Reviewerly/exHarmony huggingface dataset:
A Theoretical Perspective of Artificial Intelligence in Hostility of Cyber Threats in the Banking Sector

With the progress of new know-how and technology, various cyber centers are utilized by illegal persons to augment cyber related crime. To alleviate this digital crime and cyber threats, artificial intelligence is being implemented by banks and financial institutions. A variety of avenues are offered by artificial intelligence techniques, which facilitate the banks to amplify affluence plus wealth. As per various literatures it has been observed that the expenses of cybercrime were around $ 450 million globally. Therefore, it becomes imperative to introduce certain security programs for guarding cyber threats in banking area. To battle and overcome hackers many banks are moving hands to employ artificial intelligence. Various kinds of cybercrimes are prohibited and recognized by AI-based fraud detection systems. However, execution and preservation of artificial intelligence consist of the high cost. After it, joblessness rate is also augmented by AI techniques. In this study various artificial intelligence techniques will be discussed along with their utilization and limitations.
Cryptocurrency and the Indian Monetary System

“Change is the only constant,” wrote the ancient Greek philosopher Heraclitus of Ephesus. And in today's world, one of the greatest changes is the changing nature of money. As the world embraces the ease in accessibility to high-speed internet services across all spectrums, the medium of exchange is transforming from fiat currency to virtual and cryptocurrencies. The exchange of cryptocurrency heavily revolves around speculation by professionals, and institutional investors dealing with large sums of money. The global adoption of cryptocurrency has increased over the last few years and has seen an 880% increase in year-to-year transactions in the last year, reflecting the increased acceptance in cryptocurrency usage in emerging markets. India ranks second in the overall crypto-adoption index ranking, backing the increased interest of Indian investors in the cryptocurrency markets. The major focus of this paper is regarding the next phase of transition into digital and cryptocurrency and its implications on monetary policies with a specific focus on the Indian monetary system.
Introduction

This book examines human–computer interaction in live vocal music performance from inside the creative act of music-making. Combined with broad insights into various experiential domains of the contemporary voice, the book builds on and extends the discourse surrounding interactive music systems such as Holland et al. (2019), and conference proceedings such as the ICLI International Conference on Live Interfaces (2020) and the New Instruments for Musical Expression (NIME) research community.
Lk 24,13–35 10.4.2023 Ostermontag

No AccessLk 24,13–35 10.4.2023 OstermontagOstern to go! Oder: Wie Religionspädagogik gehtProf. Dr. Harald Schroeter-WittkeProf. Dr. Harald Schroeter-WittkeProf. Dr. Harald Schroeter-Wittke, geb. 1961, ist seit 2001 Professor für Didaktik der Ev. Religionslehre mit Kirchengeschichte am Institut für Ev. Theologie der Fakultät für Kulturwissenschaften der Universität Paderborn.Search for more papers by this authorPublished Online:Jan 2023https://doi.org/10.13109/gpre.2022.77.2.228SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail About Previous article Next article FiguresReferencesRelatedDetails Download book coverVolume 77Issue 2January 2023 ISSN: 0340-6083 eISSN: 2197-0815 History Published online:January 2023 PDF download
Embodied Interface Performance with Gestural Systems

This chapter outlines a dynamic framework for creating meaningful interactions between the embodied voice and the disembodied voice through gestural systems. Based on the practices of contemporary vocal art, I will discuss the sensor instrument as prop, object and body extension and show how the sensor’s functions or affordances influence mapping strategies. I will show how the composed mapping of functional and communicative gestures together with a software’s logic defines a creative virtual instrument design framework. For this study, the embodied practice is presented as a dynamic system with seven co-players, a visual metaphor with seven limbs that create meaning in their interactions. To clarify this, I introduce each of these co-players separately. I present a way of understanding the creation of meaning in embodied human–computer interaction as a stream of attention with shifting perspectives. This mapping framework will be useful not only for musicians, composers and creative practitioners wishing to develop an understanding of the specifics of embodied human–computer interaction in vocal music performance but also for human–robotic researchers, voice-model and artificial machine-intelligence researchers seeking to develop a more systematic and targeted focus for exploring embodied interaction approaches in gestural systems.
An improved constrained Bayesian probabilistic matrix factorization algorithm

Given the increasing growth of the Web and consequently the growth of e-commerce, the application of recommendation systems becomes more and more extensive. A good recommendation algorithm can provide a better user experience. In the collaborative filtering algorithm recommendation system, many existing approaches to collaborative filtering can neither handle very large datasets nor easily deal with users who have very few ratings, this paper proposes an improved constrained Bayesian probability matrix factorization algorithm. The algorithm introduces a potential similarity constraint matrix for specific sparsely scored users to affect the user’s feature vector, and uses the Logistic function to express the nonlinear relationship of the potential factors, combined with the Markov chain Monte Carlo method for training. Finally, the data set is used for testing and comparative evaluation. This experiment proves that the algorithmic model can be efficiently trained using Markov chain Monte Carlo methods by applying them to the MovieLens and Netflix dataset. The experimental results show that the algorithm has better predictive performance and is suitable for solving the problem of sparse rating matrix of specific users.
FACTORS AFFECTING THE RESEARCH PERFORMANCE OF SCIENCE RESEARCH INSTITUTIONS IN SRI LANKA

Research performance is recognized as one of the most critical indices for the evaluation of a research institute. However, very few previous scientific studies have focused on the factors affecting a science research institutes research performance in Sri Lanka. The study focusses to identify whether the research performance of research institutions coming under the State Ministry of Skills Development, Vocational Education, and Research &amp; Innovation is affected by individual and institutional characteristics. Purpose The study is to identify the research performance of research institutions is affected by individual and institutional characteristics Design/Methodology/Approach Primary &amp; secondary data has been predominately used in order to address the research objectives. The research is conducted perusing the recent and major Journals and articles that are published in reliable, high quality Journals on the captioned subject. The data concerning the research performance of academics has been extracted from the questionnaire, NIFS Research Repository, Annual Reports and from NASTEC reports. This quantitative study conducted using a deductive approach through a slightly adopted version of an already validated and reliability checked questionnaire Findings Individual characteristics and institutional characteristics were significantly correlated with the research performance (r=0.356, r=0.568 respectively). Of all variables, institutional characteristics had the strongest positive correlation where it could be used as a significant predictor (R2AD= 0.320) for the research performance of research institutions. Keywords Research performance, Individual characteristics, Institutional characteristics Paper Type Research paper
Constructing Traceability Links between Software Requirements and Source Code Based on Neural Networks

Software requirement changes, code changes, software reuse, and testing are important activities in software engineering that involve the traceability links between software requirements and code. Software requirement documents, design documents, code documents, and test case documents are the intermediate products of software development. The lack of interrelationship between these documents can make it extremely difficult to change and maintain the software. Frequent requirements and code changes are inevitable in software development. Software reuse, change impact analysis, and testing also require the relationship between software requirements and code. Using these traceability links can improve the efficiency and quality of related software activities. Existing methods for constructing these links need to be better automated and accurate. To address these problems, we propose to embed software requirements and source code into feature vectors containing their semantic information based on four neural networks (NBOW, RNN, CNN, and self-attention). Accurate traceability links from requirements to code are established by comparing the similarity between these vectors. We develop a prototype tool RCT based on this method. These four networks’ performances in constructing links are explored on 18 open-source projects. The experimental results show that the self-attention network performs best, with an average Recall@50 value of 0.687 on the 18 projects, which is higher than the other three neural network models and much higher than previous approaches using information retrieval and machine learning.
Automatic Compression of Neural Network with Deep Reinforcement Learning Based on Proximal Gradient Method

In recent years, the model compression technique is very effective for deep neural network compression. However, many existing model compression methods rely heavily on human experience to explore a compression strategy between network structure, speed, and accuracy, which is usually suboptimal and time-consuming. In this paper, we propose a framework for automatically compressing models through the actor–critic structured deep reinforcement learning (DRL) which interacts with each layer in the neural network, where the actor network determines the compression strategy and the critic network ensures the decision accuracy of the actor network through predicted values, thus improving the compression quality of the network. To enhance the prediction performance of the critic network, we impose the L1 norm regularizer on the weights of the critic network to obtain a distinct activation output feature on the representation, thus enhancing the prediction accuracy of the critic network. Moreover, to improve the decision performance of the actor network, we impose the L1 norm regularizer on the weights of the actor network to improve the decision accuracy of the actor network by removing the redundant weights in the actor network. Furthermore, to improve the training efficiency, we use the proximal gradient method to optimize the weights of the actor network and the critic network, which can obtain an effective weight solution and thus improve the compression performance. In the experiment, in MNIST datasets, the proposed method has only a 0.2% loss of accuracy when compressing more than 70% of neurons. Similarly, in CIFAR-10 datasets, the proposed method compresses more than 60% of neurons, with only 7.1% accuracy loss, which is superior to other existing methods. In terms of efficiency, the proposed method also cost the lowest time among the existing methods.
Word-Based Processor Structure for Montgomery Modular Multiplier Suitable for Compact IoT Edge Devices

The Internet of Things (IoT) is an emerging technology that forms a huge network of different objects and intelligent devices. IoT Security is becoming more important due to the exchange of sensitive sensor data and the potential for incorporating the virtual and real worlds. IoT edge devices create serious security threats to network systems. Due to their limited resources, it is challenging to implement cryptographic protocols on these devices to secure them. To address this problem, we should perform compact implementation of cryptographic algorithms on these devices. At the heart of most cryptographic algorithms is the modular multiplication operation. Therefore, efficient implementation of this operation will have a great impact on the implementation of the whole cryptographic protocol. In this paper, we will focus on the resource and energy efficient hardware implementation of the adopted Montgomery modular multiplication algorithm over GF(2m). The main building block of the proposed word-based processor structure is a processor array that has a modular structure with local connectivity between its processing elements. The ability to manage the saving amounts of area, delay, and consumed energy is the main benefit of the suggested hardware structure. We used ASIC technology to implement the suggested word-based processor structure. The final results show an average reduction in the area of 86.3% when compared with the competitive word-based multiplier structures. Additionally, the recommended design achieves significant average savings in area-time product, power, and consumed energy of 53.7%, 83.2%, and 72.6%, receptively, over the competitive ones. The obtained results show that the provided processor structure is best suited for application in compact IoT edge devices with limited resources.
Development of a Price Tag Detection System on Mobile Devices using Deep Learning

Ensuring customer satisfaction is an important issue in the retail industry. The way to achieve this satisfaction is to provide a quality service. The data on the price tags on the product shelves are frequently updated. These data should be included on the price tags in their current form. Customers may encounter inaccurate information on price tags in shopping places, which causes negative results in terms of customer loyalty and satisfaction. The data on the price tags is mostly checked manually, which can cause human errors. In this study, a deep learning-based solution is proposed for fast and high accuracy detection of price tag area. One of the first and important stages of a deep learning-based price recognition system is the correct detection of the price tag area. The successful execution of this stage is important for the successful execution of the next processes (barcode reading, price reading). The proposed method has been tested on mobile phones. It is envisaged that the proposed method is applicable in its current form and can be a technical reference for similar problems in the retail industry.
How to Understand Phishing

The article is based on the results of previous research, is focused on the analysis and classification of phishing emails, and documents the results of communication with the phisher attacker. In the first part of the article, an experiment carried out with a randomly selected set of emails confirms the considerable uncertainty of the correct result of automatic classification based on keywords using text analysis software. The second part of the article contains the experiment of communication with phishing attackers. A typical scenario of message exchange is presented. Thanks to the correct setting of security and protection rules, no security incident occurred. The literature search confirms the great interest in publishing in the field of phishing. Compared to the content of the published article, it turned out that its focus is completely original.
Evidence Network Inference Recognition Method Based on Cloud Model

Uncertainty is widely present in target recognition, and it is particularly important to express and reason the uncertainty. Based on the advantage of the evidence network in uncertainty processing, this paper presents an evidence network reasoning recognition method based on a cloud fuzzy belief. In this method, a hierarchical structure model of an evidence network is constructed; the MIC (maximum information coefficient) method is used to measure the degree of correlation between nodes and determine the existence of edges, and the belief of corresponding attributes is generated based on the cloud model. In addition, the method of information entropy is used to determine the conditional reliability table of non-root nodes, and the target recognition under uncertain conditions is realized afterwards by evidence network reasoning. The simulation results show that the proposed method can deal with the random uncertainty and cognitive uncertainty simultaneously, overcoming the problem that the traditional method has where it cannot carry out hierarchical recognition, and it can effectively use sensor information and expert knowledge to realize the deep cognition of the target intention.
The Destructive Influence Of Global Narcoterrorism On The National Security Of The Republic Of Serbia

The connection between terrorist organizations and the drug mafia on a global level has a destructive effect on the national security of every democratic state, and therefore on the national security of the Republic of Serbia. The work is proof of the existence of a cause-and-effect relationship between drug addiction and narco-terrorism, where the illegal production and abuse of psychoactive drugs in themselves represent narco-terrorism and a crime against humanity, while the realized drug capital finances terrorism throughout the world. The fight against the bearers of terrorist and narco-terrorist organizations should be a permanent and priority task of every state, and thus also of the Republic of Serbia. The countries of the modern world have recognized this harmful influence, about the existence of these mutual ties that directly affect the national security of the country and the normal functioning of society, and also the security at the global level.
Does an Information System Security Notice Format Influence Users’ Compliance Willingness from the Perspective of the Framing Effect?

Information security issues have triggered both academic and practical circles to think about operation management and the sustainable development of information systems. Based on the theory of framing effect, this study constructs a theoretical model of the presentation framework of security notice information on users’ compliance willingness and empirically tests the proposed research hypotheses using a combination of behavioral experiments and questionnaires to analyze the mechanism of the information presentation framework on compliance willingness. The results show that (1) the information presentation framework has a significant effect on users’ decision to comply, but it varies according to specific frameworks. While the attribute and risk frameworks have a significant effect on users’ decision to comply, the goal framework does not have a significant effect on users’ decision to comply. (2) The security notice situation moderates the relationship between the security notice information presentation frame and users’ compliance willingness, but this varies according to the specific situation of the specific framework. The security notice situation moderates the relationship between the attribute framework, the risk framework, and users’ compliance willingness but not the relationship between the goal framework and users’ compliance willingness. (3) Information security cognition has a moderating effect on the relationship between the security notice presentation framework and users’ compliance willingness, but it varies by the specific frameworks. Information security cognition moderates the relationship between attribute frames, risk frames, and users’ compliance willingness but not the relationship between goal frames and users’ compliance willingness.
ID-Based Ring Signature against Continual Side Channel Attack

The security of the signature scheme is destroyed because its secret information of the signature system is leaked due to the side channel attack. Ring signature has good application value, which can provide more flexibility and complete anonymity. It can be used in some systems such as anonymous authentication in ad hoc networks, electronic voting and crypto coin based on blockchain. Because of the side channel attack, the private key of the ring signature system may be exposed, which may cause insecurity. We present a ring signature system against continuous side channel attack. Because of the symmetry of the ring, the user’s identity has good privacy protection. The proposed scheme is completely secure without a random oracle model and the private key disclosure rate is close to 1/3. Through the dual system technique, the existential unforgeability and unconditional anonymity of the scheme are proved in the composite order group based on the subgroup decision assumption.
FCKDNet: A Feature Condensation Knowledge Distillation Network for Semantic Segmentation

As a popular research subject in the field of computer vision, knowledge distillation (KD) is widely used in semantic segmentation (SS). However, based on the learning paradigm of the teacher-student model, the poor quality of teacher network feature knowledge still hinders the development of KD technology. In this paper, we investigate the output features of the teacher-student network and propose a feature condensation-based KD network (FCKDNet), which reduces pseudo-knowledge transfer in the teacher-student network. First, combined with the pixel information entropy calculation rule, we design a feature condensation method to separate the foreground feature knowledge from the background noise of the teacher network outputs. Then, the obtained feature condensation matrix is applied to the original outputs of the teacher and student networks to improve the feature representation capability. In addition, after performing feature condensation on the teacher network, we propose a soft enhancement method of features based on spatial and channel dimensions to improve the dependency of pixels in the feature maps. Finally, we divide the outputs of the teacher network into spatial condensation features and channel condensation features and perform distillation loss calculation with the student network separately to assist the student network to converge faster. Extensive experiments on the public datasets Pascal VOC and Cityscapes demonstrate that our proposed method improves the baseline by 3.16% and 2.98% in terms of mAcc, and 2.03% and 2.30% in terms of mIoU, respectively, and has better segmentation performance and robustness than the mainstream methods.