On Guaranteed Optimal Robust Explanations for NLP Models

Abstract: We build on abduction-based explanations for machine learning and develop a method for computing local explanations for neural network models in natural language processing (NLP). Our explanations comprise a subset of the words of the input text that satisfies two key features: optimality w.r.t. a user-defined cost function, such as the length of explanation, and robustness, in that they ensure prediction invariance for any bounded perturbation in the embedding space of the left-out words. We present two solution algorithms, respectively based on implicit hitting sets and maximum universal subsets, introducing a number of algorithmic improvements to speed up convergence of hard instances. We show how our method can be configured with different perturbation sets in the embedded space and used to detect bias in predictions by enforcing include/exclude constraints on biased terms, as well as to enhance existing heuristic-based NLP explanation frameworks such as Anchors. We evaluate our framework on three widely used sentiment analysis tasks and texts of up to 100 words from SST, Twitter and IMDB datasets, demonstrating the effectiveness of the derived explanations.

Deceptive AI Explanations: Creation and Detection

Abstract: Artificial intelligence (AI) comes with great opportunities but also great risks. Automatically generated explanations for decisions are deemed helpful to better understand AI, increasing transparency and fostering trust. However, given e.g. economic incentives to create dishonest AI, can we trust its explanations? To address this issue, our paper investigates to what extent models of AI, i.e. deep learning, and existing instruments to increase transparency regarding AI decisions can be used to create and detect deceptive explanations. For empirical evaluation, we focus on text classification and alter explanations originating from GradCAM, a well-established technique for creating explanations in neural networks. We then evaluate the effect of deceptive explanations on users in an experiment with 200 participants. Our findings confirm that deceptive explanations can indeed fool humans while machine learning methods can detect seemingly minor attempts of deception with accuracy that exceeds 80\% given sufficient domain knowledge in the form of training data. Without domain knowledge, one can still infer inconsistencies in the explanations in an unsupervised manner given basic knowledge on the allegedly deceptive model.

Unfooling Perturbation-Based Post Hoc Explainers

Abstract: Monumental advancements in artificial intelligence (AI) have lured the interest of doctors, lenders, judges, and other professionals. While these high-stakes decision-makers are optimistic about the technology, those familiar with AI systems are wary about the lack of transparency of its decision-making processes. Perturbation-based post hoc explainers offer a model agnostic means of interpreting these systems while only requiring query-level access. However, recent work demonstrates that these explainers can be fooled adversarially. This discovery has adverse implications for auditors, regulators, and other sentinels. With this in mind, several natural questions arise - how can we audit these black box systems? And how can we ascertain that the auditee is complying with the audit in good faith? In this work, we rigorously formalize this problem and devise a defense against adversarial attacks on perturbation-based explainers. We propose algorithms for the detection (CAD-Detect) and defense (CAD-Defend) of these attacks, which are aided by our novel conditional anomaly detection approach, KNN-CAD. We demonstrate that our approach successfully detects whether a black box system adversarially conceals its decision-making process and mitigates the adversarial attack on real-world data for the prevalent explainers, LIME and SHAP. The code for this work is available at https://github.com/craymichael/unfooling.

Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse

Abstract: As machine learning models are increasingly being employed to make consequential decisions in real-world settings, it becomes critical to ensure that individuals who are adversely impacted (e.g., loan denied) by the predictions of these models are provided with a means for recourse. While several approaches have been proposed to construct recourses for affected individuals, the recourses output by these methods either achieve low costs (i.e., ease-of-implementation) or robustness to small perturbations (i.e., noisy implementations of recourses), but not both due to the inherent trade-offs between the recourse costs and robustness. Furthermore, prior approaches do not provide end users with any agency over navigating the aforementioned trade-offs. In this work, we address the above challenges by proposing the first algorithmic framework which enables users to effectively manage the recourse cost vs. robustness trade-offs. More specifically, our framework Probabilistically ROBust rEcourse (\texttt{PROBE}) lets users choose the probability with which a recourse could get invalidated (recourse invalidation rate) if small changes are made to the recourse i.e., the recourse is implemented somewhat noisily. To this end, we propose a novel objective function which simultaneously minimizes the gap between the achieved (resulting) and desired recourse invalidation rates, minimizes recourse costs, and also ensures that the resulting recourse achieves a positive model prediction. We develop novel theoretical results to characterize the recourse invalidation rates corresponding to any given instance w.r.t. different classes of underlying models (e.g., linear models, tree based models etc.), and leverage these results to efficiently optimize the proposed objective. Experimental evaluation with multiple real world datasets demonstrates the efficacy of the proposed framework.

Summary of defenses against the attacks on explanations of machine learning models. Each work on explanations' robustness is connected with up to two attacks that are mentioned to be potentially addressed by it. We abbreviate the following: data (D), model (M), image (I), tabular (T), language (Lg), neural network (N), black-box (B), local (L), global (G). Appendix  lists other abbreviations.

Defense against the attacks on explanations

Table {{table:112c6e07-9b09-44f9-a002-b11847d06dcd}}  lists defenses against the attacks on explanations, where for each, we record the datasets, models and explanation algorithms mentioned in experiments. Excluded from it are works that improve explanation robustness without directly relating to the potential adversarial attack scenario {{cite:846e1fc}}, {{cite:8dac493}}, {{cite:7207680}}, {{cite:ef79b9b}}. We link each defense with an attack, but omit to list all attacks potentially addressed by the defense for brevity. The three missing links are worth clarifying here. {{cite:e8b902d}} is an early work that introduces adversarial explanations, which have improved robustness against adversarial examples targeting model predictions. Similarly, {{cite:eb34488}} proposes to improve explanations of language models against adversarial perturbations. Unlike most of the contributions that focus on algorithms, {{cite:4cfdee2}} conduct a user study with artificially manipulated explanations to evaluate if humans can discover the potential attack in practice {{cite:3e97fc2}}, {{cite:2525853}}. {{cite:06181fc}} and {{cite:3e8bcac}} introduce mechanisms to improve the robustness of counterfactual explanations against adversarial perturbations (we link the latter with an otherwise unreferenced attack of {{cite:59059fb}}).

Comparing Tables {{table:ba667c20-3a33-4d95-98e4-954410f171ad}}  & {{table:112c6e07-9b09-44f9-a002-b11847d06dcd}}  highlights existing insecurities in XAI methods; namely, not clearly addressed are backdooring attacks {{cite:c14815b}}, {{cite:e3e2a42}}, data poisoning attacks {{cite:e23d526}}, {{cite:6283e99}}, attacks specific to language {{cite:6fa6944}} and concept-based explainability {{cite:46cc821}}.


Automatic Anomaly Detection in the Cloud Via Statistical Learning

Abstract: Performance and high availability have become increasingly important drivers, amongst other drivers, for user retention in the context of web services such as social networks, and web search. Exogenic and/or endogenic factors often give rise to anomalies, making it very challenging to maintain high availability, while also delivering high performance. Given that service-oriented architectures (SOA) typically have a large number of services, with each service having a large set of metrics, automatic detection of anomalies is non-trivial. Although there exists a large body of prior research in anomaly detection, existing techniques are not applicable in the context of social network data, owing to the inherent seasonal and trend components in the time series data. To this end, we developed two novel statistical techniques for automatically detecting anomalies in cloud infrastructure data. Specifically, the techniques employ statistical learning to detect anomalies in both application, and system metrics. Seasonal decomposition is employed to filter the trend and seasonal components of the time series, followed by the use of robust statistical metrics -- median and median absolute deviation (MAD) -- to accurately detect anomalies, even in the presence of seasonal spikes. We demonstrate the efficacy of the proposed techniques from three different perspectives, viz., capacity planning, user behavior, and supervised learning. In particular, we used production data for evaluation, and we report Precision, Recall, and F-measure in each case.

Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications

Abstract: To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e.g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation. However, anomaly detection for these seasonal KPIs with various patterns and data quality has been a great challenge, especially without labels. In this paper, we proposed Donut, an unsupervised anomaly detection algorithm based on VAE. Thanks to a few of our key techniques, Donut greatly outperforms a state-of-arts supervised ensemble approach and a baseline VAE approach, and its best F-scores range from 0.75 to 0.9 for the studied KPIs from a top global Internet company. We come up with a novel KDE interpretation of reconstruction for Donut, making it the first VAE-based anomaly detection algorithm with solid theoretical explanation.

Time-Series Anomaly Detection Service at Microsoft

Abstract: Large companies need to monitor various metrics (for example, Page Views and Revenue) of their applications and services in real time. At Microsoft, we develop a time-series anomaly detection service which helps customers to monitor the time-series continuously and alert for potential incidents on time. In this paper, we introduce the pipeline and algorithm of our anomaly detection service, which is designed to be accurate, efficient and general. The pipeline consists of three major modules, including data ingestion, experimentation platform and online compute. To tackle the problem of time-series anomaly detection, we propose a novel algorithm based on Spectral Residual (SR) and Convolutional Neural Network (CNN). Our work is the first attempt to borrow the SR model from visual saliency detection domain to time-series anomaly detection. Moreover, we innovatively combine SR and CNN together to improve the performance of SR model. Our approach achieves superior experimental results compared with state-of-the-art baselines on both public datasets and Microsoft production data.

Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models

Abstract: This paper introduces a new methodology for detecting anomalies in time series data, with a primary application to monitoring the health of (micro-) services and cloud resources. The main novelty in our approach is that instead of modeling time series consisting of real values or vectors of real values, we model time series of probability distributions over real values (or vectors). This extension to time series of probability distributions allows the technique to be applied to the common scenario where the data is generated by requests coming in to a service, which is then aggregated at a fixed temporal frequency. Our method is amenable to streaming anomaly detection and scales to monitoring for anomalies on millions of time series. We show the superior accuracy of our method on synthetic and public real-world data. On the Yahoo Webscope data set, we outperform the state of the art in 3 out of 4 data sets and we show that we outperform popular open-source anomaly detection tools by up to 17% average improvement for a real-world data set.

RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks

Abstract: The monitoring and management of numerous and diverse time series data at Alibaba Group calls for an effective and scalable time series anomaly detection service. In this paper, we propose RobustTAD, a Robust Time series Anomaly Detection framework by integrating robust seasonal-trend decomposition and convolutional neural network for time series data. The seasonal-trend decomposition can effectively handle complicated patterns in time series, and meanwhile significantly simplifies the architecture of the neural network, which is an encoder-decoder architecture with skip connections. This architecture can effectively capture the multi-scale information from time series, which is very useful in anomaly detection. Due to the limited labeled data in time series anomaly detection, we systematically investigate data augmentation methods in both time and frequency domains. We also introduce label-based weight and value-based weight in the loss function by utilizing the unbalanced nature of the time series anomaly detection problem. Compared with the widely used forecasting-based anomaly detection algorithms, decomposition-based algorithms, traditional statistical algorithms, as well as recent neural network based algorithms, RobustTAD performs significantly better on public benchmark datasets. It is deployed as a public online service and widely adopted in different business scenarios at Alibaba Group.

Intrinsic Anomaly Detection for Multi-Variate Time Series

Abstract: We introduce a novel, practically relevant variation of the anomaly detection problem in multi-variate time series: intrinsic anomaly detection. It appears in diverse practical scenarios ranging from DevOps to IoT, where we want to recognize failures of a system that operates under the influence of a surrounding environment. Intrinsic anomalies are changes in the functional dependency structure between time series that represent an environment and time series that represent the internal state of a system that is placed in said environment. We formalize this problem, provide under-studied public and new purpose-built data sets for it, and present methods that handle intrinsic anomaly detection. These address the short-coming of existing anomaly detection methods that cannot differentiate between expected changes in the system's state and unexpected ones, i.e., changes in the system that deviate from the environment's influence. Our most promising approach is fully unsupervised and combines adversarial learning and time series representation learning, thereby addressing problems such as label sparsity and subjectivity, while allowing to navigate and improve notoriously problematic anomaly detection data sets.

Comparison of Existing Metric Anomaly Detection Models

As metrics are a form of time series data, and incidents are expressed as an abnormal occurrence in the data, metric incident detection is most often formulated as a time series anomaly detection problem {{cite:a6cda77}}, {{cite:369b4c0}}, {{cite:59c7ec3}}.
In the following, we focus on the AIOps setting and categorize it based on several key criteria: (i) learning paradigm, (ii) dimensionality, (iii) system, and (iv) streaming updates. We further summarize a list of time series anomaly detection methods with a comparison over these criteria in Table {{table:47ecdff6-a6e6-4aa7-852c-1e1b73a23d95}} .


Medical Event Classification, Medication Identification

ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness

Abstract: The emergence of generative large language models (LLMs) raises the question: what will be its impact on crowdsourcing? Traditionally, crowdsourcing has been used for acquiring solutions to a wide variety of human-intelligence tasks, including ones involving text generation, modification or evaluation. For some of these tasks, models like ChatGPT can potentially substitute human workers. In this study, we investigate whether this is the case for the task of paraphrase generation for intent classification. We apply data collection methodology of an existing crowdsourcing study (similar scale, prompts and seed data) using ChatGPT and Falcon-40B. We show that ChatGPT-created paraphrases are more diverse and lead to at least as robust models.

LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning

Abstract: In recent years, there has been significant progress in developing pre-trained language models for NLP. However, these models often struggle when fine-tuned on small datasets. To address this issue, researchers have proposed various adaptation approaches. Prompt-based tuning is arguably the most common way, especially for larger models. Previous research shows that adding contrastive learning to prompt-based fine-tuning is effective as it helps the model generate embeddings that are more distinguishable between classes, and it can also be more sample-efficient as the model learns from positive and negative examples simultaneously. One of the most important components of contrastive learning is data augmentation, but unlike computer vision, effective data augmentation for NLP is still challenging. This paper proposes LM-CPPF, Contrastive Paraphrasing-guided Prompt-based Fine-tuning of Language Models, which leverages prompt-based few-shot paraphrasing using generative language models, especially large language models such as GPT-3 and OPT-175B, for data augmentation. Our experiments on multiple text classification benchmarks show that this augmentation method outperforms other methods, such as easy data augmentation, back translation, and multiple templates.

Medical Data Augmentation via ChatGPT: A Case Study on Medication Identification and Medication Event Classification

Abstract: The identification of key factors such as medications, diseases, and relationships within electronic health records and clinical notes has a wide range of applications in the clinical field. In the N2C2 2022 competitions, various tasks were presented to promote the identification of key factors in electronic health records (EHRs) using the Contextualized Medication Event Dataset (CMED). Pretrained large language models (LLMs) demonstrated exceptional performance in these tasks. This study aims to explore the utilization of LLMs, specifically ChatGPT, for data augmentation to overcome the limited availability of annotated data for identifying the key factors in EHRs. Additionally, different pre-trained BERT models, initially trained on extensive datasets like Wikipedia and MIMIC, were employed to develop models for identifying these key variables in EHRs through fine-tuning on augmented datasets. The experimental results of two EHR analysis tasks, namely medication identification and medication event classification, indicate that data augmentation based on ChatGPT proves beneficial in improving performance for both medication identification and medication event classification.

Exploring Zero and Few-shot Techniques for Intent Classification

Abstract: Conversational NLU providers often need to scale to thousands of intent-classification models where new customers often face the cold-start problem. Scaling to so many customers puts a constraint on storage space as well. In this paper, we explore four different zero and few-shot intent classification approaches with this low-resource constraint: 1) domain adaptation, 2) data augmentation, 3) zero-shot intent classification using descriptions large language models (LLMs), and 4) parameter-efficient fine-tuning of instruction-finetuned language models. Our results show that all these approaches are effective to different degrees in low-resource settings. Parameter-efficient fine-tuning using T-few recipe (Liu et al., 2022) on Flan-T5 (Chang et al., 2022) yields the best performance even with just one sample per intent. We also show that the zero-shot method of prompting LLMs using intent descriptions

AugGPT: Leveraging ChatGPT for Text Data Augmentation

Abstract: Text data augmentation is an effective strategy for overcoming the challenge of limited sample sizes in many natural language processing (NLP) tasks. This challenge is especially prominent in the few-shot learning scenario, where the data in the target domain is generally much scarcer and of lowered quality. A natural and widely-used strategy to mitigate such challenges is to perform data augmentation to better capture the data invariance and increase the sample size. However, current text data augmentation methods either can't ensure the correct labeling of the generated data (lacking faithfulness) or can't ensure sufficient diversity in the generated data (lacking compactness), or both. Inspired by the recent success of large language models, especially the development of ChatGPT, which demonstrated improved language comprehension abilities, in this work, we propose a text data augmentation approach based on ChatGPT (named AugGPT). AugGPT rephrases each sentence in the training samples into multiple conceptually similar but semantically different samples. The augmented samples can then be used in downstream model training. Experiment results on few-shot learning text classification tasks show the superior performance of the proposed AugGPT approach over state-of-the-art text data augmentation methods in terms of testing accuracy and distribution of the augmented samples.

ChatGPT as Data Augmentation for Compositional Generalization: A Case Study in Open Intent Detection

Abstract: Open intent detection, a crucial aspect of natural language understanding, involves the identification of previously unseen intents in user-generated text. Despite the progress made in this field, challenges persist in handling new combinations of language components, which is essential for compositional generalization. In this paper, we present a case study exploring the use of ChatGPT as a data augmentation technique to enhance compositional generalization in open intent detection tasks. We begin by discussing the limitations of existing benchmarks in evaluating this problem, highlighting the need for constructing datasets for addressing compositional generalization in open intent detection tasks. By incorporating synthetic data generated by ChatGPT into the training process, we demonstrate that our approach can effectively improve model performance. Rigorous evaluation of multiple benchmarks reveals that our method outperforms existing techniques and significantly enhances open intent detection capabilities. Our findings underscore the potential of large language models like ChatGPT for data augmentation in natural language understanding tasks.

Summary of research works exploring GLLMs for paraphrasing-based data augmentation.

Research works exploring GLLMs for paraphrasing-based data augmentation. The research community explored GLLMs for paraphrasing in various NLP tasks like intent classification {{cite:5d73bbc}}, {{cite:f52cfb7}}, {{cite:6932f46}}, machine translation {{cite:f8348e2}}, named entity recognition {{cite:1d009e8}}, question answering {{cite:1ac2406}}, medical event classification {{cite:8906e54}}, medication identification {{cite:8906e54}} etc. GLLM-based paraphrasing is explored in multiple domains like general {{cite:5d73bbc}}, {{cite:f8348e2}}, {{cite:1d009e8}}, {{cite:9030fde}}, {{cite:e4a2d65}}, {{cite:6932f46}}, news {{cite:1d009e8}}, social media {{cite:1d009e8}}, {{cite:f52cfb7}} and healthcare {{cite:1d009e8}}, {{cite:1ac2406}}, {{cite:8906e54}}, {{cite:e4a2d65}}. Table {{table:45e36039-fc28-409f-8917-8c06d466c54a}}  presents a summary of research works exploring GLLMs for paraphrasing-based data augmentation.


Turn-level Entities & Dialogue-level Goals & Recommended Items

Rules of Thumbs (RoTs) & Revised Responses

Turn-level Entities & Enriched Responses with Knowledge

Target-Guided Open-Domain Conversation

Abstract: Many real-world open-domain conversation applications have specific goals to achieve during open-ended chats, such as recommendation, psychotherapy, education, etc. We study the problem of imposing conversational goals on open-domain chat agents. In particular, we want a conversational system to chat naturally with human and proactively guide the conversation to a designated target subject. The problem is challenging as no public data is available for learning such a target-guided strategy. We propose a structured approach that introduces coarse-grained keywords to control the intended content of system responses. We then attain smooth conversation transition through turn-level supervised learning, and drive the conversation towards the target with discourse-level constraints. We further derive a keyword-augmented conversation dataset for the study. Quantitative and human evaluations show our system can produce meaningful and effective conversations, significantly improving over other approaches

Proactive Human-Machine Conversation with Explicit Conversation Goal

Abstract: Though great progress has been made for human-machine conversation, current dialogue system is still in its infancy: it usually converses passively and utters words more as a matter of response, rather than on its own initiatives. In this paper, we take a radical step towards building a human-like conversational agent: endowing it with the ability of proactively leading the conversation (introducing a new topic or maintaining the current topic). To facilitate the development of such conversation systems, we create a new dataset named Konv where one acts as a conversation leader and the other acts as the follower. The leader is provided with a knowledge graph and asked to sequentially change the discussion topics, following the given conversation goal, and meanwhile keep the dialogue as natural and engaging as possible. Konv enables a very challenging task as the model needs to both understand dialogue and plan over the given knowledge graph. We establish baseline results on this dataset (about 270K utterances and 30k dialogues) using several state-of-the-art models. Experimental results show that dialogue models that plan over the knowledge graph can make full use of related knowledge to generate more diverse multi-turn conversations. The baseline systems along with the dataset are publicly available.

Towards Conversational Recommendation over Multi-Type Dialogs

Abstract: We focus on the study of conversational recommendation in the context of multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recommendation dialog, taking into account user’s interests and feedback. To facilitate the study of this task, we create a human-to-human Chinese dialog dataset DuRecDial (about 10k dialogs, 156k utterances), where there are multiple sequential dialogs for a pair of a recommendation seeker (user) and a recommender (bot). In each dialog, the recommender proactively leads a multi-type dialog to approach recommendation targets and then makes multiple recommendations with rich interaction behavior. This dataset allows us to systematically investigate different parts of the overall problem, e.g., how to naturally lead a dialog, how to interact with users for recommendation. Finally we establish baseline results on DuRecDial for future studies.

The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems

Abstract: Conversational agents have come increasingly closer to human competence in open-domain dialogue settings; however, such models can reflect insensitive, hurtful, or entirely incoherent viewpoints that erode a user’s trust in the moral integrity of the system. Moral deviations are difficult to mitigate because moral judgments are not universal, and there may be multiple competing judgments that apply to a situation simultaneously. In this work, we introduce a new resource, not to authoritatively resolve moral ambiguities, but instead to facilitate systematic understanding of the intuitions, values and moral judgments reflected in the utterances of dialogue systems. The Moral Integrity Corpus, MIC, is such a resource, which captures the moral assumptions of 38k prompt-reply pairs, using 99k distinct Rules of Thumb (RoTs). Each RoT reflects a particular moral conviction that can explain why a chatbot’s reply may appear acceptable or problematic. We further organize RoTs with a set of 9 moral and social attributes and benchmark performance for attribute classification. Most importantly, we show that current neural language models can automatically generate new RoTs that reasonably describe previously unseen interactions, but they still struggle with certain scenarios. Our findings suggest that MIC will be a useful resource for understanding and language models’ implicit moral assumptions and flexibly benchmarking the integrity of conversational agents. To download the data, see https://github.com/GT-SALT/mic

ProsocialDialog: A Prosocial Backbone for Conversational Agents

Abstract: Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them. To address this issue, we introduce ProsocialDialog, the first large-scale multi-turn dialogue dataset to teach conversational agents to respond to problematic content following social norms. Covering diverse unethical, problematic, biased, and toxic situations, ProsocialDialog contains responses that encourage prosocial behavior, grounded in commonsense social rules (i.e., rules-of-thumb, RoTs). Created via a human-AI collaborative framework, ProsocialDialog consists of 58K dialogues, with 331K utterances, 160K unique RoTs, and 497K dialogue safety labels accompanied by free-form rationales.With this dataset, we introduce a dialogue safety detection module, Canary, capable of generating RoTs given conversational context, and a socially-informed dialogue agent, Prost. Empirical results show that Prost generates more socially acceptable dialogues compared to other state-of-the-art language and dialogue models in both in-domain and out-of-domain settings. Additionally, Canary effectively guides conversational agents and off-the-shelf language models to generate significantly more prosocial responses. Our work highlights the promise and importance of creating and steering conversational AI to be socially responsible.

Decoupling Strategy and Generation in Negotiation Dialogues

Abstract: We consider negotiation settings in which two agents use natural language to bargain on goods. Agents need to decide on both high-level strategy (e.g., proposing $50) and the execution of that strategy (e.g., generating “The bike is brand new. Selling for just $50!”). Recent work on negotiation trains neural models, but their end-to-end nature makes it hard to control their strategy, and reinforcement learning tends to lead to degenerate solutions. In this paper, we propose a modular approach based on coarse dialogue acts (e.g., propose(price=50)) that decouples strategy and generation. We show that we can flexibly set the strategy using supervised learning, reinforcement learning, or domain-specific knowledge without degeneracy, while our retrieval-based generation can maintain context-awareness and produce diverse utterances. We test our approach on the recently proposed DEALORNODEAL game, and we also collect a richer dataset based on real items on Craigslist. Human evaluation shows that our systems achieve higher task success rate and more human-like negotiation behavior than previous approaches.

Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good

Abstract: Developing intelligent persuasive conversational agents to change people’s opinions and actions for social good is the frontier in advancing the ethical development of automated dialogue systems. To do so, the first step is to understand the intricate organization of strategic disclosures and appeals employed in human persuasion conversations. We designed an online persuasion task where one participant was asked to persuade the other to donate to a specific charity. We collected a large dataset with 1,017 dialogues and annotated emerging persuasion strategies from a subset. Based on the annotation, we built a baseline classifier with context information and sentence-level features to predict the 10 persuasion strategies used in the corpus. Furthermore, to develop an understanding of personalized persuasion processes, we analyzed the relationships between individuals’ demographic and psychological backgrounds including personality, morality, value systems, and their willingness for donation. Then, we analyzed which types of persuasion strategies led to a greater amount of donation depending on the individuals’ personal backgrounds. This work lays the ground for developing a personalized persuasive dialogue system.

End-to-End Trainable Non-Collaborative Dialog System

Abstract: End-to-end task-oriented dialog models have achieved promising performance on collaborative tasks where users willingly coordinate with the system to complete a given task. While in non-collaborative settings, for example, negotiation and persuasion, users and systems do not share a common goal. As a result, compared to collaborate tasks, people use social content to build rapport and trust in these non-collaborative settings in order to advance their goals. To handle social content, we introduce a hierarchical intent annotation scheme, which can be generalized to different non-collaborative dialog tasks. Building upon TransferTransfo (Wolf et al. 2019), we propose an end-to-end neural network model to generate diverse coherent responses. Our model utilizes intent and semantic slots as the intermediate sentence representation to guide the generation process. In addition, we design a filter to select appropriate responses based on whether these intermediate representations fit the designed task and conversation constraints. Our non-collaborative dialog model guides users to complete the task while simultaneously keeps them engaged. We test our approach on our newly proposed AntiScam dataset and an existing PersuasionForGood dataset. Both automatic and human evaluations suggest that our model outperforms multiple baselines in these two non-collaborative tasks.

Adding Chit-Chat to Enhance Task-Oriented Dialogues

Abstract: Existing dialogue corpora and models are typically designed under two disjoint motives: while task-oriented systems focus on achieving functional goals (e.g., booking hotels), open-domain chatbots aim at making socially engaging conversations. In this work, we propose to integrate both types of systems by Adding Chit-Chat to ENhance Task-ORiented dialogues (ACCENTOR), with the goal of making virtual assistant conversations more engaging and interactive. Specifically, we propose a Human <-> AI collaborative data collection approach for generating diverse chit-chat responses to augment task-oriented dialogues with minimal annotation effort. We then present our new chit-chat-based annotations to 23.8K dialogues from two popular task-oriented datasets (Schema-Guided Dialogue and MultiWOZ 2.1) and demonstrate their advantage over the originals via human evaluation. Lastly, we propose three new models for adding chit-chat to task-oriented dialogues, explicitly trained to predict user goals and to generate contextually relevant chit-chat responses. Automatic and human evaluations show that, compared with the state-of-the-art task-oriented baseline, our models can code-switch between task and chit-chat to be more engaging, interesting, knowledgeable, and humanlike, while maintaining competitive task performance.

KETOD: Knowledge-Enriched Task-Oriented Dialogue

Abstract: Existing studies in dialogue system research mostly treat task-oriented dialogue and chit-chat as separate domains. Towards building a human-like assistant that can converse naturally and seamlessly with users, it is important to build a dialogue system that conducts both types of conversations effectively. In this work, we investigate how task-oriented dialogue and knowledge-grounded chit-chat can be effectively integrated into a single model. To this end, we create a new dataset, KETOD (Knowledge-Enriched Task-Oriented Dialogue), where we naturally enrich task-oriented dialogues with chit-chat based on relevant entity knowledge. We also propose two new models, SimpleToDPlus and Combiner, for the proposed task. Experimental results on both automatic and human evaluations show that the proposed methods can significantly improve the performance in knowledge-enriched response generation while maintaining a competitive task-oriented dialog performance. We believe our new dataset will be a valuable resource for future studies. Our dataset and code are publicly available at \url{https://github.com/facebookresearch/ketod}.

PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance

Abstract: To facilitate conversational question answering (CQA) over hybrid contexts in finance, we present a new dataset, named PACIFIC. Compared with existing CQA datasets, PACIFIC exhibits three key features: (i) proactivity, (ii) numerical reasoning, and (iii) hybrid context of tables and text. A new task is defined accordingly to study Proactive Conversational Question Answering (PCQA), which combines clarification question generation and CQA. In addition, we propose a novel method, namely UniPCQA, to adapt a hybrid format of input and output content in PCQA into the Seq2Seq problem, including the reformulation of the numerical reasoning process as code generation. UniPCQA performs multi-task learning over all sub-tasks in PCQA and incorporates a simple ensemble strategy to alleviate the error propagation issue in the multi-task learning by cross-validating top-k sampled Seq2Seq outputs. We benchmark the PACIFIC dataset with extensive baselines and provide comprehensive evaluations on each sub-task of PCQA.

Summary of representative data resources that are publicly available for different problems of proactive dialogue systems.

Firstly, different from echoing the user's topics, emotions, or views, several problems emerge to enable the system to lead the open-domain dialogues, such as target-guided dialogues {{cite:99d895e}} and prosocial dialogues {{cite:6eb204a}}.
As the examples illustrated in Figure {{figure:2be71b2d-43c1-4a19-ba91-33eb24a542c0}} , target-guided dialogues involve the agent leading discussions towards designated target topics (e.g., Music to K-Pop to Blackpink), while prosocial dialogues entrust the agent with constructively guiding conversations according to social norms in response to problematic user utterances (e.g., the cheating intention).
Secondly, rather than simply following the user's instruction, two distinct types of task-oriented dialogues are characterized by the necessity for the agent's proactivity:
(i) non-collaborative dialogues {{cite:404f25c}}, where the system and user may have divergent objective or conflicting interests regarding task completion (e.g., the price bargain negotiation), and (ii) enriched task-oriented dialogues {{cite:c8683ab}}, where the agent takes the initiative to provide useful supplementary information not explicitly requested by the user (e.g., additional knowledge or chitchats).
Thirdly, we discuss two groups of proactivity designs for enhancing the final performance of conversational information-seeking systems, including asking clarification questions {{cite:8ebee75}} and user preference elicitation {{cite:6a3c771}}.
Accordingly, we introduce the available data resources (summarized in Table {{table:7b224c3d-7263-42d7-a6c6-28ec231f5e4c}} ) and corresponding evaluation protocols for each problem.


Low Latency Privacy Preserving Inference

Abstract: When applying machine learning to sensitive data, one has to find a balance between accuracy, information security, and computational-complexity. Recent studies combined Homomorphic Encryption with neural networks to make inferences while protecting against information leakage. However, these methods are limited by the width and depth of neural networks that can be used (and hence the accuracy) and exhibit high latency even for relatively simple networks. In this study we provide two solutions that address these limitations. In the first solution, we present more than 10× improvement in latency and enable inference on wider networks compared to prior attempts with the same level of security. The improved performance is achieved by novel methods to represent the data during the computation. In the second solution, we apply the method of transfer learning to provide private inference services using deep networks with latency of ∼0.16 seconds. We demonstrate the efficacy of our methods on several computer vision tasks.

Neural Network Quantisation for Faster Homomorphic Encryption

Abstract: Homomorphic encryption (HE) enables calculating on encrypted data, which makes it possible to perform privacypreserving neural network inference. One disadvantage of this technique is that it is several orders of magnitudes slower than calculation on unencrypted data. Neural networks are commonly trained using floating-point, while most homomorphic encryption libraries calculate on integers, thus requiring a quantisation of the neural network. A straightforward approach would be to quantise to large integer sizes (e.g. 32 bit) to avoid large quantisation errors. In this work, we reduce the integer sizes of the networks, using quantisation-aware training, to allow more efficient computations. For the targeted MNIST architecture proposed by Badawi et al., we reduce the integer sizes by 33% without significant loss of accuracy, while for the CIFAR architecture, we can reduce the integer sizes by 43%. Implementing the resulting networks under the BFV homomorphic encryption scheme using SEAL, we could reduce the execution time of an MNIST neural network by 80% and by 40% for a CIFAR neural network.

Results of the quantisation-aware training and homomorphic inference.

Table {{table:a68f278b-042b-4f25-9c61-2ffe35d79d96}}  compares our QAT network to earlier works.
Notable is that the CryptoNets and HCNN implementations use PTQ techniques, but no QAT techniques.
Using QAT, we can quantise the weights to as low as 2, giving the network a much lower FIW with minimal to no drop in accuracy. Compared to a full precision network, i.e. quantising the parameters to 32 integers, the FIW is reduced with a factor of 8.2 for the MNIST network and a factor of 5 for the CIFAR network. Compared to the numbers presented by HCNN, our smallest networks have a 33% and 43% smaller final integer width for MNIST and CIFAR, respectively, while boasting similar accuracy.

We report the sequential times for various quantisation on the right in Table {{table:a68f278b-042b-4f25-9c61-2ffe35d79d96}} . To account for the number of instances, the `sequential time' is given, corresponding to the total time when each instance is executed sequentially and reflecting the use of computing resources.
For the CIFAR network, the work of {{cite:555309e}} uses ten instances, each possessing a plaintext size of around 21. Using the same sizes, our smallest network (2) only requires six instances.


Stellar Imager (SI): developing and testing a predictive dynamo model for the Sun by imaging other stars

Abstract: The Stellar Imager mission concept is a space-based UV/Optical interferometer designed to resolve surface magnetic activity and subsurface structure and flows of a population of Sun-like stars, in order to accelerate the development and validation of a predictive dynamo model for the Sun and enable accurate long-term forecasting of solar/stellar magnetic activity.

DARE Mission Design: Low RFI Observations from a Low-Altitude, Frozen Lunar Orbit

Abstract: The Dark Ages Radio Experiment (DARE) seeks to study the cosmic Dark Ages approximately 80 to 420 million years after the Big Bang. Observations require truly quiet radio conditions, shielded from Sun and Earth electromagnetic (EM) emissions, on the far side of the Moon. DARE's science orbit is a frozen orbit with respect to lunar gravitational perturbations. The altitude and orientation of the orbit remain nearly fixed indefinitely, maximizing science time without the need for maintenance. DARE's observation targets avoid the galactic center and enable investigation of the universe's first stars and galaxies.

Proposed space interferometer concepts (NASA/ESA)

Additional mission concepts have been developed for nearly all wavelengths ranges and also using unconventional methods such as intensity interferometry {{cite:cf29da0}}. A (nearly) exhaustive list of proposed major space interferometry mission concepts appears in Table {{table:20791ce5-c1af-43a6-be4d-d43e7f28f7d8}} .


35,687 person images and 13,524 in-shop cloth images

VITON: An Image-based Virtual Try-on Network

Abstract: We present an image-based VIirtual Try-On Network (VITON) without using 3D information in any form, which seamlessly transfers a desired clothing item onto the corresponding region of a person using a coarse-to-fine strategy. Conditioned upon a new clothing-agnostic yet descriptive person representation, our framework first generates a coarse synthesized image with the target clothing item overlaid on that same person in the same pose. We further enhance the initial blurry clothing area with a refinement network. The network is trained to learn how much detail to utilize from the target clothing item, and where to apply to the person in order to synthesize a photo-realistic image in which the target item deforms naturally with clear visual patterns. Experiments on our newly collected Zalando dataset demonstrate its promise in the image-based virtual try-on task over state-of-the-art generative models.

Towards Multi-Pose Guided Virtual Try-On Network

Abstract: Virtual try-on systems under arbitrary human poses have significant application potential, yet also raise extensive challenges, such as self-occlusions, heavy misalignment among different poses, and complex clothes textures. Existing virtual try-on methods can only transfer clothes given a fixed human pose, and still show unsatisfactory performances, often failing to preserve person identity or texture details, and with limited pose diversity. This paper makes the first attempt towards a multi-pose guided virtual try-on system, which enables clothes to transfer onto a person with diverse poses. Given an input person image, a desired clothes image, and a desired pose, the proposed Multi-pose Guided Virtual Try-On Network (MG-VTON) generates a new person image after fitting the desired clothes into the person and manipulating the pose. MG-VTON is constructed with three stages: 1) a conditional human parsing network is proposed that matches both the desired pose and the desired clothes shape; 2) a deep Warping Generative Adversarial Network (Warp-GAN) that warps the desired clothes appearance into the synthesized human parsing map and alleviates the misalignment problem between the input human pose and the desired one; 3) a refinement render network recovers the texture details of clothes and removes artifacts, based on multi-pose composition masks. Extensive experiments on commonly-used datasets and our newly-collected largest virtual try-on benchmark demonstrate that our MG-VTON significantly outperforms all state-of-the-art methods both qualitatively and quantitatively, showing promising virtual try-on performances.

MoCoGAN: Decomposing Motion and Content for Video Generation

Abstract: Visual signals in a video can be divided into content and motion. While content specifies which objects are in the video, motion describes their dynamics. Based on this prior, we propose the Motion and Content decomposed Generative Adversarial Network (MoCoGAN) framework for video generation. The proposed framework generates a video by mapping a sequence of random vectors to a sequence of video frames. Each random vector consists of a content part and a motion part. While the content part is kept fixed, the motion part is realized as a stochastic process. To learn motion and content decomposition in an unsupervised manner, we introduce a novel adversarial learning scheme utilizing both image and video discriminators. Extensive experimental results on several challenging datasets with qualitative and quantitative comparison to the state-of-the-art approaches, verify effectiveness of the proposed framework. In addition, we show that MoCoGAN allows one to generate videos with same content but different motion as well as videos with different content and same motion. Our code is available at https://github.com/sergeytulyakov/mocogan.

DwNet: Dense warp-based network for pose-guided human video generation

Abstract: Generation of realistic high-resolution videos of human subjects is a challenging and important task in computer vision. In this paper, we focus on human motion transfer - generation of a video depicting a particular subject, observed in a single image, performing a series of motions exemplified by an auxiliary (driving) video. Our GAN-based architecture, DwNet, leverages dense intermediate pose-guided representation and refinement process to warp the required subject appearance, in the form of the texture, from a source image into a desired pose. Temporal consistency is maintained by further conditioning the decoding process within a GAN on the previously generated frame. In this way a video is generated in an iterative and recurrent fashion. We illustrate the efficacy of our approach by showing state-of-the-art quantitative and qualitative performance on two benchmark datasets: TaiChi and Fashion Modeling. The latter is collected by us and will be made publicly available to the community.

Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis

Abstract: We tackle the human motion imitation, appearance transfer, and novel view synthesis within a unified framework, which means that the model once being trained can be used to handle all these tasks. The existing task-specific methods mainly use 2D keypoints (pose) to estimate the human body structure. However, they only expresses the position information with no abilities to characterize the personalized shape of the individual person and model the limbs rotations. In this paper, we propose to use a 3D body mesh recovery module to disentangle the pose and shape, which can not only model the joint location and rotation but also characterize the personalized body shape. To preserve the source information, such as texture, style, color, and face identity, we propose a Liquid Warping GAN with Liquid Warping Block (LWB) that propagates the source information in both image and feature spaces, and synthesizes an image with respect to the reference. Specifically, the source features are extracted by a denoising convolutional auto-encoder for characterizing the source identity well. Furthermore, our proposed method is able to support a more flexible warping from multiple sources. In addition, we build a new dataset, namely Impersonator (iPER) dataset, for the evaluation of human motion imitation, appearance transfer, and novel view synthesis. Extensive experiments demonstrate the effectiveness of our method in several aspects, such as robustness in occlusion case and preserving face identity, shape consistency and clothes details. All codes and datasets are available on https://svip-lab.github.io/project/impersonator.html

Datasets for pose and garment-oriented person generation. PGPIG: Pose-Guided Person Image Generation. PGPVG: Pose-Guided Person Video Generation. VTON: Virtual Try-on. VF: Virtual Fitting. VVTON: Video Virtual Try-on.

Details of popular datasets for talking-head generation are summarized in Tab. {{table:2ab0fccd-9bc4-4617-af34-3afe33048830}} , and those for pose and garment-oriented generation are in Tab. {{table:e9849718-e044-451e-80f7-a62650f20978}} . Here we only briefly highlight the datasets adopted by the major researchers.


DeepDGA: Adversarially-Tuned Domain Generation and Detection

Abstract: Many malware families utilize domain generation algorithms (DGAs) to establish command and control (C&C) connections. While there are many methods to pseudorandomly generate domains, we focus in this paper on detecting (and generating) domains on a per-domain basis which provides a simple and flexible means to detect known DGA families. Recent machine learning approaches to DGA detection have been successful on fairly simplistic DGAs, many of which produce names of fixed length. However, models trained on limited datasets are somewhat blind to new DGA variants. In this paper, we leverage the concept of generative adversarial networks to construct a deep learning based DGA that is designed to intentionally bypass a deep learning based detector. In a series of adversarial rounds, the generator learns to generate domain names that are increasingly more difficult to detect. In turn, a detector model updates its parameters to compensate for the adversarially generated domains. We test the hypothesis of whether adversarially generated domains may be used to augment training sets in order to harden other machine learning models against yet-to-be-observed DGAs. We detail solutions to several challenges in training this character-based generative adversarial network. In particular, our deep learning architecture begins as a domain name auto-encoder (encoder + decoder) trained on domains in the Alexa one million. Then the encoder and decoder are reassembled competitively in a generative adversarial network (detector + generator), with novel neural architectures and training strategies to improve convergence.

IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection

Abstract: As an essential tool in security, the intrusion detection system bears the responsibility of the defense to network attacks performed by malicious traffic. Nowadays, with the help of machine learning algorithms, intrusion detection systems develop rapidly. However, the robustness of this system is questionable when it faces adversarial attacks. For the robustness of detection systems, more potential attack approaches are under research. In this paper, a framework of the generative adversarial networks, called IDSGAN, is proposed to generate the adversarial malicious traffic records aiming to attack intrusion detection systems by deceiving and evading the detection. Given that the internal structure and parameters of the detection system are unknown to attackers, the adversarial attack examples perform the black-box attacks against the detection system. IDSGAN leverages a generator to transform original malicious traffic records into adversarial malicious ones. A discriminator classifies traffic examples and dynamically learns the real-time black-box detection system. More significantly, the restricted modification mechanism is designed for the adversarial generation to preserve original attack functionalities of adversarial traffic records. The effectiveness of the model is indicated by attacking multiple algorithm-based detection models with different attack categories. The robustness is verified by changing the number of the modified features. A comparative experiment with adversarial attack baselines demonstrates the superiority of our model.

Summary of Problem Space Adversarial Attacks in Network Security and Their Effectiveness

Feature Space and Problem Space - Dimensional Space Classification

Feature space and problem space dimensional classification of various adversarial attacks are shown in Tables {{table:6c3e42ca-94e9-43b6-91b4-8e23670afbc1}}  and {{table:f910fb26-4a7b-4c7c-b0f2-92cd3e165b00}}  respectively. From our observation, adversarial attacks in problem space are more difficult to generate and also more difficult to defend against.


Bayesian Symbolic Regression

Abstract: Interpretability is crucial for machine learning in many scenarios such as quantitative finance, banking, healthcare, etc. Symbolic regression (SR) is a classic interpretable machine learning method by bridging X and Y using mathematical expressions composed of some basic functions. However, the search space of all possible expressions grows exponentially with the length of the expression, making it infeasible for enumeration. Genetic programming (GP) has been traditionally and commonly used in SR to search for the optimal solution, but it suffers from several limitations, e.g. the difficulty in incorporating prior knowledge; overly-complicated output expression and reduced interpretability etc. To address these issues, we propose a new method to fit SR under a Bayesian framework. Firstly, Bayesian model can naturally incorporate prior knowledge (e.g., preference of basis functions, operators and raw features) to improve the efficiency of fitting SR. Secondly, to improve interpretability of expressions in SR, we aim to capture concise but informative signals. To this end, we assume the expected signal has an additive structure, i.e., a linear combination of several concise expressions, whose complexity is controlled by a well-designed prior distribution. In our setup, each expression is characterized by a symbolic tree, and the proposed SR model could be solved by sampling symbolic trees from the posterior distribution using an efficient Markov chain Monte Carlo (MCMC) algorithm. Finally, compared with GP, the proposed BSR(Bayesian Symbolic Regression) method saves computer memory with no need to keep an updated 'genome pool'. Numerical experiments show that, compared with GP, the solutions of BSR are closer to the ground truth and the expressions are more concise. Meanwhile we find the solution of BSR is robust to hyper-parameter specifications such as the number of trees.

Interpretable Scientific Discovery with Symbolic Regression: A Review

Abstract: Symbolic regression is emerging as a promising machine learning method for learning succinct underlying interpretable mathematical expressions directly from data. Whereas it has been traditionally tackled with genetic programming, it has recently gained a growing interest in deep learning as a data-driven model discovery method, achieving significant advances in various application domains ranging from fundamental to applied sciences. This survey presents a structured and comprehensive overview of symbolic regression methods and discusses their strengths and limitations.

Table summarizing ground-truth problems for symbolic regression.

Ground-truth regression problems are characterized by known mathematical equations. These include (1) physics-inspired equations {{cite:b3d11c5}}, {{cite:94726c2}} and (2) real-valued symbolic equations {{cite:111b0bb}}, {{cite:de3e582}}, {{cite:d9eba4a}}, {{cite:4581fa5}}, {{cite:dd8e057}}, {{cite:de93e47}}, {{cite:1b38a28}}, they are listed in Table {{table:413e5279-64eb-4516-8ca5-83a9a9fa80c9}} .


Very Deep Convolutional Networks for Large-Scale Image Recognition

Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

Rethinking the Inception Architecture for Computer Vision

Abstract: Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we are exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21:2% top-1 and 5:6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3:5% top-5 error and 17:3% top-1 error on the validation set and 3:6% top-5 error on the official test set.

DNN models used in the experiments.

Models.
We test with a range of typical CNN models with various input sizes: VGG11/16/19 {{cite:e93ef88}}, ResNet18/34 {{cite:ecf2bbc}}, and InceptionV3 {{cite:fbf1ab8}}, as listed in Table {{table:36157616-c353-47db-a759-cf120b76c2cd}} .
The input data to those models are either CIFAR-10 {{cite:ade4bfb}} (input size 32x32) or ImageNet {{cite:65766be}} (input size 224x224).


TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data

Abstract: Recent years have witnessed the burgeoning of pretrained language models (LMs) for text-based natural language (NL) understanding tasks. Such models are typically trained on free-form NL text, hence may not be suitable for tasks like semantic parsing over structured data, which require reasoning over both free-form NL questions and structured tabular data (e.g., database tables). In this paper we present TaBERT, a pretrained LM that jointly learns representations for NL sentences and (semi-)structured tables. TaBERT is trained on a large corpus of 26 million tables and their English contexts. In experiments, neural semantic parsers using TaBERT as feature representation layers achieve new best results on the challenging weakly-supervised semantic parsing benchmark WikiTableQuestions, while performing competitively on the text-to-SQL dataset Spider.

TaPas: Weakly Supervised Table Parsing via Pre-training

Abstract: Answering natural language questions over tables is usually seen as a semantic parsing task. To alleviate the collection cost of full logical forms, one popular approach focuses on weak supervision consisting of denotations instead of logical forms. However, training semantic parsers from weak supervision poses difficulties, and in addition, the generated logical forms are only used as an intermediate step prior to retrieving the denotation. In this paper, we present TaPas, an approach to question answering over tables without generating logical forms. TaPas trains from weak supervision, and predicts the denotation by selecting table cells and optionally applying a corresponding aggregation operator to such selection. TaPas extends BERT’s architecture to encode tables as input, initializes from an effective joint pre-training of text segments and tables crawled from Wikipedia, and is trained end-to-end. We experiment with three different semantic parsing datasets, and find that TaPas outperforms or rivals semantic parsing models by improving state-of-the-art accuracy on SQA from 55.1 to 67.2 and performing on par with the state-of-the-art on WikiSQL and WikiTQ, but with a simpler model architecture. We additionally find that transfer learning, which is trivial in our setting, from WikiSQL to WikiTQ, yields 48.7 accuracy, 4.2 points above the state-of-the-art.

GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing

Abstract: We present GraPPa, an effective pre-training approach for table semantic parsing that learns a compositional inductive bias in the joint representations of textual and tabular data. We construct synthetic question-SQL pairs over high-quality tables via a synchronous context-free grammar (SCFG) induced from existing text-to-SQL datasets. We pre-train our model on the synthetic data using a novel text-schema linking objective that predicts the syntactic role of a table field in the SQL for each question-SQL pair. To maintain the model's ability to represent real-world data, we also include masked language modeling (MLM) over several existing table-and-language datasets to regularize the pre-training process. On four popular fully supervised and weakly supervised table semantic parsing benchmarks, GraPPa significantly outperforms RoBERTa-large as the feature representation layers and establishes new state-of-the-art results on all of them.

Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training

Abstract: Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train large neural language models with self-supervised learning objectives, such as Masked Language Model (MLM). However, based on a pilot study, we observe three issues of existing general-purpose language models when they are applied to text-to-SQL semantic parsers: fail to detect column mentions in the utterances, fail to infer column mentions from cell values, and fail to compose complex SQL queries. To mitigate these issues, we present a model pre-training framework, Generation-Augmented Pre-training (GAP), that jointly learns representations of natural language utterances and table schemas by leveraging generation models to generate pre-train data. GAP MODEL is trained on 2M utterance-schema pairs and 30K utterance-schema-SQL triples, whose utterances are produced by generative models. Based on experimental results, neural semantic parsers that leverage GAP MODEL as a representation encoder obtain new state-of-the-art results on both SPIDER and CRITERIA-TO-SQL benchmarks.

Structure-Grounded Pretraining for Text-to-SQL

Abstract: Learning to capture text-table alignment is essential for tasks like text-to-SQL. A model needs to correctly recognize natural language references to columns and values and to ground them in the given database schema. In this paper, we present a novel weakly supervised Structure-Grounded pretraining framework (STRUG) for text-to-SQL that can effectively learn to capture text-table alignment based on a parallel text-table corpus. We identify a set of novel pretraining tasks: column grounding, value grounding and column-value mapping, and leverage them to pretrain a text-table encoder. Additionally, to evaluate different methods under more realistic text-table alignment settings, we create a new evaluation set Spider-Realistic based on Spider dev set with explicit mentions of column names removed, and adopt eight existing text-to-SQL datasets for cross-database evaluation. STRUG brings significant improvement over BERTLARGE in all settings. Compared with existing pretraining methods such as GRAPPA, STRUG achieves similar performance on Spider, and outperforms all baselines on more realistic sets. All the code and data used in this work will be open-sourced to facilitate future research.

Meta-Learning for Domain Generalization in Semantic Parsing

Abstract: The importance of building semantic parsers which can be applied to new domains and generate programs unseen at training has long been acknowledged, and datasets testing out-of-domain performance are becoming increasingly available. However, little or no attention has been devoted to learning algorithms or objectives which promote domain generalization, with virtually all existing approaches relying on standard supervised learning. In this work, we use a meta-learning framework which targets zero-shot domain generalization for semantic parsing. We apply a model-agnostic training algorithm that simulates zero-shot parsing by constructing virtual train and test sets from disjoint domains. The learning objective capitalizes on the intuition that gradient steps that improve source-domain performance should also improve target-domain performance, thus encouraging a parser to generalize to unseen target domains. Experimental results on the (English) Spider and Chinese Spider datasets show that the meta-learning objective significantly boosts the performance of a baseline parser.

TAPEX: Table Pre-training via Learning a Neural SQL Executor

Abstract: Recent progress in language model pre-training has achieved a great success via leveraging large-scale unstructured textual data. However, it is still a challenge to apply pre-training on structured tabular data due to the absence of large-scale high-quality tabular data. In this paper, we propose TAPEX to show that table pre-training can be achieved by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically synthesizing executable SQL queries and their execution outputs. TAPEX addresses the data scarcity challenge via guiding the language model to mimic a SQL executor on the diverse, large-scale and high-quality synthetic corpus. We evaluate TAPEX on four benchmark datasets. Experimental results demonstrate that TAPEX outperforms previous table pre-training approaches by a large margin and achieves new state-of-the-art results on all of them. This includes the improvements on the weakly-supervised WikiSQL denotation accuracy to 89.5% (+2.3%), the WikiTableQuestions denotation accuracy to 57.5% (+4.8%), the SQA denotation accuracy to 74.5% (+3.5%), and the TabFact accuracy to 84.2% (+3.2%). To our knowledge, this is the first work to exploit table pre-training via synthetic executable programs and to achieve new state-of-the-art results on various downstream tasks. Our code can be found at https://github.com/microsoft/Table-Pretraining.

TableFormer: Robust Transformer Modeling for Table-Text Encoding

Abstract: Understanding tables is an important aspect of natural language understanding. Existing models for table understanding require linearization of the table structure, where row or column order is encoded as an unwanted bias. Such spurious biases make the model vulnerable to row and column order perturbations. Additionally, prior work has not thoroughly modeled the table structures or table-text alignments, hindering the table-text understanding ability. In this work, we propose a robust and structurally aware table-text encoding architecture TableFormer, where tabular structural biases are incorporated completely through learnable attention biases. TableFormer is (1) strictly invariant to row and column orders, and, (2) could understand tables better due to its tabular inductive biases. Our evaluations showed that TableFormer outperforms strong baselines in all settings on SQA, WTQ and TabFact table reasoning datasets, and achieves state-of-the-art performance on SQA, especially when facing answer-invariant row and column order perturbations (6% improvement over the best baseline), because previous SOTA models’ performance drops by 4% - 6% when facing such perturbations while TableFormer is not affected.

The pre-training objectives for text-to-SQL parsing.

Most existing pre-training models for text-to-SQL parsing employ either a single Transformer or a Transformer-based encoder-decoder framework as the backbone, and adopt different kinds of pre-training objectives to capture the characteristics of the text-to-SQL parsing task.
As illustrated in Table {{table:54fe9da5-db0d-432a-b3ba-a36849d52b30}} , the pre-training objectives can be divided into five primary categories, including masked language modelling (MLM) {{cite:1f5a6c5}}, {{cite:62935dd}}, {{cite:4a353e2}}, {{cite:70423d4}}, {{cite:79a1fcf}}, {{cite:aa6d6d0}}, {{cite:47cd0e5}}, schema linking {{cite:4a353e2}}, {{cite:70423d4}}, {{cite:cb9e02b}}, {{cite:47cd0e5}}, SQL executor {{cite:f48a682}}, text generation {{cite:70423d4}} and context modelling {{cite:47cd0e5}}. Next, we will introduce the implementation details of each primary pre-training objective.


A comprehensive survey of issues on computing,
caching and communication techniques in MEC.

A discussion of the security threats and challenges in the edge paradigms, along with the promising solution for each specific challenge.

Describes specific applications and use cases of MEC in IoT including healthcare, wireless sensor systems, smart grid, smart home, and smart city.

No explicit discussion on IoT. Briefly discusses how IoT will benefit from edge computing and related security threats.

A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications

Abstract: As the explosive growth of smart devices and the advent of many new applications, traffic volume has been growing exponentially. The traditional centralized network architecture cannot accommodate such user demands due to heavy burden on the backhaul links and long latency. Therefore, new architectures which bring network functions and contents to the network edge are proposed, i.e., mobile edge computing and caching. Mobile edge networks provide cloud computing and caching capabilities at the edge of cellular networks. In this survey, we make an exhaustive review on the state-of-the-art research efforts on mobile edge networks. We first give an overview of mobile edge networks including definition, architecture and advantages. Next, a comprehensive survey of issues on computing, caching and communication techniques at the network edge is presented respectively. The applications and use cases of mobile edge networks are discussed. Subsequently, the key enablers of mobile edge networks such as cloud technology, SDN/NFV and smart devices are discussed. Finally, open research challenges and future directions are presented as well.

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.

Summary of important surveys on MEC.

At present, IoT has become a fairly mature technology. As a result, the recent decade has seen a plethora of surveys published in multiple research areas on IoT including enabling concepts {{cite:a5fb1f4}}, visions and challenges {{cite:1949b2a}}, technologies {{cite:9f0ba42}}, standardization {{cite:9d1010d}}, architecture {{cite:791ffae}}, security {{cite:fcd040f}}, {{cite:9db0b8a}}, privacy {{cite:8365b72}}, trust {{cite:c95906d}}, Social Internet of Things (SIoT) {{cite:7f39f85}}, communication {{cite:916ecef}}, context awareness {{cite:e39ea6c}}, and future directions {{cite:1949b2a}}, {{cite:814526c}}. Few other papers are focused on the combined aspects of IoT research and their potential application scenarios {{cite:7aa3ffc}}, {{cite:9f0ba42}}, {{cite:9bba365}}, {{cite:5162797}}. Some of these surveys were published during the time when IoT was more of a visionary paradigm than a real world platform. Many future research possibilities discussed in those papers have already been achieved and commercialized with high market values.
However, there is yet to be a sufficient number of publications on MEC technology, given that is relatively a novel technology which lies at the intersection of mobile cloud computing and wireless communication. In Table {{table:b41fb601-e90c-43e4-a3d9-9d7b64534dd4}} , we summarize the recently published surveys on MEC. These articles are focused on MEC taxonomy, future research directions, and more specific MEC attributes such as communication, computation offloading, security, and virtualization. These studies are quite shallow in addressing the MEC integration with IoT, they are mostly focusing on the requirements and usability of MEC in IoT applications. In this short magazine article {{cite:55bb2e3}}, the authors discuss the examples of MEC deployment, with special reference to IoT use cases.


Weight pruning and quantization to reduce storage and computation

Partial DNNs model inference

Accuracy-aware

Scheduling multiple DNN-based task

Resource-efficient

Learning both Weights and Connections for Efficient Neural Networks

Abstract: Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems. Also, conventional networks fix the architecture before training starts; as a result, training cannot improve the architecture. To address these limitations, we describe a method to reduce the storage and computation required by neural networks by an order of magnitude without affecting their accuracy by learning only the important connections. Our method prunes redundant connections using a three-step method. First, we train the network to learn which connections are important. Next, we prune the unimportant connections. Finally, we retrain the network to fine tune the weights of the remaining connections. On the ImageNet dataset, our method reduced the number of parameters of AlexNet by a factor of 9x, from 61 million to 6.7 million, without incurring accuracy loss. Similar experiments with VGG-16 found that the number of parameters can be reduced by 13x, from 138 million to 10.3 million, again with no loss of accuracy.

Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy

Abstract: As the backbone technology of machine learning, deep neural networks (DNNs) have have quickly ascended to the spotlight. Running DNNs on resource-constrained mobile devices is, however, by no means trivial, since it incurs high performance and energy overhead. While offloading DNNs to the cloud for execution suffers unpredictable performance, due to the uncontrolled long wide-area network latency. To address these challenges, in this paper, we propose Edgent, a collaborative and on-demand DNN co-inference framework with device-edge synergy. Edgent pursues two design knobs: (1) DNN partitioning that adaptively partitions DNN computation between device and edge, in order to leverage hybrid computation resources in proximity for real-time DNN inference. (2) DNN right-sizing that accelerates DNN inference through early-exit at a proper intermediate DNN layer to further reduce the computation latency. The prototype implementation and extensive evaluations based on Raspberry Pi demonstrate Edgent's effectiveness in enabling on-demand low-latency edge intelligence.

NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision

Abstract: Mobile vision systems such as smartphones, drones, and augmented-reality headsets are revolutionizing our lives. These systems usually run multiple applications concurrently and their available resources at runtime are dynamic due to events such as starting new applications, closing existing applications, and application priority changes. In this paper, we present NestDNN, a framework that takes the dynamics of runtime resources into account to enable resource-aware multi-tenant on-device deep learning for mobile vision systems. NestDNN enables each deep learning model to offer flexible resource-accuracy trade-offs. At runtime, it dynamically selects the optimal resource-accuracy trade-off for each deep learning model to fit the model's resource demand to the system's available runtime resources. In doing so, NestDNN efficiently utilizes the limited resources in mobile vision systems to jointly maximize the performance of all the concurrently running applications. Our experiments show that compared to the resource-agnostic status quo approach, NestDNN achieves as much as 4.2% increase in inference accuracy, 2.0× increase in video frame processing rate and 1.7× reduction on energy consumption.

Technologies for distributed DNN inference at the edge

In this subsection, we review the enabling technologies for improving one or more of the aforementioned key performance indicators for edge intelligence model inference. Table {{table:287fa55e-11c8-4cc3-ad55-3e40bcd77b8a}}  summarizes the highlights of each enabling technology.


Accurate charge dependent nucleon nucleon potential at fourth order of chiral perturbation theory

Abstract: We present the first nucleon-nucleon potential at next-to-next-to-next-to-leading order (fourth order) of chiral perturbation theory. Charge dependence is included up to next-to-leading order of the isospin-violation scheme. The accuracy for the reproduction of the nucleon-nucleon $(NN)$ data below $290\text{\ensuremath{-}}\text{MeV}$ lab energy is comparable to the one of phenomenological high-precision potentials. Since $NN$ potentials of order three and less are known to be deficient in quantitative terms, the present work shows that the fourth order is necessary and sufficient for a $NN$ potential reliable up to $290\phantom{\rule{0.3em}{0ex}}\text{MeV}$. The new potential provides a promising starting point for exact few-body calculations and microscopic nuclear structure theory (including chiral many-body forces derived on the same footing).

Shape transitions in exotic Si and S isotopes and tensor-force-driven Jahn-Teller effect

Abstract: We show how shape transitions in the neutron-rich exotic Si and S isotopes occur in terms of shell-model calculations with a newly constructed Hamiltonian based on V_MU interaction. We first compare the calculated spectroscopic-strength distributions for the proton 0d_5/2,3/2 and 1s_1/2 orbitals with results extracted from a 48Ca(e,e'p) experiment to show the importance of the tensor-force component of the Hamiltonian. Detailed calculations for the excitation energies, B(E2) and two-neutron separation energies for the Si and S isotopes show excellent agreement with experimental data. The potential energy surface exhibits rapid shape transitions along the isotopic chains towards N=28 that are different for Si and S. We explain the results in terms of an intuitive picture involving a Jahn-Teller-type effect that is sensitive to the tensor-force-driven shell evolution. The closed sub-shell nucleus 42Si is a particularly good example of how the tensor-force-driven Jahn-Teller mechanism leads to a strong oblate rather than spherical shape.

Table of nuclear data for targets we include with the program at time of publication. Each corresponds to a (.dres) density matrix file in the targets directory. The source indicates the nuclear Hamiltonian that was used to generate the wave function data. See the manual and GitHub repositoryfor updates and full information on provenance. New targets may be added in future releases.

We supply a library of nuclear structure files (one-body density matrix files) for many of the common expected targets, as listed in Table {{table:e6cfb012-2b15-4812-9c7b-a03e950fe64f}} . (We also include, for purposes of validation, the legacy density matrices included with the original
Mathematica script  {{cite:3c0ff83}}.)
These density matrix files are written in plain ASCII,
using the format output by the nuclear configuration-interaction code Bigstick {{cite:c664014}}, {{cite:605f84e}}. The only assumption is
that the single-particle basis states are harmonic oscillator states; the user must supply the harmonic oscillator single particle basis
frequency {{formula:59de8143-cc9d-4fb5-ae0a-a6536ecb8039}} , typically given in MeV as {{formula:27f1e6d2-aff2-4548-9c0c-f541376821f3}} , or the related length parameter {{formula:d53c0a2e-05c6-4c66-941c-54e255809820}} ,
where {{formula:850f853a-3410-48f7-8dd7-62130f31ec86}}  is the nucleon mass.


Xbee-Based WSN Architecture for Monitoring of Banana Ripening Process Using Knowledge-Level Artificial Intelligent Technique

Ambient Assisted Living Environment Towards Internet of Things Using Multifarious Sensors Integrated with XBee Platform

Performance Analysis of Data Transmission on a Wireless Sensor Network Using the XBee Pro Series 2B RF Module

In order to track banana ripening, the article suggests an Xbee wireless sensor network (WSN) design. In order to improve analysis and decision-making in this process, artificial intelligence techniques are used. However, it is devoid of precise methodological information.

The study focuses on exploiting IoT technology to enhance the quality of life in assisted living settings by using the XBee platform and sensors to build an ambient assisted living environment. But no precise methodological information was offered.

The XBee Pro Series 2B RF module used in the article is used to analyze data transmission on a wireless sensor network. It assesses this module's effectiveness and dependability without presenting a thorough methodology or analysis.

According to the findings, a wireless sensor network based on Xbees and knowledge-level AI were able to accurately classify bananas into three states: normal, rotten, and unknown.

Using the XBee platform and many sensors, the study successfully developed an ambient assisted living environment. Through better monitoring and assistance, this integration increased the quality of life for residents of assisted living facilities.

The design and execution of wireless sensor networks are influenced by the examination of the XBee Pro Series 2B RF module's data transmission capabilities.

Abstract: Real-time monitoring of fruit ripeness in storage and during logistics allows traders to minimize the chances of financial losses and maximize the quality of the fruit during storage through accurate prediction of the present condition of fruits. In Pakistan, banana production faces different difficulties from production, post-harvest management, and trade marketing due to atmosphere and mismanagement in storage containers. In recent research development, Wireless Sensor Networks (WSNs) are progressively under investigation in the field of fruit ripening due to their remote monitoring capability. Focused on fruit ripening monitoring, this paper demonstrates an Xbee-based wireless sensor nodes network. The role of the network architecture of the Xbee sensor node and sink end-node is discussed in detail regarding their ability to monitor the condition of all the required diagnosis parameters and stages of banana ripening. Furthermore, different features are extracted using the gas sensor, which is based on diverse values. These features are utilized for training in the Artificial Neural Network (ANN) through the Back Propagation (BP) algorithm for further data validation. The experimental results demonstrate that the projected WSN architecture can identify the banana condition in the storage area. The proposed Neural Network (NN) architectural design works well with selecting the feature data sets. It seems that the experimental and simulation outcomes and accuracy in banana ripening condition monitoring in the given feature vectors is attained and acceptable, through the classification performance, to make a better decision for effective monitoring of current fruit condition.

EFFECT OF DATA QUALITY ON WATER BODY SEGMENTATION WITH DEEPLABV3+ ALGORITHM

Abstract: Abstract. Training Deep Learning (DL) algorithms for segmenting features require hundreds to thousands of input data and corresponding labels. Generating thousands of input images and labels requires considerable resources and time. Hence, it is common practice to use opensource imagery data and labels available online. Most of these open-source data have little or no metadata describing their quality or suitability making it problematic for training or evaluating DL models. This study evaluated the effect of data quality on training DeepLabV3+, using Sentinel 2 A/B RGB images and labels obtained from Kaggle. We generated subsets of 256 × 256 pixels, and 10% of these images (802) were set aside for testing. First, we trained and validated the DeepLabV3+ model with the remaining images. Second, we removed images with incorrect labels and trained another DeepLabV3+ network. Finally, we trained the third DeepLabV3+ network after removing images with turbid water or with floating vegetation. All three trained models were evaluated with test images and then we calculated accuracy metrics. As the quality of the input images improved, accuracy of the predicted masks generated from the first model increased from 92.8% to 94.3% in the second model. The third model’s accuracy was 96.4%, demonstrating the network’s ability to better learn and predict water bodies when the input data had fewer class variations. Based on the results we recommend assessing the quality of open-source data for incorrect labels and variations in the target class prior to training DeepLabV3+ or any other DL network.

Abstract: Wireless Sensor Network (WSN) is a wireless network technology that is capable of scanning and can be used to monitor environmental conditions, both indoors and outdoors. One of the devices used in data communication at WSN is XBee. XBee is a device in WSN that uses radio frequency as a data transmission path from one device to another. In sending data, Xbee can be affected by the distance and signal strength between devices. In this research, Xbee communication distance analysis is done outdoors and indoors so as to get data in the form of maximum distance vulnerable from Xbee. The results of this study indicate that the Xbee Pro Series 2B that is measured outside the room is capable of sending data up to a distance of 110 meters, while the Xbee Pro Series 2B device that is measured indoors is capable of sending data up to a distance of 20 meters.

Literature Review

The table of literature review Table {{table:6a1a4b0e-912e-47d7-a3cf-a431265fd3c5}}  provides a summary of the key findings from the relevant research literature.


Network architecture and design, routing protocols including performance analysis and QoS metrics, and opening issues.

UAV routing schemes, including objectives, challenges, routing metrics, characteristics, and performance measures, along with open issues.

Routing classification only includes a few methods.

It only briefly mentions adaptive routing protocols, missing most of the AI-enabled routing protocols.

Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks

Abstract: With the development of Unmanned Air Vehicle (UAV) communication, Flying Ad Hoc Network (FANET) has become a hot research area in recent years, which is widely used in civil and military fields due to its unique advantages. FANET is a special kind of networks which are composed of UAV nodes, and can be used to implement data transfer in certain unique scenarios. To achieve reliable and robust communication among UAVs, a routing algorithm is the key factor and should be designed elaborately. Because of its importance and usefulness, this topic has attracted many researchers, and various routing protocols have also been put forward to improve the quality of data transmission in FANETs. Thus, in this paper, we give a survey on the state-of-the-art of routing protocols proposed in recent years. First, an in-depth research of the routing in FANETs recently has been brought out by absolutely differentiating them based on their routing mechanism. Then, we give a comparative analysis of each protocol based on their characteristics and service quality indicators. Finally, we propose some unsolved problems and future research directions for FANET routing.

Routing Schemes in FANETs: A Survey

Abstract: Flying ad hoc network (FANET) is a self-organizing wireless network that enables inexpensive, flexible, and easy-to-deploy flying nodes, such as unmanned aerial vehicles (UAVs), to communicate among themselves in the absence of fixed network infrastructure. FANET is one of the emerging networks that has an extensive range of next-generation applications. Hence, FANET plays a significant role in achieving application-based goals. Routing enables the flying nodes to collaborate and coordinate among themselves and to establish routes to radio access infrastructure, particularly FANET base station (BS). With a longer route lifetime, the effects of link disconnections and network partitions reduce. Routing must cater to two main characteristics of FANETs that reduce the route lifetime. Firstly, the collaboration nature requires the flying nodes to exchange messages and to coordinate among themselves, causing high energy consumption. Secondly, the mobility pattern of the flying nodes is highly dynamic in a three-dimensional space and they may be spaced far apart, causing link disconnection. In this paper, we present a comprehensive survey of the limited research work of routing schemes in FANETs. Different aspects, including objectives, challenges, routing metrics, characteristics, and performance measures, are covered. Furthermore, we present open issues.

Most recent survey papers for routing protocols in Wireless Sensor Networks.

There are a few recent review papers that survey routing protocols for ground and aerial networks. Other related papers that review UAV networks survey a broader set of aspects. Table {{table:55fec811-ae07-4fb2-913c-b5e8d82b5b12}}  summarizes key topics covered in these surveys, along with key topics missing in each paper. To our knowledge, no paper provides a comprehensive and up-to-date review of AI-based routing protocols for aerial networks, which is our central focus.