Reviewerly/exHarmony huggingface dataset:
THE COURSE "EDUCATION IN THE CONTEXT OF MODERN SOCIO-ECONOMIC PROCESSES"&#x0D; AS A RESOURCE FOR THE DEVELOPMENT OF THE PROFESSIONAL-PEDAGOGICAL WORLDVIEW&#x0D; IN MASTER’S STUDENTS

The article provides definitions of the concept of “professional-pedagogical worldview”, proposed by various&#x0D; researchers in a very small number of works devoted to the study into that intra-personal phenomenon. Based on the analysis&#x0D; of the definitions, the authors conclude that the content of the teacher's professional training should cover not only the&#x0D; psychological, pedagogical, methodological and technological aspects of future professional activity, but also the socioeconomic,&#x0D; legal and political aspects and the grounds for integrating their professional activity into the modern world picture.&#x0D; According to the authors of the article, a significant resource for the development of the professional and pedagogical&#x0D; worldview in Master’s students may be the course “Education in the context of modern socio-economic processes”. The&#x0D; main tasks of teaching students the course are to master the knowledge about the main directions of the functioning of&#x0D; education in the context of world and national socio-economic realities and prospects, which implies an understanding of&#x0D; the essence of education as a social practice, the situation of the development of the education system in their own country&#x0D; and in the world; modern approaches to the education economics and legal regulation for education, key elements of the&#x0D; economic mechanism of the education sector, its socio-economic effectiveness, as well as the role of the teacher and the&#x0D; results of teachers’ professional activities for the social and innovative development of society and the state. The article&#x0D; presents an approximate thematic plan of the course and describes some of its content and methodical aspects.
MFLCES: Multi-Level Federated Edge Learning Algorithm Based on Client and Edge Server Selection

This research suggests a multi-level federated edge learning algorithm by leveraging the advantages of Edge Computing Paradigm. Model aggregation is partially moved from a cloud center server to edge servers in this framework, and edge servers are connected hierarchically depending on where they are located and how much computational power they have. At the same time, we considered an important issue: the heterogeneity of different client computing resources (such as device processor computing power) and server communication channels (which may be limited by geography or device). For this situation, a client and edge server selection algorithm (CESA) based on a greedy algorithm is proposed in this paper. Given resource constraints, CESA aims to select as many clients and edge servers as possible to participate in the model computation in order to improve the accuracy of the model. The simulation results show that, when the number of clients is high, the multi-level federated edge learning algorithm can shorten the model training time and improve efficiency compared to the traditional federated learning algorithm. Meanwhile, the CESA is able to aggregate more clients for training in the same amount of time compared to the baseline algorithm, improving model training accuracy.
Authenticated Key Exchange under Bad Randomness, Revisited

A bad randomness may cause catastrophic results in security; thus, it is of importance to make cryptographic systems secure against bad randomness. In this paper, we focus on a practical situation where an adversary is able to force participants in an authenticated key exchange (AKE) system to reuse the random values and the functions of these values, called related randomness attack (RRA). Following the existing randomness resetting security model of AKE and the RRA security model of public-key encryption, we present a model of RRA security for authenticated key exchange, as well as the necessary restrictions on the related randomness functions used to obtain the security definition. Then we show how a related randomness attack adversary breaks the security of some existing AKE protocols, and propose some constructions of RRA-secure authenticated key exchange in the random oracle model and standard model, respectively.
Building of safer urban hubs: Insights from a comparative study on cyber telecom scams and early warning design

As digital technologies and smart city development continue to grow, the threat of cybercrime and scams is becoming increasingly salient to city managers, businesses, and citizens worldwide. With less effective data privacy protection, new types of scams that precisely target victims are on the rise. This research collected 6,871 crawl fraud cases from news reports and local websites between October 2018 and December 2021 in Mainland China and Hong Kong. We generated 2,747 messages from news and open-source message datasets on GitHub. Based on these novel datasets, we conducted a comparative analysis of cyber-telecom scams between Mainland China and Hong Kong, and identified victim profiles using data technology and Target Group Index (TGI) analysis. Furthermore, we developed a message-classifier scam alert model using data mining and machine learning algorithms. Our study provides valuable insights and essential implications on how data analytics can support future anti-fraud initiatives and help cities build safer urban hubs.
Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers

To guarantee that software does not fail, software quality assurance (SQA) teams play a critical part in the software development procedure. As a result, prioritizing SQA activities is a crucial stage in SQA. Software defect prediction (SDP) is a procedure for recognizing high-risk software components and determining the influence of software measurements on the likelihood of software modules failure. There is a continuous need for sophisticated and better SDP models. Therefore, this study proposed the use of dagging-based and baseline classifiers to predict software defects. The efficacy of the dagging-based SDP model for forecasting software defects was examined in this study. The models employed were naïve Bayes (NB), decision tree (DT), and k-nearest neighbor (kNN), and these models were used on nine NASA datasets. Findings from the experimental results indicated the superiority of SDP models based on dagging meta-learner. Dagging-based models significantly outperformed experimented baseline classifiers built on accuracy, the area under the curve (AUC), F-measure, and precision-recall curve (PRC) values. Specifically, dagging-based NB, DT, and kNN models had +6.62%, +3.26%, and +4.14% increments in average accuracy value over baseline NB, DT, and kNN models. Therefore, it can be concluded that the dagging meta-learner can advance the recognition performances of SDP methods and should be considered for SDP processes.
Protecting the Distribution of Color Images via Inverse Colorization, Visible-Imperceptible Watermarking and Reversible Data Hiding

Invertible color-to-gray algorithms are good alternatives for protecting color images in storage, transmission, or limited-access environments. These approaches hide color information in a gray-scale version of an image. Thus, illegal recovery of the original color becomes a challenging task, and only authorized users can restore the colorized image. However, although the color is protected, the original structure of the image remains vulnerable to illegal usage. In this paper, we present a reversible distortion of the protected gray-scale image to make illegal reconstruction more difficult. In addition, we preserve the general visibility of the original content. Furthermore, a visible imperceptible watermark is embedded to protect ownership of the colorized image in public access. The watermark remains imperceptible in the colorized image and reveals the logo of the owner when the image is protected. We propose different levels of distortions obtaining mean PSNR qualities between 14.74 dB and 32.92 dB with respect to a reference gray-scale image. Furthermore, the mean PSNR quality of the colorized images remains between 38.87 dB and 41.55 dB.
A Novel Bat Algorithm with Asymmetrical Weighed Variational Method in the Path Planning of UAVs

In this paper, a novel bat algorithm with an asymmetrical weighed variational method (AWVM-BA) is proposed. The proposed algorithm employs the BA with a point-to-point modified asymmetrical variation above the three-dimensional flying region, which treats the space as sets of geodesics in a second order Euclidean weighed warped space. Mutation and the local selection procedure can be avoided at the same time, which solves the problem of a local optimum in concave regions. As shown in the results, the proposed algorithm does not have much impact on the calculation complexity and time in convex regions. It can greatly reduce the calculation time and avoid local optimization in concave regions. The disadvantage of the proposed algorithm is that the iteration number increases comparatively faster with the increase in the deviation of the wind speed. Therefore, it requires a higher hardware calculation ability.
The police in Nigeria – recruitment, formation and responsibility – legal instruments to improve the role of the police to establish security

Every society functions on law. These laws establishes acceptable modes of conduct and prohibits certain actions and inactions. These laws ensures the security of lives and property. One major character of law is that it also provides sanctions for its violators. These sanctions are usually enforced by a Police system. Hence, policing is an intrinsic aspect of any society. This is because the security of any society is dependent largely dependent on the effectiveness of its policing system. This work is an analyses of the Police in Nigeria. The works details the formation, recruitment and responsibility of the Nigerian Police Force. It also reviews the effectiveness of the Nigerian Police based on its present structure and operations as allowed by the relevant laws and makes recommendations for an improved Police Force to meet the nation’s present security challenges.
Research on PoW Protocol Security under Optimized Long Delay Attack

In the blockchain network, the communication delay between different nodes is a great threat to the distributed ledger consistency of each miner. Blockchain is the core technology of Bitcoin. At present, some research has proven the security of the PoW protocol when the number of delay rounds is small, but in complex asynchronous networks, the research is insufficient on the security of the PoW protocol when the number of delay rounds is large. This paper improves the proposed blockchain main chain record model under the PoW protocol and then proposes the TOD model, which makes the main chain record in the model more close to the actual situation and reduces the errors caused by the establishment of the model in the analysis process. By comparing the differences between the TOD model and the original model, it is verified that the improved model has a higher success rate of attack when the probability of mining the delayable block increases. Then, the long delay attack is improved on the balance attack in this paper, which makes the adversary control part of the computing power and improves the success rate of the adversary attack within a certain limit.
Library Automation and Knowledge Sharing

Library automation has a rich history of more than 130 years of development, from the standardization machine-readable cataloging (MARC)of card catalogs to the creation of the machine-readable cataloging (MARC) communications format and bibliographic utilities. Beginning in the early 1980s university libraries and integrated library system (ILS)library automation vendors pioneered the first integrated library systems (ILS). The digital era, characterized by the proliferation of content in electronic format, brought with it the development of services for casual users as well as scholarly researchers – services such as OpenURL linking and metasearching and library staff tools such as electronic resource management systems. Libraries have now developed approaches to search that integrate data from the many disparate content sources they have historically managed, as well as the new systems being developed for digital object management. Additionally, the proliferation of services that emerged in the first decade of this century is being consolidated into emergent library service platforms that provide flexible frameworks for managing the full range of library collections. These developments have allowed libraries to evolve their mission of knowledge sharing from aggregating knowledge created globally to a local community to one in which they also aggregate and disseminate locally created knowledge to the world.
A Memory-Efficient Learning Framework for Symbol Level Precoding With Quantized NN Weights

This paper proposes a memory-efficient deep neural network (DNN) framework-based symbol level precoding (SLP). We focus on a DNN with realistic finite precision weights and adopt an unsupervised deep learning (DL) based SLP model (SLP-DNet). We apply a stochastic quantization (SQ) technique to obtain its corresponding quantized version called SLP-SQDNet. The proposed scheme offers a scalable performance vs memory trade-off, by quantizing a scalable percentage of the DNN weights, and we explore binary and ternary quantizations. Our results show that while SLP-DNet provides near-optimal performance, its quantized versions through SQ yield ~3.46× and ~2.64× model compression for binary-based and ternary-based SLP-SQDNets, respectively. We also find that our proposals offer ~20× and ~10× computational complexity reductions compared to SLP optimization-based and SLP-DNet, respectively.
Coastal Ship Tracking with Memory-Guided Perceptual Network

Coastal ship tracking is used in many applications, such as autonomous navigation, maritime rescue, and environmental monitoring. Many general object-tracking methods based on deep learning have been explored for ship tracking, but they often fail to accurately track ships in challenging scenarios, such as occlusion, scale variation, and motion blur. We propose a memory-guided perception network (MGPN) to address these issues. MGPN has two main innovative improvements. The dynamic memory mechanism (DMM) in the proposed method stores past features of the tracked target to enhance the model’s feature fusion capability in the temporal dimension. Meanwhile, the hierarchical context-aware module (HCAM) enables the interaction of different scales, global and local information, to address the scale discrepancy of targets and improve the feature fusion capability in the spatial dimension. These innovations enhance the robustness of tracking and reduce inaccuracies in the bounding boxes. We conducted an in-depth ablation study to demonstrate the effectiveness of DMM and HCAM. Finally, influenced by the above two points, MGPN has achieved state-of-the-art performance on a large offshore ship tracking dataset, which contains challenging scenarios such as complex backgrounds, ship occlusion, and varying scales.
Learning to Generate Popular Headlines

Headlines are not only essential for summarizing news articles but also for grabbing users’ attention. Headline generation is a type of text summarization that can employ either an extractive or abstractive approach, with the latter being more prevalent through deep learning models. However, creating a popular headline that can capture readers’ attention is challenging. To address this issue, we propose a hybrid headline generation approach that utilizes state-of-the-art transformer models to generate several headline variations for an article. Additionally, we use a model for predicting headline popularity that can choose the most popular headline from the generated ones. We also create a new dataset for predicting headline popularity by scraping Twitter accounts of news media. Our evaluation shows that fine-tuning summarization models for the headline generation task can significantly improve their performance. We also demonstrate that our proposed method can generate more popular headlines compared to the baseline methods that do not incorporate popularity prediction. For such an evaluation purpose, we create a popularity benchmark to automatically assess the effectiveness of our proposed headline generation approach in generating popular headlines.
Neural Network Compression via Low Frequency Preference

Network pruning has been widely used in model compression techniques, and offers a promising prospect for deploying models on devices with limited resources. Nevertheless, existing pruning methods merely consider the importance of feature maps and filters in the spatial domain. In this paper, we re-consider the model characteristics and propose a novel filter pruning method that corresponds to the human visual system, termed Low Frequency Preference (LFP), in the frequency domain. It is essentially an indicator that determines the importance of a filter based on the relative low-frequency components across channels, which can be intuitively understood as a measurement of the “low-frequency components”. When the feature map of a filter has more low-frequency components than the other feature maps, it is considered more crucial and should be preserved during the pruning process. We conduct the proposed LFP on three different scales of datasets through several models and achieve superior performances. The experimental results obtained on the CIFAR datasets and ImageNet dataset demonstrate that our method significantly reduces the model size and FLOPs. The results on the UC Merced dataset show that our approach is also significant for remote sensing image classification.
Identifying Problem-Solving Solution Patterns Using Network Analysis of Operation Sequences and Response Times

Process data from educational assessments enhance the understanding of how students answer cognitive items. However, effectively making use of these data is challenging. We propose an approach to identify solution patterns from operation sequences and response times by generating networks from process data and defining network features that extract essential information from them. With these features, we group respondents to a problem-solving task from PISA 2012 using Gaussian mixture models. The results indicate the presence of two and four clusters for groups defined by failure and success on the task, respectively. We interpret the clusters as less-able, low-effort, adaptable, back-and-forth, deliberate, and trial-and-error clusters by considering the cluster-specific feature statistics. The proposed approach sheds light on students’ problem-solving mental processes, which can aid item development and facilitate individualized feedback to students. The method is applicable to many computer-based problems, but a limitation is that the feature definitions can be task-dependent.
VideoTime<sup>3</sup>: A 40-uJ/frame 38 FPS Video Understanding Accelerator With Real-Time DiffFrame Temporal Redundancy Reduction and Temporal Modeling

VideoTime3 is an accelerator for state-of-the-art video understanding with deep learning on the edge. Different from prior work, it highlights a real-time DiffFrame convolution achieving 2.2x DRAM access reduction compared to conventional convolution, a sorter-free architecture for efficient sparse output stationary dataflow, temporal modeling capability achieving high accuracy on video understanding applications, and optimized data buffering to remove DRAM traffic overhead for temporal modeling and reduce 55%–79% input activation DRAM traffic in depth-wise layers. The chip consumes 40uJ/frame with 38 frames/second at 0.6V in 28nm CMOS.
Verbal markers of threat in public statements of the English-speaking segment of the social network Twitter

The aim of the study is to establish a correlation between the intensity of a threat and the specific linguistic means expressing it in the social network Twitter (lexemes, distribution of lexemes, grammatical constructions). Threat as a regular phenomenon of Internet public pages is realised in the appropriate contextual conditions, it is endowed with a particular style and is actualised through recurrent grammatical constructions. Such conditions of threat actualisation make it possible to associate it with the speech strategy relevant in Internet discourse. The scientific novelty of the study is accounted for by considering the functions of markers of threat in the gradation of its intensity in the English-speaking segment of the social network Twitter. As a result of the study, it has been determined that the textual space of social networks has a high degree of implicitness, expressiveness and evaluativeness, verbal markers of threat occupy a special place in the textual space of social networks as key elements of comments and messages. The intensity of a threat can be determined through the linguistic means of its actualisation in a social context. The statements containing a threat themselves have the appropriate style and expression, are built according to the most typical grammatical constructions. The analysis of the latter allows us to talk about the credibility of the action predicted by the threat and the possibility of transition from the plane of Internet communication to the real world.
Database Security in RDF Terms

Abstract Databases are perhaps the most attacked and vulnerable elements in the online environment as far as organizations are concerned. The databases contain the most important information of an organization and its confidential data, along with those of its customers. However, little of the effort dedicated to security is intended to protect data. The present paper draws attention to the need to implement a simple and then complex security in a database using RDF (Resource Description Framework) based descriptions. Our example is one implemented for Microsoft Access and covers different security level descriptions considering attack types. We choose to represent the basic concepts and abstract syntax using the RDF data modeling vocabulary and we’re using as a starting point a legacy relational database where the company already catalogued attacks.
Cybersecurity in Healthcare Through the 7-S Model Strategy

Abstract According to the 2019 HIMSS Cybersecurity Survey, most healthcare organizations are aware of the importance of cybersecurity, with 96% of respondents indicating that cybersecurity was a priority for their organization. Despite this, the survey also found that healthcare organizations are not adequately prepared for cyberattacks, with only 39% reporting that they had a comprehensive cybersecurity plan. This paper uses the 7-S Model strategy as a foundation for strategy development for healthcare organizations in a post-COVID-19 world.
Source Microphone Identification Using Swin Transformer

Microphone identification is a crucial challenge in the field of digital audio forensics. The ability to accurately identify the type of microphone used to record a piece of audio can provide important information for forensic analysis and crime investigations. In recent years, transformer-based deep-learning models have been shown to be effective in many different tasks. This paper proposes a system based on a transformer for microphone identification based on recorded audio. Two types of experiments were conducted: one to identify the model of the microphones and another in which identical microphones were identified within the same model. Furthermore, extensive experiments were performed to study the effects of different input types and sub-band frequencies on system accuracy. The proposed system is evaluated on the Audio Forensic Dataset for Digital Multimedia Forensics (AF-DB). The experimental results demonstrate that our model achieves state-of-the-art accuracy for inter-model and intra-model microphone classification with 5-fold cross-validation.