Reviewerly/exHarmony huggingface dataset:
Extending Routes in Tor to Achieve Recipient Anonymity against the Global Adversary

Tor is a famous routing overlay network based on the Onion multi-layered encryption to support communication anonymity in a threat model in which some network nodes are malicious. However, Tor does not provide any protection against the global passive adversary. In this threat model, an idea to obtain recipient anonymity, which is enough to have relationship anonymity, is to hide the recipient among a sufficiently large anonymity set. However, this would lead to high latency both in the set-up phase (which has a quadratic cost in the number of involved nodes) and in the successive communication. In this paper, we propose a way to arrange a Tor circuit with a tree-like topology, in which the anonymity set consists of all its nodes, whereas set-up and communication latency depends on the number of the sole branch nodes (which is a small fraction of all the nodes). Basically, the cost goes down from quadratic to linear. Anonymity is obtained by applying a broadcast-based technique for the forward message, and cover traffic (generated by the terminal-chain nodes) plus mixing over branch nodes, for the response.
Vulnerability Assessment for Deep Learning Based Phishing Detection System

Recently, the threats of phishing attacks have in-creased. As a countermeasure against phishing attacks, phishing detection systems using deep learning techniques have been considered. However, deep learning techniques are vulnerable to adversarial examples (AEs) that intentionally cause misclassification. When AEs are applied to a deep-learning-based phishing detection system, they pose a significant security risk. Therefore, in this paper, we assess the vulnerability of a phishing detection system by inputting AEs generated based on a dataset that consists of phishing sites’ URLs. Moreover, we consider countermeasures against AEs and clarify whether these defense methods can prevent misclassification.
Design of Multimedia Assisted English online teaching model based on flipped classroom

Aiming at the current information needs of English learning, combined with the current problems of multimedia-assisted teaching, a multimedia-assisted English online teaching model based on flipped classrooms is designed. Analyze the characteristics of multimedia-assisted English online teaching and optimize the functions of the teaching model; based on the dual-agent teaching theory of flipped classroom English teaching, the model’s login module and personalized recommendation module are designed with emphasis on the online assisted teaching function optimize management. Experimental results show that after using this model, the accuracy of classroom exercises has been effectively improved, indicating that it can provide a reference for the application of intelligent recommendation algorithms in English teaching.
Detection of Body Behavior Characteristics in Sports Training Based on Grey Relational Model

In order to deal with the problems of low detection accuracy and efficiency existing in traditional detection methods of body behavior characteristics in sports training, this paper proposes a new detection method of body behavior characteristics in sports training based on the grey correlation model. According to this method, the foreground binary image is obtained by the interframe difference method, and the action information and contour information are extracted, firstly. And then, the behavior feature descriptor is obtained. After the description of body behavior characteristics in sports training is completed, the grey correlation coefficient is calculated, and the complete observation equation of time delay estimation of image grey value under complex sports training background is established to complete the detection of body behavior characteristics. The experimental results show that, compared with the traditional methods, the detection accuracy and efficiency of the text method can always maintain a high level, indicating that the practical application performance of this method is strong. In fact, the grey analysis does not attempt to find the best solution but does provide techniques for determining a good solution.
Modulation Classification Based on Signal Constellation and Yolov3

Aiming at the modulation classification of communication signal, a new identification method based on signal constellation is proposed. In this method, the constellation of communication signal is identified as an image, and the Y olov3 based on deep learning is used for target recognition. This method is not limited by the order of signal constellation, and has the advantage of not requiring manual feature selections. Firstly, the constellation diagrams of different modulation signals are produced and then they are used for training the weight of yolov3. Finally, the testing image of received signal constellation is detected by yolov3 under different noises. Simulation results demonstrate that the proposed method can identify more kinds of signals and has better performance than the traditional cumulant-based method.
Generalizing weighted trees

Despite the tremendous interest in cryptocurrencies like Bitcoin and Ethereum today, many aspects of the underlying consensus protocols are poorly understood. Therefore, the search for protocols that improve either throughput or security (or both) continues. Bitcoin always selects the longest chain (i.e., the one with most work). Forks may occur when two miners extend the same block simultaneously, and the frequency of forks depends on how fast blocks are propagated in the network. In the GHOST protocol, used by Ethereum, all blocks involved in the fork contribute to the security. However, the greedy chain selection rule of GHOST does not consider the full information available in the block tree, which has led to some concerns about its security.
A Softmax-Free Loss Function Based on Predefined Optimal-Distribution of Latent Features for Cnn Classifier

In the field of pattern classification, the training of convolutional neural network classifiers is mostly end-to-end learning, and the loss function is the constraint on the final output (posterior probability) of the network, so the existence of Softmax is essential. In the case of end-to-end learning, there is usually no effective loss function that completely relies on the features of the middle layer to restrict learning, resulting in the distribution of sample latent features is not optimal, so there is still room for improvement in classification accuracy. Based on the concept of Predefined Evenly-Distributed Class Centroids (PEDCC), this article proposes a Softmax-free loss function based on predefined optimal-distribution of latent featuresâ€”POD Loss. The loss function only restricts the latent features of the samples, including the cosine distance between the latent feature vector of the sample and the center of the predefined evenly-distributed class, and the correlation between the latent features of the samples. Finally, cosine distance is used for classification. Compared with the commonly used Softmax Loss and the typical Softmax related AM-Softmax Loss, COT-Loss and PEDCC-Loss, experiments on several commonly used datasets on a typical network show that the classification performance of POD Loss is always better and easier to converge. Code is available in https://github.com/TianYuZu/POD-Loss.
Real-time Panoramic Video Mosaic system Based on Mapping Table and GPU Acceleration

As an important research direction of computer vision, panoramic mosaic technology has a very important position in many fields such as disaster detecting and terrain reconnaissance, target detection and tracking, drone aerial reconnaissance, satellite survey, virtual reality and so on. Although many researchers have made a lot of achievements in the field of panoramic stitching, it is found that its universality and real-time performance need to be improved in practical engineering applications. In order to solve the problems that the existing algorithms have poor performance when the feature points are scarce and the stitching speed is slow, we realized a real-time panoramic video mosaic system based on the mapping table (MT)and the GPU acceleration method. Qualitative and quantitative experimental analysis shows the effectiveness of our method.
Analysis of dominant classes in universal adversarial perturbations

The reasons why Deep Neural Networks are susceptible to being fooled by adversarial examples remains an open discussion. Indeed, many different strategies can be employed to efficiently generate adversarial attacks, some of them relying on different theoretical justifications. Among these strategies, universal (input-agnostic) perturbations are of particular interest, due to their capability to fool a network independently of the input in which the perturbation is applied. In this work, we investigate an intriguing phenomenon of universal perturbations, which has been reported previously in the literature, yet without a proven justification: universal perturbations change the predicted classes for most inputs into one particular (dominant) class, even if this behavior is not specified during the creation of the perturbation. In order to justify the cause of this phenomenon, we propose a number of hypotheses and experimentally test them using a speech command classification problem in the audio domain as a testbed. Our analyses reveal interesting properties of universal perturbations, suggest new methods to generate such attacks and provide an explanation of dominant classes, under both a geometric and a data-feature perspective.
The Construction of Software Technology Course System Based on Modern Information Technology

With the requirement of "doing a good job in the special work of higher vocational enrollment expansion" proposed by the Ministry of Education, The enrollment of "modern apprenticeship" in vocational colleges has become one of the key points in the expansion of enrollment. This paper takes the software technology major as the research object, and develops modern apprenticeship cooperation through cooperation with Shenzhen Softship Information Technology Co., Ltd. and other enterprise alliances. Both sides have built the corresponding curriculum system on the basis of fully considering the knowledge points and skills required by talents. Through teaching practice, it is found that the students trained in the apprenticeship system have significantly improved their moral skills compared with the students trained in the traditional model. It also proves that modern apprenticeship training is a relatively successful training model.
The Reception of C. S. Lewis’s<i>A Preface to</i>Paradise Lost in Milton Scholarship, 1990‐2015

Milton QuarterlyVolume 55, Issue 1 p. 1-38 ORIGINAL ARTICLE The Reception of C. S. Lewis’s A Preface to Paradise Lost in Milton Scholarship, 1990-2015 Justin W. Keena, Corresponding Author jwkeena@gmail.com Nashua Community College, Nashua, New Hampshire, USA Correspondence Justin W. Keena Email: jwkeena@gmail.comSearch for more papers by this author Justin W. Keena, Corresponding Author jwkeena@gmail.com Nashua Community College, Nashua, New Hampshire, USA Correspondence Justin W. Keena Email: jwkeena@gmail.comSearch for more papers by this author First published: 30 November 2021 https://doi.org/10.1111/milt.12373Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Volume55, Issue1March 2021Pages 1-38 RelatedInformation
Modelling Energy Production and Consumption in Relation to Climate Parameters

The research has described the key features of the co-variability of wind and insolation on the east coast of India, and their potential impact on balancing energy supply. For the first time in this region, it was found that due to changes caused by coastal currents in climatic conditions (both solar and wind) on the east coast of India, there is a 13&#x0025; change from 6720.28 to 5951.15 kWh/m<sup>2</sup> in total incident horizontal solar insolation, and a 29.4&#x0025; change from 3.4 to 4.4 m/s in the mean wind speed, which ultimately leads to a 15&#x0025; change in the levelised cost of electricity (LCOE) from $US0.185 to $US0.158 and a 10&#x0025;change from 2.1 to 1.9 years in the period of refund at different points on the coast. Gained correlations can be seen not only in coastal regions, but also can give the insights of how well the hybrid renewable energy sources systems will perform in the remote regions. Parameters of solar panels that employ Heterojunction Technology (HJT) were used in the simulation in this region&#x003E
OpenDesign of Scientific Research in Pandemic Context

The Coronavirus pandemic (COVID19) and the consequent situation of social isolation have affected research design, especially in fields working directly with people. While online tools enable communication and collaboration, some specific domains involving contemporary technology-based systems, such as Socioenactive systems, demand a deeper understanding of human embodied action in real-world scenarios with ubiquitous technology. This work addresses this issue by presenting a study in which a research team used the OpenDesign Platform to conduct a process of ideation and consolidation of a socioenactive system solution during pandemics and physical presence limitations. The contributions are twofold, addressing the demands of the research team working remotely and envisaging an interaction scenario for people in a socioenactive system, even without their copresence in the same physical space. Results have shown the strengths and weaknesses of the OpenDesign Platform and lessons learned towards supporting systems for teamwork in such systems’ ideation context.
An Uncertainty Correlation Metric in Gradual Inference to Improve Domain Adaptation for Cross-location Fault Diagnosis

In recent years, domain adaptation methods have achieved a certain success in cross-domain unsupervised fault diagnosis. For reducing the distribution discrepancies, the most existing multi-classifier minimax optimization methods employed traditional metrics such as LI, L2, Wasserstein distance to measure the difference of multi-classifiers, but there are still three disadvantages: 1. The difference between multi-classifiers is optimized only by maximizing the distance metric. 2. The category confusion (uncertainty) of each classifier cannot be optimized when metrics minimized. 3. The prediction uncertainty of classifiers is excessively optimized when maximized. Therefore, this paper proposes a novel uncertainty correlation metric (UC) in the gradual inference multi-classifier structure to improve domain adaptation for cross-location fault diagnosis. The UC metric is constructed from class correlation and uncertainty weight, and applied to the proposed gradual inference multi-classifier structure for minimax training (UC-GI). First, the information recognition difference from the bottom to the top of the gradual inference structure constructs the original difference of the multiple classifiers. Second, when maximizing the UC metric, each classifier performs multi-category information trade-offs. Third, when the metric is minimized to unity the prediction results of the classifiers, UC-GI can optimize the prediction uncertainty of each classifier, thereby reducing category confusion. Final, built on UC-GI, a novel class-level distribution alignment (UC-GIDA) is constructed by implementing classic domain adversarial learning in gradual inference. Extensive comparison and evaluation verity the effectiveness and advantages of the proposed method.
Classifying Potentially Unbounded Hierarchical Data Streams with Incremental Gaussian Naive Bayes

Hierarchical data stream classification inherits the properties and constraints of hierarchical classification and data stream classification concomitantly. Therefore, it requires novel approaches that (i) can handle class hierarchies, (ii) can be updated over time, and (iii) are computationally light-weighted regarding processing time and memory usage. In this study, we propose the Gaussian Naive Bayes for Hierarchical Data Streams (GNB-hDS) method: an incremental Gaussian Naive Bayes for classifying potentially unbounded hierarchical data streams. GNB-hDS uses statistical summaries of the data stream instead of storing actual instances. These statistical summaries allow more efficient data storage, keep constant computational time and memory, and calculate the probability of an instance belonging to a specific class via the Bayes' Theorem. We compare our method against a technique that stores raw instances, and results show that our method obtains equivalent prediction rates while being significantly faster.
The Acceptance and Interest of Students in Using Virtual Reality (VR) for Learning Mathematics

Nowadays, Virtual Reality (VR) is one of the new technologies that have been used in learning session that have a positive impact on the students.This article is intended to study the students' acceptance and interest in the use of VR in learning Mathematics.This study involves 37 students, of whom are 15 male students and 22 female students with age range of 15 and 16 years from Sekolah Menengah Maahad Tahfiz Ilmuan (SMITIN) located in Bangi, Selangor.The module that contains 13 questions and 1 set of questionnaires is used to collect the research data.The findings reveal that majority of the students show good results in the use of VR as learning tools in the lesson, whereby 89% of them got grade A in the task whilst the rest of them got grade A-.These technologies have a positive impact on the students in understanding Mathematics.Furthermore, the findings show that the usage of VR in the group can not only help improve students' social skills in doing tutorial in the class but also increase their interests in learning Mathematics.The implementation of VR application has a positive impact on the students in learning Mathematics with the help of their teachers and parents.Nevertheless, parents need to control the usage of the gadgets.
Enhancing Monitoring in Online Exams Using Artificial Intelligence

Online teaching accommodates learners from distant locations and also saves time to commute, reduces traffic, etc. Exams are an important component of online educational programs, and students' cheating during the exam is a widespread phenomenon around the world. Also, applicants tend to cheat more under no monitoring in the online exam. So, exam proctoring and monitoring methodology to detect cheating and reduce its possibility is the need of an hour. In the past few years, various research methods have been introduced to ensure smooth and efficient online exam monitoring. But, such methodologies are costly and require much human intervention. Also, proctoring in online exams requires precision and accuracy. The proposed methodology involves continuous user validation and verification to ensure his/her integrity. Proposed approach of monitoring during the test includes subtle micro-expression detection such as laughter detection, eye gaze tracking to predict applicant's viewing direction, eyes blinking/close duration, and head activity/head movement detection. Any suspicious activity or moment by the applicant will be tracked, and associated penalty is imposed. The work uses artificial intelligence for classifying the applicant's activity. Having remotely proctored exams can significantly reduce logistic efforts, reduce evaluation time, and make it easier to reach distant test takers. The preliminary results demonstrate efficiency of the proposed approach for the same.
NEAT Activity Detection using Smartwatch at Low Sampling Frequency

Our paper aims to build a classification model to discern the typical NEAT (Non-Exercise Activity Thermogenesis) activities done in a home setting. The concept of NEAT is broadly defined as the energy spent in everything which is not sleeping, eating, or a traditional form of physical exercise. We focus on the following NEAT and non-NEAT activities in this paper - cooking, sweeping, mopping, walking, climbing up, climbing down, and non-NEAT activities (e.g., watching television and working on a desk). This aim is to build a classification model which can work with data sampled at a low frequency of 1Hz. However, building such a classifier is non-trivial because the NEAT activities are not easily separable in low-frequency data. The state-of-the-art in the area of human activity recognition either uses multiple physical devices (e.g., accelerometers on arms, waist, and feet) for data collection or use data that is sampled at high frequency (20Hz or above). In contrast, our model performs NEAT activity recognition using data sampled at 1Hz and from a single smartwatch worn on the dominant hand. Thus, making it more energy-efficient and easily usable for widespread use. We evaluate our proposed model using actual data collected on a smartwatch, and we compare it with alternative models. Our results indicate that the proposed model is able to achieve much higher accuracy than the alternative approaches.
ZND-ZeaD Models and Theoretics Including Proofs for Takagi Factorization of Complex Time-Dependent Symmetric Matrix

Takagi factorization (TF) is a kind of matrix factorization (also termed, decomposition) method. In this paper, the problem of TF for complex time-dependent symmetric matrix (simply termed, time-dependent Takagi factorization) is presented and theoretically studied. To simplify the tough problem, we first consider converting it into an equivalent equation system. Then, a continuous-time (CT) TF (CTTF) model is presented and derived theoretically, by adopting ZND (Zhang neural dynamics, or termed, Zhang neural network) method and a dimensional reduction technique. In order to facilitate the implementation on digital hardware platforms, the corresponding discrete-time (DT) ZND solution models (or termed, algorithms) with different discretization precisions of the CTTF model are presented and taken into theoretical consideration. Specifically, we present and investigate a discrete-time TF (DTTF) model, termed eight-instant DTTF (8IDTTF) model, by using a new eight-instant Zhang et al discretization (ZeaD, also termed, Zhang time discretization, ZTD) formula to discretize the CTTF model. Besides, two other DTTF models, termed four-instant DTTF (4IDTTF) and six-instant DTTF (6IDTTF) models, are presented respectively, on the basis of two corresponding multiinstant ZeaD formulas. Moreover, the effective solution step-size intervals of the three DTTF models are further presented and proved.