Reviewerly/exHarmony huggingface dataset:
SAR Image Despeckling Based on Combination of Fractional-Order Total Variation and Nonlocal Low Rank Regularization

Regularization method is an effective tool for synthetic aperture radar (SAR) image despeckling. Design of the effective regularization terms describing the image priors plays a vital role in this kind of method. In this article, a new combinational regularization model for speckle reduction (CRM-SR) is proposed, in which a regularization term is elaborately designed to contain both a fractional-order total variation (FrTV) regularization and a nonlocal low rank (NLR) regularization. The new regularization model inherits both the advantages of FrTV and NLR and improves the performance of SAR despeckling and, therefore, better preserves the edges and geometrical features of the images during the despeckling process. An efficient algorithm based on alternating direction optimization is derived to solve the proposed combinational regularization model. Experimental results show that the proposed model can effectively remove SAR image speckle and preserve the geometrical features of images according to both subjective visual assessment of image quality and objective evaluation.
ABSynthe: Automatic Blackbox Side-channel Synthesis on Commodity Microarchitectures

The past decade has seen a plethora of side-channel attacks on various CPU components.Each new attack typically follows a whitebox analysis approach, which involves (i) identifying a specific shared CPU component, (ii) reversing its behavior on a specific microarchitecture, and (iii) surgically exploiting such knowledge to leak information (e.g., by actively evicting shared entries to monitor victim accesses).This approach requires lengthy reverse engineering, repeated for every component and microarchitecture, and does not allow for attacking unknown shared resources.In this paper, we present ABSynthe, a system that takes a target program and a microarchitecture as inputs and automatically synthesizes new side channels.The key insight is that by limiting ourselves to (typically on-core) contention-based side channels, we can treat the target CPU microarchitecture as a black box, enabling automation.To make ABSynthe possible, we have automatically generated leakage maps for a variety of x86_64 microarchitectures.These leakage maps show a complex picture of interaction between different x86_64 instructions and justify a black box approach to finding the best sequence of instructions that cause information to leak from a given software target, which we also treat as a black box.To recover the secret information using the optimized sequence of instructions, ABSynthe relies on a recurrent neural network to craft practical side-channel attacks that recover a secret bit stream.Our evaluation shows that ABSynthe can synthesize better attacks by exploiting contention on multiple components at the same time compared to state of the art contention-based attacks that focus on a single component.Furthermore, the automation made possible by ABSynthe allows us to synthesize cross-thread attacks for a variety of microarchitectures (from Intel, AMD and ARM) on four different cryptographic software targets, in both native and virtualized environments.The results show that ABSynthe can recover cryptographic key bit streams with high accuracy.As an example, ABSynthe recovers a full 256-bit EdDSA key from just a single trace capture with 100% success rate on one of our test beds.
A Database for the Radio Frequency Fingerprinting of Bluetooth Devices

Radio frequency fingerprinting (RFF) is a promising physical layer protection technique which can be used to defend wireless networks from malicious attacks. It is based on the use of the distinctive features of the physical waveforms (signals) transmitted from wireless devices in order to classify authorized users. The most important requirement to develop an RFF method is the existence of a precise, robust, and extensive database of the emitted signals. In this context, this paper introduces a database consisting of Bluetooth (BT) signals collected at different sampling rates from 27 different smartphones (six manufacturers with several models for each). Firstly, the data acquisition system to create the database is described in detail. Then, the two well-known methods based on transient BT signals are experimentally tested by using the provided data to check their solidity. The results show that the created database may be useful for many researchers working on the development of the RFF of BT devices.
The Facebook Oversight Board: Creating an Independent Institution to Adjudicate Online Free Expression

For a decade and a half, Facebook has dominated the landscape of digital social networks, becoming one of the most powerful arbiters of online speech. Twenty-four hours a day, seven days a week, over two billion users leverage the platform to post, share, discuss, react to, and access content from all over the globe. Through a system of semipublic rules called “Community Standards,” Facebook has created a body of “laws” and a system of governance that dictate what users may say on the platform. In recent years, as this intricately built system to dispatch the company’s immense private power over the public right of speech has become more visible, Facebook has experienced intense pressure to become more accountable, transparent, and democratic, not only in how it creates its fundamental policies for speech but also in how it enforces them. In November 2018, after years of entreaty from the press, advocacy groups, and users, CEO and founder Mark Zuckerberg announced that Facebook would construct an independent oversight body to be researched, created, and launched within the year. The express purpose of this body was to serve as an appellate review system for user content and to make content-moderation policy recommendations to Facebook. This Feature empirically documents the creation of this institution, now called the Facebook Oversight Board. The Board is a historic endeavor both in scope and scale.The Feature traces the events and influences that led to Facebook’s decision to create the Oversight Board. It details the year-long process of creating the Board, relying on hundreds of hours of interviews and embedded research with the Governance Team charged with researching, planning, and building this new institution.The creation of the Oversight Board and its aims are a novel articulation of internet governance. This Feature illuminates the future implications of the new institution for global freedom of expression. Using the lens of adjudication, it analyzes what the Board is, what the Board means to users, and what the Board means for industry and governments. Ultimately, the Feature concludes that the Facebook Oversight Board has great potential to set new precedent for user participation in private platforms’ governance and a user right to procedure in content moderation.
s-LWSR: Super Lightweight Super-Resolution Network

In recent years, deep-based models have achieved great success in the field of single image super-resolution (SISR), where tremendous parameters are always needed to obtain a satisfying performance. However, the high computational complexity extremely limits its applications to some mobile devices that possess less computing and storage resources. To address this problem, in this paper, we propose a flexibly adjustable super lightweight SR network: s-LWSR. Firstly, in order to efficiently abstract features from the low resolution image, we design a high-efficient U-shape based block, where an information pool is constructed to mix multi-level information from the first half part of the pipeline. Secondly, a compression mechanism based on depth-wise separable convolution is employed to further reduce the numbers of parameters with negligible performance degradation. Thirdly, by revealing the specific role of activation in deep models, we remove several activation layers in our SR model to retain more information, thus leading to the final performance improvement. Extensive experiments show that our s-LWSR, with limited parameters and operations, can achieve similar performance compared with other cumbersome DL-SR methods.
A Novel Optimized Semi-Blind Scheme for Color Image Watermarking

Image watermarking is a robust solution for solving key issues like copyright protection and proof of ownership of digital data.Existing schemes of image watermarking mostly used grayscale or binary images as embedded watermarks, while only a few watermarking schemes are developed for color images.In this article, we propose a novel robust semi-blind image watermarking scheme based on finite ridgelet transform (FRT), discrete wavelet transform (DWT), singular value decomposition (SVD), particle swarm optimization (PSO), and Arnold transform to protect copyright and verify the authenticity of color images.Firstly, the color image is converted from RGB to YCbCr color space, and the luminance component (Y) is taken into account to insert the watermark data.In this study, the principal component (PC) of the watermark image is directly inserted into the corresponding singular value of the cover image by the scaling factor to avoid the false positive problem (FPP).To further improve security, Arnold transform is applied to process the Y channel of the watermark image before inserting it in the cover image.Besides, PSO optimizes the embedding factor matrices.The qualitative evaluation factors like peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are used to assess the visual quality, while normalized correlation coefficient (NCC) is used to assess the resemblance between the watermarked and the restored watermarked images.The performance of the proposed scheme is evaluated using geometric, non-geometric, and combinational attacks, and its comparison is performed with different image watermarking schemes to prove its robustness.
Enhance Prototypical Network with Text Descriptions for Few-shot Relation Classification

Recently few-shot relation classification has drawn much attention. It devotes to addressing the long-tail relation problem by recognizing the relations from few instances. The existing metric learning methods aim to learn the prototype of classes and make prediction according to distances between query and prototypes. However, it is likely to make unreliable predictions due to the text diversity. It is intuitive that the text descriptions of relation and entity can provide auxiliary support evidence for relation classification. In this paper, we propose TD-Proto, which enhances prototypical network with relation and entity descriptions. We design a collaborative attention module to extract beneficial and instructional information of sentence and entity respectively. A gate mechanism is proposed to fuse both information dynamically so as to obtain a knowledge-aware instance. Experimental results demonstrate that our method achieves excellent performance.
Learning from Task Descriptions

Typically, machine learning systems solve new tasks by training on thousands of examples.In contrast, humans can solve new tasks by reading some instructions, with perhaps an example or two.To take a step toward closing this gap, we introduce a framework for developing NLP systems that solve new tasks after reading their descriptions, synthesizing prior work in this area.We instantiate this framework with a new English language dataset, ZEST, structured for task-oriented evaluation on unseen tasks.Formulating task descriptions as questions, we ensure each is general enough to apply to many possible inputs, thus comprehensively evaluating a model's ability to solve each task.Moreover, the dataset's structure tests specific types of systematic generalization.We find that the state-of-the-art T5 model achieves a score of 12% on ZEST, leaving a significant challenge for NLP researchers.
Contrastive Distillation on Intermediate Representations for Language Model Compression

Existing language model compression methods mostly use a simple L 2 loss to distill knowledge in the intermediate representations of a large BERT model to a smaller one.Although widely used, this objective by design assumes that all the dimensions of hidden representations are independent, failing to capture important structural knowledge in the intermediate layers of the teacher network.To achieve better distillation efficacy, we propose Contrastive Distillation on Intermediate Representations (CODIR), a principled knowledge distillation framework where the student is trained to distill knowledge through intermediate layers of the teacher via a contrastive objective.By learning to distinguish positive sample from a large set of negative samples, CoDIR facilitates the student's exploitation of rich information in teacher's hidden layers.CoDIR can be readily applied to compress large-scale language models in both pretraining and finetuning stages, and achieves superb performance on the GLUE benchmark, outperforming state-of-the-art compression methods. 1
SMAUG

In recent years, there has been tremendous advances in hardware acceleration of deep neural networks. However, most of the research has focused on optimizing accelerator microarchitecture for higher performance and energy efficiency on a per-layer basis. We find that for overall single-batch inference latency, the accelerator may only make up 25–40%, with the rest spent on data movement and in the deep learning software framework. Thus far, it has been very difficult to study end-to-end DNN performance during early stage design (before RTL is available), because there are no existing DNN frameworks that support end-to-end simulation with easy custom hardware accelerator integration. To address this gap in research infrastructure, we present SMAUG, the first DNN framework that is purpose-built for simulation of end-to-end deep learning applications. SMAUG offers researchers a wide range of capabilities for evaluating DNN workloads, from diverse network topologies to easy accelerator modeling and SoC integration. To demonstrate the power and value of SMAUG, we present case studies that show how we can optimize overall performance and energy efficiency for up to 1.8×–5× speedup over a baseline system, without changing any part of the accelerator microarchitecture, as well as show how SMAUG can tune an SoC for a camera-powered deep learning pipeline.
Error Bounded Foreground and Background Modeling for Moving Object Detection in Satellite Videos

Detecting moving objects from ground-based videos is commonly achieved by using background subtraction (BS) techniques. Low-rank matrix decomposition inspires a set of state-of-the-art approaches for this task. It is integrated with structured sparsity regularization to achieve BS in the developed method of low-rank and structured sparse decomposition (LSD). However, when this method is applied to satellite videos where spatial resolution is poor and targets' contrast to the background is low, its performance is limited as the data no longer fit adequately either the foreground structure or the background model. In this article, we handle these unexplained data explicitly and address the moving target detection from space as one of the pioneering studies. We propose a new technique by extending the decomposition formulation with bounded errors, named Extended LSD (E-LSD). This formulation integrates low-rank background, structured sparse foreground, as well as their residuals in a matrix decomposition problem. Solving this optimization problem is challenging. We provide an effective solution by introducing an alternative treatment and adopting the direct extension of alternating direction method of multipliers (ADMM). The proposed E-LSD was validated on two satellite videos, and the experimental results demonstrate the improvement in background modeling with boosted moving object detection precision over state-of-the-art methods.
Highly‐Sensitive Skin‐Attachable Eye‐Movement Sensor Using Flexible Nonhazardous Piezoelectric Thin Film

Abstract Accurate and continuous monitoring of eye movements using compact, low‐power‐consuming, and easily‐wearable sensors is necessary in personal and public health and safety, selected medical diagnosis techniques (point‐of‐care diagnostics), and personal entertainment systems. In this study, a highly sensitive, noninvasive, and skin‐attachable sensor made of a stable flexible piezoelectric thin film that is also free of hazardous elements to overcome the limitations of current computer‐vision‐based eye‐tracking systems and piezoelectric strain sensors is developed. The sensor fabricated from single‐crystalline III‐N thin film by a layer‐transfer technique is highly sensitive and can detect subtle movements of the eye. The flexible eye movement sensor converts the mechanical deformation (skin deflection by eye blinking and eyeball motion) with various frequencies and levels into electrical outputs. The sensor can detect abnormal eye flickering and conditions caused by fatigue and drowsiness, including overlong closure, hasty eye blinking, and half‐closed eyes. The abnormal eyeball motions, which may be the sign of several brain‐related diseases, can also be measured, as the sensor generates discernable output voltages from the direction of eyeball movements. This study provides a practical solution for continuous sensing of human eye blinking and eyeball motion as a critical part of personal healthcare, safety, and entertainment systems.
Phishing Happens Beyond Technology: The Effects of Human Behaviors and Demographics on Each Step of a Phishing Process

Prior studies have shown that the behaviours and attitudes of Internet users influence the likelihood of being victimised by phishing attacks.Many scammers design a step-by-step approach to phishing in order to gain the potential victim's trust and convince them to take the desired actions.It is important to understand which behaviours and attitudes can influence following the attacker in each step of a phishing scam.This will enable us to identify the root causes of phishing and to develop specific mitigation plans for each step of the phishing process and to increase prevention points.This study investigates to what extent people's risk-taking and decision-making styles influence the likelihood of phishing victimisation in three specific phishing steps.We asked participants to play a risk-taking game and to answer questions related to two psychological scales to measure their behaviours, and then conducted a simulated phishing campaign to assess their phishability throughout the three phishing steps selected.We find that the attitude to risk-taking and gender can predict users' phishability in the different steps selected.There are however other possible direct and indirect behavioural factors that could be investigated in future studies.The results of this study and the model developed can be used to build a comprehensive framework to prevent the success of phishing attempts, starting from their root causes.
Foreign Language Learning Gamification Using Virtual Reality—A Systematic Review of Empirical Research

Virtual reality has shown to have great potential as an educational tool when it comes to new learning methods. With the growth and dissemination of this technology, there is a massive opportunity for teachers to add this technology to their methods of teaching a second/foreign language, since students keep showing a growing interest in new technologies. This systematic review of empirical research aims at understanding whether the use of gaming strategies in virtual reality is beneficial for the learning of a second/foreign language or not. Results show that more than half of the articles proved that virtual reality technologies with gaming strategies can be used to learn a foreign language. It was also found that “learning” was the most evaluated dependent variable among the chosen records, augmented reality was the leading technology used, primary education and lower secondary was the most researched school stages, and the most used language to evaluate the use of gamified technology was by far the English language. Given the lack of directed investigation, it is recommended to use these technologies to support second language learning and not entirely replace traditional approaches. A research agenda is also proposed by the authors.
Learning to Restore Hazy Video: A New Real-World Dataset and A New Method

Most of the existing deep learning-based dehazing methods are trained and evaluated on the image dehazing datasets, where the dehazed images are generated by only exploiting the information from the corresponding hazy ones. On the other hand, video dehazing algorithms, which can acquire more satisfying dehazing results by exploiting the temporal redundancy from neighborhood hazy frames, receive less attention due to the absence of the video dehazing datasets. Therefore, we propose the first REal-world VIdeo DEhazing (REVIDE) dataset which can be used for the supervised learning of the video dehazing algorithms. By utilizing a well-designed video acquisition system, we can capture paired real-world hazy and haze-free videos that are perfectly aligned by recording the same scene (with or without haze) twice. Considering the challenge of exploiting temporal redundancy among the hazy frames, we also develop a Confidence Guided and Improved Deformable Network (CG-IDN) for video dehazing. The experiments demonstrate that the hazy scenes in the REVIDE dataset are more realistic than the synthetic datasets and the proposed algorithm also performs favorably against state-of-the-art dehazing methods.
MFR 2021: Masked Face Recognition Competition

This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attracted a total of 10 participating teams with valid submissions. The affiliations of these teams are diverse and associated with academia and industry in nine different countries. These teams successfully submitted 18 valid solutions. The competition is designed to motivate solutions aiming at enhancing the face recognition accuracy of masked faces. Moreover, the competition considered the deployability of the proposed solutions by taking the compactness of the face recognition models into account. A private dataset representing a collaborative, multisession, real masked, capture scenario is used to evaluate the submitted solutions. In comparison to one of the topperforming academic face recognition solutions, 10 out of the 18 submitted solutions did score higher masked face verification accuracy.