LSTM-based Deep Learning Models for Non-factoid Answer Selection

Abstract: In this paper, we apply a general deep learning (DL) framework for the answer selection task, which does not depend on manually defined features or linguistic tools. The basic framework is to build the embeddings of questions and answers based on bidirectional long short-term memory (biLSTM) models, and measure their closeness by cosine similarity. We further extend this basic model in two directions. One direction is to define a more composite representation for questions and answers by combining convolutional neural network with the basic framework. The other direction is to utilize a simple but efficient attention mechanism in order to generate the answer representation according to the question context. Several variations of models are provided. The models are examined by two datasets, including TREC-QA and InsuranceQA. Experimental results demonstrate that the proposed models substantially outperform several strong baselines.

Speech recognition with deep recurrent neural networks

Abstract: Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is unknown. The combination of these methods with the Long Short-term Memory RNN architecture has proved particularly fruitful, delivering state-of-the-art results in cursive handwriting recognition. However RNN performance in speech recognition has so far been disappointing, with better results returned by deep feedforward networks. This paper investigates deep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs. When trained end-to-end with suitable regularisation, we find that deep Long Short-term Memory RNNs achieve a test set error of 17.7% on the TIMIT phoneme recognition benchmark, which to our knowledge is the best recorded score.

XGBoost: A Scalable Tree Boosting System

Abstract: Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.

Tasks in a legal document review scenario. *The format of the samples for the classifier.

The formal and technical language of legal documents (i.e., legalese) is by nature often difficult and time consuming to read through. A practical facilitating tool is the one that can navigate the human reviewers to their points of interest in a single or multiple documents while asking for minimum inputs. Here, we have identified four main navigation scenarios in a legal document review process that can be facilitated by an automated tool. We elaborate on the main requirements that should be satisfied in each scenario and show how we model each task as an NLP problem. We explain the format of the labelled data we need as well as the learning technique we use to address each task. We eventually introduce a baseline algorithm for each task that can be used as a benchmark when evaluating the impact of using language models. Table {{table:725a3ddb-33d1-45eb-b5dc-14b8607a2de7}}  presents a summary of the details of each review task. Due to the complexities of legal texts, we define snippet as the unit of text that is more general than a grammatical sentence (see Section REF  for an example of a legal text snippet). In our experiments, splitting the text into snippets is performed using a customized rule based system that relies only on text punctuation.


Overviews the existing federated learning studies and promotes its data aggregation aspect.

Removes the requirement on csi for the distributed ml method.

The method is for broadband communications and reduces latency as well as promoting a trade-off between communication and learning performance.

Reduces the convergence rate of the learning algorithm by considering device selection and beamforming.

Federated Transfer Learning: concept and applications

Abstract: Development of Artificial Intelligence (AI) is inherently tied to the development of data. However, in most industries data exists in form of isolated islands, with limited scope of sharing between different organizations. This is an hindrance to the further development of AI. Federated learning has emerged as a possible solution to this problem in the last few years without compromising user privacy. Among different variants of the federated learning, noteworthy is federated transfer learning (FTL) that allows knowledge to be transferred across domains that do not have many overlapping features and users. In this work we provide a comprehensive survey of the existing works on this topic. In more details, we study the background of FTL and its different existing applications. We further analyze FTL from privacy and machine learning perspective.

Collaborative Machine Learning at the Wireless Edge with Blind Transmitters

Abstract: We study wireless collaborative machine learning (ML), where mobile edge devices, each with its own dataset, carry out distributed stochastic gradient descent (DSGD) over-the-air with the help of a wireless access point acting as the parameter server (PS). At each iteration of the DSGD algorithm wireless devices compute gradient estimates with their local datasets, and send them to the PS over a wireless fading multiple access channel (MAC). Motivated by the additive nature of the wireless MAC, we propose an analog DSGD scheme, in which the devices transmit scaled versions of their gradient estimates in an uncoded fashion. We assume that the channel state information (CSI) is available only at the PS. We instead allow the PS to employ multiple antennas to alleviate the destructive fading effect, which cannot be cancelled by the transmitters due to the lack of CSI. Theoretical analysis indicates that, with the proposed DSGD scheme, increasing the number of PS antennas mitigates the fading effect, and, in the limit, the effects of fading and noise disappear, and the PS receives aligned signals used to update the model parameter. The theoretical results are then corroborated with the experimental ones.

Broadband Analog Aggregation for Low-Latency Federated Edge Learning

Abstract: To leverage rich data distributed at the network edge, a new machine-learning paradigm, called edge learning, has emerged where learning algorithms are deployed at the edge for providing intelligent services to mobile users. While computing speeds are advancing rapidly, the communication latency is becoming the bottleneck of fast edge learning. To address this issue, this work is focused on designing a low-latency multi-access scheme for edge learning. To this end, we consider a popular privacy-preserving framework, federated edge learning (FEEL), where a global AI-model at an edge-server is updated by aggregating (averaging) local models trained at edge devices. It is proposed that the updates simultaneously transmitted by devices over broadband channels should be analog aggregated “over-the-air” by exploiting the waveform-superposition property of a multi-access channel. Such broadband analog aggregation (BAA) results in dramatical communication-latency reduction compared with the conventional orthogonal access (i.e., OFDMA). In this work, the effects of BAA on learning performance are quantified targeting a single-cell random network. First, we derive two tradeoffs between communication-and-learning metrics, which are useful for network planning and optimization. The power control (“truncated channel inversion”) required for BAA results in a tradeoff between the update-reliability [as measured by the receive signal-to-noise ratio (SNR)] and the expected update-truncation ratio. Consider the scheduling of cell-interior devices to constrain path loss. This gives rise to the other tradeoff between the receive SNR and fraction of data exploited in learning. Next, the latency-reduction ratio of the proposed BAA with respect to the traditional OFDMA scheme is proved to scale almost linearly with the device population. Experiments based on a neural network and a real dataset are conducted for corroborating the theoretical results.

Federated Learning via Over-the-Air Computation

Abstract: The stringent requirements for low-latency and privacy of the emerging high-stake applications with intelligent devices such as drones and smart vehicles make the cloud computing inapplicable in these scenarios. Instead, edge machine learning becomes increasingly attractive for performing training and inference directly at network edges without sending data to a centralized data center. This stimulates a nascent field termed as federated learning for training a machine learning model on computation, storage, energy and bandwidth limited mobile devices in a distributed manner. To preserve data privacy and address the issues of unbalanced and non-IID data points across different devices, the federated averaging algorithm has been proposed for global model aggregation by computing the weighted average of locally updated model at each selected device. However, the limited communication bandwidth becomes the main bottleneck for aggregating the locally computed updates. We thus propose a novel over-the-air computation based approach for fast global model aggregation via exploring the superposition property of a wireless multiple-access channel. This is achieved by joint device selection and beamforming design, which is modeled as a sparse and low-rank optimization problem to support efficient algorithms design. To achieve this goal, we provide a difference-of-convex-functions (DC) representation for the sparse and low-rank function to enhance sparsity and accurately detect the fixed-rank constraint in the procedure of device selection. A DC algorithm is further developed to solve the resulting DC program with global convergence guarantees. The algorithmic advantages and admirable performance of the proposed methodologies are demonstrated through extensive numerical results.

An overview of federated learning studies.

The data-driven nature of the ml requires the collection of massive data in centralized points before the learning process. However, data sources of today's technology are often at the wireless edges. As a result, collecting massive data from wireless devices to a center can cost a high amount of energy and bandwidth {{cite:72d9b3d}}. Collaborative machine learning or federated learning is a solution to the data collection problem which proposes the process of the data at the edge users or distributed centers instead of a local center {{cite:56e9125}}, {{cite:1721d58}}. The superposition property of the wireless channel can further relieve some of the costs in the federated learning schemes by performing computations over the wireless channel. A list of federated learning studies can be found in Table {{table:341ec585-ddc9-4aae-940b-e61d891e93c2}} .


Learning Multiagent Communication with Backpropagation

Abstract: Many tasks in AI require the collaboration of multiple agents. Typically, the communication protocol between agents is manually specified and not altered during training. In this paper we explore a simple neural model, called CommNet, that uses continuous communication for fully cooperative tasks. The model consists of multiple agents and the communication between them is learned alongside their policy. We apply this model to a diverse set of tasks, demonstrating the ability of the agents to learn to communicate amongst themselves, yielding improved performance over non-communicative agents and baselines. In some cases, it is possible to interpret the language devised by the agents, revealing simple but effective strategies for solving the task at hand.

Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks

Abstract: Learning when to communicate and doing that effectively is essential in multi-agent tasks. Recent works show that continuous communication allows efficient training with back-propagation in multi-agent scenarios, but have been restricted to fully-cooperative tasks. In this paper, we present Individualized Controlled Continuous Communication Model (IC3Net) which has better training efficiency than simple continuous communication model, and can be applied to semi-cooperative and competitive settings along with the cooperative settings. IC3Net controls continuous communication with a gating mechanism and uses individualized rewards foreach agent to gain better performance and scalability while fixing credit assignment issues. Using variety of tasks including StarCraft BroodWars explore and combat scenarios, we show that our network yields improved performance and convergence rates than the baselines as the scale increases. Our results convey that IC3Net agents learn when to communicate based on the scenario and profitability.

TarMAC: Targeted Multi-Agent Communication

Abstract: We propose a targeted communication architecture for multi-agent reinforcement learning, where agents learn both what messages to send and whom to address them to while performing cooperative tasks in partially-observable environments. This targeting behavior is learnt solely from downstream task-specific reward without any communication supervision. We additionally augment this with a multi-round communication approach where agents coordinate via multiple rounds of communication before taking actions in the environment. We evaluate our approach on a diverse set of cooperative multi-agent tasks, of varying difficulties, with varying number of agents, in a variety of environments ranging from 2D grid layouts of shapes and simulated traffic junctions to 3D indoor environments, and demonstrate the benefits of targeted and multi-round communication. Moreover, we show that the targeted communication strategies learned by agents are interpretable and intuitive. Finally, we show that our architecture can be easily extended to mixed and competitive environments, leading to improved performance and sample complexity over recent state-of-the-art approaches.

Architecture of the Baselines

For evaluation, we adopt a diverse selection of MARL communication methods which fall under the GDN paradigm. These are shown in Table {{table:f368c23c-756b-447f-a015-1e2776473304}} , along with the communication graph structure, agent model, and GNN architecture. We use the code provided by {{cite:ff5b13f}}, {{cite:29f105c}} as starting points. All of the implementations are extended to support multiple rounds of message-passing and the baselines are augmented with the ability for their communication to be masked by the environment (e.g. based on distance or obstacles in the environment). We fix the number of message-passing rounds to be 4 and otherwise use the original models and hyperparameters from {{cite:ff5b13f}}, {{cite:29f105c}}. Full experiment and hyperparameter details can be found in Appendix , and full results are shown in Appendix .


Architecture of the Baselines

For evaluation, we adopt a diverse selection of MARL communication methods which fall under the GDN paradigm. These are shown in Table {{table:1e289317-bffc-433d-95a3-7234b4206cee}} , along with the respective paradigm (whether the method simply falls within GDNs or whether GNNs are explicitly used for communication), the MARL paradigm, and communication graph structure. We use the code provided by {{cite:29f105c}}, {{cite:ff5b13f}} as starting points. The code of {{cite:ff5b13f}} uses an MIT license and the code of {{cite:29f105c}} does not have one. All of the implementations are extended to be able to support multiple rounds of message-passing and the baselines are augmented with the ability for their communication to be masked by the environment (e.g. based on distance or obstacles in the environment).


Reducing the classifier's oversensitivity to identity terms

Detecting biases related to named entities

Detecting over- and under-sensitivity to identity tokens

hate speech and abusive language detection

Contextualizing Hate Speech Classifiers with Post-hoc Explanation

Abstract: Hate speech classifiers trained on imbalanced datasets struggle to determine if group identifiers like “gay” or “black” are used in offensive or prejudiced ways. Such biases manifest in false positives when these identifiers are present, due to models’ inability to learn the contexts which constitute a hateful usage of identifiers. We extract post-hoc explanations from fine-tuned BERT classifiers to detect bias towards identity terms. Then, we propose a novel regularization technique based on these explanations that encourages models to learn from the context of group identifiers in addition to the identifiers themselves. Our approach improved over baselines in limiting false positives on out-of-domain data while maintaining and in cases improving in-domain performance.

HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection

Abstract: Hate speech is a challenging issue plaguing the online social media. While better models for hate speech detection are continuously being developed, there is little research on the bias and interpretability aspects of hate speech. In this paper, we introduce HateXplain, the first benchmark hate speech dataset covering multiple aspects of the issue. Each post in our dataset is annotated from three different perspectives: the basic, commonly used 3-class classification (i.e., hate, offensive or normal), the target community (i.e., the community that has been the victim of hate speech/offensive speech in the post), and the rationales, i.e., the portions of the post on which their labelling decision (as hate, offensive or normal) is based. We utilize existing state-of-the-art models and observe that even models that perform very well in classification do not score high on explainability metrics like model plausibility and faithfulness. We also observe that models, which utilize the human rationales for training, perform better in reducing unintended bias towards target communities. We have made our code and dataset public at https://github.com/punyajoy/HateXplain

Perturbation Sensitivity Analysis to Detect Unintended Model Biases

Abstract: Data-driven statistical Natural Language Processing (NLP) techniques leverage large amounts of language data to build models that can understand language. However, most language data reflect the public discourse at the time the data was produced, and hence NLP models are susceptible to learning incidental associations around named referents at a particular point in time, in addition to general linguistic meaning. An NLP system designed to model notions such as sentiment and toxicity should ideally produce scores that are independent of the identity of such entities mentioned in text and their social associations. For example, in a general purpose sentiment analysis system, a phrase such as I hate Katy Perry should be interpreted as having the same sentiment as I hate Taylor Swift. Based on this idea, we propose a generic evaluation framework, Perturbation Sensitivity Analysis, which detects unintended model biases related to named entities, and requires no new annotations or corpora. We demonstrate the utility of this analysis by employing it on two different NLP models — a sentiment model and a toxicity model — applied on online comments in English language from four different genres.

Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection

Abstract: We present a novel feature attribution method for explaining text classifiers, and analyze it in the context of hate speech detection. Although feature attribution models usually provide a single importance score for each token, we instead provide two complementary and theoretically-grounded scores -- necessity and sufficiency -- resulting in more informative explanations. We propose a transparent method that calculates these values by generating explicit perturbations of the input text, allowing the importance scores themselves to be explainable. We employ our method to explain the predictions of different hate speech detection models on the same set of curated examples from a test suite, and show that different values of necessity and sufficiency for identity terms correspond to different kinds of false positive errors, exposing sources of classifier bias against marginalized groups.

Summary of the studies that apply explainability techniques to uncover unintended biases in NLP systems.

As summarized in Table {{table:d317f158-cc2d-4439-9ec0-2a0d831b62dd}} , almost all these works focus exclusively on hate speech detection, and use local feature attribution methods. The range of bias types is also quite limited. This demonstrates the very narrow context in which explainability has been linked to fairness in NLP.


Massive Online Crowdsourced Study of Subjective and Objective Picture Quality

Abstract: Most publicly available image quality databases have been created under highly controlled conditions by introducing graded simulated distortions onto high-quality photographs. However, images captured using typical real-world mobile camera devices are usually afflicted by complex mixtures of multiple distortions, which are not necessarily well-modeled by the synthetic distortions found in existing databases. The originators of existing legacy databases usually conducted human psychometric studies to obtain statistically meaningful sets of human opinion scores on images in a stringently controlled visual environment, resulting in small data collections relative to other kinds of image analysis databases. Toward overcoming these limitations, we designed and created a new database that we call the LIVE In the Wild Image Quality Challenge Database, which contains widely diverse authentic image distortions on a large number of images captured using a representative variety of modern mobile devices. We also designed and implemented a new online crowdsourcing system, which we have used to conduct a very large-scale, multi-month image quality assessment (IQA) subjective study. Our database consists of over 350 000 opinion scores on 1162 images evaluated by over 8100 unique human observers. Despite the lack of control over the experimental environments of the numerous study participants, we demonstrate excellent internal consistency of the subjective data set. We also evaluate several top-performing blind IQA algorithms on it and present insights on how the mixtures of distortions challenge both end users as well as automatic perceptual quality prediction models. The new database is available for public use at <;uri xlink:href="http://live.ece.utexas.edu/research/ChallengeDB/index.html" xlink:type="simple">http://live.ece.utexas.edu/research/ChallengeDB/index.html<;/uri>.

KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment

Abstract: Deep learning methods for image quality assessment (IQA) are limited due to the small size of existing datasets. Extensive datasets require substantial resources both for generating publishable content and annotating it accurately. We present a systematic and scalable approach to creating KonIQ-10k, the largest IQA dataset to date, consisting of 10,073 quality scored images. It is the first in-the-wild database aiming for ecological validity, concerning the authenticity of distortions, the diversity of content, and quality-related indicators. Through the use of crowdsourcing, we obtained 1.2 million reliable quality ratings from 1,459 crowd workers, paving the way for more general IQA models. We propose a novel, deep learning model (KonCept512), to show an excellent generalization beyond the test set (0.921 SROCC), to the current state-of-the-art database LIVE-in-the-Wild (0.825 SROCC). The model derives its core performance from the InceptionResNet architecture, being trained at a higher resolution than previous models (512x384). Correlation analysis shows that KonCept512 performs similar to having 9 subjective scores for each test image.

Statistics of IQA datasets.

We evaluate GreenBIQA on four IQA datasets, including
two synthetic datasets and two authentic datasets. Their statistics are
given in Table {{table:2040117d-b0e1-4e35-99d6-a560a2855987}} . CSIQ {{cite:90d0594}} and
KADID-10K {{cite:3ca5a3c}} are two synthetic-distortion datasets,
where multiple distortions of various levels are applied to a set of
reference images. LIVE-C {{cite:62aab7b}} and KonIQ-10K
{{cite:4b87c90}} are two authentic-distortion datasets, which
contains a diverse range of distorted images captured by various cameras
in the real world.


Triplet loss {{cite:e62b28b}}, {{cite:53268be}}

PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition

Abstract: Audio pattern recognition is an important research topic in the machine learning area, and includes several tasks such as audio tagging, acoustic scene classification, music classification, speech emotion classification and sound event detection. Recently, neural networks have been applied to tackle audio pattern recognition problems. However, previous systems are built on specific datasets with limited durations. Recently, in computer vision and natural language processing, systems pretrained on large-scale datasets have generalized well to several tasks. However, there is limited research on pretraining systems on large-scale datasets for audio pattern recognition. In this paper, we propose pretrained audio neural networks (PANNs) trained on the large-scale AudioSet dataset. These PANNs are transferred to other audio related tasks. We investigate the performance and computational complexity of PANNs modeled by a variety of convolutional neural networks. We propose an architecture called Wavegram-Logmel-CNN using both log-mel spectrogram and waveform as input feature. Our best PANN system achieves a state-of-the-art mean average precision (mAP) of 0.439 on AudioSet tagging, outperforming the best previous system of 0.392. We transfer PANNs to six audio pattern recognition tasks, and demonstrate state-of-the-art performance in several of those tasks. We have released the source code and pretrained models of PANNs: https://github.com/qiuqiangkong/audioset_tagging_cnn.

Efficient Training of Audio Transformers with Patchout

Abstract: The great success of transformer-based models in natural language processing (NLP) has led to various attempts at adapting these architectures to other domains such as vision and audio. Recent work has shown that transformers can outperform Convolutional Neural Networks (CNNs) on vision and audio tasks. However, one of the main shortcomings of transformer models, compared to the well-established CNNs, is the computational complexity. In transformers, the compute and memory complexity is known to grow quadratically with the input length. Therefore, there has been extensive work on optimizing transformers, but often at the cost of degrading predictive performance. In this work, we propose a novel method to optimize and regularize transformers on audio spectrograms. Our proposed models achieve a new state-of-the-art performance on Audioset and can be trained on a single consumer-grade GPU. Furthermore, we propose a transformer model that outperforms CNNs in terms of both performance and training speed. Source code: https://github.com/kkoutini/PaSST

ESResNet: Environmental Sound Classification Based on Visual Domain Models

Abstract: Environmental Sound Classification (ESC) is an active research area in the audio domain and has seen a lot of progress in the past years. However, many of the existing approaches achieve high accuracy by relying on domain-specific features and architectures, making it harder to benefit from advances in other fields (e.g., the image domain). Additionally, some of the past successes have been attributed to a discrepancy of how results are evaluated (i.e., on unofficial splits of the UrbanSound8K (US8K) dataset), distorting the overall progression of the field. The contribution of this paper is twofold. First, we present a model that is inherently compatible with mono and stereo sound inputs. Our model is based on simple log-power Short-Time Fourier Transform (STFT) spectrograms and combines them with several well-known approaches from the image domain (i.e., ResNet, Siamese-like networks and attention). We investigate the influence of cross-domain pre-training, architectural changes, and evaluate our model on standard datasets. We find that our model out-performs all previously known approaches in a fair comparison by achieving accuracies of 97.0 % (ESC-10), 91.5 % (ESC-50) and 84.2 % / 85.4 % (US8K mono / stereo). Second, we provide a comprehensive overview of the actual state of the field, by differentiating several previously reported results on the US8K dataset between official or unofficial splits. For better reproducibility, our code (including any re- implementations) is made available.

Summary of systems with best performance from all teams.

Audio encoders.
Among the submitted systems, PANNs {{cite:b2a97ef}} (e.g., CNN14, Wavegram-Logmel-CNN14 and CNN10) were the most common choice for encoding audio data.
There were in total 21 systems from six teams utilizing PANNs as their audio encoders, including the top three teams {{cite:da2d580}}, {{cite:f8b4372}}, {{cite:ec4453e}}.
Particularly, the CNN14 model was most preferred (20 systems).
Xu et al. {{cite:da2d580}} experimented with multiple pre-trained audio expert models, including CNN14 and Wavegram-Logmel-CNN14.
With ensemble of different models, they achieved the best performance.
Mei et al. {{cite:f8b4372}} adopted CNN14 as their audio encoder without model ensembles, having their best system ranked second.
Tied on third rank, Lamort et al. {{cite:ec4453e}} experimented with aggregating the most number of pre-trained audio expert models (i.e., five models in total, as shown in Table {{table:237f6925-42c5-46d8-a43b-21cb67059ab2}} ).
The audio encoders utilized in other systems were PaSST {{cite:9913932}}, ESResNet {{cite:3ac6a90}}, HTS-AT {{cite:a62bd84}}, and CRNN {{cite:4c3240f}}.

Caption encoders.
Most of the participating teams (i.e., seven out of nine) preferred Transformer-based language embedding models (e.g., BERT {{cite:48d4060}}) over word embedding models (e.g., Word2Vec {{cite:516bf59}}), to learn caption embeddings.
As shown in Table {{table:237f6925-42c5-46d8-a43b-21cb67059ab2}} , pre-trained Transformer-based language embedding models, including BERT {{cite:48d4060}}, RoBERTa {{cite:cd1a174}}, and Sentence-BERT {{cite:c4bbbc8}}, were utilized as caption encoders by seven teams (28 systems in total).
The other two teams and the baseline converted captions into embeddings with pre-trained Word2Vec word vectors {{cite:516bf59}}.
All teams with Transformer-based models ranked higher than those with pre-trained Word2Vec {{cite:516bf59}}, which showed that Transformer-based models learned caption embeddings more efficiently.


node2vec: Scalable Feature Learning for Networks

Abstract: Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node's network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.

LINE: Large-scale Information Network Embedding

Abstract: This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction. Most existing graph embedding methods do not scale for real world information networks which usually contain millions of nodes. In this paper, we propose a novel network embedding method called the "LINE," which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted. The method optimizes a carefully designed objective function that preserves both the local and global network structures. An edge-sampling algorithm is proposed that addresses the limitation of the classical stochastic gradient descent and improves both the effectiveness and the efficiency of the inference. Empirical experiments prove the effectiveness of the LINE on a variety of real-world information networks, including language networks, social networks, and citation networks. The algorithm is very efficient, which is able to learn the embedding of a network with millions of vertices and billions of edges in a few hours on a typical single machine. The source code of the LINE is available online.

Graph Neural Network-Based Anomaly Detection in Multivariate Time Series

Abstract: Given high-dimensional time series data (e.g., sensor data), how can we detect anomalous events, such as system faults and attacks? More challengingly, how can we do this in a way that captures complex inter-sensor relationships, and detects and explains anomalies which deviate from these relationships? Recently, deep learning approaches have enabled improvements in anomaly detection in high-dimensional datasets; however, existing methods do not explicitly learn the structure of existing relationships between variables, or use them to predict the expected behavior of time series. Our approach combines a structure learning approach with graph neural networks, additionally using attention weights to provide explainability for the detected anomalies. Experiments on two real-world sensor datasets with ground truth anomalies show that our method detects anomalies more accurately than baseline approaches, accurately captures correlations between sensors, and allows users to deduce the root cause of a detected anomaly.

Summary of Reviewed Approaches and Their Capabilities

Graph-based Tools and Techniques for Anomaly Detection

In this section, we aim to introduce and compare techniques that combined with graphs have a great potential for solving the problem of anomaly detection in distributed systems. The summary of all reviewed works and their capabilities can be seen in Table {{table:8b417be4-9159-47af-b6cf-fd782cdaeb74}} .

Table {{table:8b417be4-9159-47af-b6cf-fd782cdaeb74}}  summarizes the strong points and weaknesses of two discussed works DIoT {{cite:87eec8e}} and VFL {{cite:0744a5a}}. Both of these works are unable to process the additional information provided by attributed graphs which in real-world scenarios are common. It should be mentioned that for supervised models such as DIot {{cite:87eec8e}} a crucial requirement is available labeled data that is hard to provide. Based on mentioned shortcomings, although FGL addresses data diversity, data security and real-time continuous learning it is not mature enough yet for real-world applications.

For more clear scrutiny, Table {{table:8b417be4-9159-47af-b6cf-fd782cdaeb74}}  shows the different features of reviewed works. As mentioned before, one important characteristic of the autoencoder structure is that it is trained in an unsupervised manner that directly solves the problem of the availability of labeled data. Also, the changing nature of anomalies will not be a problem in these types of models since they will learn the normal behavior of the system and anything different from that will be counted as an outlier.

Fig {{figure:cd91df75-9fd4-41c6-a2b5-6a2e3c276e01}}  shows the structure of AddGraph which is applied to the LAN Cluster example. Previous hidden states and current snapshot of the network graph is used for constructing the node embedding. Also, an attention module and GRU combine the long-term and short-term states to generate the current hidden state. In the final step, a scoring function is responsible to assign a normality score to the current hidden state. The main points of discussed techniques are available in Table {{table:8b417be4-9159-47af-b6cf-fd782cdaeb74}} .

Capabilities of all mentioned models can be seen in Table {{table:8b417be4-9159-47af-b6cf-fd782cdaeb74}} . Unfortunately, none of the anomaly detection models using graph transformers are compatible with attributed graphs which makes them less practical in real applications. Future research can move in the direction of solving this issue to overcome the complex nature of heterogeneous distributed systems.

Anomaly detection with GSP filtering on Wireless Sensor Networks (WSNs) has been a topic of interest in several works. {{cite:217d569}} captures proximity information such as data between sensors to capture local anomalous behavior. They present three graph designs and use GSP filtering to find the cut-off frequency and Lambda for the filters. This is used to separate normal and anomalous sub-spaces for unsupervised detection. The anomalous space projections are finally utilized to generate anomaly scores as shown in Fig. {{figure:d933ef01-a6ce-402c-b80d-8484ea5b8018}} . The main advantage of such a system is that in addition to raw sensor data, relational characteristics between the nodes, like proximity information between sensors and their environment, can be effectively captured. Graph-based filtering is found to be particularly useful for both regular and irregular graph structures for unsupervised anomaly detection. In Table {{table:8b417be4-9159-47af-b6cf-fd782cdaeb74}} , specification of the reviewed model is available. The ability to work on dynamic attributed graphs makes {{cite:217d569}} aligned with real-world requirements. On the other hand, leveraging GSP techniques can add deeper perspectives to anomaly detection problems and equip them with more relational information helpful for detecting outliers which is essential in real-world applications.

Characteristics of all mentioned models can be seen in Table {{table:8b417be4-9159-47af-b6cf-fd782cdaeb74}} . As discussed, Graph Contrastive Learning approaches show promising results and are extremely adaptable to real-world scenarios. The discussed works are all able to overcome the dynamic complex nature of distributed heterogeneous systems and the unsupervised/self-supervised learning adaptability of these models can alleviate the problem of available labeled data mentioned in Section .


Resource consumption (e.g., heap used, user CPU time, system CPU time, voluntary context switches, bytes written to file system, and bytes received over network)

Number of functions, function popularity, arrival rate, service rate, cold start rate, and idle lifetime rate

Sizeless: predicting the optimal size of serverless functions

Abstract: Serverless functions are an emerging cloud computing paradigm that is being rapidly adopted by both industry and academia. In this cloud computing model, the provider opaquely handles resource management tasks such as resource provisioning, deployment, and auto-scaling. The only resource management task that developers are still in charge of is selecting how much resources are allocated to each worker instance. However, selecting the optimal size of serverless functions is quite challenging, so developers often neglect it despite its significant cost and performance benefits. Existing approaches aiming to automate serverless functions resource sizing require dedicated performance tests, which are time-consuming to implement and maintain. In this paper, we introduce an approach to predict the optimal resource size of a serverless function using monitoring data from a single resource size. As our approach does not require dedicated performance tests, it enables cloud providers to implement resource sizing on a platform level and automate the last resource management task associated with serverless functions. We evaluate our approach on four different serverless applications on AWS, where it predicts the execution time of the other memory sizes based on monitoring data for a single memory size with an average prediction error of 15.3%. Based on these predictions, it selects the optimal memory size for 79.0% of the serverless functions and the second-best memory size for 12.3% of the serverless functions, which results in an average speedup of 39.7% while also decreasing average costs by 2.6%.

COCOA: Cold Start Aware Capacity Planning for Function-as-a-Service Platforms

Abstract: Function-as-a-Service (FaaS) has become increasingly popular in the software industry due to the implied cost-savings in event-driven workloads and its synergy with DevOps. To size an on-premise FaaS platform, it is important to estimate the required CPU and memory capacity to serve the expected loads. Given the service-level agreements, it is however challenging to take the cold start issue into account during the sizing process. We have investigated the similarity of this problem with the hit rate improvement problem in Time to Live (TTL) caches and concluded that solutions for TTL cache, although potentially applicable, lead to over-provisioning in FaaS. Thus, we propose a novel approach, COCOA, to solve this issue. COCOA uses a queueing-based approach to assess the effect of cold starts on FaaS response times. It also considers different memory consumption values depending on whether the function is idle or in execution. Using an event-driven FaaS simulator, FaasSim, that we have developed, we show that COCOA can reduce overprovisioning by over 70% under some of the workloads we have considered, while satisfying the service-level agreements.

A summary of studies about performance prediction.

For studies related to performance prediction, regression model-based prediction and statistical learning-based prediction are two common solutions. A summary of studies on performance prediction is shown in Table {{table:e791a2b1-8ed7-4dfb-8e4f-865561a9eb84}} .


Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks

Abstract: We present Wiki-CS, a novel dataset derived from Wikipedia for benchmarking Graph Neural Networks. The dataset consists of nodes corresponding to Computer Science articles, with edges based on hyperlinks and 10 classes representing different branches of the field. We use the dataset to evaluate semi-supervised node classification and single-relation link prediction models. Our experiments show that these methods perform well on a new domain, with structural properties different from earlier benchmarks. The dataset is publicly available, along with the implementation of the data pipeline and the benchmark experiments, at this https URL .

Image-based Recommendations on Styles and Substitutes

Abstract: Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance. Some pairs of objects might be seen as being alternatives to each other (such as two pairs of jeans), while others may be seen as being complementary (such as a pair of jeans and a matching shirt). This information guides many of the choices that people make, from buying clothes to their interactions with each other. We seek here to model this human sense of the relationships between objects based on their appearance. Our approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within. We cast this as a network inference problem defined on graphs of related images, and provide a large-scale dataset for the training and evaluation of the same. The system we develop is capable of recommending which clothes and accessories will go well together (and which will not), amongst a host of other applications.

Semi-Supervised Classification with Graph Convolutional Networks

Abstract: We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.

Statistics of datasets used in our experiments.

Below we describe datasets from Table {{table:9386cde8-0c36-4f43-be89-ef5188ee855c}} :


1) Partition the large RIS into tiles and calculate tile response function; 2) Low rank channel matrix decomposition; 3) Transmission mode selection

Model by the summation of impinging plane waves

Model the small-scale fading as a zero-mean, spatially-stationary, and correlated Gaussian scalar random field

View RIS as a controlled scattering cluster

Model the three sub-channels by ellipses

Consider the spherical wavefront and THz band path loss

Can derive the joint spatial-temporal correlation functions under both isotropic and non-isotropic scattering

Satisfy the Helmholtz equation in the frequency domain

1) Apply to RIS-assisted FSO systems; 2) Consider the non-uniform power distribution of Gaussian beams, the impact of RIS size, the positions of the laser source, the lens, and the phase-shift configuration of the RIS

Can be applied to various indoor/outdoor scenarios and frequency bands

2D, consider the spatial CCFs of sub-channels associated with different RIS elements

Wideband non-stationary properties and practical phase shifts of RIS are studied

Tx and Rx are equipped with a single antennas

Physics-based Modeling of Large Intelligent Reflecting Surfaces for Scalable Optimization

Abstract: In this paper, we develop a physics-based model that allows a scalable optimization of large intelligent reflecting surfaces (IRSs). The basic idea is to partition the IRS unit cells into several subsets, referred to as tiles, and model the impact of each tile on the wireless channel. Borrowing concepts from the radar literature, we model each tile as an anomalous reflector, and derive its impact on the wireless channel for given unit cell phase shifts by solving the corresponding integral equations for the electric and magnetic vector fields. Based on this model, one can design the phase shifts of the unit cells of a tile offline for the support of several transmission modes and then select the best mode online for a given channel realization. Therefore, the number of tiles and transmission modes in the proposed model are design parameters that can be adjusted to trade performance for complexity.

Small-Scale Spatial-Temporal Correlation and Degrees of Freedom for Reconfigurable Intelligent Surfaces

Abstract: The reconfigurable intelligent surface (RIS) is an emerging promising candidate technology for future wireless networks, where the element spacing is usually of sub-wavelength. Only limited knowledge, however, has been gained about the spatial-temporal correlation behavior among the elements in an RIS. In this paper, we investigate the spatial-temporal correlation for an RIS-enabled wireless communication system. Specifically, a joint small-scale spatial-temporal correlation model is derived under isotropic scattering, which can be represented by a four-dimensional sinc function. Furthermore, based upon the spatial-only correlation at a certain time instant, an essential RIS property -- the spatial degrees of freedom (DoF) -- is revisited, and an analytical expression is propounded to characterize the spatial DoF for RISs with realistic hence non-infinitesimal element spacing and finite aperture sizes. The results are vital to the accurate evaluation of various system performance metrics.

Holographic MIMO Communications Under Spatially-Stationary Scattering

Abstract: Holographic MIMO is a spatially-constrained MIMO system with a massive number of antennas, possibly thought of, in its ultimate form, as a spatially-continuous electromagnetic aperture. Accurate and tractable channel modeling is critical to understanding the full potential of this technology. This paper considers arbitrary spatially-stationary scattering and provides a 4D plane-wave representation in Cartesian coordinates, which captures the essence of electromagnetic propagation and allows to evaluate the capacity of Holographic MIMO systems with rectangular volumetric arrays. The developed framework generalizes the virtual channel representation, which was originally developed for uniform linear arrays.

Channel Modeling for IRS-Assisted FSO Systems

Abstract: In this paper, we develop an analytical channel model for intelligent reflecting surface (IRS)-assisted free space optical (FSO) systems. Unlike IRS-assisted radio frequency systems, where it is typically assumed that a plane wave is incident on the IRS, in FSO systems, the incident wave is a Gaussian beam with non-uniform power distribution across the IRS. Taking this property into account, we develop an analytical end-to-end channel model for IRS-assisted FSO systems based on the Huygens-Fresnel principle. Our analytical model reveals the impact of the size, position, orientation, and phase-shift configuration of the IRS on the end-to-end channel. Furthermore, we show that results obtained based on geometric optics under the far-field approximation are only valid for a specific range of IRS-receiver lens distances depending on the IRS size, incident beam width, and wavelength. Simulation results validate the accuracy of the proposed analytical results for the FSO beam reflected from the IRS and compare the bit error rate performance obtained for the proposed analytical channel model with that obtained for geometric optics under the far-field approximation.

IRS-Enabled Beam-Space Channel

Abstract: The intelligent reflecting surface (IRS) is emphasized as a controlled scattering cluster. To this end, scatterers and traveling paths of multipath components are classified to build a new channel model. Unlike the conventional modeling, where the channels between system units are modeled independently, the new model considers the channel as a whole and decomposes it based on the traveling paths. The model shows clearly how IRS, in the beam-space context, converts the channel from a problem into a design element. After investigating IRS as a scattering cluster, based on a proposed segmentation scheme, the beamforming problem is considered with a focus on first-order reflections. Passive beamforming at IRS is shown to have two tiers; at the scatterer and antenna levels. A segment-activation scheme is proposed to maximize the received signal power, where the number of transmitting antenna elements to be used is given as a function of IRS positioning and beamforming at the receiver. The results show that while using more transmitting antenna elements to get narrower beams is possible, using fewer elements can give better performance, especially for larger IRS at close distances. The developed model also proves useful in addressing emerging issues in massive MIMO communication, namely, stationarity and spherical wavefronts.

SimRIS Channel Simulator for Reconfigurable Intelligent Surface-Empowered Communication Systems

Abstract: Reconfigurable intelligent surface (RIS)-assisted communication appears as one of the potential enablers for sixth generation (6G) wireless networks by providing a new way to optimize the communication system performance. This paper aims to fill an important gap in the open literature by providing an accurate, source, and widely applicable RIS channel model for mmWave frequencies. Our model is not only applicable in various indoor and outdoor environments but also includes the physical aspects of wireless propagation in the presence of an RIS as well as various practical 5G channel modeling issues. The open-source and comprehensive SimRIS Channel Simulator is also introduced in this paper to be used in computer simulations of RIS-assisted communication systems.

A geometry-based stochastic channel model and its application for intelligent reflecting surface assisted wireless communication

Abstract: Intelligent reﬂecting surface (IRS) is a new concept originating from metamaterials, which can achieve beamforming through controllable passive reﬂecting. This device makes it possible to engineer the wireless communication environment, and has drawn increasing attention. However, the associated channel models in current literature are mainly borrowed from conventional wireless channel models directly, omitting the unique features of IRS. In this paper, a geometry-based stochastic channel model for IRS-assisted wireless communication system is employed. The model has certain accuracy and low computational complexity. In particular, it captures the correlations of subchannels associated with different IRS elements, which is typically not considered in current works. Based on this channel model and the derived channel spatial correlation functions (CFs), an iterative reﬂection coefﬁcients conﬁguration method is proposed exploiting statistical channel state information to maximise the ergodic channel capacity. The impacts of the IRS spatial positions as well as the number of the IRS elements on the ergodic channel capacity is investigated through simulations. It is found that to obtain a larger ergodic channel capacity, the IRS should be placed in the vicinity of either the transmitter side or the receiver side, which is a useful guideline for practical deployment

A 3D Non-Stationary Channel Model for 6G Wireless Systems Employing Intelligent Reflecting Surface

Abstract: As one of the key technologies for the sixth generation (6G) mobile communications, intelligent reflecting surface IRS) has the advantages of low power consumption, low cost, and simple design methods. But channel modeling is still an open issue in this field currently. In this paper, we propose a three-dimensional (3D) geometry based stochastic model (GBSM) for a massive multiple-input multiple-output (MIMO) communication system employing IRS. The model supports the movements of the transmitter, the receiver, and clusters. The evolution of clusters on the linear array and planar array is also considered in the proposed model. In addition, the generation of reflecting coefficient is incorporated into the model and the path loss of the sub-channel assisted by IRS is also proposed. The steering vector is set up at the base station for the cooperation with IRS. Through studying statistical properties such as the temporal autocorrelation function and space correlation function, the nonstationary properties are verified. The good agreement between the simulation results and the analytical results illustrates the correctness of the proposed channel model.

Intelligent Reflecting Surfaces at Terahertz Bands: Channel Modeling and Analysis

Abstract: An intelligent reflecting surface (IRS) at terahertz (THz) bands is expected to have a massive number of reflecting elements to compensate for the severe propagation losses. However, as the IRS size grows, the conventional far-field assumption starts becoming invalid and the spherical wavefront of the radiated waves should be taken into account. In this work, we consider a spherical wave channel model and pursue a comprehensive study of IRS-aided multiple-input multiple-output (MIMO) in terms of power gain and energy efficiency (EE). Specifically, we first analyze the power gain under beamfocusing and beamforming, and show that the latter is suboptimal even for multiple meters away from the IRS. To this end, we derive an approximate, yet accurate, closed-form expression for the loss in the power gain under beamforming. Building on the derived model, we next show that an IRS can significantly improve the EE of MIMO when it operates in the radiating near-field and performs beamfocusing. Numerical results corroborate our analysis and provide novel insights into the design and performance of IRS-assisted THz communication.

A summary of RIS small-scale channel models.

A few RIS-assisted small-scale channel models have been proposed recently, including phase-shift model, transmission-mode model, physics based channel model, beamspace channel model, and geometry based stochastic model (GBSM), which are summarized in Table {{table:9bbe0a18-7eb9-4b33-887f-5b10eb65cfe8}}  and thereby overviewed below.


Reinforcement Learning for Efficient Network Penetration Testing

Abstract: Penetration testing (also known as pentesting or PT) is a common practice for actively assessing the defenses of a computer network by planning and executing all possible attacks to discover and exploit existing vulnerabilities. Current penetration testing methods are increasingly becoming non-standard, composite and resource-consuming despite the use of evolving tools. In this paper, we propose and evaluate an AI-based pentesting system which makes use of machine learning techniques, namely reinforcement learning (RL) to learn and reproduce average and complex pentesting activities. The proposed system is named Intelligent Automated Penetration Testing System (IAPTS) consisting of a module that integrates with industrial PT frameworks to enable them to capture information, learn from experience, and reproduce tests in future similar testing cases. IAPTS aims to save human resources while producing much-enhanced results in terms of time consumption, reliability and frequency of testing. IAPTS takes the approach of modeling PT environments and tasks as a partially observed Markov decision process (POMDP) problem which is solved by POMDP-solver. Although the scope of this paper is limited to network infrastructures PT planning and not the entire practice, the obtained results support the hypothesis that RL can enhance PT beyond the capabilities of any human PT expert in terms of time consumed, covered attacking vectors, accuracy and reliability of the outputs. In addition, this work tackles the complex problem of expertise capturing and re-use by allowing the IAPTS learning module to store and re-use PT policies in the same way that a human PT expert would learn but in a more efficient way.

Using Cyber Terrain in Reinforcement Learning for Penetration Testing

Abstract: Reinforcement learning (RL) has been applied to attack graphs for penetration testing, however, trained agents do not reflect reality because the attack graphs lack operational nuances typically captured within the intelligence preparation of the battlefield (IPB) that include notions of (cyber) terrain. In particular, current practice constructs attack graphs exclusively using the Common Vulnerability Scoring System (CVSS) and its components. We present methods for constructing attack graphs using notions from IPB on cyber terrain analysis of obstacles, avenues of approach, key terrain, observation and fields of fire, and cover and concealment. We demonstrate our methods on an example where firewalls are treated as obstacles and represented in (1) the reward space and (2) the state dynamics. We show that terrain analysis can be used to bring realism to attack graphs for RL.

Crown Jewels Analysis using Reinforcement Learning with Attack Graphs

Abstract: Cyber attacks pose existential threats to nations and enterprises. Current practice favors piece-wise analysis using threat-models in the stead of rigorous cyber terrain analysis and intelligence preparation of the battlefield. Automated penetration testing using reinforcement learning offers a new and promising approach for developing methodologies that are driven by network structure and cyber terrain, that can be later interpreted in terms of threat-models, but that are principally network-driven analyses. This paper presents a novel method for crown jewel analysis termed CJA-RL that uses reinforcement learning to identify key terrain and avenues of approach for exploiting crown jewels. In our experiment, CJA-RL identified ideal entry points, choke points, and pivots for exploiting a network with multiple crown jewels, exemplifying how CJA-RL and reinforcement learning for penetration testing generally can benefit computer network operations workflows.

Autonomous Penetration Testing Based on Improved Deep Q-Network

Abstract: Penetration testing is an effective way to test and evaluate cybersecurity by simulating a cyberattack. However, the traditional methods deeply rely on domain expert knowledge, which requires prohibitive labor and time costs. Autonomous penetration testing is a more efficient and intelligent way to solve this problem. In this paper, we model penetration testing as a Markov decision process problem and use reinforcement learning technology for autonomous penetration testing in large scale networks. We propose an improved deep Q-network (DQN) named NDSPI-DQN to address the sparse reward problem and large action space problem in large-scale scenarios. First, we reasonably integrate five extensions to DQN, including noisy nets, soft Q-learning, dueling architectures, prioritized experience replay, and intrinsic curiosity model to improve the exploration efficiency. Second, we decouple the action and split the estimators of the neural network to calculate two elements of action separately, so as to decrease the action space. Finally, the performance of algorithms is investigated in a range of scenarios. The experiment results demonstrate that our methods have better convergence and scaling performance.

Modeling Penetration Testing with Reinforcement Learning Using Capture-the-Flag Challenges: Trade-offs between Model-free Learning and A Priori Knowledge

Abstract: Penetration testing is a security exercise aimed at assessing the security of a system by simulating attacks against it. So far, penetration testing has been carried out mainly by trained human attackers and its success critically depended on the available expertise. Automating this practice constitutes a non-trivial problem, as the range of actions that a human expert may attempts against a system and the range of knowledge she relies on to take her decisions are hard to capture. In this paper, we focus our attention on simplified penetration testing problems expressed in the form of capture the flag hacking challenges, and we analyze how model-free reinforcement learning algorithms may help to solve them. In modeling these capture the flag competitions as reinforcement learning problems we highlight that a specific challenge that characterize penetration testing is the problem of discovering the structure of the problem at hand. We then show how this challenge may be eased by relying on different forms of prior knowledge that may be provided to the agent. In this way we demonstrate how the feasibility of tackling penetration testing using reinforcement learning may rest on a careful trade-off between model-free and model-based algorithms. By using techniques to inject a priori knowledge, we show it is possible to better direct the agent and restrict the space of its exploration problem, thus achieving solutions more efficiently.

Deep hierarchical reinforcement agents for automated penetration testing

Abstract: Penetration testing the organised attack of a computer system in order to test existing defences has been used extensively to evaluate network security. This is a time consuming process and requires in-depth knowledge for the establishment of a strategy that resembles a real cyber-attack. This paper presents a novel deep reinforcement learning architecture with hierarchically structured agents called HA-DRL, which employs an algebraic action decomposition strategy to address the large discrete action space of an autonomous penetration testing simulator where the number of actions is exponentially increased with the complexity of the designed cybersecurity network. The proposed architecture is shown to find the optimal attacking policy faster and more stably than a conventional deep Q-learning agent which is commonly used as a method to apply artificial intelligence in automatic penetration testing.

Discovering Exfiltration Paths Using Reinforcement Learning with Attack Graphs

Abstract: Reinforcement learning (RL), in conjunction with attack graphs and cyber terrain, are used to develop reward and state associated with determination of optimal paths for exfiltration of data in enterprise networks. This work builds on previous crown jewels (CJ) identification that focused on the target goal of computing optimal paths that adversaries may traverse toward compromising CJs or hosts within their proximity. This work inverts the previous CJ approach based on the assumption that data has been stolen and now must be quietly exfiltrated from the network. RL is utilized to support the development of a reward function based on the identification of those paths where adversaries desire reduced detection. Results demonstrate promising performance for a sizable network environment.

Penetration Testing Using RL with Attack Graphs

Recently, enabled by advances in deep learning, RL has seen broad application in cyber {{cite:001aa45}}, and a revival of interest from the penetration testing community in particular {{cite:a00ddeb}}, {{cite:2fa58bb}}, {{cite:3de740d}}, {{cite:bba9771}}, {{cite:ab94520}}, {{cite:2b4e054}}, {{cite:2aa9f54}}, {{cite:d308d92}}, {{cite:fe2d4d5}}, {{cite:91d42c3}}, {{cite:bdaa7a8}}, {{cite:d430b21}}. Recently published literature on penetration testing with RL and attack graphs is summarized in Table {{table:31fe879e-4aae-4401-b6e3-6d7debb7bb90}} .

As noted in Table {{table:31fe879e-4aae-4401-b6e3-6d7debb7bb90}} , other than Ghanem and Chen {{cite:3de740d}}, the authors in the RL with attack graphs literature use fully observable MDPs to model networks. Many authors use the CVSS to furnish their MDPs. Yousefi et al. provide the earliest work doing so in deep RL for penetration testing {{cite:a00ddeb}}. Hu et al. extend the use of the CVSS by proposing to use exploitability scores weight rewards {{cite:ab94520}}. Gangupantulu et al. {{cite:2b4e054}}, {{cite:2aa9f54}} and Cody et al. {{cite:d430b21}} explicitly extend the methods of Hu et al. with concepts of terrain. Gangupantulu et al. advocate defining models of terrain in terms of the rewards and transition probabilities of MDPs, first in the case of firewalls as obstacles {{cite:2b4e054}}, then in the case of lateral pivots nearby key terrain {{cite:2aa9f54}}. Cody et al. apply these concepts to exfiltration {{cite:d430b21}}. Other authors either handcraft the MDP or do not remark on how its components are estimated.

Many authors apply generic deep Q learning (DQN) {{cite:afe6ac1}}, {{cite:62b752c}} to solve point-to-point network traversal tasks, termed `pathing' in Table {{table:31fe879e-4aae-4401-b6e3-6d7debb7bb90}}  {{cite:a00ddeb}}, {{cite:2fa58bb}}, {{cite:bba9771}}, {{cite:ab94520}}, {{cite:2b4e054}}. Typically the terminal state is unknown and solutions take the form of individual paths. Others develop domain-specific modifications for deep RL including the double agent architecture {{cite:d308d92}}, a hierarchical action decomposition approach {{cite:bdaa7a8}}, and various improvements to DQN termed NDSPI-DQN {{cite:fe2d4d5}}. Another line of research focuses on developing more specific penetration testing tasks than `pathing'. A number of authors define more specific tasks by reward engineering and other modifications to the MDP including formulations of capture the flag {{cite:91d42c3}}, crown jewel analysis {{cite:2aa9f54}}, and discovering exfiltration paths {{cite:d430b21}}. Existing literature will be discussed in the context of LRM-RAG in Section .

The existing literature has taken two approaches to the generation challenge. As reported in Table {{table:31fe879e-4aae-4401-b6e3-6d7debb7bb90}} , Yousefi et al. {{cite:a00ddeb}}, Chowdhary et al. {{cite:bba9771}}, Hu et al. {{cite:ab94520}}, and Gangupantulu et al. {{cite:2b4e054}}, {{cite:2aa9f54}} all use MulVal, while others use some form of network model—i.e., the network model is not processed into a graph of attack paths prior to interaction with RL. Instead, RL agents use network attack simulators or handcrafted network models. This divide seems to be tied to the role of supplemental information, as all authors using MulVal use the CVSS to provide vulnerability information.

LRM-RAG breaks MDP engineering into four parts. The generic MDP uses the CVSS to furnish incremental rewards for exploiting vulnerabilities and assigning transition probabilities {{cite:a00ddeb}}, {{cite:bba9771}}, {{cite:ab94520}}, {{cite:2b4e054}}. Nearly half the authors in Table {{table:31fe879e-4aae-4401-b6e3-6d7debb7bb90}}  implicitly use a generic MDP, however, even those that use it remark on its deficiencies. The terrain MDP addresses these deficiencies by introducing general concepts of terrain which apply to adversaries and tasks generally. Gangupantulu et al. {{cite:2b4e054}}, {{cite:2aa9f54}} and Cody et al. {{cite:d430b21}} propose several terrain concepts for RL with attack graphs including obstacles, key terrain, and cover and concealment.


A Stylometric Inquiry into Hyperpartisan and Fake News

Abstract: We report on a comparative style analysis of hyperpartisan (extremely one-sided) news and fake news. A corpus of 1,627 articles from 9 political publishers, three each from the mainstream, the hyperpartisan left, and the hyperpartisan right, have been fact-checked by professional journalists at BuzzFeed: 97% of the 299 fake news articles identified are also hyperpartisan. We show how a style analysis can distinguish hyperpartisan news from the mainstream (F1 = 0.78), and satire from both (F1 = 0.81). But stylometry is no silver bullet as style-based fake news detection does not work (F1 = 0.46). We further reveal that left-wing and right-wing news share significantly more stylistic similarities than either does with the mainstream. This result is robust: it has been confirmed by three different modeling approaches, one of which employs Unmasking in a novel way. Applications of our results include partisanship detection and pre-screening for semi-automatic fake news detection.

Harvey Mudd College at SemEval-2019 Task 4: The Clint Buchanan Hyperpartisan News Detector

Abstract: We investigate the recently developed Bidirectional Encoder Representations from Transformers (BERT) model for the hyperpartisan news detection task. Using a subset of hand-labeled articles from SemEval as a validation set, we test the performance of different parameters for BERT models. We find that accuracy from two different BERT models using different proportions of the articles is consistently high, with our best-performing model on the validation set achieving 85% accuracy and the best-performing model on the test set achieving 77%. We further determined that our model exhibits strong consistency, labeling independent slices of the same article identically. Finally, we find that randomizing the order of word pieces dramatically reduces validation accuracy (to approximately 60%), but that shuffling groups of four or more word pieces maintains an accuracy of about 80%, indicating the model mainly gains value from local context.

A Bias Aware News Recommendation System

Abstract: In this era of fake news and political polarization, it is desirable to have a system to enable users to access balanced news content. Current solutions focus on top down, server based approaches to decide whether a news article is fake or biased, and display only trusted news to the end users. In this paper, we follow a different approach to help the users make informed choices about which news they want to read, making users aware in real time of the bias in news articles they were browsing and recommending news articles from other sources on the same topic with different levels of bias. We use a recent Pew research report to collect news sources that readers with varying political inclinations prefer to read. We then scrape news articles on a variety of topics from these varied news sources. After this, we perform clustering to find similar topics of the articles, as well as calculate a bias score for each article. For a news article the user is currently reading, we display the bias score and also display other articles on the same topic, out of the previously collected articles, from different news sources. This we present to the user. This approach, we hope, would make it possible for users to access more balanced articles on given news topics. We present the implementation details of the system along with some preliminary results on news articles.

Topic-Specific Sentiment Analysis Can Help Identify Political Ideology

Abstract: Ideological leanings of an individual can often be gauged by the sentiment one expresses about different issues. We propose a simple framework that represents a political ideology as a distribution of sentiment polarities towards a set of topics. This representation can then be used to detect ideological leanings of documents (speeches, news articles, etc.) based on the sentiments expressed towards different topics. Experiments performed using a widely used dataset show the promise of our proposed approach that achieves comparable performance to other methods despite being much simpler and more interpretable.

Multi-view Models for Political Ideology Detection of News Articles

Abstract: A news article’s title, content and link structure often reveal its political ideology. However, most existing works on automatic political ideology detection only leverage textual cues. Drawing inspiration from recent advances in neural inference, we propose a novel attention based multi-view model to leverage cues from all of the above views to identify the ideology evinced by a news article. Our model draws on advances in representation learning in natural language processing and network science to capture cues from both textual content and the network structure of news articles. We empirically evaluate our model against a battery of baselines and show that our model outperforms state of the art by 10 percentage points F1 score.

We Can Detect Your Bias: Predicting the Political Ideology of News Articles

Abstract: We explore the task of predicting the leading political ideology or bias of news articles. First, we collect and release a large dataset of 34,737 articles that were manually annotated for political ideology -left, center, or right-, which is well-balanced across both topics and media. We further use a challenging experimental setup where the test examples come from media that were not seen during training, which prevents the model from learning to detect the source of the target news article instead of predicting its political ideology. From a modeling perspective, we propose an adversarial media adaptation, as well as a specially adapted triplet loss. We further add background information about the source, and we show that it is quite helpful for improving article-level prediction. Our experimental results show very sizable improvements over using state-of-the-art pre-trained Transformers in this challenging setup.

Related work in political bias (top) and political ideology (bottom) inference from text. For each study, we report task category as either `bias' (e.g., hyperpartisan news detection) or `ideology' (political ideology, stance, and alignment), label granularity (t = text-level, a = author-level), main learning features (text or network relations), learning methods (e.g., RNN = recurrent networks, U = stylometry, CNN = convolutional networks, LogReg = logistic regression, SVM = Support Vector Machines, RGC = graph networks, NPOV=neutral point of view), and text genre (D = discourse/debate, N = news, T = Twitter, W = Wikipedia.)

Table {{table:c25cf241-5797-49c3-945b-2390c4a09f0b}}  presents an overview of recent NLP work on political bias and ideology inference from text data. All selected studies happen to be devoted to the English language. Further details are discussed below.


Neural AMR: Sequence-to-Sequence Models for Parsing and Generation

Abstract: Sequence-to-sequence models have shown strong performance across a broad range of applications. However, their application to parsing and generating text usingAbstract Meaning Representation (AMR)has been limited, due to the relatively limited amount of labeled data and the non-sequential nature of the AMR graphs. We present a novel training procedure that can lift this limitation using millions of unlabeled sentences and careful preprocessing of the AMR graphs. For AMR parsing, our model achieves competitive results of 62.1SMATCH, the current best score reported without significant use of external semantic resources. For AMR generation, our model establishes a new state-of-the-art performance of BLEU 33.8. We present extensive ablative and qualitative analysis including strong evidence that sequence-based AMR models are robust against ordering variations of graph-to-sequence conversions.

A Graph-to-Sequence Model for AMR-to-Text Generation

Abstract: The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph. The current state-of-the-art method uses a sequence-to-sequence model, leveraging LSTM for encoding a linearized AMR structure. Although being able to model non-local semantic information, a sequence LSTM can lose information from the AMR graph structure, and thus faces challenges with large graphs, which result in long sequences. We introduce a neural graph-to-sequence model, using a novel LSTM structure for directly encoding graph-level semantics. On a standard benchmark, our model shows superior results to existing methods in the literature.

Modeling Graph Structure in Transformer for Better AMR-to-Text Generation

Abstract: Recent studies on AMR-to-text generation often formalize the task as a sequence-to-sequence (seq2seq) learning problem by converting an Abstract Meaning Representation (AMR) graph into a word sequences. Graph structures are further modeled into the seq2seq framework in order to utilize the structural information in the AMR graphs. However, previous approaches only consider the relations between directly connected concepts while ignoring the rich structure in AMR graphs. In this paper we eliminate such a strong limitation and propose a novel structure-aware self-attention approach to better model the relations between indirectly connected concepts in the state-of-the-art seq2seq model, i.e. the Transformer. In particular, a few different methods are explored to learn structural representations between two concepts. Experimental results on English AMR benchmark datasets show that our approach significantly outperforms the state-of-the-art with 29.66 and 31.82 BLEU scores on LDC2015E86 and LDC2017T10, respectively. To the best of our knowledge, these are the best results achieved so far by supervised models on the benchmarks.

GPT-too: A language-model-first approach for AMR-to-text generation

Abstract: Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs. Existing approaches to generating text from AMR have focused on training sequence-to-sequence or graph-to-sequence models on AMR annotated data only. In this paper, we propose an alternative approach that combines a strong pre-trained language model with cycle consistency-based re-scoring. Despite the simplicity of the approach, our experimental results show these models outperform all previous techniques on the English LDC2017T10dataset, including the recent use of transformer architectures. In addition to the standard evaluation metrics, we provide human evaluation experiments that further substantiate the strength of our approach.

Structural Information Preserving for Graph-to-Text Generation

Abstract: The task of graph-to-text generation aims at producing sentences that preserve the meaning of input graphs. As a crucial defect, the current state-of-the-art models may mess up or even drop the core structural information of input graphs when generating outputs. We propose to tackle this problem by leveraging richer training signals that can guide our model for preserving input information. In particular, we introduce two types of autoencoding losses, each individually focusing on different aspects (a.k.a. views) of input graphs. The losses are then back-propagated to better calibrate our model via multi-task training. Experiments on two benchmarks for graph-to-text generation show the effectiveness of our approach over a state-of-the-art baseline. Our code is available at \url{http://github.com/Soistesimmer/AMR-multiview}.

Online Back-Parsing for AMR-to-Text Generation

Abstract: AMR-to-text generation aims to recover a text containing the same meaning as an input AMR graph. Current research develops increasingly powerful graph encoders to better represent AMR graphs, with decoders based on standard language modeling being used to generate outputs. We propose a decoder that back predicts projected AMR graphs on the target sentence during text generation. As the result, our outputs can better preserve the input meaning than standard decoders. Experiments on two AMR benchmarks show the superiority of our model over the previous state-of-the-art system based on graph Transformer.

Main methods of AMR-to-Text

As illustrated in Figure {{figure:9a6c8cab-ddc1-402c-8008-bb6780b059bf}} , the main methods of AMR-to-Text are revolved around the Encoder area and other derivatives, such as Decoder and training process. In particular, most of the literature in AMR-to-Text focused on the Encoder area in an attempt to better represent AMR graphs. The focus area and used techniques between each paper with AMR-to-Text are shown in Table {{table:1a4c30f2-0809-4902-859e-dfed2bedab91}} . In this section, we present the different techniques used in the Encoder area and their corresponding analysis.


BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

Abstract: Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Researchers are usually constrained to study a small set of problems on one dataset, while real-world computer vision applications require performing tasks of various complexities. We construct BDD100K, the largest driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving. The dataset possesses geographic, environmental, and weather diversity, which is useful for training models that are less likely to be surprised by new conditions. Based on this diverse dataset, we build a benchmark for heterogeneous multitask learning and study how to solve the tasks together. Our experiments show that special training strategies are needed for existing models to perform such heterogeneous tasks. BDD100K opens the door for future studies in this important venue.

A data set providing synthetic and real-world fisheye video sequences

Abstract: In video surveillance as well as automotive applications, so-called fisheye cameras are often employed to capture a very wide angle of view. As such cameras depend on projections quite different from the classical perspective projection, the resulting fisheye image and video data correspondingly exhibits non-rectilinear image characteristics. Typical image and video processing algorithms, however, are not designed for these fisheye characteristics. To be able to develop and evaluate algorithms specifically adapted to fisheye images and videos, a corresponding test data set is therefore introduced in this paper. The first of those sequences were generated during the authors' own work on motion estimation for fish-eye videos and further sequences have gradually been added to create a more extensive collection. The data set now comprises synthetically generated fisheye sequences, ranging from simple patterns to more complex scenes, as well as fisheye video sequences captured with an actual fisheye camera. For the synthetic sequences, exact information on the lens employed is available, thus facilitating both verification and evaluation of any adapted algorithms. For the real-world sequences, we provide calibration data as well as the settings used during acquisition. The sequences are freely available via www.lms.lnt.de/fisheyedataset/.

The detailed training and testing split on four datasets.

Moreover, we also validate the generalization ability of different methods on the real-world fisheye image dataset LMS {{cite:13ed9eb}}, in which the Fujinon FE185C057HA-1 fisheye lens was used to capture various scenes. The details of datasets are listed in Table {{table:38c22a87-6a7b-4d64-9752-58e8e85f34f5}} .


Secure Transmission with Multiple Antennas: The MISOME Wiretap Channel

Abstract: The role of multiple antennas for secure communication is investigated within the framework of Wyner's wiretap channel. We characterize the secrecy capacity in terms of generalized eigenvalues when the sender and eavesdropper have multiple antennas, the intended receiver has a single antenna, and the channel matrices are fixed and known to all the terminals, and show that a beamforming strategy is capacity-achieving. In addition, we show that in the high signal-to-noise (SNR) ratio regime the penalty for not knowing eavesdropper's channel is small--a simple ``secure space-time code'' that can be thought of as masked beamforming and radiates power isotropically attains near-optimal performance. In the limit of large number of antennas, we obtain a realization-independent characterization of the secrecy capacity as a function of the number $\beta$: the number of eavesdropper antennas per sender antenna. We show that the eavesdropper is comparatively ineffective when $\beta<1$, but that for $\beta\ge2$ the eavesdropper can drive the secrecy capacity to zero, thereby blocking secure communication to the intended receiver. Extensions to ergodic fading channels are also provided.

Comparison of MIMO wiretap transmission strategies for various ECSIT assumptions

A summary of transmission strategies in the MIMO wiretap channel for various assumptions regarding eavesdropper channel state information at the transmitter (ECSIT) is presented in Table {{table:ff21d477-7aae-4757-9428-a993e9105117}} .


Near linear time algorithm to detect community structures in large-scale networks

Abstract: Community detection and analysis is an important methodology for understanding the organization of various real-world networks and has applications in problems as diverse as consensus formation in social communities or the identification of functional modules in biochemical networks. Currently used algorithms that identify the community structures in large-scale real-world networks require a priori information such as the number and sizes of communities or are computationally expensive. In this paper we investigate a simple label propagation algorithm that uses the network structure alone as its guide and requires neither optimization of a pre-defined objective function nor prior information about the communities. In our algorithm every node is initialized with a unique label and at every step each node adopts the label that most of its neighbors currently have. In this iterative process densely connected groups of nodes form a consensus on a unique label to form communities. We validate the algorithm by applying it to networks whose community structures are known. We also demonstrate that the algorithm takes an almost linear time and hence it is computationally less expensive than what was possible so far.

SLPA: Uncovering Overlapping Communities in Social Networks via A Speaker-listener Interaction Dynamic Process

Abstract: Overlap is one of the characteristics of social networks, in which a person may belong to more than one social group. For this reason, discovering overlapping structures is necessary for realistic social analysis. In this paper, we present a novel, general framework to detect and analyze both individual overlapping nodes and entire communities. In this framework, nodes exchange labels according to dynamic interaction rules. A specific implementation called Speaker-listener Label Propagation Algorithm (SLPA1) demonstrates an excellent performance in identifying both overlapping nodes and overlapping communities with different degrees of diversity.

Community Detection via Local Dynamic Interaction

Abstract: How can we uncover the natural communities in a real network that allows insight into its underlying structure and also potential functions? In this paper, we introduce a new community detection algorithm, called Attractor, which automatically spots the communities or groups in a network over time via local dynamic interaction. The basic idea is to envision a network as a dynamical system, and each agent interacts with its local partners. Instead of investigating the node dynamics, we actually examine the change of "distances" among linked nodes. As time evolves, these distances will be shrunk or stretched gradually based on their topological structures. Finally all distances among linked nodes will converge into a stable pattern, and communities can be intuitively identified. Thanks to the dynamic viewpoint of community detection, Attractor has several potential attractive properties: (a) Attractor provides an intuitive solution to analyze the community structure of a network, and faithfully captures the natural communities (with high quality). (b) Owing to its time complexity $O(|E|)$, Attractor allows finding communities on large networks. (c) The small communities or anomalies, usually existing in real-world networks, can be well pinpointed. (d) Attractor is easy to parameterize, since there is no need to specify the number of clusters. Extensive experiments on synthetic and real-world networks further demonstrate the effectiveness and efficiency of the proposed approach.

Fast unfolding of communities in large networks

Abstract: We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2 million customers and by analysing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad hoc modular networks.

Dynamic Structural Similarity on Graphs

Abstract: One way of characterizing the topological and structural properties of vertices and edges in a graph is by using structural similarity measures. Measures like Cosine, Jaccard and Dice compute the similarities restricted to the immediate neighborhood of the vertices, bypassing important structural properties beyond the locality. Others measures, such as the generalized edge clustering coefficient, go beyond the locality but with high computational complexity, making them impractical in large-scale scenarios. In this paper we propose a novel similarity measure that determines the structural similarity by dynamically diffusing and capturing information beyond the locality. This new similarity is modeled as an iterated function that can be solved by fixed point iteration in super-linear time and memory complexity, so it is able to analyze large-scale graphs. In order to show the advantages of the proposed similarity in the community detection task, we replace the local structural similarity used in the SCAN algorithm with the proposed similarity measure, improving the quality of the detected community structure and also reducing the sensitivity to the parameter $\epsilon$ of the SCAN algorithm.

DEMON: a Local-First Discovery Method for Overlapping Communities

Abstract: Community discovery in complex networks is an interesting problem with a number of applications, especially in the knowledge extraction task in social and information networks. However, many large networks often lack a particular community organization at a global level. In these cases, traditional graph partitioning algorithms fail to let the latent knowledge embedded in modular structure emerge, because they impose a top-down global view of a network. We propose here a simple local-first approach to community discovery, able to unveil the modular organization of real complex networks. This is achieved by democratically letting each node vote for the communities it sees surrounding it in its limited view of the global system, i.e. its ego neighborhood, using a label propagation algorithm; finally, the local communities are merged into a global collection. We tested this intuition against the state-of-the-art overlapping and non-overlapping community discovery methods, and found that our new method clearly outperforms the others in the quality of the obtained communities, evaluated by using the extracted communities to predict the metadata about the nodes of several real world networks. We also show how our method is deterministic, fully incremental, and has a limited time complexity, so that it can be used on web-scale real networks.

Set of fast and unsupervised state-of-the-art algorithms selected for the comparative analysis.

In order to compare our proposed algorithms, a set of algorithms was chosen from literature. Those algorithms are top because of their efficiency and efficacy in the community detection task. Only unsupervised algorithms with average time complexity of {{formula:14514b36-083d-4e67-91e0-c45f9276c046}}  in complex networks are considered, because they not require the number of communities to detect as parameter, and are applicable to large-scale networks. Table {{table:71757d8b-b1ee-47f4-8858-b54d0f98d4d4}}  summarizes the selected algorithms.


Proposed a security framework for virtualized Small Cell Networks, with the aim of further extending MEC in the broader 5G environment

Addresses the utility based matching or pairing problem within the same domain of IoT nodes by using Irving's matching algorithm under the node specified preferences to endure a stable IoT node pairing

Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges

Abstract: For various reasons, the cloud computing paradigm is unable to meet certain requirements (e.g. low latency and jitter, context awareness, mobility support) that are crucial for several applications (e.g. vehicular networks, augmented reality). To fulfil these requirements, various paradigms, such as fog computing, mobile edge computing, and mobile cloud computing, have emerged in recent years. While these edge paradigms share several features, most of the existing research is compartmentalised; no synergies have been explored. This is especially true in the field of security, where most analyses focus only on one edge paradigm, while ignoring the others. The main goal of this study is to holistically analyse the security threats, challenges, and mechanisms inherent in all edge paradigms, while highlighting potential synergies and venues of collaboration. In our results, we will show that all edge paradigms should consider the advances in other paradigms.

Abstract: This is an official pytorch implementation of Deep High-Resolution Representation Learning for Human Pose Estimation. In this work, we are interested in the human pose estimation problem with a focus on learning reliable high-resolution representations. Most existing methods recover high-resolution representations from low-resolution representations produced by a high-to-low resolution network. Instead, our proposed network maintains high-resolution representations through the whole process. We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the mutli-resolution subnetworks in parallel. We conduct repeated multi-scale fusions such that each of the high-to-low resolution representations receives information from other parallel representations over and over, leading to rich high-resolution representations. As a result, the predicted keypoint heatmap is potentially more accurate and spatially more precise. We empirically demonstrate the effectiveness of our network through the superior pose estimation results over two benchmark datasets: the COCO keypoint detection dataset and the MPII Human Pose dataset. The code and models have been publicly available at \url{https://github.com/leoxiaobin/deep-high-resolution-net.pytorch}.

Comparison of the reviewed state-of-the-art security, privacy, and trust management in MEC enabled IoT.

In many previous literatures, data perception trust is addressed in the context of security and privacy, mainly by mitigating security attacks on data aggregation and processing, as well as exploiting some key management techniques {{cite:c95906d}}. Some recent literatures have also addressed data protection and performance improvement at the edge computing servers by trust management among fog servers . Furthermore, trust is paramount to the effectiveness of node interaction in SIoT where the objects are building up a social network and becoming more autonomous {{cite:7f39f85}}. Table {{table:d9a16370-26c2-4ace-9b0b-a0b0ca2f0d2c}}  summarizes the reviewed state-of-the-art security, privacy, and trust management in MEC enabled IoT.