Reviewerly/exHarmony huggingface dataset:
DIGITALIZATION OF EDUCATION AND STUDENTS' ABILITY TO LEARN POST COVID 19 PANDEMICS

This research focuses on the topic of students' ability to receive material in digital learning. This research aims to see in detail how students receive material, where different student abilities are motivated by geographical, economic and social conditions in their lives. The differences that exist will distinguish the range of students in capturing material delivered digitally. This research uses a qualitative method with a phenomenological approach to see the real experiences experienced by students. Determination of informants using probability design and purposive sampling. Data collection techniques used primary and secondary data by conducting semi-structured interviews. Data analysis uses data processing, analysis and making conclusions on the results of the study. The results show that the digitalization of education has a positive impact on students' abilities in online-based learning, especially in the aspects of technology skills and the ability to learn independently. However, there are still obstacles in using technology and limited access that need to be overcome. The implication of this research is the importance of technology integration in the learning process and the expansion of digital access for all students to optimize students' ability to learn based on technology.
Voice Controlled Robot using Node-Mcu

Abstract: The development of voice-controlled robots has gained significant attention due to their potential to improve humanrobot interaction. In this project, we propose the design and development of a voice-controlled robot that can perform various tasks based on voice commands. The robot is designed to respond to voice commands and execute various actions such as moving, turning, and picking up objects
RECONCILIATION (MEDIATION) SERVICES IN EDUCATIONAL ORGANIZATIONS AS AN OBJECT OF MANAGEMENT

The purpose of the article is to substantiate the expediency of considering the reconciliation (mediation) service (hereinafter - R(M)S) in an educational organization as an element of the educational system and the object of adaptive management. The necessity of scientific research of the problem of the introduction of R(M)S into educational practice is actualized. The essence of the joint R(M)S is revealed through the restoration of the abilities of the subjects of the educational organization to understand each other and peacefully resolve emerging problems.
INCREASING THE PERSONAL EFFICIENCY OF STUDENTS IN CONDITIONS OF DIGITALIZATION OF EDUCATION

The article substantiates the role of digitalization of the education system in increasing the personal effectiveness of students as arts and sciences to be successful, productive, productive in achieving their goals, combining rationality and harmony in their own actions, to be distinguished by a clear concentration on the implementation of specific tasks at the optimal level. In the article, the authors associate the success of the process of personal effectiveness in the conditions of the modern information society with: the method of mind maps, time management, self-motivation, the need for high achievements. Undoubtedly, the listed approaches to the formation of personal effectiveness cannot be implemented without the use of digital technologies, since they provide a strengthening of positions in the struggle for human resources on the world stage.
Fuzzy modelling techniques for improving multi-label classification of software bugs

Software bug repositories stores a wealth of information related to the problems that occurred during the software development. Today's software development is a modular approach, with multiple developers working in different locations all around the world. A software bug may belong to multiple categories and can be resolved by more than one developer. For understanding the multiple causes of software bugs and proper bug information management at large bug repositories, better classification of software bugs is needed. In the proposed work, a multi-label fuzzy system-based classification (ML-FBC) is proposed. A fuzzy system is used to compute the membership of software bugs into multiple categories. Then a fuzzy c-means clustering algorithm is used to create various clusters. Once the clusters are created, the cluster-category mapping is done for various software bugs. For a new bug, the fuzzy similarity values are computed, and the created cluster-category mappings are utilised to categorise it. Using a user-defined threshold value, a new bug is classified into multi-label categories. Experiments are carried out on available benchmark datasets to compare the performance measures F1 score, BEP score, Hloss, accuracy, training time, and testing time of various multi-label classifiers. The proposed ML-FBC outperforms existing multi-label classifiers.
Web-Based Motorcycle Loan Management System with credit risk and approval analysis using Data Mining Techniques

Using information systems can dramatically improve a business's productivity and efficiency. Moreover, the ability of information systems to provide users with the information they need to complete jobs effectively can be the most significant advantage. This project development plan proposes a Web-Based Motorcycle Loan Management System with credit risk and approval analysis using Data Mining Techniques for a private motorcycle dealer who encounters challenges, particularly in the decision-making of motorcycle loans. It will facilitate the automation of business processes, including online motorcycle loan applications and payments, access to real-time data and reporting, and credit risk and approval analysis to assist the credit department in decision- making by applying data mining techniques. The project will employ the Scrum framework for software development and testing method. A total of 150.625 man-days with a total cost of P1,350,231.78 were computed to complete the proposed information system. A one-year financial viability analysis was defined to determine the proposed project's total cost and benefits. Based on the results, time spent on activities is reduced to 89.19%, indicating that the business can handle more applications every day compared to the average twenty daily applications since processing time is significantly decreased. It is recommended for future improvement of this plan to implement Augmented Reality for a better user online experience in previewing motorcycle units.
Josephson Junction, the Quantum Engine: from S.QU.I.D. Sensors to Qubits for Quantum Computers

Research & Development in Material Science Josephson Junction, the Quantum Engine: from S.QU.I.D. Sensors to Qubits for Quantum Computers Eliene B Simão-Silva, Galdino JF, Faddul-Stelzenberger AS, Boareto-Mendes AJ and Fernando M Araujo-Moreira* Department of Science and Technology, Military Institute of Engineering/IME, Brazil *Corresponding author:Fernando M Araujo-Moreira, Department of Science and Technology, Military Institute of Engineering/IME, Brazilian Army, Brazil Submission: May 16, 2023;Published: May 24 2023 DOI: 10.31031/RDMS.2023.18.000947 ISSN: 2576-8840 Volume 18 Issue 5
Exploring the Problems and Optimization Strategies of Class Teacher Management in Primary Schools

It is well known that the management of classroom teachers is of great significance to the building of classroom communities and the proper functioning of school affairs. The class teacher is not only the manager of the daily operation of the class, but also the facilitator of students' learning and healthy growth. However, in the actual practice of education and teaching, there are still many shortcomings in the classroom management of class teachers. This paper attempts to analyses the strategies of classroom teachers' management and improve the quality of their daily management through the interpretation of the meaningful value of classroom teachers' management and the analysis of problems in the process of classroom teachers' management.
JitSCA: Jitter-based Side-Channel Analysis in Picoscale Resolution

In safety and security conscious environments, isolated communication channels are often deemed necessary. Galvanically isolated communication channels are typically expected not to allow physical side-channel attacks through that channel. However, in this paper, we show that they can inadvertently leak side channel information in the form of minuscule jitter on the communication signal. We observe worst-case signal jitter within 54 ± 45 ps using an FPGA-based receiver employing a time-to-digital converter (TDC), which is a higher time resolution than a typical oscilloscope can measure, while in many other systems such measurements are also possible. A transmitter device runs a cryptographic accelerator, while we connect an FPGA on the receiver side and measure the signal jitter using a TDC. We can indeed show sufficient side-channel leakage in the jitter of the signal by performing a key recovery of an AES accelerator running on the transmitter. Furthermore, we compare this leakage to a power side channel also measured with a TDC and prove that the timing jitter alone contains sufficient side-channel information. While for an on-chip power analysis attack about 27k traces are needed for key recovery, our cross-device jitter-based attack only needs as few as 47k traces, depending on the setup. Galvanic isolation does not change that significantly. That is an increase by only 1.7x, showing that fine-grained jitter timing information can be a very potent attack vector even under galvanic isolation. In summary, we introduce a new side-channel attack vector that can leak information in many presumably secure systems. Communication channels can inadvertently leak information through tiny timing variations, known as signal jitter. This could affect millions of devices and needs to be considered.
Way Points and Reference Speed Based Trajectory Generator for an Unmanned Surface Vehicle

On the basis of successive way points (WPs) and piecewise reference speed, a trajectory generator (TG) is elaborately devised to generate a smooth trajectory for an unmanned surface vehicle (USV) in this paper. Firstly, the seventh-order polynominal is exclusively deployed to fit foregoing WPs, resulting in the parameterized path with sufficient differentiability. Then, the reference speed passes through the second-order filter, such that the speed command is uniformly continuous. Together with the parameterized path and speed command, the path speed is readily designed, and thereby contributing to the continuously differentiable trajectory. Certainly, the desired trajectory is much easier for users to predesignate in the electronic chart, and is more favorable to be exactly tracked by the USV in practice. Eventually, the model-based controller for the USV is synthesized to asymptotically track the generated trajectory in the absence of unmodeled dynamics and external disturbances. Comprehensive simulations are conducted on the benchmark prototype CyberShip II, so as to validate the effectiveness of both the proposed TG and trajectory-tracking controller.
Employee Performance Appraisal Process and Its Impact on Employee Satisfaction

Appraising employee performance can lead to improving employee engagement and satisfaction and eventually improves their work performance. In the present study, the impact of the employee performance appraisal process on employee satisfaction and business performance has been explored. It has been observed that appraising employee performance has a direct positive impact on job satisfaction and work performance, which eventually provides effective benefits to the organisation. A secondary qualitative data collection method has been followed in the study and reliable and valid data has been gathered from various journals, marketing websites and newspaper articles. The results have been illustrated by conducting a thematic analysis and the findings indicate that performance appraisal has been identified to be a vital approach for employees as well as business organisations. Additionally, several types of performance appraisal have been addressed and their potential impact on the workplace has been illustrated. These types include self-appraisal, the traditional approach of performance appraisal, upward appraisal, 360-degree reviews and performance appraisal of groups. An effective correlation between these performance appraisal processes and employee satisfaction has been observed along with evaluating its positive impact on the organisation. Thus, this study has provided efficient insights and details associated with the impact of the performance appraisal process on employee performance, satisfaction and business success. Keywords : Business Success, Employee Performance, Employee Satisfaction, Performance Appraisal Process
Pandoc resources

These are the resources that I’d want to have if I was to learn about pandoc (the universal document converter) all over again: Installing latest release The GitHub release page has installers for Windows, macOS, and Linux. Docker images There are three types of pandoc Docker images: minimal – very small, just the bare pandoc binary; core – includes pandoc-crossref and helpers programs, e.g. those used by pandoc for SVG image conversion; latex – like core, plus TeXLive with all packages...
Preface to the Special Issue on “Recent Advances in Swarm Intelligence Algorithms and Their Applications”—Special Issue Book

Swarm intelligence algorithms represent a rapidly growing research domain and have recently attracted a great deal of attention [...]
Media management of corporate university media in the USA as a public relations tool of a university (using the example of Ivy League University media)

The subject of the study is the university press of US universities that are part of the so-called "Ivy League" - an informal association of the oldest higher education institutions in the country. The object of the research is the media management of corporate media of universities as a tool of public relations among students, graduates and potential applicants. The authors consider in detail such aspects of the topic as ways of forming an individual profile of a particular media outlet, as well as its contribution to the image of a particular university. Particular attention is paid to the formation of the content of university media and their interaction with the traditional media system, as well as options for their development in a broader media profile. The main conclusions of the study are the data obtained during the comparison of Ivy League university press samples on the patterns of formation of the image of a typical student and a graduate of a university. A special contribution of the authors to the research of the topic is the development of specific recommendations on the media management of corporate publications on the example of the university press, which can be applied in domestic practice. The novelty of the research lies in the generalization of the media practices of the university press of the USA and the formulation of recommendations for creating the image of a student, graduate and teacher on the pages of the university press. Also of particular interest is the practice of creating a modern convergent media based on the traditional format of the press that has existed since the XIX century.
Grid-guided localization network based on the spatial attention mechanism for synthetic aperture radar ship detection

Synthetic aperture radar (SAR) has become an important part of ship detection due to its all-day all-weather characteristics. Ship monitoring is important for coastal traffic control and territorial safety. Ship detection requires high accuracy, but the speed of ship detection is also an important parameter in making rapid decisions, such as those in maritime rescue and military strategy. However, fast networks tend to be less accurate. To address this problem, we propose an efficient localization method based on a spatial attention mechanism. This mechanism uses grid-guided localization instead of a frame regression mechanism, which makes the network faster but also lowers the accuracy to a level insufficient for direct application to SAR image target detection. A tested and selected spatial attention mechanism improves the detection accuracy while guaranteeing its speed. It is shown that four factors affect the design of the spatial attention mechanism, namely query and key content, query content and relative position, key content, and relative position. These factors are ordered, and the group with the best precision is added to the proposed network. The proposed network can achieve a good detection effect for sparsely distributed targets in SAR images, and the proposed algorithm can achieve AP50 ∶ 75, AP50, AP75, and APS values of 71.3%, 96.9%, 86.6%, and 72.1%, respectively, on the SAR ship detection dataset.
RWD182 Influence of Multiple Instance Learning on the Generation of Computational Pathology Algorithms and Its Value-Based Impact

One of the significant factors influencing the cost of building computer vision algorithms applied to pathology images is the need for per-pixel labeling (PPL) by a trained physician. Multiple instance learning (MIL) technique allows to train algorithms without having PPL, only with the global diagnosis of the patient. Therefore, we evaluate the work done with this technique and its influence in reducing healthcare resources (HRU).
Study on novel cross-chain mechanism in internet healthcare environment

The scalability and interoperability of traditional blockchain can hardly meet the value delivery of multiple systems in internet healthcare. Therefore, to enable the interaction of values between heterogeneous blockchains, we propose a cross-chain mechanism based on a generic global relay chain strategy and a scalable heterogeneous multi-chain system. Also, an unused transaction output (UTXO) verification mechanism based on RSA accumulator is proposed to address the shortcomings of using simplified payment verification (SPV) to verify transactions. In this paper, five experimental tests were performed: pressure, stability, response time, throughput, and latency. Experiments show that its computational complexity for cross-chain transaction validation remains constant as the number of transactions increases. Thus, an efficient cross-chain value transfer is achieved.
Transformer-Based Text Clustering for Newspaper Articles

Text document clustering is a set of large textual documents which are more contextually similar in the same group. This paper has studied the existing methodologies for clustering large text documents. The analysis of word embeddings began with bag-of-word models, but it has progressed to transformer-based architectures. This work has found that transformer-based models can generate contextual text embeddings that incorporate both the position and context of a word in a sentence. Bidirectional Encoder Representation from Transformers (BERT) is a state-of-the-art approach for producing contextual word embeddings that have been used for clustering. This research has introduced a novel text clustering method using BERT’s contextual word embeddings. The paper also presents a novel cluster to domain (class label) mapping algorithm to evaluate the performance of the clustering algorithms with respect to the documents’ actual domain (class label). Several clustering algorithms such as K-Means, K-Medoids, and DBSCAN have been applied over the embeddings of BERT to cluster large text documents based on contextual meaning. Two cluster quality metrics were used to measure the algorithm’s performance: Dunn index and Silhouette score. K-Means delivers better results as per these two quality metrics. DBSCAN, however, maps the documents to the appropriate domain more accurately. The paper demonstrates the viability of text clustering using transformer-based embeddings.
Bangla Speech-Based Person Identification Using LSTM Networks

The Iris, face, fingers, conduct, voice, and other things of the human body are employed for security and identification since they are unique in each person. Biometric systems are popular and widely used in Bangladesh to identify people, e.g. in cybercrime. Apart from biometrics, a person can be identified by their voice. Since each person’s speech has a distinct timbre, vocal pattern, and frequency spectrogram. A human can easily identify the voice of a known person, but it is difficult for a machine. As a result, researchers are interested in processing human voices and recognizing them by machines. To predict the human voice, various traditional machine learning models such as GMM, HMM, SVM, and MLP are used. Voice data is a complex time-series signal and massive datasets are required to train ML models. As a result, traditional ML has low accuracy and takes a long time to train. In contrast, LSTM neural networks, which are the branch of ML, require less time to train a model with high accuracy. This paper focuses on an LSTM network for identifying a person based on Bangla speech because the Bangla language has 50 alphabets and their pronunciation differs from other languages such as English and Chinese. We extracted features from Bangla Voice using MFCCs. Our proposed model’s performance is measured using the K-fold validation, accuracy, precision, recall, and F1 score. Experimental results of our proposed model achieved a high recognition accuracy of 99.98%.
A Hybrid Watermarking Technique Based on LH-HL Subbands of DWT and SVD

Watermarking of digital images is a valuable practice to discuss numerous stimulating topics such as content verification, copyright shield, and integrity confirmation. Blending of discrete wavelet transforms (DWT), and SVD (singular value decomposition) is employed to the cover image’s LH and HL subbands to defend multimedia substances from several illegal access in this paper. A multi-level DWT and SVD procedures are used for watermark insertion and removal. The Peak Signal to Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) of the watermarked image are established as 45.90 (average) and 0.9930, respectively in this method. The suggested approach is also tested against several attacks. The studies indicate that it outperforms recently developed techniques regarding PSNR and NC (normalized correlation).
Novel pseudo-linear Kalman filtering for 3D angle-only tracking in the presence of observer’s location errors

This paper presents novel pseudo-linear Kalman filtering algorithms for three-dimensional (3D) angle-only tracking (AOT) in the presence of observer’s locations perturbed by random noises. These random observer’s location errors (OLEs) seriously deteriorate the filtering precision, since the additional errors are introduced into the angle measurements when the noisy observer location is directly used to estimate the target state of interest. A new pseudo-linear measurement model is firstly constructed in the presence of OLEs, and the first two-order statistical moments of the pseudo-linear measurement noises are analyzed conditioned on the exact observer location. Then, on the premise of deriving the azimuth and elevation errors caused by OLEs and re-analyzing the first two-order statistical moments of the pseudo-linear measurement noises conditioned on the noisy observer location, a novel pseudo-linear Kalman filter in the presence of OLEs (abbreviated as OPKF) is put forward. A closed-form bias estimate of this proposed OPKF is further derived recursively, and the corresponding bias-compensated and instrumental variable-based pseudo-linear Kalman filters in the presence of OLEs are finally designed to improve filtering precision. The advantages of filtering precision of the proposed method over the existing pseudo-linear Kaman filter and its variants are demonstrated by an example of 3D AOT in the presence of OLEs, in terms of different levels of sensor accuracy and observer’s location errors.