Reviewerly/exHarmony huggingface dataset:
Adversarial Example Attacks in Internet of Things (IoT)

Recently, the Internet of Things (IoT) technology has made tremendous progress, and it is beginning to enter many areas of social life, such as autonomous driving, medical care, etc. Due to the massive data in IoT, deep neural networks (DNN) are often involved in helping process and analyzing data, but DNNs still face many security threats. Adversarial example attack is a common attack against DNN models, which interferes with model decisions through processed samples. It will undoubtedly threaten DNN-based IoT systems. This paper presents the possible attack scenarios of adversarial example attacks in IoT systems and extensively studies the defense methods of adversarial example attacks in IoT systems.
Identity-based Interactive Aggregate Signatures from Lattices

Aggregate signature allows users to compress multiple signatures into a short signature (called an aggregate signature), and can reduce a total amount of signature-size on a channel. In particular, identity-based aggregate signature can reduce not only total signature-size but also total verification key-size, because it is possible to check the validity of multiple messages and an aggregate signature by using signers’ IDs, instead of verification keys. Furthermore, we focus on lattice-based constructions as post-quantum cryptography, due to recent advancement of quantum computers. In this paper, we propose the first identity-based interactive aggregate signature scheme from lattices. The security of our scheme is based on a standard lattice assumption, and its aggregate signature-size is logarithmic in the number of signatures.
Exploration and Practice of Course Homework Metaverse Based on Extended Reality Under Edge Computing

The metaverse originated from science fiction at first, but it gradually came into reality with the continuous power of technology. Aiming at the problems of course work in Chinese universities, such as its function is weakened or suppressed, its form is too abstract, and it lacks elaborate design, this paper proposes a design method of course work meta-universe based on extended reality under edge computing. XR technology is used to realize the interactive immersion of the coursework meta-universe, in order to improve students’ learning enthusiasm and ability to solve abstract problems. Based on the international perspective, this paper systematically combs and analyzes the integration architecture, application scenarios and future trends of the meta-universe education application based on Edge computing (MEC). Four key processes are described: teacher-student interaction, data collection, data feedback and comprehensive evaluation. In order to promote the application of the metaverse in the field of education to provide mirror. The validity of the method is illustrated by the data.
From Machine Learning to Transfer Learning

Transfer learning is an important branch of machine learning. They have very tight connections. Therefore, we should first start familiarizing with the basics of machine learning. With these bases, we can then deeply understand their problems with more insights. We have briefly introduced the background and concepts of transfer learning in the last chapter. We will dive into this area starting from this chapter. We introduce the definition of machine learning and probability distribution. Then, we present the definition of transfer learning, along with its fundamental problems and negative transfer case. Finally, we present a complete transfer learning process.
Adaptive Recurrent Iterative Updating Stereo Matching Network

When training a stereo matching network with a single training dataset, the network may overly rely on the learned features of the single training dataset due to differences in the training dataset scenes, resulting in poor performance on all datasets.Therefore, feature consistency between matched pixels is a key factor in solving the network's generalization ability.To address this issue, this paper proposed a more widely applicable stereo matching network that introduced whitening loss into the feature extraction module of stereo matching, and significantly improved the applicability of the network model by constraining the variation between salient feature pixels.In addition, this paper used a GRU iterative update module in the disparity update calculation stage, which expanded the model's receptive field at multiple resolutions, allowing for precise disparity estimation not only in rich texture areas but also in low texture areas.The model was trained only on the Scene Flow large-scale dataset, and the disparity estimation was conducted on mainstream datasets such as Middlebury, KITTI 2015, and ETH3D.Compared with earlier stereo matching algorithms, this method not only achieves more accurate disparity estimation but also has wider applicability and stronger robustness.
Digital Evidence in the Process of the International Court of Justice

The experience of the International Court of Justice of the United Nations, the oldest existing international court, is extremely important for the development of international judicial procedure. One of the important elements is the practice of collecting and using evidence, which serve as the basis for a Court decision. The problem of recent years has been the problem of using digital (electronic) evidence, which can be any material that exists or is transmitted in the form of numbers. Its source can be a computer disk, stationary or mobile, USB drive, smartphone, the Internet. The digital proof may also have a graphical form in the form of a computer file or an email. The source of evidence may also be data obtained from satellites as a result of remote sensing of the Earth. There are no rules of customary international law concerning electronic evidence. It is logical to assume that the relevant regulation is adopted by each international court separately for itself in the form of its internal law or accepted practice. There are no rules in the Statute and Rules of the International Court of Justice that directly regulate the handling of digital evidence. The main task that the Court faces is to select such data that would allow it to make an effective and fair decision in accordance with international law. Since digital information can be on the Internet, and therefore be accessible to everyone, such information can be classified as publicly available, and therefore freely available. There are concerns that its use could make the trial uncertain. In our opinion, for digital data, the same conditions of treatment must be observed as with evidence in another form: deadlines, registration of copies, etc. The final word on the suitability of the submitted digital evidence is taken by the Court, taking into account the clarity of the image and information, the reliability of sources, etc.
E-governance with cloud computing

Cloud computing offers services to the end users, or the consumers, in a convenient and on-demand fashion. This computing model has several other features which increase its dependency to an incomparable degree, such as resource pooling, pay-per-use (economic), scalability, rapid elasticity, security, and availability. This model allows big or small organizations to get started with the work and to focus primarily on the business modules without worrying about the underlying infrastructure setup costs, i.e., the cost of powerful servers, expensive network devices, etc. Cloud computing finds a very important utility in e-Governance, which allows government applications to offer uninterrupted services to its citizens. Cost-effective, scalable, and secure e-services are possible with the integration of cloud computing. This paper shows the importance of cloud computing in e-Governance.
Federated Learning for Personalized Healthcare

Federated learning aims at building machine learning models without compromising data privacy from the clients. Since different clients naturally have different data distributions (i.e., the non-i.i.d. issue), it is intuitive to embed transfer learning technology into the federated learning system. In this chapter, we first introduce a healthcare task and show how to prepare data for federated learning.
Digit Versus Logos

The paper addresses the issues of human thinking in the information world. An attempt is made to answer the question of whether the digit can replace the logos. Digital technologies make people’s lives more comfortable, and they often overlook the negative consequences behind them. In the classical paradigm, thinking means consciousness, logos, and language. It is possible to create a picture of the world, set tasks, and seek survival in the surrounding world. There is no consciousness in the information world. The person is not the subject; they do not think but calculate. People are sacrificing the logos for the digit. The paper notes how this provision is reflected in postmodern views on the world and the subject and argues that postmodernism is a reaction to the modern information society. The alienation of the subject in modernity is replaced by fragmentation and then by the death of the subject in postmodernism. The picture of the world is changing in the consciousness of the modern person. Consciousness becomes increasingly mosaic, and this fragmented knowledge forms a rhizome. The rhizomatic design of the internet influences the thinking of the modern person and gives rise to the mosaic, pluralism, and fragmentation of knowledge. Such a situation generates a clip consciousness, a product of hypertextuality, since hypertext became an expression of modern network culture. The hypertext of the internet became a reality; it is centralized because, in infinite links, the idea of centralization and integrity is blurred. The person gets used to this reality, feels comfortable in it, depends on it. The virtualization of consciousness occurs. Real relationships become secondary and irrelevant. Digital codes replace language forms. The world of information technology has both advantages and disadvantages, offering people great opportunities; however, it does not protect them from risks.
UNet Network-Based Infrared Image Segmentation Method for Induction Motor

Intelligent analysis of infrared images has become an efficient means for fault diagnosis of electrical equipment, and the key technology is segmentation of the target equipment. In this paper, we take the overheating region of induction motor as the research object, use UNet neural network based on VGG16, construct a sample data set, build a semantic segmentation network to train a semantic segmentation model, and bifurcate the pixels of the high temperature region of the motor with the background pixels. The method in the paper is used to test 273 induction motor infrared images, and the experimental results show that the MIoU of the method is 96.0%, which can segment the induction motor high temperature region from the test images efficiently and pave the way for the subsequent intelligent diagnosis of induction motor faults.
Value and Semantic Grounds for Obtaining Education in the Postgraduate Course of the National Research University

The article deals with a range of issues related to motivation factors for admission to graduate school and behavioral reactions in the process of studying under the programs of scientific specialties in a modern national research university. Using the example of the implementation of postgraduate programs in political sciences at a technical university, the key motives of young specialists to continue their studies at the university are revealed. The features of the motivational and value sphere of graduate students mastering the training curriculum and performing the tasks of research work are determined. The reasons for expulsion from graduate school, the reasons for failure in the educational program, as well as the motives for frequent academic leave are analyzed. The article presents arguments in favor of the author's hypothesis about the need to develop postgraduate studies in the humanities and social sciences in technical universities. Political science education contributes not only to the conscious choice of the profession of an engineer or a highly qualified specialist for state and municipal service bodies. A young researcher gets the opportunity to recognize the value-semantic grounds for working in the organization of higher education. A potential high school teacher develops stable ideas about pedagogical ethics, about the psychology of communication with students of different age groups, about the inadmissibility of forms of deviant behavior when performing professional tasks.
Efficiency improving in organizing regional olympiad in pedagogy using lean production tools: project development experience

To achieve unique results in university development strategy education sector focuses on lean management. The team of changes in Pedagogy Department of Belgorod State National Research University defined process (organization of a regional Olympiad in Pedagogy for students of Pedagogical Institute of the Belgorod State National Research University and professional educational organizations of secondary vocational education of Belgorod and the Belgorod Region, hereinafter referred to as the process) and established its challenges (long flow time). Research problem: what are the ways to improve the efficiency of the process? Purpose and objectives of the study: the efficiency of the process will increase with a developed (stage 1) and implemented (stage 2) set of measures in the format of a systemic lean project. Purpose: to present the experience of developing the project “Optimization of the organization of the regional Olympiad in Pedagogy for students of Pedagogical Institute of the Belgorod State National Research University and professional educational organizations of secondary vocational education of Belgorod and the Belgorod Region” (hereinafter referred to as the project). Information about the main scientific results: a carried out pre-project analysis of the current situation; a developed target process map; a comparative analysis of the process current and target state; a developed action plan to improve the process. Scope: the results of the 1st stage of the study may be of interest to Russian universities in the implementation of lean management tools, the choice of change management methods at the level of a structural unit. Conclusions on achieving the purpose of the 1st stage of the study: the team of changes in Pedagogy Department developed a project, which development is presented in the article.
Cross-Chain-Based Distributed Digital Identity: A Survey

Due to the high degree of privacy and sensitivity, it is difficult to share distributed digital identity with multiple parties. Blockchain-based distributed digital identities could address the issues of data sharing. However, the blockchain systems contain a wide variety of heterogeneous autonomous systems, and each blockchain system performs independent identity authentication. To be specific, it is difficult to realize mutual recognition and trust in digital identity. In this survey, we introduce the technical architecture of distributed digital identity and research the technical principle and development application of cross-chain technology. In addition, we expound the research status of cross-chain technology and the cross-chain-based distributed digital identity mechanism to realize unified and trusted cross-chain identity mutual recognition.
Track Obstacle Real-Time Detection of Underground Electric Locomotive Based on Improved YOLOX

Based on the influence of dark obstacles caused by insufficient light in an underground mine on the driving safety of an electric locomotive. This paper proposes an improved YOLOX target detection algorithm to effectively identify and classify the track obstacles of the unmanned electric mine locomotive. On the basis of the YOLOX target detection network, the CBAM attention module is added to the CSPDarket and the FPN part of the feature pyramid, and the loss function of YOLO head part is replaced by SIOU. The collected image data of track obstacles of electric locomotive under different lighting conditions are used as the training set. The Pytorch deep learning framework is used to construct an object detection model for training and verification. Experiments show that the average accuracy and recall rate of the improved YOLOX underground electric locomotive track obstacle detection model can reach 93.05% and 88.29%, and the speed is improved to 45.3 fps. Compared with other target detection models, this model can better realize the accuracy and real-time detection of underground electric locomotive track obstacles. It provides the basis for the intelligence of underground mine transportation equipment.
Block-gram: Mining Knowledgeable Features for Smart Contract Vulnerability Detection

Effective vulnerability detection of large-scale smart contracts is critical because smart contract attacks frequently bring about tremendous economic loss. However, code analysis requiring traversal paths and learning methods requiring many features training is too time-consuming to detect large-scale on-chain contracts. This paper focuses on improving detection efficiency by reducing the dimension of the features, combined with expert knowledge. We propose a feature extraction method Block-gram to form low-dimensional knowledgeable features from the bytecode. We first separate the metadata and convert the runtime code to opcode sequence, dividing the opcode sequence into segments according to some instructions (jump, etc.). Then, we mine extensible Block-gram features for learning-based model training, consisting of 4-dimensional block features and 8-dimensional attribute features. We evaluate these knowledge-based features using seven state-of-the-art learning algorithms to show that the average detection latency speeds up 25 to 650 times, compared with the features extracted by N-gram.
Recognition of Abnormal Proxy Voice Traffic in 5G Environment Based on Deep Learning<sup>*</sup>

With the commercial use of the fifth generation (5G), the rapid popularization of mobile Over- The- Top (OTT) voice applications has brought high-quality voice communication methods to users. The intelligent Internet in the 5G era makes communication terminals not limited to mobile phones. The complex communication environment has higher requirements for the security of data transmission between various terminals to prevent the system from being monitored or breached. At present, many OTT users use encrypted proxy technology to get rid of certain restrictions of network operators, prevent their private information from leaking, and ensure communication security. However, in some cases the encryption proxy may be subject to configuration error or maliciously attacked makes the encryption ineffective. The resulting abnormal proxy traffic may cause privacy leakage when users use voice services. However, little effort has been put on fingerprint the effectiveness of encryption for proxy voice traffic in a 5G environment. To this end, we adopt the VGG deep learning method to identify agent speech traffic, compare it with common deep learning methods, and study the impact on model performance with less abnormal traffic. Extensive experimental results show that the deep learning method we use can identify abnormal encrypted proxy voice traffic with the accuracy up to 99.77%. Moreover, VGG outperform other DL methods on indentifying the encryption algorithms of normal encrypted proxy traffic.
Global Target Tracking Algorithm Based on Improved Twin Neural Network

The target tracking algorithm based on twin neural network is a classical deep learning target tracking method, and has attracted much attention due to its superior and stable tracking performance. GlobalTrack is a tracker for global instance search based on twin neural network architecture. Tracking globally can greatly improve the success rate of tracking. However, when the tracking target has motion blur, fast motion and other challenges, the tracker will lose track. To solve the above problems, a global target tracking algorithm based on improved twin neural network is proposed. Swin Transformer with better feature extraction ability, is introduced as the backbone network of feature extraction to improve the ability of target feature extraction. A regional proposal network that guides anchor points is introduced to improve the quality of generation of candidate frames for models in motion blurred scenes. A regional convolution neural network is used, According to the ROI characteristics of the target template, the prediction of its label and border is refined, and then the final prediction is obtained by classification and regression. Through a large number of experiments on OTB2015 and VOT2018 datasets, the tracking effect of this method is good, and it has strong robustness and adaptability in a variety of complex situations such as motion blur, fast motion, and meets the real-time requirements of the tracking algorithm.
SPEM: Self-adaptive Pooling Enhanced Attention Module for Image Recognition

Recently, many effective attention modules are proposed to boot the model performance by exploiting the internal information of convolutional neural networks in computer vision. In general, many previous works overlook the design of the pooling strategy of the attention mechanism since they adopt the global average pooling for granted, which hinders the further improvement of the performance of the attention mechanism. However, we empirically find and verify a phenomenon that the simple linear combination of global max-pooling and global min-pooling can produce effective pooling strategies that match or exceed the performance of global average pooling. Based on this empirical observation, we propose a simple-yet-effective attention module SPEM which adopts a self-adaptive pooling strategy based on global max-pooling and global min-pooling and a lightweight module for producing the attention map. The effectiveness of SPEM is demonstrated by extensive experiments on widely-used benchmark datasets and popular attention networks.
A Conceptual and Computational Framework for Aspect-Based Collaborative Filtering Recommender Systems

Many datasets in E-commerce have rich information about items and users who purchase or rate them.This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspects of the items thus leading to more sophisticated and justifiable recommendations.However, most Collaborative Filtering (CF) techniques rely mainly on the overall preferences of users toward items only.And there is lack of conceptual and computational framework that enables an understandable aspect-based AI approach to recommending items to users.In this paper, we propose concepts and computational tools that can sharpen the logic of recommendations and that rely on users' sentiments along various aspects of items.These concepts include: The sentiment of a user towards a specific aspect of a specific item, the emphasis that a given user places on a specific aspect in general, the popularity and controversy of an aspect among groups of users, clusters of users emphasizing a given aspect, clusters of items that are popular among a group of users and so forth.The framework introduced in this study is developed in terms of user emphasis, aspect popularity, aspect controversy, and users and items similarity.Towards this end, we introduce the Aspect-Based Collaborative Filtering Toolbox (ABCFT), where the tools are all developed based on the three-index sentiment tensor with the indices being the user, item, and aspect.The toolbox computes solutions to the questions alluded to above.We illustrate the methodology using a hotel review dataset having around 6000 users, 400 hotels and 6 aspects.