Reviewerly/exHarmony huggingface dataset:
Perception Variances between Administrative and Academic Staff on Ethical Practices and Information Security: The Question of Students Examination Records in Public Universities in Uganda

Investigations on the ethical practices and information security variance perceptions between academic and administrative staff were confirmed in Public Universities in Uganda. Four data collection phases included: testing the impact of ethical practices on information security among administrative staff; their ranking of factors likely to improve loyalty; academic staffs’ perception on the highly ranked factors that impact loyalty and, key informant interviews to substantiate key findings. Results confirmed loyalty as a stronger predictor of information security among administrative staff, which finding was considered inconsequential in matters of examination security by the academic staff. Whereas job satisfaction, high salaries, training and development were reported to increase loyalty among administrative staff, academic staff ranked personal integrity and commitment to excellence as most important. The varying perceptions could be attributed to divergent personal values, different technical and or professional backgrounds and corporate cultures. Findings provide new information security policy interventions, highlight the departure from conventional approaches of fighting examination security vices and call for innovations that address diverse stakeholders’ work dynamics. Key recommendations include stringent recruitment practices, rapid re-skilling and regular sensitisation, improved remuneration, and high scores on examination security attributes in performance agreements for all staff handling examinations. Undertaking a single study to investigate the different perceptions of both administrative and academic staff using a four step procedural interrelated approach is a major methodological contribution to research quality. The conceptualised ethical practices’ dimensions could stimulate current debate in Universities.
A Case of Exponential Convergence Rates for SVM

Classification is often the first problem described in introductory machine learning classes. Generalization guarantees of classification have historically been offered by Vapnik-Chervonenkis theory. Yet those guarantees are based on intractable algorithms, which has led to the theory of surrogate methods in classification. Guarantees offered by surrogate methods are based on calibration inequalities, which have been shown to be highly sub-optimal under some margin conditions, failing short to capture exponential convergence phenomena. Those "super" fast rates are becoming to be well understood for smooth surrogates, but the picture remains blurry for non-smooth losses such as the hinge loss, associated with the renowned support vector machines. In this paper, we present a simple mechanism to obtain fast convergence rates and we investigate its usage for SVM. In particular, we show that SVM can exhibit exponential convergence rates even without assuming the hard Tsybakov margin condition.
FEATURES OF THE DEVELOPMENT OF THE MUNICIPAL EDUCATIONAL SYSTEM BASED ON SOCIALLY ORIENTED PROJECTS

No abstract.
Survey on Phishing Websites Detection using Machine Learning

Abstract: Phishing is a widespread method of tricking unsuspecting people into disclosing personal information by using fake websites. Phishing website URLs are designed to steal personal information such as user names, passwords, and online banking activities. Phishers employ webpages that are visually and semantically identical to legitimate websites. As technology advances, phishing strategies have become more sophisticated, necessitating the use of anti-phishing measures to identify phishing. Machine learning is an effective method for combating phishing assaults. This study examines the features utilised in detection as well as machine learning-based detection approaches. Phishing is popular among attackers because it is easier to persuade someone to click on a malicious link that appears to be legitimate than it is to break through a computer's protection measures. The malicious links in the message body are made to look like they go to the faked organisation by utilising the spoofed organization's logos and other valid material. We'll go through the characteristics of phishing domains (also known as fraudulent domains), the qualities that distinguish them from real domains, why it's crucial to detect them, and how they can be discovered using machine learning and natural language processing techniques. Keywords: Phishing, personal information, machine learning, malicious links, and phishing domain characteristics are all terms that come up when people think of phishing
On the SDEs and Scaling Rules for Adaptive Gradient Algorithms

Approximating Stochastic Gradient Descent (SGD) as a Stochastic Differential Equation (SDE) has allowed researchers to enjoy the benefits of studying a continuous optimization trajectory while carefully preserving the stochasticity of SGD. Analogous study of adaptive gradient methods, such as RMSprop and Adam, has been challenging because there were no rigorously proven SDE approximations for these methods. This paper derives the SDE approximations for RMSprop and Adam, giving theoretical guarantees of their correctness as well as experimental validation of their applicability to common large-scaling vision and language settings. A key practical result is the derivation of a $\textit{square root scaling rule}$ to adjust the optimization hyperparameters of RMSprop and Adam when changing batch size, and its empirical validation in deep learning settings.
NISQ algorithm for the matrix elements of a generic observable

The calculation of off-diagonal matrix elements has various applications in fields such as nuclear physics and quantum chemistry. In this paper, we present a noisy intermediate scale quantum algorithm for estimating the diagonal and off-diagonal matrix elements of a generic observable in the energy eigenbasis of a given Hamiltonian. Several numerical simulations indicate that this approach can find many of the matrix elements even when the trial functions are randomly initialized across a wide range of parameter values without, at the same time, the need to prepare the energy eigenstates.
Optimal quantum reservoir computing for the NISQ era

Universal fault-tolerant quantum computers require millions of qubits with low error rates. Since this technology is years ahead, noisy intermediate-scale quantum (NISQ) computation is receiving tremendous interest. In this setup, quantum reservoir computing is a relevant machine learning algorithm. Its simplicity of training and implementation allows to perform challenging computations on today available machines. In this Letter, we provide a criterion to select optimal quantum reservoirs, requiring few and simple gates. Our findings demonstrate that they render better results than other commonly used models with significantly less gates, and also provide insight on the theoretical gap between quantum reservoir computing and the theory of quantum states complexity.
DESIGN TECHNOLOGY INDIVIDUAL MODEL METHODOLOGICAL TEACHER'S SYSTEMS AS A CONDITION OF HIS PREPARATIONS FOR IMPLEMENTATION DEVELOPING CAPACITY MUSICAL AND PEDAGOGICAL ACTIVITIES

No abstract.
Noise Suppression with Label Graph in Distantly Supervised Relation Extraction

Distantly supervised relation extraction suffers from the influence of noise data. Some works solved this issue with relation-aware attention based on multi-instance learning to reduce the weights of noise data in sentence bag, which achieved remarkable results. However, the relationships of label-label and label-sentence in semantic space that can enhance label representation effectively was ignored. The enhanced label representation can keep the representation of noise data that does not contain the corresponding relation away from the label in semantic space. In view of this, we propose a novel method to capture the two relationships above to suppress noise in distantly supervised relation extraction with a label graph. To be specific, the single label of each sentence is first expanded to multi-label and the label graph is built based on the label hierarchy to capture implicit relation among them. Then the relation-aware attention in semantic space is deployed to assign low weights to noise sentences for the connection of label-sentence. Finally, the distantly supervised relation extraction task is optimized as a multi-label multi-classification problem. Experiment results on New York Times indicate that our method shows significantly improvement compared with the strong baseline methods.
PRACTICAL-ORIENTED PHILOSOPHY METHODOLOGIES OF PEDAGOGICAL ACTION

Personality, possibilities of its development, its freedom and inequity are the subject of spiritual, intellectual, moral and esthetic search of home philosophers educationalist and psychologists. The article is devoted to the study of laws of personality development, to its perfectioning taking into account the conceptual philosophical approach to the problem.
CREATIVE CLIMATE IN METHODICAL WORK AS ONE OF CONDITIONS FOR THE DEVELOPMENT OF CREATIVE STYLE ACTIVITIES OF TEACHER

There is a dependence of the development of teacher's Creative style from the present conditions of school work. The organization of creative climate during the method work as one of the conditions is a high possibility of teacher's positive attitude to professionalfursonal work.
PSYCHOLOGICAL SUPPORT OF THE TEACHER AS CONDITION OF HUMANIZATION EDUCATIONAL ENVIRONMENT

No abstract.
Face Detection Method based on Lightweight Network and Weak Semantic Segmentation Attention Mechanism

A face detection method based on lightweight network and weak semantic segmentation attention mechanism is proposed in this paper, aiming at the problems of low detection accuracy and slow detection speed in face detection in complex scenes. K-means++ algorithm is employed to perform clustering analysis on YOLOv4 model prior frames in this paper, and smaller size prior frames are set to capture small face information to solve the missing detection problem of small face targets in scenes. The backbone network structure is improved by introducing Mobile Net lightweight network model, to reduce the number of parameters and calculation of the model and improve the detection speed. The convolutional block attention module model with dual attention mechanism is embedded to improve the sensitivity of the model to target features, which can suppress interference information and improve the accuracy of target detection. A dynamic enhancement attachment based on weak semantic segmentation is added in front of the detector head, whose output is used as the spatial weight distribution to correct the activation area, to suppress the false detection and missed detection caused by the decrease of extraction ability evoked by the pursuit of lightweight. The experimental results on WIDEFACE dataset indicate that this method not only can detect face in real time and with high accuracy, but also has better performance than other existing methods.
Interpolating Compressed Parameter Subspaces

Inspired by recent work on neural subspaces and mode connectivity, we revisit parameter subspace sampling for shifted and/or interpolatable input distributions (instead of a single, unshifted distribution). We enforce a compressed geometric structure upon a set of trained parameters mapped to a set of train-time distributions, denoting the resulting subspaces as Compressed Parameter Subspaces (CPS). We show the success and failure modes of the types of shifted distributions whose optimal parameters reside in the CPS. We find that ensembling point-estimates within a CPS can yield a high average accuracy across a range of test-time distributions, including backdoor, adversarial, permutation, stylization and rotation perturbations. We also find that the CPS can contain low-loss point-estimates for various task shifts (albeit interpolated, perturbed, unseen or non-identical coarse labels). We further demonstrate this property in a continual learning setting with CIFAR100.
MAIN TRENDS OF EMOTIONAL RESPONSE TEACHERS IN TENSION PEDAGOGICAL SITUATIONS

No abstract.
M.I. DEMKOV ON THE PRINCIPLES OF EDUCATION

The author examines the views of M.I.Demkov, a representative of the national pedagogics at the end of the XIX c.Beginning of the rote, as for the process of upbringing.
ISSUES OF VOCAL AND TECHNICAL IMPROVEMENT STUDENTS 'SKILLS FACULTIES OF MUSIC AND PEDAGOGICAL

In vocal pedagogies the large role belongs to a professional knowledge, experience and personal qualities of the teacher.
Test-time Batch Normalization

Deep neural networks often suffer the data distribution shift between training and testing, and the batch statistics are observed to reflect the shift. In this paper, targeting of alleviating distribution shift in test time, we revisit the batch normalization (BN) in the training process and reveals two key insights benefiting test-time optimization: $(i)$ preserving the same gradient backpropagation form as training, and $(ii)$ using dataset-level statistics for robust optimization and inference. Based on the two insights, we propose a novel test-time BN layer design, GpreBN, which is optimized during testing by minimizing Entropy loss. We verify the effectiveness of our method on two typical settings with distribution shift, i.e., domain generalization and robustness tasks. Our GpreBN significantly improves the test-time performance and achieves the state of the art results.
Hubungan Penggunaan E-Learning terhadap Hasil Belajar Kognitif Siswa di SMPN 31 Pekanbaru pada Mata Pelajaran IPA Biologi Tahun Ajaran 2020/2021

This study is a correlation study that aims to determine the relationship between the use of e-learning on UTS cognitive learning outcomes of students. This research is quantitative research with correlation method. The sampling technique in this study used the Simple Random Sampling technique with a total sample of 125 students. Students' cognitive learning outcomes are included in the medium category, which is 53.6%. The results of the data analysis of the correlation coefficient (rcount) of 0.24 with a significance level of 5% have a correlation in the low category. The results of the analysis of the determination of the variable use of e-learning (X) it gave a positive contribution to the learning outcomes (Y) obtained by students was 5.76% while 94.24% was determined by other factors not discussed in this study. The results of this study indicate that there is a relationship between the use of e-learning on students' cognitive learning outcomes with a low category.
CHATTERLY

Chatterly application is a feature or a program on the Internet to communicate directly among Internet users who are online or who were equally using the internet. Chatterly applications allow users to communicate even though from a great distance. Therefore, this web application must be real-time and multi platform to be used by many users.There has been an emerging trend of a vast number of chat applications which are present in the recent years to help people to connect with each other across different mediums, like Hike, WhatsApp, Telegram, etc. The proposed network-based android chat application used for chatting purpose with remote clients or users connected to the internet, and it will not let the user send inappropriate messages. This paper proposes the mechanism of creating professional chat application that will not permit the user to send inappropriate or improper messages to the participants by incorporating base level implementation of natural language processing (NLP). The programming language used to build server is Node.js with express framework and socket.io and for front-end their is involvement of HTML,CSS and javascript
THE DEVELOPMENT OF SKILLS OF THE ABILITY TO ACT IN THE MIND OF JUNIOR SCHOOLCHILDREN STUDYING THE CHESS GAME

The article reveals the essence of the concept of “the ability to act in the mind”, which is a fundamental specific component of the human intellect and consists in providing the opportunity to act indirectly with things, that is, to operate with their sign-symbolic images without changing the things themselves. This quality is necessary for the successful implementation of any activity, since it underlies such important components as goal creating and goal setting, the ability to imagine the result in advance, which is the motivation for the student in his research work to successfully master the educational material. The history of appearance and various definitions of this concept are given. The relevance of the article is due to the importance of the widespread introduction of chess in elementary school as an activity that forms and develops the “ability to act in the mind” in primary school age. it is at this age that the formation of two main psychological neoplasms takes place: the arbitrariness of mental processes and the internal plan of action.&#x0D; The purpose of the article is to describe a comparative study of the development of the ability to act in the mind in junior schoolchildren, studying and not studying chess.&#x0D; The method or methodology. The study was carried out using the test of T.A. Ogneva «Architect and builders» to determine the predisposition to success in the chess game of younger schoolchildren, method A.Z. Zak «Logical problems» to determine the level of formation of theoretical analysis and internal action plan.&#x0D; Results. Thanks to the development of the ability to act in the mind, it has been proven that children chess players have better developed this ability and the ability to predict probable solutions in different game situations.&#x0D; Practical implications. The results of the study can be applied in the field of diagnostics of preschoolers and primary schoolchildren, as well as in general to determine the intellectual and volitional readiness of children to study at school.&#x0D; Based on the research carried out by means of the test by T.A. Ogneva “Architect and Builders” to determine the predisposition to success in the chess game of junior schoolchildren due to the development of the ability to act in the mind, it was proved that children-chess players have better developed this ability and the ability to predict probable options decisions in different game situations.
POLYCULTURALITY AS A METHODOLOGY AND PEDAGOGICAL NORMS OF MODERN EDUCATION

No abstract.
HUMANITARIZATION OF HIGHER TECHNICAL EDUCATION: PSYCHOLOGICAL AND PEDAGOGICAL ASPECT

The article deals with the organization of studying psychological and pedagogical subjects for students of technical universities.
FOLKLORE PICTURE OF THE WORLD AS A PEDAGOGICAL ECOCULTURAL TECHNOLOGY PRESCHOOL EDUCATION

No abstract.
EFEKTIVITAS PEMBELAJARAN DARING MENGGUNAKAN GOOGLE CLASSROOM TERHADAP MINAT BELAJAR MATEMATIKA PADA SISWA SEKOLAH DASAR

The aim of this study was to find out the effectiveness of using Google classroom during online learning toward students' elementary Interest in math learning. The study adopt descriptive quantitative Which focus on students' interest during online learning. the partisipant for the study were elementary student on Banten province that were using Google classroom during online learning. The students were choose by Random sampling technique. The instrument to collect the data was using quesioner on google form. Data was analysis by using prosentase uji. The results of the study showed that online learning using google classroom on interest in learning mathematics in elementary school students has been effective. This is indicated by the acquisition of an average score for indicators of interest in learning mathematics using Google Classroom (61%), the use of Google Classroom features (72%) and the effectiveness of online learning using Google Classroom (62%). Although there are still students disagree that online learning is not effective (4.4%).
STUDENT PERSONALITY AS A SUBJECT INDEPENDENT LEARNING ACTIVITY IN CONDITIONS HIGHER PEDAGOGICAL EDUCATIONAL INSTITUTION

The article deals with a personality of the student as the subject of self directed learning activities under conditions of a higher teacher training institution.
FUEL RECOMMENDATION INFORMATION SYSTEM (BBM) CASE STUDY: MINISTRY OF MARINE, FISHERIES AND FOOD OF NAGAN RAYA REGENCY

The Department of Maritime Affairs, Fisheries and Food (DKPP) of Nagan Raya Regency is a fully authorized body in managing all fisheries, marine and food activities in Nagan Raya Regency, Aceh Province. One important factor in supporting the productivity of capture fisheries is fuel oil (BBM) for small and medium fishermen. The fulfillment of fuel for fishermen is influenced by the length of stay at sea, the distance to the fishing ground, the size of the vessel, the type of equipment used, the vessel used and the fuel used. The fuel recommendation information system for fishermen is influential as the carrying capacity of information effectiveness between the service and fishermen. The information system itself is a group of elements that are interconnected or interact to form a single unit with an approach using quantitative methods and participatory observation. make a solution to the DKPP Nagan Raya as an effort to create a BBM Information System Application with the aim of facilitating an information system or fuel data collection, data collection of fishermen, and data collection of fishermen's catches. The BBM Information System application with a click using the fisherman's android will be greatly facilitated in the BBM recommendation process because it is in accordance with the quota received. In addition, this application method also makes it easier to monitor and report fuel distribution to fishermen, because everything can be integrated and can be monitored directly by the Marine Fisheries Service of Nagan Raya Regency.
Understanding and Mitigating the Uncertainty in Zero-Shot Translation

Zero-shot translation is a promising direction for building a comprehensive multilingual neural machine translation (MNMT) system. However, its quality is still not satisfactory due to off-target issues. In this paper, we aim to understand and alleviate the off-target issues from the perspective of uncertainty in zero-shot translation. By carefully examining the translation output and model confidence, we identify two uncertainties that are responsible for the off-target issues, namely, extrinsic data uncertainty and intrinsic model uncertainty. Based on the observations, we propose two light-weight and complementary approaches to denoise the training data for model training, and mask out the vocabulary of the off-target languages in inference. Extensive experiments on both balanced and unbalanced datasets show that our approaches significantly improve the performance of zero-shot translation over strong MNMT baselines. Qualitative analyses provide insights into where our approaches reduce off-target translations
Desert grass grid paving device and intelligent control equipment

At present, sand prevention and control in the desert mainly adopt the method of laying sand barriers, and laying grass grids is a simple and effective control method with low cost and good effect. In the process of laying grass grids, large engineering vehicles are used for laying, which has high cost and low flexibility; manual laying is low in efficiency and labor-intensive. In response to the above problems, this device has designed a desert grass grid paving device. This device is a semi-automatic machine with low cost, stable work and high efficiency. The device and manual cooperation can greatly improve the efficiency of grass grid laying. DC control box control, stable operation, can be combined with intelligent monitoring and other equipment at the same time, the application prospect is broad.