Reviewerly/exHarmony huggingface dataset:
A Robust Packet-Dropout Covert Channel over Wireless Networks

Wireless communication is the most important carrier for covert channels because increasing security threats have made covert channels an alternative to transmitting confidential information in untrusted wireless communications. As a solution for next-generation mobile communication, voice over LTE (VoLTE) is developed using a high-speed packet-switched core network to provide high-quality end-to-end communication services. Unlike traditional circuit-switched communication technologies, both inter-packet delays and packet orders in VoLTE traffic are limited by specific rules. Even minor modifications to the overt traffic can be detected, so the existing solutions do not apply to VoLTE. To solve this problem, we build a robust packet-dropout covert timing channel through parity casecade coding. Hash-based inter-codewords verification, cyclic redundancy check based codeword self-verification, and an adaptive mapping matrix are designed to ensure robustness and undetectability. In our scheme, the covert message is modulated into the sequence numbers of the actively dropped packets, which can be retrieved by the receiver. With the help of the verification section, the actual codewords combination can be identified to retrieve the embedded covert message. Several experiments are performed to evaluate the performance of the scheme, and the parameters of the covert timing channel (CTC) are adjusted to estimate robustness and throughput. Finally, the CTC scheme is proved to be feasible since both average bit error rate and throughput are acceptable while the covert message is stealthily transmitted.
Bridging the Gap Between $f$-GANs and Wasserstein GANs

Generative adversarial networks (GANs) have enjoyed much success in learning high-dimensional distributions. Learning objectives approximately minimize an $f$-divergence ($f$-GANs) or an integral probability metric (Wasserstein GANs) between the model and the data distribution using a discriminator. Wasserstein GANs enjoy superior empirical performance, but in $f$-GANs the discriminator can be interpreted as a density ratio estimator which is necessary in some GAN applications. In this paper, we bridge the gap between $f$-GANs and Wasserstein GANs (WGANs). First, we list two constraints over variational $f$-divergence estimation objectives that preserves the optimal solution. Next, we minimize over a Lagrangian relaxation of the constrained objective, and show that it generalizes critic objectives of both $f$-GAN and WGAN. Based on this generalization, we propose a novel practical objective, named KL-Wasserstein GAN (KL-WGAN). We demonstrate empirical success of KL-WGAN on synthetic datasets and real-world image generation benchmarks, and achieve state-of-the-art FID scores on CIFAR10 image generation.
Evidence-Aware Hierarchical Interactive Attention Networks for Explainable Claim Verification

Exploring evidence from relevant articles to confirm the veracity of claims is a trend towards explainable claim verification. However, most strategies capture the top-k check-worthy articles or salient words as evidence, but this evidence is difficult to focus on the questionable parts of unverified claims. Besides, they utilize relevant articles indiscriminately, ignoring the source credibility of these articles, which may cause quiet a few unreliable articles to interfere with the assessment results. In this paper, we propose Evidence-aware Hierarchical Interactive Attention Networks (EHIAN) by considering the capture of evidence fragments and the fusion of source credibility to explore more credible evidence semantics discussing the questionable parts of claims for explainable claim verification. EHIAN first designs internal interaction layer (IIL) to strengthen deep interaction and matching between claims and relevant articles for obtaining key evidence fragments, and then proposes global inference layer (GIL) that fuses source features of articles and interacts globally with the average semantics of all articles and finally earns the more credible evidence semantics discussing the questionable parts of claims. Experiments on two datasets demonstrate that EHIAN not only achieves the state-of-the-art performance but also secures effective evidence to explain the results.
Attribute Restoration Framework for Anomaly Detection

With the recent advances in deep neural networks, anomaly detection in multimedia has received much attention in the computer vision community. While reconstruction-based methods have recently shown great promise for anomaly detection, the information equivalence among input and supervision for reconstruction tasks can not effectively force the network to learn semantic feature embeddings. We here propose to break this equivalence by erasing selected attributes from the original data and reformulate it as a restoration task, where the normal and the anomalous data are expected to be distinguishable based on restoration errors. Through forcing the network to restore the original image, the semantic feature embeddings related to the erased attributes are learned by the network. During testing phases, because anomalous data are restored with the attribute learned from the normal data, the restoration error is expected to be large. Extensive experiments have demonstrated that the proposed method significantly outperforms several state-of-the-arts on multiple benchmark datasets, especially on ImageNet, increasing the AUROC of the top-performing baseline by 10.1%. We also evaluate our method on a real-world anomaly detection dataset MVTec AD and a video anomaly detection dataset ShanghaiTech.
Learning to Segment Actions from Observation and Narration

We apply a generative segmental model of task structure, guided by narration, to action segmentation in video.We focus on unsupervised and weakly-supervised settings where no action labels are known during training.Despite its simplicity, our model performs competitively with previous work on a dataset of naturalistic instructional videos.Our model allows us to vary the sources of supervision used in training, and we find that both task structure and narrative language provide large benefits in segmentation quality.
New Techniques for SIDH-based NIKE

Abstract We consider the problem of producing an efficient, practical, quantum-resistant non-interactive key exchange (NIKE) protocol based on Supersingular Isogeny Diffie-Hellman (SIDH). An attack of Galbraith, Petit, Shani and Ti rules out the use of naïve forms of the SIDH construction for this application, as they showed that an adversary can recover private key information when supplying an honest party with malformed public keys. Subsequently, Azarderakhsh, Jao and Leonardi presented a method for overcoming this attack using multiple instances of the SIDH protocol, but which increases the costs associated with performing a key exchange by factors of up to several thousand at typical security levels. In this paper, we present two new techniques to reduce the cost of SIDH-based NIKE, with various possible tradeoffs between key size and computational cost.
Analyzing the real-world applicability of DGA classifiers

Separating benign domains from domains generated by DGAs with the help of a binary classifier is a well-studied problem for which promising performance results have been published. The corresponding multiclass task of determining the exact DGA that generated a domain enabling targeted remediation measures is less well studied. Selecting the most promising classifier for these tasks in practice raises a number of questions that have not been addressed in prior work so far. These include the questions on which traffic to train in which network and when, just as well as how to assess robustness against adversarial attacks. Moreover, it is unclear which features lead a classifier to a decision and whether the classifiers are real-time capable. In this paper, we address these issues and thus contribute to bringing DGA detection classifiers closer to practical use. In this context, we propose one novel classifier based on residual neural networks for each of the two tasks and extensively evaluate them as well as previously proposed classifiers in a unified setting. We not only evaluate their classification performance but also compare them with respect to explainability, robustness, and training and classification speed. Finally, we show that our newly proposed binary classifier generalizes well to other networks, is time-robust, and able to identify previously unknown DGAs.
The field-field and dipole-dipole coupling effects on the entanglement of the interaction between two qutrits with a two-mode field

In this paper we study the interaction between two qutrits ([Formula: see text]-type three-level atoms) with a two-mode field in a high quality cavity, considering the field-field (F-F) correlation and dipole-dipole (D-D) interaction. By defining a Hamiltonian corresponds to D-D interaction related to the two three-level atoms, we introduce a generalized Hamiltonian of Jaynes-Cummings model for the desired system and then the associated Schrödinger equation is solved. Finally, by achieving the reduced density matrix and then the linear entropy as a criterion for the entanglement, we investigate the influence of the F-F and D-D coupling on the degree of entanglement of the system. Our results show that there exists specific initial superposition of atomic states wherein the stable value of linear entropy is accessible. In addition, in such cases the F-F correlation and D-D interaction have not considerable effect on the entropy. Moreover, in general, as we have shown, the entanglement can be tuned appropriately by choosing the parameters involve in the model.
Development and evaluation of e‐learning materials for dental hygiene students in six schools: Using smartphones to learn dental treatment procedures

The purpose of this study was to develop and evaluate the effectiveness of using e-learning on smartphones to provide dental hygiene education on dental treatment procedures.This was a randomized controlled trial. Two-hundred ninety-three dental hygiene students in four universities, one junior college and one technical school took a preliminary examination, and based on the results, participants in each school were randomly divided into two groups, a test or control group. Both groups took pre- and post-examinations at a 4-week interval. The test groups learned dental treatment procedures and four-handed techniques with interactive learning materials using smartphones. The learning materials allowed them to watch videos of dental treatments. The control groups were not provided any learning material.Results of all schools combined showed that the changes from pre- to post-examination scores in the test groups were significantly higher than those of the control groups (p < .05). Post-examination scores were significantly higher than pre-examination scores in the test groups in all schools (p < .05). Also, post-examination scores of the test groups were significantly higher than those of the control groups (p < .05). The changes from pre- to post-examination scores in the test groups of two schools were significantly higher than those of control groups (p < .05). Post-examination scores of the control groups in two schools were significantly higher than pre-examination scores (p < .05).Learning dental treatment procedures through e-learning on a smartphone was effective in developing participants' understanding of dental treatment procedures and four-handed techniques.
A Theoretical Framework for Target Propagation

The success of deep learning, a brain-inspired form of AI, has sparked interest in understanding how the brain could similarly learn across multiple layers of neurons. However, the majority of biologically-plausible learning algorithms have not yet reached the performance of backpropagation (BP), nor are they built on strong theoretical foundations. Here, we analyze target propagation (TP), a popular but not yet fully understood alternative to BP, from the standpoint of mathematical optimization. Our theory shows that TP is closely related to Gauss-Newton optimization and thus substantially differs from BP. Furthermore, our analysis reveals a fundamental limitation of difference target propagation (DTP), a well-known variant of TP, in the realistic scenario of non-invertible neural networks. We provide a first solution to this problem through a novel reconstruction loss that improves feedback weight training, while simultaneously introducing architectural flexibility by allowing for direct feedback connections from the output to each hidden layer. Our theory is corroborated by experimental results that show significant improvements in performance and in the alignment of forward weight updates with loss gradients, compared to DTP.
Empirical Evaluation of Three Common Assumptions in Building Political Media Bias Datasets

In this work, we empirically validate three common assumptions in building political media bias datasets, which are (i) labelers' political leanings do not affect labeling tasks, (ii) news articles follow their source outlet's political leaning, and (iii) political leaning of a news outlet is stable across different topics. We build a ground-truth dataset of manually annotated article-level political leaning and validate the three assumptions. Our findings warn that the three assumptions could be invalid even for a small dataset. We hope that our work calls attention to the (in)validity of common assumptions in building political media bias datasets.
Blockchain for Internet of Medical Things: A Technical Review

The Internet of Medical Things (IoMT) represents a network of implantable or wearable medical devices that continuously collect medical data about the patient’s health status. These data are heavy, sensitive and require high level of security. With the emergence of blockchain technology, researchers are focusing on using blockchain strategies to bring security to healthcare applications. However, such integration is very difficult and challenging due to the different requirements in these two technologies. We present in this paper a technical review of existing solutions applying blockchain technology on IoMT. We analyze these studies, discuss the proposed architectures and how they managed the integration challenges. The open issues regarding the application of blockchain over IoMT are also specified.
Adversarial Training for Commonsense Inference

We propose an AdversariaL training algorithm for commonsense InferenCE (ALICE).We apply small perturbations to word embeddings and minimize the resultant adversarial risk to regularize the model.We exploit a novel combination of two different approaches to estimate these perturbations: 1) using the true label and 2) using the model prediction.Without relying on any human-crafted features, knowledge bases or additional datasets other than the target datasets, our model boosts the finetuning performance of RoBERTa, achieving competitive results on multiple reading comprehension datasets that require commonsense inference.
An Improved Water Surface Images Segmentation Algorithm Based on the Otsu Method

The one-dimensional Otsu method is an adaptive threshold method. It obtains the optimal threshold for image segmentation by the maximum between-class variance, without considering the minimum within-class variance. As the background of water surface image is mostly uniform, using this feature, the threshold selection tactics adopt the combination of the one-dimensional Otsu method and the uniformity measurement, proposes the threshold segmentation method based on uniformity measurement, and adopts the performance evaluation method based on GT image to compare the segmentation result. Experimental results demonstrate that effectiveness of the improved Otsu method is generally better than the traditional Otsu method, and the other four commonly used threshold segmentation methods for the water surface image, which improves the segmentation accuracy of such images and reduces the segmentation error rate. At the same time, as the water surface image is usually affected by light intensity, water ripple and other factors, this paper also adopts the relevant correction algorithm to further improve the segmentation accuracy.
Blockchain technology in energy markets – An interview with the European Energy Exchange

Abstract What is the impact of blockchain technology on electronic markets in the energy sector? In this interview with Electronic Markets, Dr. Tobias Paulun, chief strategy officer of the European Energy Exchange (EEX), explains where the leading European energy exchange recognizes potentials of blockchain technology compared to existing electronic platforms and which blockchain projects EEX is working on. In his view, the impact of blockchain technology depends on the respective market segment and on the availability of solutions for safeguarding guarantees of origin. He expects that established exchange systems and blockchain-based systems will coexist in this strongly regulated and specialized industry.
Partial Discharge Signal Denoising Based on Wavelet Pair and Block Thresholding

In electrical engineering, it is of great importance to monitor partial discharge (PD) of high voltage apparatus. However, on-site PD signal easily gets corrupted by white noise, and hence denoising the measured PD signal is necessary to acquire pure PD. In traditional method, single wavelet and regular thresholding are utilized. By combining wavelets of the same family, dual-tree complex wavelet pairs can be constructed. Specific combinations are approximately translation invariant, and they are also able to achieve better denoising effect without sophisticated computation. Meanwhile, considering the correlation between adjacent wavelet coefficients, the block thresholding is employed. In this paper, it is proposed to combine wavelet pair and block thresholding for PD denoising. There are four combinations based on single wavelet/wavelet pair and regular thresholding/block thresholding, and they are compared with each other. Based on a numerical study, the results demonstrate that the proposed algorithm outperforms the traditional method.
Digital Horizons in Teacher Education Development

Modern global challenges reflect the essential features of the Fourth Industrial Revolution and are directly related to the digital transformation of life. The teachers are obliged to master the relevant competences to meet the challenges, as their teaching practices experience significant transformations through the enrichment with the digital component. The current problems in the education system transformation force the researchers to identify the principle transformation characteristics, acquire some new types of pedagogical action and design new teacher education formats. The paper presents the authors’ vision of the new teacher education formats at the target, organizational, activity and technological levels. The authors analyze the regional experience of modernization in the higher pedagogical education on the basis of Federal Standards in Higher Education 3++ at the bachelor and master program levels. The realization experience of the federal project “Modern Digital Educational Environment in the Russian Federation” as a part of the national project “Education” is conceptualized in terms of professional skill enhancement of pedagogical personnel in the Tomsk region. The authors reflect on the development trend of the region research and academic complex in the concept of cooperation and resource consolidation for the “Big University” model. The guidelines for strategic development of Tomsk State Pedagogical University, associated with its digitalization, are outlined. The paper shows the development conditions in the digital transformation of the national system for continuous pedagogical education. The paper materials are based on such research methods as theoretical analysis, expert method, and survey.
On Analyzing Video Transmission Over Wireless WiFi and 5G C-Band in Harsh IIoT Environments

This paper analyzes the quality of Virtual Reality (VR)/Augmented Reality (AR) video streamed in harsh industrial environments. VR/AR is being used in the Industrial Internet of Things (IIoT) as one of the most promising applications for favouring labour conditions and increasing security, while reducing costs of manufacturing. We provide an empirical work that analyzes the impact of industrial environment within the assessment of the video quality transmitted over WiFi and 5G C-band access and discuss the impact of electromagnetic emission of industrial machines in radiocommunications (microwave bands). Even if the presented measurements are limited, they clearly show some aspects of the impact of industrial environments that should be taken into account when developing multimedia IIoT applications. Moreover, we discuss the differences between Radiocommunications technologies for building separated (private) Small Area Wireless Networks (SAWN) for the IIoT in factories.
Modeling and mitigating human annotation errors to design efficient stream processing systems with human-in-the-loop machine learning

High-quality human annotations are necessary for creating effective machine learning-driven stream processing systems. We study hybrid stream processing systems based on a Human-In-The-Loop Machine Learning (HITL-ML) paradigm, in which one or many human annotators and an automatic classifier (trained at least partially by the human annotators) label an incoming stream of instances. This is typical of many near-real-time social media analytics and web applications, including annotating social media posts during emergencies by digital volunteer groups. From a practical perspective, low-quality human annotations result in wrong labels for retraining automated classifiers and indirectly contribute to the creation of inaccurate classifiers. Considering human annotation as a psychological process allows us to address these limitations. We show that human annotation quality is dependent on the ordering of instances shown to annotators and can be improved by local changes in the instance sequence/order provided to the annotators, yielding a more accurate annotation of the stream. We adapt a theoretically-motivated human error framework of mistakes and slips for the human annotation task to study the effect of ordering instances (i.e., an "annotation schedule"). Further, we propose an error-avoidance approach to the active learning paradigm for stream processing applications robust to these likely human errors (in the form of slips) when deciding a human annotation schedule. We support the human error framework using crowdsourcing experiments and evaluate the proposed algorithm against standard baselines for active learning via extensive experimentation on classification tasks of filtering relevant social media posts during natural disasters. According to these experiments, considering the order in which data instances are presented to a human annotator leads to increased accuracy for machine learning and awareness of the potential properties of human memory for the class concept, which may affect annotation for automated classifiers. Our results allow the design of hybrid stream processing systems based on the HITL-ML paradigm, which requires the same amount of human annotations, but that has fewer human annotation errors. Automated systems that help reduce human annotation errors could benefit several web stream processing applications, including social media analytics and news filtering.