Reviewerly/exHarmony huggingface dataset:
Application of the type-2 fuzzy logic controller and the fractional order controller to regulate the DTC speed in an induction motor

Introduction/purpose: Among excellent strategies available to control the torque of asynchronous motors, we distinguish direct torque control. This technique of control allows direct control of magnetic flux and electromagnetic torque without the need to decouple them. Also, direct torque control like each control strategy has some drawbacks, the major drawbacks of this technique being operation at a variable switching frequency and flux and electromagnetic ripples due to the use of hysteresis regulators. It worsens acoustic noise, especially at low speeds, as well as the control performances. Methods: To improve the performance of direct torque control especially at low speeds, the authors propose using fractional order PID in combination with type-2 fuzzy logic controllers to regulate the speed of an induction motor controlled by direct torque control. Results: The results obtained by the proposed regulators show the improvements made to the system. Conclusion: The proposed contribution can exert better control efforts.
Towards Blockchain-based Remote Management Systems for Patients with Movement Disorders

Secure storage and sharing of patients’ medical data over the Internet are part of the challenges for emerging healthcare systems. The use of blockchain technology in medical Internet of things systems can be considered a safe and novel solution to overcome such challenges. Patients with movement disorders require multi-disciplinary management and must be continuously receive medical care by a specialist. Due to the increasing costs of face-to-face treatment, especially during the pandemic, patients would highly benefit from remote monitoring and management. The proposed work presents a model for blockchain-based remote management systems for patients with movement disorders, especially those with Parkinson’s disease. The model ensures a high level of integrity and decreases the security risks of medical data sharing.
Deep learning channel estimation for 5G wireless communications

Introduction/purpose: In recent years, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have demonstrated remarkable performance in 5G communication systems by significantly improving the accuracy of channel estimation compared to conventional methods. This article aims to provide a comprehensive review of the existing literature on CNN-based channel estimation techniques, as well as to enhance the state-of-the-art CNN-based channel estimation methods by proposing a novel method called VDSR (Very Deep Super Resolution), inspired by Image Super-Resolution techniques. Methods: To evaluate the effectiveness of various approaches, we conduct a comprehensive comparison considering different scenarios, including low Signal-to-Noise Ratio (SNR) and high SNR, as well as Lineof-Sight (LOS) and Non-Line-of-Sight (NLOS) scenarios. Through this comparative analysis, we assess the performance of the existing methods and highlight the advantages offered by the proposed VDSR-based technique. Results: Our findings reveal a significant potential of CNN-based channel estimation in 5G communication systems, with the VDSR method demonstrating a consistent performance across all scenarios. This research contributes to the advancement of channel estimation techniques in 5G networks, paving the way for enhanced wireless communication systems with improved reliability. Conclusion: The VDSR architecture demonstrates remarkable adaptability to different types of channels, which results in achieving requested performances for all analyzed SNR values.
Prevention of hate speech in the practice of the European Court of Human Rights

Different types of communication enable an intensive exchange of diverse ideas and content, thereby enabling the development and enrichment of human thought. On the other hand, modern forms of communication additionally increase the risk of violation and endangerment of human rights and freedoms by those who use hate speech. Hence, many international documents are dedicated to defining and preventing hate speech, while various international institutions, including the European Court of Human Rights, try to determine the criteria under which certain restrictions on freedom of speech could be acceptable. Bearing in mind the above, the author has analyzed the cases from the practice of the European Court of Human Rights in order to distinguish the standards which could be applicable in the process of assessment if certain expressions constitute hate speech that should be sanctioned, and also to establish to what extent such standards are applicable in different situations. The author concludes that the formulated standards do have a certain utility value, but that there is still a risk of extensive restriction of freedom of speech.
eHealthcare system data privacy concept based on blockchain technology

Introduction/purpose: Advances in information and communication technologies have enabled the creation of a symbiotic environment of humans and machines in which humans interact with machines to get better quality of everyday life. In that interaction, environment problems of information security and in particular data privacy are at the forefront. In many countries, there is legal regulation that regulates this problem in terms of securing the goals that must be realized when manipulating private data, and the technology itself is the choice of the creators of information systems. Blockchain technology is one of the methods of choice to ensure the integrity of data and undeniable transactions while digital certificates in conjunction with it enable the realization of data privacy of patients. Methods: The cryptographic methods of asymmetric cryptography apply blockchain technology and reliable methods of identification in cyberspace, which enables the preservation of data privacy at a high level. Results: This paper describes the method of patient health data privacy protection in a healthcare system based on digital certificates as an identification method in cyberspace and Blockchain technology as a method for preserving the integrity of transactions and a healthcare information system. The proposed concept enables the separation of private and medical data in such a way that with the accepted principle of patient ownership of medical data, it is possible to achieve primary and secondary use of healthcare data without compromising the patient's privacy. Conclusions: The concept of identity assignment to every element in the healthcare information system and the organization/storage of data in accordance with the principles of Blockchain technology proposed in this paper enable the realization of a high level of data privacy in accordance with the European Union General Data Protection Regulation at the international level. In addition, the proposed concept enables the detection of unregistered devices or entities in the system and thus preserves the integrity of the system and increases its overall information security.
Number-Theoretic Transform with Constant Time Computation for Embedded Post-Quantum Cryptography

Abstract In this article, we describe the principles and advantages of using the Number-Theoretic Transform (NTT) in post-quantum cryptography. We deal with usages of NTT in post-quantum algorithms included in the competition announced by the National Institute of Standards and Technology. Attention is paid to the fast multiplication of polynomials using NTT and negacyclic convolution. We also focus on the existing implementation of NTT and its modifications to analyze the effectiveness of individual modifications. Separate attention is paid to the Constant Time implementation of NTT because the constant computation time of the transformation decreases a possibility of side channel attack. We describe measurements performed on OS Linux Ubuntu 20.04 LTS environment in Linux kernel mode, with the highest attention to the measurement executed on a microcontroller with an ARM 32-bit core. Measurements performed on microcontroller units are done using 32-bit and 16-bit arithmetic to demonstrate how to achieve constant computation time of the transformation. We present the results and analysis of measurements performed using modified implementations.
Analysis of Reactions of Mobile Phone Users

Abstract In this work, we analyze the aspects of user reactions during their work with a mobile phone. We tried to find the most crucial positions within the mobile screen, which provide essential information and stimulus. One of the main factors is to know which part of the screen is the first interest of the user and which part the user reacts to the fastest. Sixty-four persons participated in an experiment. We used the gamification approach to motivate users for the best results and created a game. Users competed with each other during the game, and they tried to react to the new point of the screen as soon as possible. The experiment showed that about a quarter of the screen below the centre was an area where users react the fastest. There were interesting differences between men and women and signs of background impact.
Hints on the Multicore CPU Design for Personal Computers

Abstract Abstract - Multi-core processors are created by combining multiple units, each called the core, containing the processing unit, registers and a cache. This study provides a detailed analysis on the performances of multicore CPUs for personal computers. We parallelized some cryptographic algorithms with OpenMP tool and tested one 2-core and one 4-core CPU with two cryptographic software one of which imposes high data load and the other one high work load. In the light of the experiments that stress the CPUs from these two different aspects, we present our hints and suggestions for designing multicore chips for the usage in personal computers.
Towards Program Comprehension: Knowledge Profiles for C Programmers

Abstract Following up on the issue of programmer knowledge profiles, the present article deals with the generation of profiles for C language programmers. The main idea is based on static analysis of source codes, assembling the most significant data about their author. Such a profile can point out some abilities and habits of the programmer. The process of static analysis applies methods and techniques of size metrics, complexity metrics, and clone detection. We also present an experiment focused on novice programmers. The results show that knowledge profiles provide an easy way of novice programmers’ progress tracking. The main contribution is believed to be in the area of program comprehension.
Design Information Systems for Malnutrition Analysis Apriori Algorithm

Malnutrition drives lasting detriments across individual and community wellbeing, requiring data-informed action. Advanced analytics through information systems present pathways for revelatory pattern detection from multidimensional health data. This paper outlines a system design encompassing preprocessing, modeling, analysis and interpretation techniques for mining malnutrition dataset through Apriori algorithm. The core data mining methodology enables extraction of frequencies, associations and prediction rules linking nutritional status parameters and food intake patterns. Custom algorithms filter results to high-confidence associations via statistical measures before expert evaluation. System testing verifies accurate architecture for surfaced dietary risk factors of malnutrition down to village-level. The systemization and computational augmentation of health insight derivation provides a template for needs-based analytics platforms. By targeting analysis to community data, impactful interventions become possible. The potential of customized information systems with data mining at the core is highlighted alongside domain challenges requiring cross-disciplinary impetus. The data-to-decisions system with embedded Apriori pipelines demonstrates applied informatics transforming malnutrition strategy through unveiling actionable patterns within intricacies of public welfare data.
Designing An Attendance Application With A Web-Based Face Camera

This research discuss the design of a web-based student attendance application aimed at facilitating student attendance at the Medan Tourism Polytechnic, a vocational higher education institution supervised by the Ministry of Tourism and Creative Economy of the Republic of Indonesia. The Medan Tourism Polytechnic is a state university focusing on providing education for the tourism and hospitality industry in Indonesia. The Three Pillars of Higher Education in this field were established by the Medan Tourism Polytechnic with the objective of disseminating tourism-based education, instilling student discipline, and fostering Indonesian personality traits. Currently, the Medan Tourism Polytechnic employs a semi-manual attendance procedure using Microsoft Excel. This attendance application was developed with a database created using PHP and MySQL programming languages. The research findings indicate that students can perform self-attendance using facial recognition technology, which would make attendance more efficient and reduce fraud. Furthermore, this research demonstrates and anticipates that more effective attendance methods can enhance student discipline
Toward Optimal Real-Time Volumetric Video Streaming: A Rolling Optimization and Deep Reinforcement Learning Based Approach

Volumetric video provides users with a good viewing experience of six degrees of freedom (DoF) and has wide applications in many fields such as teleconferencing and online games. However, the huge data volume and strict latency requirements of point cloud video, the most popular representative of volumetric video, pose a challenge to its transmission. Existing point cloud video transmission algorithms usually segment a long video by every one or several group of frames, predict network bandwidth and FoV information, then perform adaptive transmission by solving the quality of experience (QoE) optimization problem. However, such segmentation neglects the impact of current optimization decisions on the subsequent video streaming process, as well as the large prediction error for a long interval, severely degrading user’s QoE. Moreover, the complex constrained optimization problem makes the solution time too long to meet the real-time video streaming requirements. To this end, in this paper, we propose a rolling prediction-optimization-transmission (POT) framework, which makes predictions of network bandwidth and FoV in each short rolling window to reduce prediction error. And our framework takes into account the upper bounded QoE contribution of the subsequent point cloud video to improve the system performance. In addition, we design a deep reinforcement learning based real-time solver to make decisions of the fixed structure optimization problem in each roll, allowing our system to run in real-time. We have performed simulations and experiments, and the results show that our solution outperforms existing methods.
Creative education – an area of increased interest for politics and the politicians of all time

In such a changing world today, when the vast majority of governing principles turn out to be VOLATILE, cultural archetypes are the ONLY ONES you can rely on, since other fundamental principles prove to be false, illusory. Culture, as the primary decision-making factor, determines the formation of most of the behavioral models in society. The CULTURAL factor and its role of major importance, is even crucial, in the period of revolutionary transformation of societies. At present, the most prestigious international strategic / political consultancy HOMES INSIST on the valorization of the CULTURAL MATRIX of the consulted companies (nations, states), when forming Country Projects. In order to confer additional authority, importance, notoriety to a candidate, to a political project, modern social and political Strategies use, in a compulsory way, in all types of political campaigns, content, forms, methods and the authority of people of culture. Thus, Culture becomes an area of increased interest for politics and the politicians of all times.
A Physical Zero-Knowledge Proof for Sumplete, a Puzzle Generated by ChatGPT

In March 2023, ChatGPT generated a new puzzle, Sumplete. Sumplete consists of an $$n \times n$$ grid, each whose cell has an integer. In addition, each row and column of the grid has an integer, which we call a target value. The goal of Sumplete is to make the sum of integers in each row and column equal to the target value by deleting some integers of the cells. In this paper, we prove that Sumplete is NP-complete and propose a physical zero-knowledge proof for Sumplete. To show the NP-completeness, we give a polynomial reduction from the subset sum problem to Sumplete. In our physical zero-knowledge proof protocol, we use a card protocol that realizes the addition of negative and positive integers using cyclic permutation on a sequence of cards. To keep the solution secret, we use a technique named decoy technique.
Manuscript Submission

Nearly all academic journals now utilize digital editorial management systems. These systems streamline manuscript submission and facilitate post-submission accessibility for authors and editorial staff. Critics of digital editorial systems note challenges associated with variations in procedures and requirements between submission systems and across journals. Errors in the submission process can mean significant delays in manuscript review or even desk rejection. Fortunately, common errors can be avoided with pre-planning, organization, and attention to detail. This chapter takes the reader step-by-step through the manuscript submission process. Using examples from flagship journals in the social sciences, we review variations in journal requirements. We discuss current standards for data reporting and sharing. We provide an overview of attestations and declarations. We present a framework for the cover letter as a tool for succinctly describing the manuscript and related components. We alert the reader to common pitfalls in the submission process as cited in the literature, and we offer practical strategies for facilitating an error-free submission.
Application of tectono-geochemistry method for weak information extraction of Carlin-type gold deposits in Yunnan–Guizhou–Guangxi, SW China

Following decades of exploration of Carlin-type gold deposits in the Yunnan, Guizhou, and Guangxi provinces of China (known as the Dian–Qian–Gui Golden Triangle), surface mines are almost exhausted and undiscovered gold orebodies are deeply buried; thus, surficial geochemical information associated with concealed gold orebodies is poor. In this study, we compared methods of extracting this weak information based on soil geochemistry and tectono-geochemistry, and then applied the tectono-geochemical method to the Huijiabao and Nibao gold ore fields to evaluate its validity and identify favorable prospecting targets for deep-mineral exploration. We analyzed 8,842 soil geochemical samples and 4,049 tectono-geochemical samples from the Nibao gold ore field and its surroundings, and 253 tectono-geochemical samples from the Huijiabao gold ore field by statistical analysis and geochemical mapping. Tectono-geochemical samples were less abundant, accounting for only approximately 60 % of the number of soil geochemical samples; however, the number and proportion of high-value samples were greater and the elemental content was typically higher. Primary geochemical information related to deep-seated Carlin-type gold orebodies is profoundly affected by structures such as faults, anticlines, joints, and fissures, and was effectively identified by the tectono-geochemical technique, which focuses on tectonic and altered rocks. Conversely, soil geochemical information derived from these primary sources was significantly weaker because of the high mixing and homogeneity of elements during weathering. Eleven tectono-geochemical high composite anomalies of Au, Hg, Sb, As, and W were recognized in the east of the Nibao ore field, with Baogudi, Shuijingwan, Jiaozishan, Houzipo, Anzhe, and Hetaoshu predicted to be future targets of deep-mineral exploration. The Nayang–Yandengpo target was delineated according to high tectono-geochemical composite anomalies of Au, Hg, Sb, As, and Tl in the southeast part of the Huijiabao ore field, and nine concealed gold orebodies were identified at depths of 800–1,400 m through 37 boreholes, with a total gold resource of 16,338 kg and an average grade of 3.74 g/t. Successful case studies and applications demonstrate the effectiveness of tectono-geochemical information extraction method for detecting subtle signals from concealed orebodies, which will promote future deep exploration of the Dian–Qian–Gui Golden Triangle.
Effective fusion module with dilation convolution for monocular panoramic depth estimate

Abstract Depth estimation from monocular panoramic image is a crucial step in 3D reconstruction, which is a close relationship with virtual reality and metaverse technologies. In recent years, some methods, such as HRDFuse, BiFuse++, and UniFuse, have employed a two‐branch neural network leveraging two common projections: equirectangular and cubemap projections (CMPs). The equirectangular projection (ERP) provides a complete field of view but introduces distortion, while the CMP avoids distortion but introduces discontinuity at the boundary of the cube. In order to address the issue of distortion and discontinuity, the authors propose an efficient depth estimation fusion module to balance the feature mapping of the two projections. Moreover, for the ERP, the authors propose a novel inflated network architecture to extend the receptive field and effectively harness visual information. Extensive experiments show that the authors’ method predicts more clear boundaries and accurate depth results while outperforming mainstream panoramic depth estimation algorithms.
Block Chain Technology: Emerging Trend in Manufacturing Industry

Block chain technology has emerged as a transformative force, disrupting traditional systems across industries. Block chain has interdisciplinary applications, making it relevant across fields such as finance, law, healthcare and logistics. Applying block chain to supply chain management not only enhances the efficiency of the process but also builds a foundation of trust among participants and consumers by providing a transparent and secure record of the product's entire journey
Smart Security Using QR-Code System

In today’s world, security has become very important and it is must in any field like organization, educational institutes, hospitals and so on. In recent studies, it is found that QR code system has been arising to make payments, to access Wi-fi, to get course’s information and so on. QR code is 2D type of barcode that stores data and can be retrieved by scanning device or scanning application. Overall, this paper aims to provide a secure and convenient way to check when an individual will enter or leave the premises in an organization, educational institutes, hospital and so on.