Lack of diversity on ISC, SGC and GSC links due to direct signal propagation

Conventional, cooperative, cognitive NOMA techniques can be used according to the network architecture and MTCS characteristics

Multi-cast and multi-group beamforming design over large geographical regions, exploitation of atmospheric conditions and rain attenuation

User Activity Detection and Channel Estimation for Grant-Free Random Access in LEO Satellite-Enabled Internet of Things

Abstract: With recent advances on the dense low-Earth orbit (LEO) constellation, the LEO satellite network has become one promising solution for providing global coverage for Internet-of-Things (IoT) services. Confronted with the sporadic transmission from randomly activated IoT devices, we consider the random access (RA) mechanism and propose a grant-free RA (GF-RA) scheme to reduce the access delay to the mobile LEO satellites. A Bernoulli–Rician message passing with expectation–maximization (BR-MP-EM) algorithm is proposed for this terrestrial–satellite GF-RA system to address the user activity detection (UAD) and channel estimation (CE) problem. This BR-MP-EM algorithm is divided into two stages. In the inner iterations, the Bernoulli messages and Rician messages are updated for the joint UAD and CE problem. Based on the output of the inner iterations, the expectation–maximization (EM) method is employed in the outer iterations to update the hyperparameters related to the channel impairments. Finally, simulation results show the UAD and CE accuracy of the proposed BR-MP-EM algorithm, as well as the robustness against the channel impairments.

Multicast Multigroup Precoding and User Scheduling for Frame-Based Satellite Communications

Abstract: The present work focuses on the forward link of a broadband multibeam satellite system that aggressively reuses the user link frequency resources. Two fundamental practical challenges, namely the need to frame multiple users per transmission and the per-antenna transmit power limitations, are addressed. To this end, the so-called frame-based precoding problem is optimally solved using the principles of physical layer multicasting to multiple co-channel groups under per-antenna constraints. In this context, a novel optimization problem that aims at maximizing the system sum rate under individual power constraints is proposed. Added to that, the formulation is further extended to include availability constraints. As a result, the high gains of the sum rate optimal design are traded off to satisfy the stringent availability requirements of satellite systems. Moreover, the throughput maximization with a granular spectral efficiency versus SINR function, is formulated and solved. Finally, a multicast-aware user scheduling policy, based on the channel state information, is developed. Thus, substantial multiuser diversity gains are gleaned. Numerical results over a realistic simulation environment exhibit as much as 30% gains over conventional systems, even for 7 users per frame, without modifying the framing structure of legacy communication standards.

DSSN Technologies, Challenges and Solutions

In this section, we discuss important technologies that can help achieve DSSN benefits for MTCS. Different technologies, associated challenges, and possible solutions are also provided in Table {{table:7f9f15e8-9c82-4f88-bc82-f1d8f8fa9f24}} .


Deep Learning for Classical Japanese Literature

Abstract: Much of machine learning research focuses on producing models which perform well on benchmark tasks, in turn improving our understanding of the challenges associated with those tasks. From the perspective of ML researchers, the content of the task itself is largely irrelevant, and thus there have increasingly been calls for benchmark tasks to more heavily focus on problems which are of social or cultural relevance. In this work, we introduce Kuzushiji-MNIST, a dataset which focuses on Kuzushiji (cursive Japanese), as well as two larger, more challenging datasets, Kuzushiji-49 and Kuzushiji-Kanji. Through these datasets, we wish to engage the machine learning community into the world of classical Japanese literature. Dataset available at https://github.com/rois-codh/kmnist

Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Abstract: We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images. Fashion-MNIST is intended to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms, as it shares the same image size, data format and the structure of training and testing splits. The dataset is freely available at this https URL

Specifics of Datasets used in Experiments

Seven datasets are used in experiments: MNIST, KMNIST, FashionMNIST, CIFAR-10, CIFAR-100, SVHN. Table {{table:f8e85e08-bd26-4790-beeb-b87f96232564}}  shows the sizes of each dataset, prior to training set splitting. The training set is, in fact, randomly split into a proper training set (80% of the original training set) and a validation set (20% of the original training set), using torch.utils.data.random_split. The random split is controlled by the same experimental seed that makes the rest of the experiments reproducible.


Enable fast, parallel training/inference of deep learning models in mobile applications

High-level toolboxes that enable network engineers to build purpose-specific deep learning architectures

In-Datacenter Performance Analysis of a Tensor Processing Unit

Abstract: Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit (TPU)---deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs (caches, out-of-order execution, multithreading, multiprocessing, prefetching, ...) that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X - 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X - 80X higher. Moreover, using the GPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.

TensorFlow: A system for large-scale machine learning

Abstract: TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous "parameter server" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with particularly strong support for training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model in contrast to existing systems, and demonstrate the compelling performance that TensorFlow achieves for several real-world applications.

Summary of tools and techniques that enable deploying deep learning in mobile systems.

Enabling Deep Learning in Mobile Networking

5G systems seek to provide high throughput and ultra-low latency communication services, to improve users' QoE {{cite:7138ed5}}. Implementing deep learning to build intelligence into 5G systems, so as to meet these objectives is expensive. This is because powerful hardware and software is required to support training and inference in complex settings. Fortunately, several tools are emerging, which make deep learning in mobile networks tangible; namely, (i) advanced parallel computing, (ii) distributed machine learning systems, (iii) dedicated deep learning libraries, (iv) fast optimization algorithms, and (v) fog computing. These tools can be seen as forming a hierarchical structure, as illustrated in Fig. {{figure:d400f51d-fe33-4286-a22f-8b4da8df989e}} ; synergies between them exist that make networking problem amenable to deep learning based solutions. By employing these tools, once the training is completed, inferences can be made within millisecond timescales, as already reported by a number of papers for a range of tasks (e.g., {{cite:8fa1432}}, {{cite:4c2122c}}, {{cite:0df59f6}} ). We summarize these advances in Table {{table:b57cc05e-3363-406f-a2eb-3b2af6882469}}  and review them in what follows.


Filter size shrinking, reducing input channels and late downsampling

Point-wise group convolution and channel shuffle

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

Abstract: Recent research on deep neural networks has focused primarily on improving accuracy. For a given accuracy level, it is typically possible to identify multiple DNN architectures that achieve that accuracy level. With equivalent accuracy, smaller DNN architectures offer at least three advantages: (1) Smaller DNNs require less communication across servers during distributed training. (2) Smaller DNNs require less bandwidth to export a new model from the cloud to an autonomous car. (3) Smaller DNNs are more feasible to deploy on FPGAs and other hardware with limited memory. To provide all of these advantages, we propose a small DNN architecture called SqueezeNet. SqueezeNet achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters. Additionally, with model compression techniques we are able to compress SqueezeNet to less than 0.5MB (510x smaller than AlexNet). The SqueezeNet architecture is available for download here: https://github.com/DeepScale/SqueezeNet

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Abstract: We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

Abstract: We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, e.g. lower top-1 error (absolute 7.8%) than recent MobileNet [12] on ImageNet classification task, under the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves ~13Ã— actual speedup over AlexNet while maintaining comparable accuracy.

MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU

Abstract: In this paper, we explore optimizations to run Recurrent Neural Network (RNN) models locally on mobile devices. RNN models are widely used for Natural Language Processing, Machine Translation, and other tasks. However, existing mobile applications that use RNN models do so on the cloud. To address privacy and efficiency concerns, we show how RNN models can be run locally on mobile devices. Existing work on porting deep learning models to mobile devices focus on Convolution Neural Networks (CNNs) and cannot be applied directly to RNN models. In response, we present MobiRNN, a mobile-specific optimization framework that implements GPU offloading specifically for mobile GPUs. Evaluations using an RNN model for activity recognition shows that MobiRNN does significantly decrease the latency of running RNN models on phones.

Quantized Convolutional Neural Networks for Mobile Devices

Abstract: Recently, convolutional neural networks (CNN) have demonstrated impressive performance in various computer vision tasks. However, high performance hardware is typically indispensable for the application of CNN models due to the high computation complexity, which prohibits their further extensions. In this paper, we propose an efficient framework, namely Quantized CNN, to simultaneously speed-up the computation and reduce the storage and memory overhead of CNN models. Both filter kernels in convolutional layers and weighting matrices in fully-connected layers are quantized, aiming at minimizing the estimation error of each layer's response. Extensive experiments on the ILSVRC-12 benchmark demonstrate 4 ~ 6× speed-up and 15 ~ 20× compression with merely one percentage loss of classification accuracy. With our quantized CNN model, even mobile devices can accurately classify images within one second.

MEC: Memory-efficient Convolution for Deep Neural Network

Abstract: Convolution is a critical component in modern deep neural networks, thus several algorithms for convolution have been developed. Direct convolution is simple but suffers from poor performance. As an alternative, multiple indirect methods have been proposed including im2col-based convolution, FFT-based convolution, or Winograd-based algorithm. However, all these indirect methods have high memory-overhead, which creates performance degradation and offers a poor trade-off between performance and memory consumption. In this work, we propose a memory-efficient convolution or MEC with compact lowering, which reduces memory-overhead substantially and accelerates convolution process. MEC lowers the input matrix in a simple yet efficient/compact way (i.e., much less memory-overhead), and then executes multiple small matrix multiplications in parallel to get convolution completed. Additionally, the reduced memory footprint improves memory sub-system efficiency, improving performance. Our experimental results show that MEC reduces memory consumption significantly with good speedup on both mobile and server platforms, compared with other indirect convolution algorithms.

Fast, Compact, and High Quality LSTM-RNN Based Statistical Parametric Speech Synthesizers for Mobile Devices

Abstract: Acoustic models based on long short-term memory recurrent neural networks (LSTM-RNNs) were applied to statistical parametric speech synthesis (SPSS) and showed significant improvements in naturalness and latency over those based on hidden Markov models (HMMs). This paper describes further optimizations of LSTM-RNN-based SPSS for deployment on mobile devices; weight quantization, multi-frame inference, and robust inference using an {\epsilon}-contaminated Gaussian loss function. Experimental results in subjective listening tests show that these optimizations can make LSTM-RNN-based SPSS comparable to HMM-based SPSS in runtime speed while maintaining naturalness. Evaluations between LSTM-RNN- based SPSS and HMM-driven unit selection speech synthesis are also presented.

Summary of works on improving deep learning for mobile devices and systems.

Tailoring Deep Learning to Mobile Devices and Systems

The ultra-low latency requirements of future 5G networks demand runtime efficiency from all operations performed by mobile systems. This also applies to deep learning driven applications. However, current mobile devices have limited hardware capabilities, which means that implementing complex deep learning architectures on such equipment may be computationally unfeasible, unless appropriate model tuning is performed. To address this issue, ongoing research improves existing deep learning architectures {{cite:0ce3b03}}, such that the inference process does not violate latency or energy constraints {{cite:b51d4a1}}, {{cite:f9079d4}}. We outline these works in Table {{table:466d7aba-d0c9-4b76-9742-6f88e36f2ae9}}  and discuss their key contributions next.


Poor lighting conditions and visibility; suspended particles; color distortions

Presence of a paired USBL transceiver; noisy reading

Computer vision-based algorithms are used for detection and tracking

Diver's position information is estimated by communicating with the transceiver

Towards a Generic Diver-Following Algorithm: Balancing Robustness and Efficiency in Deep Visual Detection

Abstract: This paper explores the design and development of a class of robust diver-following algorithms for autonomous underwater robots. By considering the operational challenges for underwater visual tracking in diverse real-world settings, we formulate a set of desired features of a generic diver following algorithm. We attempt to accommodate these features and maximize general tracking performance by exploiting the state-of-the-art deep object detection models. We fine-tune the building blocks of these models with a goal of balancing the trade-off between robustness and efficiency in an onboard setting under real-time constraints. Subsequently, we design an architecturally simple Convolutional Neural Network (CNN)-based diver-detection model that is much faster than the state-of-the-art deep models yet provides comparable detection performances. In addition, we validate the performance and effectiveness of the proposed diver-following modules through a number of field experiments in closed-water and open-water environments.

Underwater Object Tracking Using Sonar and USBL Measurements

Abstract: In the scenario where an underwater vehicle tracks an underwater target, reliable estimation of the target position is required. While USBL measurements provide target position measurements at low but regular update rate, multibeam sonar imagery gives high precision measurements but in a limited field of view. This paper describes the development of the tracking filter that fuses USBL and processed sonar image measurements for tracking underwater targets for the purpose of obtaining reliable tracking estimates at steady rate, even in cases when either sonar or USBL measurements are not available or are faulty. The proposed algorithms significantly increase safety in scenarios where underwater vehicle has to maneuver in close vicinity to human diver who emits air bubbles that can deteriorate tracking performance. In addition to the tracking filter development, special attention is devoted to adaptation of the region of interest within the sonar image by using tracking filter covariance transformation for the purpose of improving detection and avoiding false sonar measurements. Developed algorithms are tested on real experimental data obtained in field conditions. Statistical analysis shows superior performance of the proposed filter compared to conventional tracking using pure USBL or sonar measurements.

Choice of sensors and the corresponding design issues for person-following underwater robots.

Proprioceptive sensors such as IMUs are used by underwater robots for internal state estimation {{cite:eac446b}};
 in addition, pressure sensors are used for measuring the depth of the robot (from surface) using external pressure {{cite:dd24477}}.
The depth information is useful for the depth-control and altitude-control modules of the robot. Moreover, inertial navigation systems (INSs) and other navigation sensors are often used to determine the robot's instantaneous pose and velocity information {{cite:dd24477}}; however, these systems drift, thus require repeated correction using secondary sensing system(s). Table {{table:301d5b43-340e-44b3-aace-870dad1a7c00}}  summarizes the challenges and operational issues based on different choices of sensors for person-following underwater robots.


High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis

Abstract: Recent advances in deep learning have shown exciting promise in filling large holes in natural images with semantically plausible and context aware details, impacting fundamental image manipulation tasks such as object removal. While these learning-based methods are significantly more effective in capturing high-level features than prior techniques, they can only handle very low-resolution inputs due to memory limitations and difficulty in training. Even for slightly larger images, the inpainted regions would appear blurry and unpleasant boundaries become visible. We propose a multi-scale neural patch synthesis approach based on joint optimization of image content and texture constraints, which not only preserves contextual structures but also produces high-frequency details by matching and adapting patches with the most similar mid-layer feature correlations of a deep classification network. We evaluate our method on the ImageNet and Paris Streetview datasets and achieved state-of-the-art inpainting accuracy. We show our approach produces sharper and more coherent results than prior methods, especially for high-resolution images.

Deep Image Harmonization

Abstract: Compositing is one of the most common operations in photo editing. To generate realistic composites, the appearances of foreground and background need to be adjusted to make them compatible. Previous approaches to harmonize composites have focused on learning statistical relationships between hand-crafted appearance features of the foreground and background, which is unreliable especially when the contents in the two layers are vastly different. In this work, we propose an end-to-end deep convolutional neural network for image harmonization, which can capture both the context and semantic information of the composite images during harmonization. We also introduce an efficient way to collect large-scale and high-quality training data that can facilitate the training process. Experiments on the synthesized dataset and real composite images show that the proposed network outperforms previous state-of-the-art methods.

Context Encoders: Feature Learning by Inpainting

Abstract: We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction. By analogy with auto-encoders, we propose Context Encoders -- a convolutional neural network trained to generate the contents of an arbitrary image region conditioned on its surroundings. In order to succeed at this task, context encoders need to both understand the content of the entire image, as well as produce a plausible hypothesis for the missing part(s). When training context encoders, we have experimented with both a standard pixel-wise reconstruction loss, as well as a reconstruction plus an adversarial loss. The latter produces much sharper results because it can better handle multiple modes in the output. We found that a context encoder learns a representation that captures not just appearance but also the semantics of visual structures. We quantitatively demonstrate the effectiveness of our learned features for CNN pre-training on classification, detection, and segmentation tasks. Furthermore, context encoders can be used for semantic inpainting tasks, either stand-alone or as initialization for non-parametric methods.

Shown are results from our relative realism study. Each cell lists the percentage of pairs in which synthesized images by our approach are rated more realistic than the corresponding baseline in human perception experiments. Chance is at {{formula:5895c542-80ca-4f55-9e89-7283de675d30}}  result.

Results: As shown in Table {{table:3d697dd8-5f11-4ea3-bff4-850fd439f8c3}} , people rate our synthesized images more realistic than baselines for at least {{formula:2882b19f-bf28-4692-af4b-1b9bc40d59fb}}  of test images. For example, our results are more realistic than Content-Aware-Fill, which is the best baseline in the absolute realism experiment for {{formula:bab8a2b5-66e3-459c-9276-7a174eab4881}}  of images (using Retrieval method (a) to find a guidance image). These results reveal greater improvements of our synthesis over baselines than is evident from the absolute realism study. Our results are similar whether using Retrieval method (a) or (b) to choose a guidance image.


Learning Transferable Features with Deep Adaptation Networks

Abstract: Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation Network (DAN) architecture, which generalizes deep convolutional neural network to the domain adaptation scenario. In DAN, hidden representations of all task-specific layers are embedded in a reproducing kernel Hilbert space where the mean embeddings of different domain distributions can be explicitly matched. The domain discrepancy is further reduced using an optimal multi-kernel selection method for mean embedding matching. DAN can learn transferable features with statistical guarantees, and can scale linearly by unbiased estimate of kernel embedding. Extensive empirical evidence shows that the proposed architecture yields state-of-the-art image classification error rates on standard domain adaptation benchmarks.

Correlation Alignment for Unsupervised Domain Adaptation

Abstract: In this chapter, we present CORrelation ALignment (CORAL), a simple yet effective method for unsupervised domain adaptation. CORAL minimizes domain shift by aligning the second-order statistics of source and target distributions, without requiring any target labels. In contrast to subspace manifold methods, it aligns the original feature distributions of the source and target domains, rather than the bases of lower-dimensional subspaces. It is also much simpler than other distribution matching methods. CORAL performs remarkably well in extensive evaluations on standard benchmark datasets. We first describe a solution that applies a linear transformation to source features to align them with target features before classifier training. For linear classifiers, we propose to equivalently apply CORAL to the classifier weights, leading to added efficiency when the number of classifiers is small but the number and dimensionality of target examples are very high. The resulting CORAL Linear Discriminant Analysis (CORAL-LDA) outperforms LDA by a large margin on standard domain adaptation benchmarks. Finally, we extend CORAL to learn a nonlinear transformation that aligns correlations of layer activations in deep neural networks (DNNs). The resulting Deep CORAL approach works seamlessly with DNNs and achieves state-of-the-art performance on standard benchmark datasets. Our code is available at:~\url{https://github.com/VisionLearningGroup/CORAL}

Beyond Sharing Weights for Deep Domain Adaptation

Abstract: The performance of a classifier trained on data coming from a specific domain typically degrades when applied to a related but different one. While annotating many samples from the new domain would address this issue, it is often too expensive or impractical. Domain Adaptation has therefore emerged as a solution to this problem; It leverages annotated data from a source domain, in which it is abundant, to train a classifier to operate in a target domain, in which it is either sparse or even lacking altogether. In this context, the recent trend consists of learning deep architectures whose weights are shared for both domains, which essentially amounts to learning domain invariant features. Here, we show that it is more effective to explicitly model the shift from one domain to the other. To this end, we introduce a two-stream architecture, where one operates in the source domain and the other in the target domain. In contrast to other approaches, the weights in corresponding layers are related but not shared. We demonstrate that this both yields higher accuracy than state-of-the-art methods on several object recognition and detection tasks and consistently outperforms networks with shared weights in both supervised and unsupervised settings.

Comparison of different discrepancy-based methods, where `loss' indicates the loss objectives without the common cross-entropy loss, `layer' represents the layers that the loss functions on, `weight' indicates whether the weights of the two networks are shared or not, `base net' is the existing network that the compared methods are based on.

The comparison of these methods is summarized in Table {{table:32f8e260-805c-40a4-ae8f-732c02d0dad7}} . Since the domain invariant features of deep UDA methods are learned end-to-end, we will not compare the detailed difference on {{formula:973f6eda-8a85-4cdc-ab2a-6294d5fd152d}} , {{formula:e0f01c3e-f59e-4d6c-9796-fb502ab31a09}}  and {{formula:68803c39-d6b7-4c2f-8949-4f578649d10d}}  unless otherwise specified.


Energy depletion, interruption of charging operation

Safe and Secure Wireless Power Transfer Networks: Challenges and Opportunities in RF-Based Systems

Abstract: RF-based wireless power transfer networks (WPTNs) are deployed to transfer power to embedded devices over the air via RF waves. Up until now, a considerable amount of effort has been devoted by researchers to design WPTNs that maximize several objectives such as harvested power, energy outage and charging delay. However, inherent security and safety issues are generally overlooked and these need to be solved if WPTNs are to be become widespread. This article focuses on safety and security problems related WPTNs and highlight their cruciality in terms of efficient and dependable operation of RF-based WPTNs. We provide a overview of new research opportunities in this emerging domain.

ShieldScatter: Improving IoT Security with Backscatter Assistance

Abstract: The lightweight protocols and low-power radio technologies open up many opportunities to facilitate Internet-of-Things (IoT) into our daily life, while their minimalist design also makes IoT devices vulnerable to many active attacks due to the lack of sophisticated security protocols. Recent advances advocate the use of an antenna array to extract fine-grained physical-layer signatures to mitigate these active attacks. However, it adds burdens in terms of energy consumption and hardware cost that IoT devices cannot afford. To overcome this predicament, we present ShieldScatter, a lightweight system that attaches battery-free backscatter tags to single-antenna devices to shield the system from active attacks. The key insight of ShieldScatter is to intentionally create multi-path propagation signatures with the careful deployment of backscatter tags. These signatures can be used to construct a sensitive profile to identify the location of the signals' arrival, and thus detect the threat. We prototype ShieldScatter with USRPs and ambient backscatter tags to evaluate our system in various environments. The experimental results show that even when the attacker is located only 15 cm away from the legitimate device, ShieldScatter with merely three backscatter tags can mitigate 97% of spoofing attack attempts while at the same time trigger false alarms on just 7% of legitimate traffic.

Overview of Attacks affecting EH Networks

Attacks and Threats in Energy Harvesting Networks

Different attacks have been identified and discussed in the literature, each requiring specific tools and features by the adversary and targeting specific enabling technologies. A summary of these attacks is provided in Table {{table:0d5cf549-8463-46eb-86d7-23744d3d3743}} , concerning the techniques and capabilities employed by the adversary.


Secure Communication via a Wireless Energy Harvesting Untrusted Relay

Abstract: The broadcast nature of the wireless medium allows unintended users to eavesdrop on confidential information transmission. In this regard, we investigate the problem of secure communication between a source and a destination via a wireless energy harvesting untrusted node that acts as a helper to relay the information; however, the source and destination nodes wish to keep the information confidential from the relay node. To realize the positive secrecy rate, we use destination-assisted jamming. Being an energy-starved node, the untrusted relay harvests energy from the received radio-frequency (RF) signals, which include the source's information signal and the destination's jamming signal. Thus, we utilize the jamming signal efficiently by leveraging it as a useful energy source. At the relay, to enable energy harvesting and information processing, we adopt power splitting (PS) and time switching (TS) policies. To evaluate the secrecy performance of this proposed scenario, we derive analytical expressions for two important metrics, viz., the secrecy outage probability and the ergodic secrecy rate. The numerical analysis reveals design insights into the effects of different system parameters such as PS ratio, energy harvesting time, target secrecy rate, transmit signal-to-noise ratio (SNR), relay location, and energy conversion efficiency factor, on secrecy performance. Specifically, the PS policy achieves better optimal secrecy outage probability and optimal ergodic secrecy rate than that of the TS policy at higher target secrecy rate and transmit SNR, respectively.

Secure Transmission for Simultaneous Wireless Information and Power Transfer in AF Untrusted Relay Networks

Abstract: This paper investigates secure communications of energy harvesting untrusted relay networks, where the destination assists jamming signal to prevent the untrusted relay from eavesdropping and to improve the forwarding ability of the energy constrained relay. Firstly, the source and the destination transmit the signals to the relay with maximal ratio transmission (MRT) technique or transmit antenna selection (TAS) technique. Then, the destination utilizes maximal ratio combining (MRC) technique or receive antenna selection (RAS) technique to receive the forwarded information. Therefore, four transmission and reception schemes are considered. For each scheme, the closed-form expressions of the secrecy outage probability (SOP) and the connection outage probability (COP) are derived. Besides, the effective secrecy throughput (EST) metric is analyzed to achieve a good tradeoff between security and reliability. In addition, the asymptotic performance of EST is also considered at the high signal-to-noise ratio (SNR). Finally, simulation results illustrate that: (1) the EST of the system with MRT and MRC scheme are superior to other schemes, however, in the high SNR regime, the EST of the system with MRT scheme is inferior to TAS; and (2) for the source node, there exists an optimal number of antennas to maximize the EST of the proposed schemes.

Qualitative Comparison among Approaches investigating PHY-layer Data Secrecy in EH Networks assuming untrusted relays.

A classification of the scientific contributions assuming untrusted relays in RF EH networks is provided in Table {{table:87fd632e-fde8-4979-8fc7-6ebc15de1755}} , considering the features and capabilities of the untrusted relay(s).

Despite the authors in {{cite:6e59f68}} assumed the use of the DF strategy on the relay, all the other works listed in Tab. {{table:87fd632e-fde8-4979-8fc7-6ebc15de1755}}  assumed a relay working according to the AF strategy. In general, this latter technique is preferable from the security perspective, as it does not allow the relay to decode the information while in transit toward the destination.


Jamming, Spoofing, Monitoring, Charging Attacks, Malicious Applications

Jamming, Deauthentication, Replay, Channel Spoofing

FDMA, TDMA, frequency hopping, Interference Alignment; Digital Signatures; Data and Energy exchanging estimation

Ambient Backscattering, Multi-path Propagation Signatures

Mitigating Jamming Attacks Using Energy Harvesting

Abstract: The use of energy harvesting as a counter-jamming measure is investigated on the premise that part of the harmful interference can be harvested to increase the transmit power. We formulate the strategic interaction between a pair of legitimate nodes and a malicious jammer as a zero-sum game. Our analysis demonstrates that the legitimate nodes are able to neutralize the jammer. However, this policy is not necessarily a Nash equilibrium and hence is sub-optimal. Instead, harvesting the jamming interference can lead to relative gains of up to 95%, on average, in terms of Shannon capacity, when the jamming interference is high.

Overview of physical-layer protection techniques adopted in EH networks

A security-oriented overview of such scientific contributions is provided in Table {{table:60255d15-494a-4571-802e-0ab51514173e}} .

The following discussion provides more details about the schemes listed in Table {{table:60255d15-494a-4571-802e-0ab51514173e}} , introducing them based on the target security service.


Membership Inference Attacks Against Machine Learning Models

Abstract: We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if the record was in the model's training dataset. To perform membership inference against a target model, we make adversarial use of machine learning and train our own inference model to recognize differences in the target model's predictions on the inputs that it trained on versus the inputs that it did not train on. We empirically evaluate our inference techniques on classification models trained by commercial "machine learning as a service" providers such as Google and Amazon. Using realistic datasets and classification tasks, including a hospital discharge dataset whose membership is sensitive from the privacy perspective, we show that these models can be vulnerable to membership inference attacks. We then investigate the factors that influence this leakage and evaluate mitigation strategies.

Towards Measuring Membership Privacy

Abstract: Machine learning models are increasingly made available to the masses through public query interfaces. Recent academic work has demonstrated that malicious users who can query such models are able to infer sensitive information about records within the training data. Differential privacy can thwart such attacks, but not all models can be readily trained to achieve this guarantee or to achieve it with acceptable utility loss. As a result, if a model is trained without differential privacy guarantee, little is known or can be said about the privacy risk of releasing it. In this work, we investigate and analyze membership attacks to understand why and how they succeed. Based on this understanding, we propose Differential Training Privacy (DTP), an empirical metric to estimate the privacy risk of publishing a classier when methods such as differential privacy cannot be applied. DTP is a measure of a classier with respect to its training dataset, and we show that calculating DTP is efficient in many practical cases. We empirically validate DTP using state-of-the-art machine learning models such as neural networks trained on real-world datasets. Our results show that DTP is highly predictive of the success of membership attacks and therefore reducing DTP also reduces the privacy risk. We advocate for DTP to be used as part of the decision-making process when considering publishing a classifier. To this end, we also suggest adopting the DTP-1 hypothesis: if a classifier has a DTP value above 1, it should not be published.

ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Abstract:
Machine learning (ML) has become a core component of many real-world applications and training data is a key factor that drives current progress. This huge success has led Internet companies to deploy machine learning as a service (MLaaS). Recently, the first membership inference attack has shown that extraction of information on the training set is possible in such MLaaS settings, which has severe security and privacy implications.
However, the early demonstrations of the feasibility of such attacks have many assumptions on the adversary, such as using multiple so-called shadow models, knowledge of the target model structure, and having a dataset from the same distribution as the target model's training data. We relax all these key assumptions, thereby showing that such attacks are very broadly applicable at low cost and thereby pose a more severe risk than previously thought. We present the most comprehensive study so far on this emerging and developing threat using eight diverse datasets which show the viability of the proposed attacks across domains.
In addition, we propose the first effective defense mechanisms against such broader class of membership inference attacks that maintain a high level of utility of the ML model.

Auditing Data Provenance in Text-Generation Models

Abstract: To help enforce data-protection regulations such as GDPR and detect unauthorized uses of personal data, we develop a new \emph{model auditing} technique that helps users check if their data was used to train a machine learning model. We focus on auditing deep-learning models that generate natural-language text, including word prediction and dialog generation. These models are at the core of popular online services and are often trained on personal data such as users' messages, searches, chats, and comments. We design and evaluate a black-box auditing method that can detect, with very few queries to a model, if a particular user's texts were used to train it (among thousands of other users). We empirically show that our method can successfully audit well-generalized models that are not overfitted to the training data. We also analyze how text-generation models memorize word sequences and explain why this memorization makes them amenable to auditing.

Adversarial Neural Network Inversion via Auxiliary Knowledge Alignment

Abstract: The rise of deep learning technique has raised new privacy concerns about the training data and test data. In this work, we investigate the model inversion problem in the adversarial settings, where the adversary aims at inferring information about the target model's training data and test data from the model's prediction values. We develop a solution to train a second neural network that acts as the inverse of the target model to perform the inversion. The inversion model can be trained with black-box accesses to the target model. We propose two main techniques towards training the inversion model in the adversarial settings. First, we leverage the adversary's background knowledge to compose an auxiliary set to train the inversion model, which does not require access to the original training data. Second, we design a truncation-based technique to align the inversion model to enable effective inversion of the target model from partial predictions that the adversary obtains on victim user's data. We systematically evaluate our inversion approach in various machine learning tasks and model architectures on multiple image datasets. Our experimental results show that even with no full knowledge about the target model's training data, and with only partial prediction values, our inversion approach is still able to perform accurate inversion of the target model, and outperform previous approaches.

Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning

Abstract: Machine learning (ML) has progressed rapidly during the past decade and the major factor that drives such development is the unprecedented large-scale data. As data generation is a continuous process, this leads to ML model owners updating their models frequently with newly-collected data in an online learning scenario. In consequence, if an ML model is queried with the same set of data samples at two different points in time, it will provide different results. In this paper, we investigate whether the change in the output of a black-box ML model before and after being updated can leak information of the dataset used to perform the update, namely the updating set. This constitutes a new attack surface against black-box ML models and such information leakage may compromise the intellectual property and data privacy of the ML model owner. We propose four attacks following an encoder-decoder formulation, which allows inferring diverse information of the updating set. Our new attacks are facilitated by state-of-the-art deep learning techniques. In particular, we propose a hybrid generative model (CBM-GAN) that is based on generative adversarial networks (GANs) but includes a reconstructive loss that allows reconstructing accurate samples. Our experiments show that the proposed attacks achieve strong performance.

Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers

Abstract: Machine Learning (ML) algorithms are used to train computers to perform a variety of complex tasks and improve with experience. Computers learn how to recognize patterns, make unintended decisions, or react to a dynamic environment. Certain trained machines may be more effective than others because they are based on more suitable ML algorithms or because they were trained through superior training sets. Although ML algorithms are known and publicly released, training sets may not be reasonably ascertainable and, indeed, may be guarded as trade secrets. While much research has been performed about the privacy of the elements of training sets, in this paper we focus our attention on ML classifiers and on the statistical information that can be unconsciously or maliciously revealed from them. We show that it is possible to infer unexpected but useful information from ML classifiers. In particular, we build a novel meta-classifier and train it to hack other classifiers, obtaining meaningful information about their training sets. This kind of information leakage can be exploited, for example, by a vendor to build more effective classifiers or to simply acquire trade secrets from a competitor's apparatus, potentially violating its intellectual property rights.

Stealing Hyperparameters in Machine Learning

Abstract: Hyperparameters are critical in machine learning, as different hyperparameters often result in models with significantly different performance. Hyperparameters may be deemed confidential because of their commercial value and the confidentiality of the proprietary algorithms that the learner uses to learn them. In this work, we propose attacks on stealing the hyperparameters that are learned by a learner. We call our attacks hyperparameter stealing attacks. Our attacks are applicable to a variety of popular machine learning algorithms such as ridge regression, logistic regression, support vector machine, and neural network. We evaluate the effectiveness of our attacks both theoretically and empirically. For instance, we evaluate our attacks on Amazon Machine Learning. Our results demonstrate that our attacks can accurately steal hyperparameters. We also study countermeasures. Our results highlight the need for new defenses against our hyperparameter stealing attacks for certain machine learning algorithms.

Properties of some notable attacks against machine learning privacy. MIA denotes Model Inversion Attack in the table.

Indirect (Inferred) Information Exposure

As shown in figure {{figure:a588a950-c0d3-4476-b4d7-9a4d2365ef5b}} , we categorize indirect attacks into 5 main groups of membership inference, model inversion, hyperparameter inference, parameter inference, and property inference attacks. Table {{table:25a79ca6-1d5a-44d9-b811-e438550352fd}}  shows a summary of different attacks and their properties. The “Access to Model” column determines whether the attack needs white-box or black-box access to model to successfully mount. White-box access assumes access to the full target model, whereas black-box assumes only query access to the model, without knowledge on the architecture or parameters of the target model. The last column shows whether the attacker needs access to the output confidence values of the model (the probabilities, logits), or whether only the predicted labels suffice.


Abnormal Event Detection in Videos using Generative Adversarial Nets

Abstract: In this paper we address the abnormality detection problem in crowded scenes. We propose to use Generative Adversarial Nets (GANs), which are trained using normal frames and corresponding optical-flow images in order to learn an internal representation of the scene normality. Since our GANs are trained with only normal data, they are not able to generate abnormal events. At testing time the real data are compared with both the appearance and the motion representations reconstructed by our GANs and abnormal areas are detected by computing local differences. Experimental results on challenging abnormality detection datasets show the superiority of the proposed method compared to the state of the art in both frame-level and pixel-level abnormality detection tasks.

Training Adversarial Discriminators for Cross-channel Abnormal Event Detection in Crowds

Abstract: Abnormal crowd behaviour detection attracts a large interest due to its importance in video surveillance scenarios. However, the ambiguity and the lack of sufficient abnormal ground truth data makes end-to-end training of large deep networks hard in this domain. In this paper we propose to use Generative Adversarial Nets (GANs), which are trained to generate only the normal distribution of the data. During the adversarial GAN training, a discriminator (D) is used as a supervisor for the generator network (G) and vice versa. At testing time we use D to solve our discriminative task (abnormality detection), where D has been trained without the need of manually-annotated abnormal data. Moreover, in order to prevent G learn a trivial identity function, we use a cross-channel approach, forcing G to transform raw-pixel data in motion information and vice versa. The quantitative results on standard benchmarks show that our method outperforms previous state-of-the-art methods in both the frame-level and the pixel-level evaluation.

Learning Deep Representations of Appearance and Motion for Anomalous Event Detection

Abstract: We present a novel unsupervised deep learning framework for anomalous event detection in complex video scenes. While most existing works merely use hand-crafted appearance and motion features, we propose Appearance and Motion DeepNet (AMDN) which utilizes deep neural networks to automatically learn feature representations. To exploit the complementary information of both appearance and motion patterns, we introduce a novel double fusion framework, combining both the benefits of traditional early fusion and late fusion strategies. Specifically, stacked denoising autoencoders are proposed to separately learn both appearance and motion features as well as a joint representation (early fusion). Based on the learned representations, multiple one-class SVM models are used to predict the anomaly scores of each input, which are then integrated with a late fusion strategy for final anomaly detection. We evaluate the proposed method on two publicly available video surveillance datasets, showing competitive performance with respect to state of the art approaches.

EER results comparison with existing works on UCSD Ped2 dataset. blackLower numbers mean better results.

Performance evaluation. Frame-level AUC and EER are the two evaluation metrics used to compare our approach with a series of existing works {{cite:6e46651}}, {{cite:f5438d2}}, {{cite:5c4da5f}}, {{cite:4c25cba}}, {{cite:6149933}}, {{cite:04eb078}}, {{cite:61e6eac}}, {{cite:62f41ec}}, {{cite:14e8a3b}}, {{cite:3de9f14}}, {{cite:64f6f0d}}, {{cite:2c397b0}}, {{cite:5177379}}, {{cite:1e1ee3f}}, {{cite:d9da5e2}}, {{cite:b168094}}, {{cite:7bd4ac2}}, {{cite:d9abb1b}} published within last black5 years. The corresponding results provided in blackTable {{table:a3aad6fa-4c4e-4db3-90c3-c346f47a6911}}  and Table {{table:bde36980-ed33-4adc-9386-fbe0d7e555b6}}  show that our method outperforms recent state-of-the-art methodologies in the task of anomaly detection. blackComparing with the baseline, our approach achieves an absolute gain of 5.2% in terms of AUC. Examples of the reconstructed patches are provided in Figure {{figure:d863d288-3a4e-4a78-b5e1-b06af721c4fc}} . As shown in Figure {{figure:d863d288-3a4e-4a78-b5e1-b06af721c4fc}} b, although {{formula:11c59c1c-2882-4287-88da-d99a03c0d26d}}  generates blacknoticeably good reconstructions of anomalous inputs, due to the presence of our proposed pseudo-anomaly module, {{formula:2225c16d-f665-44e1-ac62-b260f318f4a5}}  blackgets to learn the underlying patterns of reconstructed anomalous images. This is why, in contrast to the baseline, our framework provides consistent performance across a wide range of training epochs (Figure {{figure:091d5abb-05f1-449e-82d7-be664fa5a4aa}} ).


Supervised Learning of Universal Sentence Representations from Natural Language Inference Data

Abstract: Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features. Efforts to obtain embeddings for larger chunks of text, such as sentences, have however not been so successful. Several attempts at learning unsupervised representations of sentences have not reached satisfactory enough performance to be widely adopted. In this paper, we show how universal sentence representations trained using the supervised data of the Stanford Natural Language Inference datasets can consistently outperform unsupervised methods like SkipThought vectors on a wide range of transfer tasks. Much like how computer vision uses ImageNet to obtain features, which can then be transferred to other tasks, our work tends to indicate the suitability of natural language inference for transfer learning to other NLP tasks. Our encoder is publicly available.

Deep contextualized word representations

Abstract: We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals.

Models tested (disambiguated with notation from cited paper), aggregation functions applied across token representations, and representation dimensions.

We apply SEAT to seven sentence encoders (listed in Table {{table:b8e0c56d-bbfb-41b8-8a6c-1f5985038dbb}} ) including simple bag-of-words encoders, sentence-to-vector models, and state-of-the-art sequence models.We provide further details and explore variations on these model configurations in the supplement.
For all models, we use publicly available pretrained parameters.


These approaches combine existing NLP components to build effective deployments. Typical components include named entity extraction and text classification.

Features based on word embeddings and modification of general-purpose word embeddings to the specific domain space have been reported, along with typical neural network models.

Health mention classification can be broken down into a sequence of NLP components fitting into one another.

Deep learning approaches have proven to be useful since they do not rely on human-engineered features.

NLP components may be trained on documents in domains unrelated to health-care. In such cases, their efficacy for health mention classification needs validation.

Lack of availability of large labeled datasets may be an impediment.

Global Health Monitor: A Web-based System for Detecting and Mapping Infectious Diseases

Abstract: We present the Global Health Monitor, an online Web-based system for detecting and mapping infectious disease outbreaks that appear in news stories. The system analyzes English news stories from news feed providers, classifies them for topical relevance and plots them onto a Google map using geo-coding information, helping public health workers to monitor the spread of diseases in a geo-temporal context. The background knowledge for the system is contained in the BioCaster ontology (BCO) (Collier et al., 2007a) which includes both information on infectious diseases as well as geographical locations with their latitudes/longitudes. The system consists of four main stages: topic classification, named entity recognition (NER), disease/location detection and visualization. Evaluation of the system shows that it achieved high accuracy on a gold standard corpus. The system is now in practical use. Running on a clustercomputer, it monitors more than 1500 news feeds 24/7, updating the map every hour.

Did You Really Just Have a Heart Attack?: Towards Robust Detection of Personal Health Mentions in Social Media

Abstract: Millions of users share their experiences on social media sites, such as Twitter, which in turn generate valuable data for public health monitoring, digital epidemiology, and other analyses of population health at global scale. The first, critical, task for these applications is classifying whether a personal health event was mentioned, which we call the (PHM) problem. This task is challenging for many reasons, including typically short length of social media posts, inventive spelling and lexicons, and figurative language, including hyperbole using diseases like "heart attack»» or "cancer»» for emphasis, and not as a health self-report. This problem is even more challenging for rarely reported, or frequent but ambiguously expressed conditions, such as "stroke»». To address this problem, we propose a general, robust method for detecting PHMs in social media, which we call WESPAD, that combines lexical, syntactic, word embedding-based, and context-based features. WESPAD is able to generalize from few examples by automatically distorting the word embedding space to most effectively detect the true health mentions. Unlike previously proposed state-of-the-art supervised and deep-learning techniques, WESPAD requires relatively little training data, which makes it possible to adapt, with minimal effort, to each new disease and condition. We evaluate WESPAD on both an established publicly available Flu detection benchmark, and on a new dataset that we have constructed with mentions of multiple health conditions. Our experiments show that WESPAD outperforms the baselines and state-of-the-art methods, especially in cases when the number and proportion of true health mentions in the training data is small.

Summary of Approaches for Health Mention Classification.

In the survey so far, we introduced the problem of epidemic intelligence, motivated it in terms of its challenges and then described the resources that can be used for epidemic intelligence. This section describes text-based approaches that have been reported for health mention classification. We classify past approaches in categories that are commonly used in computational linguistics, summarised in Table {{table:abc796d4-aab3-4f30-b663-418d4e07da68}} . We detail out these approaches in the forthcoming subsections.


Point-to-point link, random data and energy arrivals

Harvest from PB, transmit to AP, circuit power consumption, TDMA, NOMA

STTM and TCTM, optimal offline and online policies

Joint optimization of HAP power and time allocation, myopic policy

Optimal time allocation in TDMA and NOMA, myopic policy

Transmission with Energy Harvesting Nodes in Fading Wireless Channels: Optimal Policies

Abstract: Wireless systems comprised of rechargeable nodes have a significantly prolonged lifetime and are sustainable. A distinct characteristic of these systems is the fact that the nodes can harvest energy throughout the duration in which communication takes place. As such, transmission policies of the nodes need to adapt to these harvested energy arrivals. In this paper, we consider optimization of point-to-point data transmission with an energy harvesting transmitter which has a limited battery capacity, communicating in a wireless fading channel. We consider two objectives: maximizing the throughput by a deadline, and minimizing the transmission completion time of the communication session. We optimize these objectives by controlling the time sequence of transmit powers subject to energy storage capacity and causality constraints. We, first, study optimal offline policies. We introduce a directional water-filling algorithm which provides a simple and concise interpretation of the necessary optimality conditions. We show the optimality of an adaptive directional water-filling algorithm for the throughput maximization problem. We solve the transmission completion time minimization problem by utilizing its equivalence to its throughput maximization counterpart. Next, we consider online policies. We use stochastic dynamic programming to solve for the optimal online policy that maximizes the average number of bits delivered by a deadline under stochastic fading and energy arrival processes with causal channel state feedback. We also propose near-optimal policies with reduced complexity, and numerically study their performances along with the performances of the offline and online optimal policies under various different configurations.

Throughput Maximization in Wireless Powered Communication Networks

Abstract: This paper studies the newly emerging wireless powered communication network (WPCN) in which one hybrid access point (H-AP) with constant power supply coordinates the wireless energy/information transmissions to/from distributed users that do not have energy sources. A "harvest-then-transmit" protocol is proposed where all users first harvest the wireless energy broadcast by the H-AP in the downlink (DL) and then send their independent information to the H-AP in the uplink (UL) by time-division-multiple-access (TDMA). First, we study the sum-throughput maximization of all users by jointly optimizing the time allocation for the DL wireless power transfer versus the users' UL information transmissions given a total time constraint based on the users' DL and UL channels as well as their average harvested energy values. By applying convex optimization techniques, we obtain the closed-form expressions for the optimal time allocations to maximize the sum-throughput. Our solution reveals "doubly near-far" phenomenon due to both the DL and UL distance-dependent signal attenuation, where a far user from the H-AP, which receives less wireless energy than a nearer user in the DL, has to transmit with more power in the UL for reliable information transmission. Consequently, the maximum sum-throughput is achieved by allocating substantially more time to the near users than the far users, thus resulting in unfair rate allocation among different users. To overcome this problem, we furthermore propose a new performance metric so-called common-throughput with the additional constraint that all users should be allocated with an equal rate regardless of their distances to the H-AP. We present an efficient algorithm to solve the common-throughput maximization problem. Simulation results demonstrate the effectiveness of the common-throughput approach for solving the new doubly near-far problem in WPCNs.

Wireless Networks with Energy Harvesting and Power Transfer: Joint Power and Time Allocation

Abstract: In this letter, we consider wireless powered communication networks which could operate perpetually, as the base station (BS) broadcasts energy to the multiple energy harvesting (EH) information transmitters. These employ “harvest then transmit” mechanism, as they spend all of their energy harvested during the previous BS energy broadcast to transmit the information towards the BS. Assuming time division multiple access (TDMA), we propose a novel transmission scheme for jointly optimal allocation of the BS broadcasting power and time sharing among the wireless nodes, which maximizes the overall network throughput, under the constraint of average transmit power and maximum transmit power at the BS. The proposed scheme significantly outperforms “state of the art” schemes that employ only the optimal time allocation. If a single EH transmitter is considered, we generalize the optimal solutions for the case of fixed circuit power consumption, which refers to a much more practical scenario.

Full-Duplex Wireless-Powered Communication Network with Energy Causality

Abstract: In this paper, we consider a wireless communication network with a full-duplex hybrid access point (HAP) and a set of wireless users with energy harvesting capabilities. The HAP implements the full-duplex through two antennas: one for broadcasting wireless energy to users in the downlink and one for receiving independent information from users via time-division-multiple-access (TDMA) in the uplink at the same time. All users can continuously harvest wireless power from the HAP until its transmission slot, i.e., the energy causality constraint is modeled by assuming that energy harvested in the future cannot be used for tranmission. Hence, latter users' energy harvesting time is coupled with the transmission time of previous users. Under this setup, we investigate the sum-throughput maximization (STM) problem and the total-time minimization (TTM) problem for the proposed multi-user full-duplex wireless-powered network. The STM problem is proved to be a convex optimization problem. The optimal solution strategy is then obtained in closed-form expression, which can be computed with linear complexity. It is also shown that the sum throughput is non-decreasing with increasing of the number of users. For the TTM problem, by exploiting the properties of the coupling constraints, we propose a two-step algorithm to obtain an optimal solution. Then, for each problem, two suboptimal solutions are proposed and investigated. Finally, the effect of user scheduling on STM and TTM are investigated through simulations. It is also shown that different user scheduling strategies should be used for STM and TTM.

Optimal Resource Allocation in Full-Duplex Wireless-Powered Communication Network

Abstract: This paper studies optimal resource allocation in the wireless-powered communication network (WPCN), where one hybrid access-point (H-AP) operating in full-duplex (FD) broadcasts wireless energy to a set of distributed users in the downlink (DL) and at the same time receives independent information from the users via time-division-multiple-access (TDMA) in the uplink (UL). We design an efficient protocol to support simultaneous wireless energy transfer (WET) in the DL and wireless information transmission (WIT) in the UL for the proposed FD-WPCN. We jointly optimize the time allocations to the H-AP for DL WET and different users for UL WIT as well as the transmit power allocations over time at the H-AP to maximize the users' weighted sum-rate of UL information transmission with harvested energy. We consider both the cases with perfect and imperfect self-interference cancellation (SIC) at the H-AP, for which we obtain optimal and suboptimal time and power allocation solutions, respectively. Furthermore, we consider the half-duplex (HD) WPCN as a baseline scheme and derive its optimal resource allocation solution. Simulation results show that the FD-WPCN outperforms HD-WPCN when effective SIC can be implemented and more stringent peak power constraint is applied at the H-AP.

Spectral and Energy-Efficient Wireless Powered IoT Networks: NOMA or TDMA?

Abstract: Wireless powered communication networks (WPCNs), where multiple energy-limited devices first harvest energy in the downlink and then transmit information in the uplink, have been envisioned as a promising solution for the future Internet-of-Things (IoT). Meanwhile, nonorthogonal multiple access (NOMA) has been proposed to improve the system spectral efficiency (SE) of the fifth-generation (5G) networks by allowing concurrent transmissions of multiple users in the same spectrum. As such, NOMA has been recently considered for the uplink of WPCNs based IoT networks with a massive number of devices. However, simultaneous transmissions in NOMA may also incur more transmit energy consumption as well as circuit energy consumption in practice which is critical for energy constrained IoT devices. As a result, compared to orthogonal multiple access schemes such as time-division multiple access (TDMA), whether the SE can be improved and/or the total energy consumption can be reduced with NOMA in such a scenario still remains unknown. To answer this question, we first derive the optimal time allocations for maximizing the SE of a TDMA-based WPCN (T-WPCN) and a NOMA-based WPCN (N-WPCN), respectively. Subsequently, we analyze the total energy consumption as well as the maximum SE achieved by these two networks. Surprisingly, it is found that N-WPCN not only consumes more energy, but also is less spectral efficient than T-WPCN. Simulation results verify our theoretical findings and unveil the fundamental performance bottleneck, i.e., “worst user bottleneck problem”, in multiuser NOMA systems.

Background works

The aforementioned works on scheduling policies for EH networks considered only a fixed transmission order with myopic policies and perfect CSI. However, the presence of the noise makes it difficult to estimate channel coefficients perfectly. Thus the study of EH wireless networks under channel estimation errors is important. Robust offline resource allocation policies for the decode-and-forward (DF) relay network were proposed in {{cite:55a5cc0}} for the multiple access broadcast channel and in {{cite:3fa10b2}} for a half-duplex relay. In {{cite:ae46128}}, the authors considered the distribution uncertainty model for imperfect CSI, where the distribution of channel coefficients was unknown but had a finite divergence from an empirical distribution. Under this setting, the authors obtained a robust power allocation policy for SUs in an underlay CRN. In {{cite:6baaca0}}, the authors considered a non-linear EH model and obtained a robust time and power allocation policy in a wireless powered TDMA-MIMO communication network with a single-slot setting. In {{cite:391593d}}, the authors considered a two-user EH multiple input multiple output (MIMO) interference channel with simultaneous wireless information and power transfer (SWIPT). The authors proposed a robust transceiver design considering the bounded channel uncertainty model. In {{cite:3dd4b79}}, the authors considered a multiple-input single-output (MISO) interference channel where multiple multi-antenna transmitters communicated with their respective single-antenna receivers. Using the bounded uncertainty model, the authors obtained a robust transmit beamforming and power splitting that minimized the total transmit power. Table {{table:4bfc03fa-5035-4926-93d9-db4dc7d0ef14}}  summarizes the background work.


Computing in Memory with Spin-Transfer Torque Magnetic RAM

Abstract: In-memory computing is a promising approach to addressing the processor-memory data transfer bottleneck in computing systems. We propose Spin-Transfer Torque Compute-in-Memory (STT-CiM), a design for in-memory computing with Spin-Transfer Torque Magnetic RAM (STT-MRAM). The unique properties of spintronic memory allow multiple wordlines within an array to be simultaneously enabled, opening up the possibility of directly sensing functions of the values stored in multiple rows using a single access. We propose modifications to STT-MRAM peripheral circuits that leverage this principle to perform logic, arithmetic, and complex vector operations. We address the challenge of reliable in-memory computing under process variations by extending ECC schemes to detect and correct errors that occur during CiM operations. We also address the question of how STT-CiM should be integrated within a general-purpose computing system. To this end, we propose architectural enhancements to processor instruction sets and on-chip buses that enable STT-CiM to be utilized as a scratchpad memory. Finally, we present data mapping techniques to increase the effectiveness of STT-CiM. We evaluate STT-CiM using a device-to-architecture modeling framework, and integrate cycle-accurate models of STT-CiM with a commercial processor and on-chip bus (Nios II and Avalon from Intel). Our system-level evaluation shows that STT-CiM provides system-level performance improvements of 3.93x on average (upto 10.4x), and concurrently reduces memory system energy by 3.83x on average (upto 12.4x).

Temporal correlation detection using computational phase-change memory

Abstract: For decades, conventional computers based on the von Neumann architecture have performed computation by repeatedly transferring data between their processing and their memory units, which are physically separated. As computation becomes increasingly data-centric and as the scalability limits in terms of performance and power are being reached, alternative computing paradigms are searched for in which computation and storage are collocated. A fascinating new approach is that of computational memory where the physics of nanoscale memory devices are used to perform certain computational tasks within the memory unit in a non-von Neumann manner. Here we present a large-scale experimental demonstration using one million phase-change memory devices organized to perform a high-level computational primitive by exploiting the crystallization dynamics. Also presented is an application of such a computational memory to process real-world data-sets. The results show that this co-existence of computation and storage at the nanometer scale could be the enabler for new, ultra-dense, low power, and massively parallel computing systems.

GraphR: Accelerating Graph Processing Using ReRAM

Abstract: Graph processing recently received intensive interests in light of a wide range of needs to understand relationships. It is well-known for the poor locality and high memory bandwidth requirement. In conventional architectures, they incur a significant amount of data movements and energy consumption which motivates several hardware graph processing accelerators. The current graph processing accelerators rely on memory access optimizations or placing computation logics close to memory. Distinct from all existing approaches, we leverage an emerging memory technology to accelerate graph processing with analog computation. This paper presents GRAPHR, the first ReRAM-based graph processing accelerator. GRAPHR follows the principle of near-data processing and explores the opportunity of performing massive parallel analog operations with low hardware and energy cost. The analog computation is suitable for graph processing because: 1) The algorithms are iterative and could inherently tolerate the imprecision; 2) Both probability calculation (e.g., PageRank and Collaborative Filtering) and typical graph algorithms involving integers (e.g., BFS/SSSP) are resilient to errors. The key insight of GRAPHR is that if a vertex program of a graph algorithm can be expressed in sparse matrix vector multiplication (SpMV), it can be efficiently performed by ReRAM crossbar. We show that this assumption is generally true for a large set of graph algorithms. GRAPHR is a novel accelerator architecture consisting of two components: memory ReRAM and graph engine (GE). The core graph computations are performed in sparse matrix format in GEs (ReRAM crossbars). The vector/matrix-based graph computation is not new, but ReRAM offers the unique opportunity to realize the massive parallelism with unprecedented energy efficiency and low hardware cost. With small subgraphs processed by GEs, the gain of performing parallel operations overshadows the wastes due to sparsity. The experiment results show that GRAPHR achieves a 16.01× (up to 132.67×) speedup and a 33.82× energy saving on geometric mean compared to a CPU baseline system. Compared to GPU, GRAPHR achieves 1.69× to 2.19× speedup and consumes 4.77× to 8.91× less energy. GRAPHR gains a speedup of 1.16× to 4.12×, and is 3.67× to 10.96× more energy efficiency compared to PIM-based architecture.

An Overview of In-memory Processing Designs

Table {{table:4fd1dd78-6a8c-4f51-9e45-6e7dd67b8b65}}  presents a summary of the latest eNVM-based in-memory processing designs reviewed in this paper.
Each of them is classified according to the following five main categories.


Name and URL of the top-5 projects by stars (in any programming language)

Precise Condition Synthesis for Program Repair

Abstract: Due to the difficulty of repairing defect, many research efforts have been devoted into automatic defect repair. Given a buggy program that fails some test cases, a typical automatic repair technique tries to modify the program to make all tests pass. However, since the test suites in real world projects are usually insufficient, aiming at passing the test suites often leads to incorrect patches. In this paper we aim to produce precise patches, that is, any patch we produce has a relatively high probability to be correct. More concretely, we focus on condition synthesis, which was shown to be able to repair more than half of the defects in existing approaches. Our key insight is threefold. First, it is important to know what variables in a local context should be used in an "if" condition, and we propose a sorting method based on the dependency relations between variables. Second, we observe that the API document can be used to guide the repair process, and propose document analysis technique to further filter the variables. Third, it is important to know what predicates should be performed on the set of variables, and we propose to mine a set of frequently used predicates in similar contexts from existing projects. We develop a novel program repair system, ACS, that could generate precise conditions at faulty locations. Furthermore, given the generated conditions are very precise, we can perform a repair operation that is previously deemed to be too overfitting: directly returning the test oracle to repair the defect. Using our approach, we successfully repaired 17 defects on four projects of Defects4J, which is the largest number of fully automatically repaired defects reported on the dataset so far. More importantly, the precision of our approach in the evaluation is 73.9%, which is significantly higher than previous approaches, which are usually less than 40%.

Extracting Build Changes with BUILDDIFF

Abstract: Build systems are an essential part of modern software engineering projects. As software projects change continuously, it is crucial to understand how the build system changes because neglecting its maintenance can lead to expensive build breakage. Recent studies have investigated the (co-)evolution of build configurations and reasons for build breakage, but they did this only on a coarse grained level. In this paper, we present BUILDDIFF, an approach to extract detailed build changes from MAVEN build files and classify them into 95 change types. In a manual evaluation of 400 build changing commits, we show that BUILDDIFF can extract and classify build changes with an average precision and recall of 0.96 and 0.98, respectively. We then present two studies using the build changes extracted from 30 open source Java projects to study the frequency and time of build changes. The results show that the top 10 most frequent change types account for 73% of the build changes. Among them, changes to version numbers and changes to dependencies of the projects occur most frequently. Furthermore, our results show that build changes occur frequently around releases. With these results, we provide the basis for further research, such as for analyzing the (co-)evolution of build files with other artifacts or improving effort estimation approaches. Furthermore, our detailed change information enables improvements of refactoring approaches for build configurations and improvements of models to identify error-prone build files.

Extracting Build Changes with BUILDDIFF

Papers and queries

After selecting the papers, we carefully implemented queries to collect the datasets, first using GitHub's REST API and then using the GraphQL API. The GraphQL queries retrieve only the data used in the papers. In total, we reimplemented 14 queries (denoted by Q1 to Q14, see Table {{table:65e0013e-ce90-4ee2-8949-e8c9666741bd}} ), which are used by seven papers.
Finally, we executed the queries and computed the size in bytes of the returned JSON documents.


A review on developing Blockchain-based IoT (BIoT) applications.

A systematic survey that covers the security and the privacy aspects of Bitcoin.

A Review on the Use of Blockchain for the Internet of Things

Abstract: The paradigm of Internet of Things (IoT) is paving the way for a world, where many of our daily objects will be interconnected and will interact with their environment in order to collect information and automate certain tasks. Such a vision requires, among other things, seamless authentication, data privacy, security, robustness against attacks, easy deployment, and self-maintenance. Such features can be brought by blockchain, a technology born with a cryptocurrency called Bitcoin. In this paper, a thorough review on how to adapt blockchain to the specific needs of IoT in order to develop Blockchain-based IoT (BIoT) applications is presented. After describing the basics of blockchain, the most relevant BIoT applications are described with the objective of emphasizing how blockchain can impact traditional cloud-centered IoT applications. Then, the current challenges and possible optimizations are detailed regarding many aspects that affect the design, development, and deployment of a BIoT application. Finally, some recommendations are enumerated with the aim of guiding future BIoT researchers and developers on some of the issues that will have to be tackled before deploying the next generation of BIoT applications.

A Survey on Security and Privacy Issues of Bitcoin

Abstract: Bitcoin is a popular cryptocurrency that records alltransactions in a distributed append-only public ledger calledblockchain. The security of Bitcoin heavily relies on the incentive-compatible proof-of-work (PoW) based distributed consensus pro-tocol, which is run by network nodes called miners. In exchangefor the incentive, the miners are expected to honestly maintainthe blockchain. Since its launch in 2009, Bitcoin economy hasgrown at an enormous rate, and it is now worth about 170 billions of dollars. This exponential growth in the market valueof Bitcoin motivates adversaries to exploit weaknesses for profit,and researchers to discover new vulnerabilities in the system,propose countermeasures, and predict upcoming trends.In this paper, we present a systematic survey that covers thesecurity and privacy aspects of Bitcoin. We start by presenting anoverview of the Bitcoin protocol and its major components alongwith their functionality and interactions within the system. Wereview the existing vulnerabilities in Bitcoin and its underlyingmajor technologies such as blockchain and PoW based consensusprotocol. These vulnerabilities lead to the execution of varioussecurity threats to the normal functionality of Bitcoin. Wethen discuss the feasibility and robustness of the state-of-the-art security solutions. Additionally, we present current privacyand anonymity considerations in Bitcoin and discuss the privacy-related threats to Bitcoin users along with the analysis of theexisting privacy-preserving solutions. Finally, we summarize thecritical open challenges and suggest directions for future researchtowards provisioning stringent security and privacy techniquesfor Bitcoin.

Related Surveys on Blockchain Technologies

There are related survey papers {{cite:0f6ef58}}, {{cite:145eab8}}, {{cite:26f0ade}}, {{cite:b835f66}}, {{cite:e9d75f1}}, {{cite:3ebe04a}} that covered different aspects of the blockchain technology. For example, a brief overview of blockchain for bitcoin was discussed in {{cite:145eab8}}, {{cite:26f0ade}}. However, these surveys are very limited regarding detailed discussion on research challenges in blockchain.
Moreover, Sankar et al. {{cite:145eab8}} briefly presented the feasibility of various consensus protocols in the blockchain. The detailed insights of bitcon were presented in {{cite:0f6ef58}}. Recently, the surveys {{cite:e9d75f1}} presented the overview of Blockchain-based IoT (BIoT) applications. The security and privacy aspects are presented in {{cite:3ebe04a}}, {{cite:b835f66}} for bitcoin, one of the blockchain applications. Table {{table:0234c46f-330e-410d-9b65-21d690c41eb2}}  summarizes the main focuses and major contributions of the previous comprehensive surveys on blockchain technologies.
Although the above mentioned surveys {{cite:0f6ef58}}, {{cite:b835f66}}, {{cite:e9d75f1}}, {{cite:3ebe04a}} have laid a solid foundation for blockchain technologies, our survey differs in several aspects. The main contributions of this paper are:


3D Human Pose Estimation using Spatio-Temporal Networks with Explicit Occlusion Training

Abstract: Estimating 3D poses from a monocular video is still a challenging task, despite the significant progress that has been made in the recent years. Generally, the performance of existing methods drops when the target person is too small/large, or the motion is too fast/slow relative to the scale and speed of the training data. Moreover, to our knowledge, many of these methods are not designed or trained under severe occlusion explicitly, making their performance on handling occlusion compromised. Addressing these problems, we introduce a spatio-temporal network for robust 3D human pose estimation. As humans in videos may appear in different scales and have various motion speeds, we apply multi-scale spatial features for 2D joints or keypoints prediction in each individual frame, and multi-stride temporal convolutional networks (TCNs) to estimate 3D joints or keypoints. Furthermore, we design a spatio-temporal discriminator based on body structures as well as limb motions to assess whether the predicted pose forms a valid pose and a valid movement. During training, we explicitly mask out some keypoints to simulate various occlusion cases, from minor to severe occlusion, so that our network can learn better and becomes robust to various degrees of occlusion. As there are limited 3D ground truth data, we further utilize 2D video data to inject a semi-supervised learning capability to our network. Experiments on public data sets validate the effectiveness of our method, and our ablation studies show the strengths of our network's individual submodules.

Motion Guided 3D Pose Estimation from Videos

Abstract: We propose a new loss function, called motion loss, for the problem of monocular 3D Human pose estimation from 2D pose. In computing motion loss, a simple yet effective representation for keypoint motion, called pairwise motion encoding, is introduced. We design a new graph convolutional network architecture, U-shaped GCN (UGCN). It captures both short-term and long-term motion information to fully leverage the additional supervision from the motion loss. We experiment training UGCN with the motion loss on two large scale benchmarks: Human3.6M and MPI-INF-3DHP. Our model surpasses other state-of-the-art models by a large margin. It also demonstrates strong capacity in producing smooth 3D sequences and recovering keypoint motion.

Monocular temporal models assumptions. Needed number of frames to obtain the optimal accuracy and whether the system can be adapted to only use past frames (for real-time use)

Former markerless motion tracking systems were sometimes constrained to slow motion of only few limbs to perform good detection. It is less and less the case, but there is still some difficulty in predicting quick motions (e.g., in sports video). {{cite:f45856e}} suggest that temporal and spatial data at different resolutions can be a solution to this issue. A new assumption that can be added for methods based on temporal data : if the video footage is not long enough to provide sufficient information, this can be an issue for real-time inference and even for accuracy. Additionally, temporal methods often use information in the future frame, which is not suitable for real-time. Table {{table:db7622d9-27a7-4bd4-ac0e-3c79181b243b}}  show that these methods perform best with varying temporal receptive fields. Some methods only need a few past and future images, while the accuracy of others saturates at more than 200. These methods can be adapted to shorter video clips and to real-time application using only past frames, but at the price of a decrease in accuracy. Another strong constraint is the use of video from moving cameras, but this is rarely addressed {{cite:d56b3ea}}. Many applications can work with the assumption of fixed cameras, but there is a substantial amount of video data produced with moving camera coordinate systems (e.g., in outdoor sports).