Reviewerly/exHarmony huggingface dataset:
Camera Proxy based Contrastive Learning with Hard Sampling for Unsupervised Person Re-identification

Because of the advantages of dealing with large-scale unlabelled data, unsupervised learning has recently attracted more attention for person re-identification. Particularly, the combination of the unsupervised learning paradigm with contrastive learning shows promising efficiency in network optimization. This work adopts the successful camera-aware contrastive learning approach and further explores its capability on the camera proxy level to improve the data pair consistency. Thus, it is more robust to the camera change, which still challenges the unsupervised person re-identification. This work proposed a Camera Proxy-based Contrastive Learning framework, which explicitly considers inter-camera scenario and intra-camera scenario. Moreover, this work is motivated by the strategy of selecting a hard negative sample in triplet loss learning and further extends it to contrastive learning for both negative and positive pair creation on the camera proxy level. Extensive experiments demonstrate the superiority of the proposed framework over state-of-the-art approaches on purely unsupervised re-identification.
MIXLIC: Mixing Global and Local Context Model for learned Image Compression

Learned Image Compression (LIC) is considered as a future direction for image compression. However, existing LIC methods only marginally outperform the latest traditional codec VVC. This is because current LIC methods have not fully utilized the global and local information of images. In this paper, we propose a MIXing global and local context Module (MIXM), which combines global context extractor (GCE) with local context extractor (LCE) in a parallel design, capturing both global and local dependencies. Based on the MIXM, we further build the MIXLIC, a mixing global and local context model for learned image compression, which can fully utilize global and local context to improve performance. Experimental results show that our proposed method MIXLIC achieves state-of-the-art RD performance and gets more visually pleasant results compared with other learning-based methods and traditional codecs.
Leveraging K-hop based Graph for the Staffing Recommender System with Parametric Geolocation

The graph-based methods in the staffing Recommender Systems (RecSys) can make a more accurate recommendation by leveraging the relationships. In order to discover implicit relationships, a Knowledge Graph (KG) which correlates Curriculum Vitae (CV), competence, jobs can be enriched with a large amount of semantics. However, the previous study is strictly based on the relationships of competences in both CV sand jobs. It is interesting to propose an approach to predict the jobs in which the competencies are not explicitly described in the CVs. To achieve this approach, it is necessary to estimate the distances between different competences. Moreover, the estimation of the transportation time has a key role in providing better recommended jobs as it can improve the quality of candidates working conditions. To alleviate the above issues, at first, a pipeline is reused to extract the competence keywords from CVs and jobs, and a normalized system is developed to enrich their semantics. We propose two transformer models – Sentence Bidirectional Encoder Representations from Transformers (SBERT) and Bidirectional and Auto-Regressive Transformers (BART) for the similarity measurements between the semantics of competences. And then we propose a k-hop based causality method to predict the potential jobs and a method of parametric geolocation to transform the transportation time into proximity index. At last, we apply the weight average method to calculate the final matching score for the recommended jobs. The experiment is based on Normalized Discounted Cumulative Gain (NDCG).
Prediction of Solar Radiation using Empirical Models over Lowland Region Nepal

Renewable energy sources are becoming more and more popular as a result of the world's rapidly rising energy needs and the depletion of fossil fuel stocks. Solar energy significantly increases the need for electricity. Due to high energy demands, solar energy most viable out among all of energy sources. Before choosing a location for a solar-powered power plant, it is necessary to predict or anticipate solar energy. For the estimation of the solar energy we use meteorological parameters such as sunshine hour, temperature and relative humidity on selected empirical models to find the empirical coefficients at the study site Biratnagar (Lat. 26° 27' 15" E, Long. 87° 16' 47" N, and alt. 72 m) Nepal. Regression technique is utilized on meteorological parameters including global solar radiation at different models viz. Angstrom-Prescott (A-P) model, Ogunlade model, Gopinathan model and Hargreaves model. Among the four models, the A-P model is better than other models by analyzing the statistical tools. Finally, the empirical constants of A-P model and are found. The finding coefficients can be used to estimate the solar radiation and solar energy at similar geographical locations of Nepal.
An Approach to Evaluate User Interfaces in a Scholarly Knowledge Communication Domain

The amount of research articles produced every day is overwhelming: scholarly knowledge is getting harder to communicate and easier to get lost. A possible solution is to represent the information in knowledge graphs: structures representing knowledge in networks of entities, their semantic types, and relationships between them. But this solution has its own drawback: given its very specific task, it requires new methods for designing and evaluating user interfaces. In this paper, we propose an approach for user interface evaluation in the knowledge communication domain. We base our methodology on the well-established Cognitive Walkthough approach but employ a different set of questions, tailoring the method towards domain-specific needs. We demonstrate our approach on a scholarly knowledge graph implementation called Open Research Knowledge Graph (ORKG).
The Effectiveness of Interactive Multimedia-Based Learning Using Powtoon and Renderforest in Teaching Writing to Students with Different Learning Styles

This study aimed to assess the effectiveness of Powtoon in teaching writing to visual students; to assess the effectiveness of Powtoon in teaching writing to auditory students; to assess the effectiveness of Renderforest in teaching writing to visual students; to assess the effectiveness of Renderforest in teaching writing to auditory students; to show the difference between the effectiveness of Powtoon and Renderforest in teaching writing to visual students; to show the difference between the effectiveness of Powtoon and Renderforest in teaching writing to auditory students; and to explain the relationship between interactive multimedia-based learning and students’ learning styles in teaching writing. This study used experimental research with 2x2 factorial designs. The population were the tenth-grade students at MAN 2 Model Medan with the samples were 74 students of experimental I and II that categorized into visual and auditory learning styles. The result indicated that (1) Powtoon was effective in teaching writing to visual students; (2) Powtoon was effective in teaching writing to auditory students; (3) Renderforest was effective in teaching writing to visual students; (4) Renderforest was effective in teaching writing to auditory students; (5) Powtoon and Renderforest were equally effective in teaching writing to visual students; (6) Powtoon and Renderforest were equally effective in teaching writing to auditory students; and (7) Interactive multimedia were effective in teaching writing to visual and auditory students. Keywords: Powtoon, Renderforest, Teaching Writing, Learning Styles
E-mail Fraud Detection

Spam issues have become worse on social media platforms and apps with the growth of IoT. To solve the problem, researchers have suggested several spam detection techniques. Spam rates are still high despite the use of anti-spam technologies and tactics, especially given the ubiquity of rogue e-mails that lead to dangerous websites. By using up memory or storage space, spam e-mails may cause servers to run slowly. One of the most essential methods for identifying and eliminating spam is filtering e-mails. To this end, various deep learning and machine learning technologies have been used, including Naive Bayes, decision trees, SVM, and random forest. E-mail and Internet of Things spam filters use various machine learning approaches and systems are categorized in this research. Additionally, as more people use mobile devices and SMS services become more affordable, the issue of spam SMS messages is spreading worldwide. This study suggests using a variety of machine learning approaches to detect and get rid of spam as a solution to this problem. According to the trial findings, the TF-IDF with Random Forest classification algorithm outperformed the other examined algorithms in accuracy %. It is only possible to gauge performance on accuracy since the dataset is imbalanced. Therefore, the algorithms must have good precision, recall, and F-measure.
7TH INTERNATIONAL CONFERENCE ALISOVA’S READINGS (MSU, FEBRUARY 20, 2023)

This is an overview of the reports presented at the International conference on Italian Languages, Literature, and Culture Alisovskiye chteniya held on February 20, 2023 at the Faculty of Philology, Lomonosov Moscow State University. The academic gathering celebrated the anniversary of Professor Alisova, a founder of the italianistics in Russia. More then 50 attendees included language, history, literary, art and cultural experts from Russia, Belarus and Italy
Height Above Nearest Drainage (HAND) as a Model for Rapid Flood Inundation Mapping Based on Remote Sensing and Geographic Information Systems in the Kapuas Sintang Sub Watershed

This study aims to map the flood inundation and the extent of the inundation in the study area using the HAND model. The data used in this study is DEM. The DEM is used to generate a hydrologic framework, including flow accumulation, drainage network, flow direction, elevation, and flow distance. The method used in this study is the HAND descriptor. The analysis in this study used spatial hydrological analysis and hypsometric analysis using zonal statistical tables in ArcGIS. Based on the results of the analysis of height above the nearest drainage it is known that the Kapuas Sintang sub-watershed has five classes of inundation, namely very high inundation, high inundation, moderate inundation, low inundation, and no inundation. Very high, high, and moderate inundation classes are spread over three sub-districts, namely Sintang, Dedai, and Tempunak sections. Sintang District has the widest distribution, followed by Dedai District and Tempunak District is the narrowest. Prediction of inundation area and flood area with HAND can be used to improve the new mapping model without involving additional data sources. The HAND model is a nice and simple tool that is useful for inundation studies as well as in inundation area prediction.
Development of Mobile Learning-Based Electronic Student Worksheets with Guided Inquiry Models on Newton's Law Material

Education is an endeavour to infuse civilization in the lives of individuals so that they can reach their greatest potential as human beings. Individuals are expected to be able to monitor the advancement of science and technology, as well as science and information and communication technology, through education. As a consequence, the study's goals are to establish the outcomes of the e-requirements Student Worksheets analysis, define the feasibility level of guided inquiry-based e-SW; and determine guided inquiry-based e-practicality. Electronic Student Worksheets in this study focuses on the e-SW, which is based on guided inquiry. Six media and materials professionals, one physics instructor, and 24 students took part in this project. The steps of ADDIE development are used as a research technique, comprising analysis, design, development, and implementation, however the assessment stage is not completed. The findings of this research led to the conclusion that educational product creation is viable and practicable. This is based on the results of a validation test conducted by media and material experts that scored above 80% in the very excellent category. And as well as a poll of teacher and student responses that scored above 90% in the very excellent category.
Decision Support System for Financial Aid for Underprivileged Students using the TOPSIS Method

The distribution of Financial Aid for Underprivileged Students is considered not to be on target due to the unmeasured selection process. So a decision-making system is needed that can select beneficiaries objectively. This research was conducted to assist the school in determining students who deserve this assistance. The purpose of this research is to build a decision-making system for selecting beneficiaries from poor students by applying the Technique for Order of Preference by Similarity to Ideal Solution method. The formulation of the problem is how to build a decision support system using the Technique for Order of Preference by Similarity to Ideal Solution method in selecting students who receive financial aid for underprivileged students. The method used goes through several stages, namely: determining alternatives and criteria, building a normalized decision matrix, building a weighted normalized decision matrix, determining positive and negative ideal solutions, determining the distance between ideal solutions, and determining preference values. There are 7 criteria used, namely social protection card recipients, total income, number of dependents, parental status, distance, class, and report card scores. The results showed that the highest preference value for each alternative was in alternative A3, with a score of 0.6665. While the lowest preference value is in alternative A20 with a score of 0.0719, From the results of the study, it was concluded that the Technique for Order of Preference by Similarity to Ideal Solution method can be used in making decisions on the selection of beneficiaries of poor students based on preference value rankings.
The Main Aspects of the Impact of Cybercrimes on the Business Environment in the Digital Era: Literature Review

The aim of this research is to identify the main aspects of the impact of cybercrime on the business environment through quantitative research and comparative analysis by reviewing international literature. The applied research methodology also consisted of processing and analysing information gathered from the literature of international regulatory bodies, which formed the basis for syntheses and extracts of judgements and findings. The results obtained revealed a significant increase in cybercrime over the last decade, which is why several measures to combat cybercrime in business were proposed and discussed. The chapter concludes with the authors’ conclusions, the limitations of this study and future research directions proposed by them. The importance of this chapter is aimed at both academic and business specialists, as well as at all readers interested in learning more about the subject.
Implementation of Personal Digital Inquiry Assisted by Infographics to Increase Science Literacy in Ecosystem Materials

This type of research is a quasi-experimental study,the research subjects were 70 Grade X students of SMAN X Cimahi. The purpose of this study was to see the effect of applying the Infographic-assisted Personal Digital Inquiry learning strategy on scientific literacy. The results of implementing an infographic-assisted personal digital inquiry learning strategy for science literacy test, the experiment class about indicator explaining the phenomenon scientifically has changed the medium category with an average value 65.14. For indicators evaluating and designing scientific questions even though there is the average value 54.28 still in the medium category on the results. And then, the average value of each indicator aspect of scientific literacy competence has changed, namely the indicator of interpreting data and evidence scientifically from the high category with an average value of 80.6. The Gain score obtained by the experimental class around 69.2% shows that these results are quite effective in learning. This can be seen from the Asymp value. Sig. (2-Tails) ranging from 0.000 &lt;0.05, meaning that there is a significant differences in students' scientific literacy abilities using infographic-assisted digital personal inquiry learning strategies.
Spatial Analysis of Cadmium and Lead in Soils of Nagaon District of Assam, India

Aims: The study was carried out in order to find the hotspots of Cadmium and Lead accumulation in the soil due to the anthropogenic activities, so that proper management strategies can be taken on time for their remediation.&#x0D; Study Design: Different geospatial techniques as Normal Q-Q plot, histogram, trend analysis and semi-variogram cloud model is used.&#x0D; Place and Duration of Study: The study was conducted emphasizing basically in high agricultural chemicals dependent Nagaon District of Assam, India, from 2018 to 2021.&#x0D; Methodology: Heavy metals were assessed using a random sampling method with a 5 x 5 km2 grid, and a total of 160 samples were collected from a depth of 0 – 15 cm for soil. Choropleth maps were created to show the distribution and hotspots of pollution.&#x0D; Results: Geospatial analyses from different thematic maps of heavy metals revealed significant vulnerable points of elevated concentrations of Pb (&gt;24.45 mg/kg) and Cd (&gt;0.31 mg/kg) in soil which is presumed to be due to anthropogenic factors. The three-dimensional trend over the distribution of metals throughout the district best fitted the second-order polynomial for Cd, and Pb in soils. Significant numbers of pairs of heavy metal pairs to a certain extent were found to be spatially autocorrelated and all the pairs away from X-axis towards the extreme right corner and far above the axis reflected less influence of local characteristics of the heavy metal. Co-variance cloud with search direction from North to South revealed the existence of spatial autocorrelation and a wider spatial shift of correlation towards the southern direction.&#x0D; Conclusion: The current study provides baseline data to update the mitigation approaches to better manage the heavy metal contamination in soil in the entire district.
THE QUESTION OF MOTIVATION OF THE COGNITIVE INTEREST OF STUDENTS OF EUROPEAN COUNTRIES IN FOREIGN LANGUAGE EDUCATION

The article deals with questions on the formation and development of the motivation of the cognitive interest of students of European countries in learning foreign languages.The relevance of the research is determined by the social need and the increased interest in the study of foreign languages by the societies of European countries.After all, modern Europe is a common home for many nations and nationalities with a significant cultural heritage and economic achievements, familiarization with which is possible only under the condition of mastering foreign languages.It is noted that the effectiveness of learning foreign languages depends more on the depth of needs and satisfaction.It is important that the level of success in the development of cognitive interest depends on the strength of motivation associated with the growth and improvement of students' knowledge.The key role of motivation in learning foreign languages is noted and it is shown that a powerful means of motivating students to creative activity is the formation of professional competence and competitiveness of future specialists.It is emphasized that the problem of motivation of educational activity is one of the most theoretically developed in pedagogy and at the same time one of the most difficult in practice of the educational process of learning foreign languages.The dependence of students' levels of mastery of a foreign language on the manifestation of their internal and external motivation to learning has been revealed.The analysis of a significant amount of scientific material made it possible to trace the opinion of scientists on the need for the purposeful formation of high motivation for educational activities among students.Scientists note that educational motivation of students' cognitive interest is understood as the driving force of personality development and its Věda a perspektivy № 8(27) 2023
A Study of Data Augmentation for Imbalance Problem in Network Traffic

The complexity and diversity of smart grid systems increase the likelihood of anomalies in communications between devices in the system, and how these anomalies are detected is critical to the security of the grid system. However, the distribution of traffic types in the grid is often highly unbalanced, with the total amount of normal traffic data often far outweighing the anomalous data. This imbalance compromises the effectiveness of the detection model and poses a significant threat to the overall security of the grid. This paper improves on traditional conditional generation adversarial neural networks and designs a new approach to overcome this obstacle and enhance the security of smart grid systems. In the generator part, the spatial features of the data are captured by convolutional neural networks, and the temporal features of the data are captured by gated recurrent units. By integrating these two components into the generative network, we can generate synthetic data with both temporal and spatial features, thus enhancing the minority class samples. By incorporating the augmented minority data into the training process, the proposed approach enables the detection model to understand the distribution of features of the minority anomaly samples more accurately. This improved learning accuracy translates into improved model performance, resulting in higher detection rates for previously under-represented anomalies. In addition, our approach effectively addresses the inherent bias towards traffic types with higher flows typically observed in asymmetric datasets, thus providing a more equitable and comprehensive security solution for smart grid systems.
Leasing the Cloud-Edge Continuum, à la Carte

Next-gen computing paradigms foresee deploying applications to virtualised resources along a continuum of Cloud-Edge nodes. Much literature focussed on how to place applications onto such resources so as to meet their requirements. To lease resources to application operators, infrastructure providers need to identify a portion of their Cloud-Edge assets to meet set requirements. This article proposes a novel declarative resource selection strategy prototyped in Prolog to determine a suitable infrastructure portion that satisfies all requirements. The proposal is showcased over a lifelike scenario.
The Janus System: Multi-paradigm Programming in Prolog and Python

Python and Prolog express different programming paradigms, with different strengths.Python is wildly popular because it is well-structured, easy to use, and mixes well with thousands of scientific and machine learning programs written in C. Prolog's logic-based approach provides powerful reasoning capabilities, especially when combined with constraint evaluation, probabilistic reasoning, well-founded negation, and other advances.Both languages have commonalities as well: both are usually written in C, both are dynamically typed, and both use data structures based on a small number of recursive types.This paper describes the design and implementation of Janus, a system that tightly combines Prolog and Python into a single process.Janus bi-translates data structures and offers performance of many hundreds of thousands of round-trip inter-language calls per second.Although Janus is still new, it has been used in commercial applications including natural language processing, visual query answering and robotic automation.Janus was developed for XSB, but porting Janus code to a second Prolog has been straightforward, indicating that Janus is a tool that other Prologs may easily adopt.