Reviewerly/exHarmony huggingface dataset:
The Impact of Isolation Measures during COVID-19 Pandemic on Russian Students’ Motivation for Learning

One of the leading problems of teaching is maintaining the students’ motivation for learning. In the current epidemiological situation, improvement of education quality using digital platforms and educational services by creating conditions for increasing the learning motivation of students is an essential and relevant task. Therefore, the aim of the research was to study the motivation of the educational activity of junior students during the COVID-19 lockdown period and the development of effective approaches to increase the learning motivation of students in the context of distance learning using the Microsoft Teams platform. Research methods: Theoretical analysis of psychological and pedagogical literature, generalization; experiment, questioning, testing and data processing methods. In total, 107 undergraduates took part in the study. The difficulties and benefits that students encountered during the lockdown, as well as the attitude of students to the distance learning format, were revealed. There were specified dominant motivators, intentions and motives that determine the educational activity of students. We have presented approaches to the content management of the educational process in a distance format, with various ways and techniques of increasing the educational motivation of students under the conditions of distance learning.
Multi-Hierarchical Category Supervision for Weakly-Supervised Temporal Action Localization

Weakly Supervised Temporal Action Localization (WTAL) aims to localize action segments in untrimmed videos with only video-level category labels in the training phase. In WTAL, an action generally consists of a series of sub-actions, and different categories of actions may share the common sub-actions. However, to distinguish different categories of actions with only video-level class labels, current WTAL models tend to focus on discriminative sub-actions of the action, while ignoring those common sub-actions shared with different categories of actions. This negligence of common sub-actions would lead to the located action segments incomplete, i.e., only containing discriminative sub-actions. Different from current approaches of designing complex network architectures to explore more complete actions, in this paper, we introduce a novel supervision method named multi-hierarchical category supervision (MHCS) to find more sub-actions rather than only the discriminative ones. Specifically, action categories sharing similar sub-actions will be constructed as super-classes through hierarchical clustering. Hence, training with the new generated super-classes would encourage the model to pay more attention to the common sub-actions, which are ignored training with the original classes. Furthermore, our proposed MHCS is model-agnostic and non-intrusive, which can be directly applied to existing methods without changing their structures. Through extensive experiments, we verify that our supervision method can improve the performance of four state-of-the-art WTAL methods on three public datasets: THUMOS14, ActivityNet1.2, and ActivityNet1.3.
Small-Scale Linguistic Steganalysis for Multi-Concealed Scenarios

Recently, due to the considerable feature expression ability of neural networks, deep linguistic steganalysis methods have been greatly developed. However, there are still two issues that need to be ameliorated. First, the prevailing linguistic steganalysis methods rely heavily on massive training data, which is labor-intensive and time-consuming. Second, these methods implement steganalysis only in different weak-concealed scenarios, the stego texts in each of which have only a single language style and payload. But in practice, the intercepted network samples are probably the mixture of the stego texts that possess different language styles and payloads, in which the semantic spatial distribution may be more chaotic than that in weak-concealed scenarios, thus making steganalysis more difficult. To address the above issues, a novel linguistic steganalysis method is proposed in this letter. First, the pre-trained BERT language model is constructed as an embedder to compensate for the shortage of data. Then, in addition to learning local and global semantic features, a feature interaction module is designed for exploring mutual effects between them. Furthermore, besides the typical cross-entropy loss, triplet loss is also introduced for the model training. In this way, the proposed method can refine more comprehensive and discriminative deep features in the intricate semantic space. The performance of the proposed method is compared with the representative linguistic steganalysis methods on datasets of different scales, and the experimental results reveal the superiority of the proposed method.
A Fast View Synthesis Implementation Method for Light Field Applications

View synthesis (VS) for light field images is a very time-consuming task due to the great quantity of involved pixels and intensive computations, which may prevent it from the practical three-dimensional real-time systems. In this article, we propose an acceleration approach for deep learning-based light field view synthesis, which can significantly reduce calculations by using compact-resolution (CR) representation and super-resolution (SR) techniques, as well as light-weight neural networks. The proposed architecture has three cascaded neural networks, including a CR network to generate the compact representation for original input views, a VS network to synthesize new views from down-scaled compact views, and a SR network to reconstruct high-quality views with full resolution. All these networks are jointly trained with the integrated losses of CR, VS, and SR networks. Moreover, due to the redundancy of deep neural networks, we use the efficient light-weight strategy to prune filters for simplification and inference acceleration. Experimental results demonstrate that the proposed method can greatly reduce the processing time and become much more computationally efficient with competitive image quality.
Quantum Computing for Supply Chain Finance

Applying quantum computing to real world applications to assess the potential efficacy is a daunting task for non-quantum specialists. This paper shows an implementation of two quantum optimization algorithms applied to portfolios of trade finance portfolios and compares the selections to those chosen by experienced underwriters and a classical optimizer. The method used is to map the financial risk and returns for a trade finance portfolio to an optimization function of a quantum algorithm developed in a Qiskit tutorial. The results show that whilst there is no advantage seen by using the quantum algorithms, the performance of the quantum algorithms has no statistically significant degradation. Therefore, it is promising that in the future, with expected improvements in quantum hardware, the theoretically superior processing speeds, and data volumes that quantum offers, will also be applicable to trade finance.
DETER: Denial of Ethereum Txpool sERvices

On an Ethereum node, txpool (a.k.a. mempool) is a buffer storing unconfirmed transactions and controls what downstream services can see, such as mining and transaction propagation. This work presents the first security study on Ethereum txpool designs.
Backdoor Attack on Deep Neural Networks Triggered by Fault Injection Attack on Image Sensor Interface

Recent automobiles use image sensors to take in the physical world information, and deep neural networks (DNNs) are used to recognize the surroundings to control the vehicles. Adversarial examples and backdoor attacks that induce misclassification by tampering with input images to DNN have been proposed as methods of attacking DNNs. As an example of attacks on DNNs equipped in automobiles, a method has been reported in which an adversarial mark is added to input images by physically putting a sticker on a road sign. However, the method reduces reproducibility due to the influence of the shooting environment. The tampering area needs to be increased to improve reproducibility. However, these large marks are easily seen by people.
Parallel multiscale context-based edge-preserving optical flow estimation with occlusion detection

Although convolutional neural network (CNN)-based optical flow approaches have exhibited good performance in terms of computational accuracy and efficiency in recent years, the issue of edge-blurring caused by motion occlusions remains. In this paper, we propose a parallel multiscale context-based edge-preserving optical flow estimation method with occlusion detection, named PMC-PWC. First, we exploit a parallel multiscale context (PMC) network for occlusion detection, in which the proposed PMC model is able to aggregate the multiscale context information to develop the performance of occlusion detection near motion boundaries. Second, we combine the PMC model with a context network to plan an occlusion estimation module and incorporate it into a pyramid, warping, and cost volume model to construct an edge-preserving optical flow computation network. Third, we design a novel loss function including an endpoint error (EPE)-based loss, a binary cross-entropy loss and an edge loss to supervise the proposed PMC-PWC network to produce optical flow and occlusion simultaneously. Finally, we run the proposed PMC-PWC method on the MPI-Sintel and KITTI datasets to conduct a comprehensive comparison with several state-of-the-art approaches. The experimental results indicate that the proposed PMC-PWC method performed well in terms of both accuracy and robustness, especially due to the significant benefits of edge preservation and occlusion handling.
Conveying the Perception of Humor Arising from Ambiguous Grammatical Constructs in Human-Chatbot Interaction

Chatbots have long been advocated for computer-assisted language learning systems to support learners with conversational practice. A particular challenge in such systems is explaining mistakes stemming from ambiguous grammatical constructs. Misplaced modifiers, for instance, do not make sentences ungrammatical, but introduce ambiguity through the misplacement of an adverb or prepositional phrase. In certain cases, the ambiguity gives rise to humor, which can serve to illustrate the mistake itself. We conducted an online experiment with 400 native English speakers to explore the use of a chatbot to harness such humor. In an interaction resembling an advanced grammar exercise, the chatbot presented participants with a phrase containing a misplaced modifier, explained the ambiguity in the phrase, acknowledged (or ignored) the humor that the ambiguity gave rise to, and suggested a correction. Participants then completed a questionnaire, rating the chatbot with respect to ten traits. A quantitative analysis showed a significant increase in how participants rated the chatbot's personality, humor, and friendliness when it acknowledged the humor arising from the misplaced modifier. This effect was observed whether the acknowledgment was conveyed using verbal, nonverbal (emoji), or mixed cues.
Adaptive Priority-based Conflict Resolution of IoT Services

We propose a novel conflict resolution framework for IoT services in multi-resident smart homes. An adaptive priority model is developed considering the residents' contextual factors (e.g., age, illness, impairment). The proposed priority model is designed using the concept of the analytic hierarchy process. A set of experiments on real-world datasets are conducted to show the efficiency of the proposed approach.
Yes, no, maybe, I don’t know: Complexity and application of abstract argumentation with incomplete knowledge

Abstract argumentation, as originally defined by Dung, is a model that allows the description of certain information about arguments and relationships between them: in an abstract argumentation framework (AF), the agent knows for sure whether a given argument or attack exists. It means that the absence of an attack between two arguments can be interpreted as “we know that the first argument does not attack the second one”. But the question of uncertainty in abstract argumentation has received much attention in the last years. In this paper, we survey approaches that allow to express information like “There may (or may not) be an attack between these arguments”. We describe the main models that incorporate qualitative uncertainty (or ignorance) in abstract argumentation, as well as some applications of these models. We also highlight some open questions that deserve some attention in the future.
Using Personas with Visual Impairments to Explore the Design of an Accessible Self-Driving Vehicle Human-Machine Interface

Recent reports have suggested that most self-driving vehicle technology being developed is not currently accessible to users with disabilities. We purport that this problem may be at least partially attributable to knowledge gaps in practice-oriented user-centered design research. Missing, we argue, are studies that demonstrate the practical application of user-centered design methodologies in capturing the needs of users with disabilities in the design of automotive systems specifically. We have investigated user-centered design, specifically the use of personas, as a methodological tool to inform the design of a self-driving vehicle human-machine interface for blind and low vision users. We then explore the use of these derived personas in a series of participatory design sessions involving visually impaired co-designers. Our findings suggest that a robust, multi-method UCD process culminating with persona development may be effective in capturing the conceptual model of persons with disabilities and informing the design of automotive system.
AI-Lancet: Locating Error-inducing Neurons to Optimize Neural Networks

Deep neural network (DNN) has been widely utilized in many areas due to its increasingly high accuracy. However, DNN models could also produce wrong outputs due to internal errors, which may lead to severe security issues. Unlike fixing bugs in traditional computer software, tracing the errors in DNN models and fixing them are much more difficult due to the uninterpretability of DNN. In this paper, we present a novel and systematic approach to trace and fix the errors in deep learning models. In particular, we locate the error-inducing neurons that play a leading role in the erroneous output. With the knowledge of error-inducing neurons, we propose two methods to fix the errors: the neuron-flip and the neuron-fine-tuning. We evaluate our approach using five different training datasets and seven different model architectures. The experimental results demonstrate its efficacy in different application scenarios, including backdoor removal and general defects fixing.
Chronos: Timing Interference as a New Attack Vector on Autonomous Cyber-physical Systems

Timing property plays a vital role in the Cyber-Physical System(CPS) due to its interaction with the physical world. The smooth operation of these robotic systems often relies on an accurate and timely perception and actuation of the physical world. In this poster, we demonstrated a unique new class of attack, Chronos, that exploits timing interference to cause system destabilization in cyber-physical systems. Using a compromised non-privileged non-critical task on the system, we launch timing interference attacks on both drone and autonomous vehicle platforms. Through both open-loop and close-loop testing on the end-to-end stack, we showed that the timing attack could lead to complete loss of control of the autonomous system, crashing them onto the surroundings when there is no software vulnerability. To further understand this novel attack vector, we perform preliminary investigations on the localization component of these two platforms, because they both make use of well-known simultaneous localization and mapping (SLAM) algorithms that depend on timing-sensitive multimodal data from different sensors. Building on the insights from the case study, we present our formulation of the timing attack surface and highlight future directions.
An end-to-end multi-task learning to link framework for emotion-cause pair extraction

Emotion-cause pair extraction (ECPE), as an emergent natural language processing task, aims at jointly investigating emotions and their underlying causes in documents. It extends the previous emotion cause extraction (ECE) task, yet without requiring a set of pre-given emotion clauses as in ECE. To solve ECPE task, we regards emotion-cause pair extraction as a link prediction task, and learns to link from emotion clauses to cause clauses, i.e., the links are directional. We propose a multi-task learning model that can extract emotions, causes and emotion-cause pairs simultaneously in an end-to-end manner. Emotion extraction and cause extraction are incorporated into the model as auxiliary tasks, which further boost the pair extraction. Experiments are conducted on an ECPE benchmarking dataset. The results show that our proposed model outperforms a range of state-of-the-art approaches.
Deep Learning Empowered MAC Protocol Identification With Squeeze-and-Excitation Networks

Medium access control (MAC) protocol identification in the context of cognitive radio is a challenging issue, which is usually solved by the support vector machine (SVM) method. To avoid manual feature extraction in SVM and realize accurate identification, a graphical scheme is adopted in this work. Firstly, the identification problem is modeled to analyze the distinctions of four MAC protocols by exploiting time and power features of the received signal. Then, we propose a convolutional neural network (CNN)-based MAC protocol identification (C-MPI) method, which combines CNN with the time-frequency image that contains time, frequency and energy three dimensional information. Moreover, in order to further improve the identification performance, a modified CNN with a squeeze-and-excitation mechanism-based MAC protocol identification (CSE-MPI) approach is developed, which plugs the SE block, a channel-wise attention mechanism, into classical CNN. Numerical simulations are provided to demonstrate the effectiveness of the proposed approaches.
Malicious URL Detection using Multilayer CNN

Due to developing Internet-based technologies, the number of online domains and URLs is increasing globally. Parallel several cybersecurity threats and phishing attacks have been encountered while accessing these websites. Accessing a malicious webpage can create serious harm to the physical system. Data loss, privacy breach, credential theft and many security threats are entered while an Internet user clicks a malicious URL. Several defence and detection strategies have been proposed in the previous research works. But, the works have used a traditional classifier which is not adequate. This is because the size of the URL is huge, and the URL patterns are changed over time so finding the correlation between old and new patterns is almost impossible. Hence, this paper proposed malicious URL detection using multilayer Convolutional Neural Network (CNN). The proposed model first considered one layer of CNN. After that, to improve the accuracy, a two-layer of CNN will be used. The achieved result illustrated that malicious website detection accuracy is enhanced 89% to 91% when the model uses two layers of CNN.
Periscope: A Keystroke Inference Attack Using Human Coupled Electromagnetic Emanations

This study presents Periscope, a novel side-channel attack that exploits human-coupled electromagnetic (EM) emanations from touchscreens to infer sensitive inputs on a mobile device. Periscope is motivated by the observation that finger movement over the touchscreen leads to time-varying coupling between these two. Consequently, it impacts the screen's EM emanations that can be picked up by a remote sensory device. We intend to map between EM measurements and finger movements to recover the inputs. As the significant technical contribution of this work, we build an analytic model that outputs finger movement trajectories based on given EM readings. Our approach does not need a large amount of labeled dataset for offline model training, but instead a couple of samples to parameterize the user-specific analytic model. We implement Periscope with simple electronic components and conduct a suite of experiments to validate this attack's impact. Experimental results show that Periscope achieves a recovery rate over 6-digit PINs of 56.2% from a distance of 90 cm. Periscope is robust against environment dynamics and can well adapt to different device models and setting contexts.
An Efficient Face Detector on a CPU Using Dual-Camera Sensors for Intelligent Surveillance Systems

Intelligent surveillance systems require face detection to identify human facial areas. This system should be able to utilize dual-camera sensors (color/IR) for working every time. Additionally, practical application demands a detector to be operated in real-time on a low-cost device or CPU. The deep Convolutional Neural Network (DCNN) technique has successfully used a robust facial extractor, but it requires a high amount of computation for high-resolution input. On the other hand, the light architecture generates a large number of false positives as a fast detector. This feature extractor pays less attention to specific facial features and often ignores global and local relationships between elements. This paper proposes an efficient face detector to accurately localize faces using light architecture. The one-stage detector consists of an efficient backbone to rapidly extract features and a four-level detection layer to predict variations in facial scales. To improve the non-robust feature extractor, it implements an enhancement module to enhance specific facial features at each level without significantly increasing the parameters. The proposed detector uses knowledge from the WIDER FACE dataset to train the model with a gradual learning rate. The experiment results show the effectiveness of the detector in outperforming CPU-based detectors on benchmark datasets. It also runs in real-time at 27 frames per second on a CPU using the RGB and infrared cameras for Full High Definition (Full HD) resolution, faster than other published detectors.
Model Protection: Real-Time Privacy-Preserving Inference Service for Model Privacy at the Edge

Major cloud service providers with well-equipped infrastructure, experienced machine learning (ML) expertise, and enriched training datasets are building ML-as-a-Service (MLaaS) systems, in which clients can query ML-based prediction services with their data. Instead of moving private data to the cloud, in this work, we design, implement, and evaluate a novel secure ML system to enable MLaaS on edge devices. To protect the proprietary ML models on edge devices from revealing to the clients while maintaining a real-time inference is challenging. Existing privacy-preserving ML techniques can hardly satisfy real-time requirements. In our solution, we employ a secure enclave (e.g., SGX) to offer security and provide better efficiency than cryptographic techniques. However, the enclave alone cannot achieve real-time capability due to its limited capacity. We observe that the ML model imposes a severe accuracy degradation when adding noise to a few model weights. Based on this, we design a suite of novel solutions to optimize the performance of secure enclave-based inference service at the edge by enclosing only <inline-formula><tex-math notation="LaTeX">$1\%$</tex-math></inline-formula> computation within secure enclaves. Our work can achieve up to a <inline-formula><tex-math notation="LaTeX">$7.8\times$</tex-math></inline-formula> increase in efficiency and a <inline-formula><tex-math notation="LaTeX">$27\times$</tex-math></inline-formula> reduction in memory usage compared to the state-of-the-art.