Reviewerly/exHarmony huggingface dataset:
Stone Tools Illustrations with Vector Art: The &amp;#39;STIVA&amp;#39; Method v3

Lithic illustrations are often used in scientific publications to efficiently communicate the technological and morphological characteristics of stone tools. They offer invaluable information and insights not only on how stone raw materials were transformed into their final form, but also on the individuals that made them. Here, the “Stone Tools Illustrations with Vector Art” (STIVA) Method is presented, which involves the illustration of lithic artefacts using vectorial graphics software (Adobe® Illustrator®). This protocol follows an optimised step-by-step method, presenting ten major sections that constitute the creation of a lithic illustration: photography, vectorial software configuration, scale, outline, scar borders, ripples, cortex, symbols, composition, and export. This method has been developed to allow researchers, students and educators to create clear and competent illustrations for any application, from scientific publications to public outreach. (The last step contains a supplemental video with extra context and tips, as part of the protocols.io Spotlight series, featuring conversations with protocol authors.)
Feature selection for transfer learning using particle swarm optimization and complexity measures

Particle Swarm Optimization is an optimization algorithm that explores a search space guided by a fitness function in order to find a good solution.We apply it to perform feature selection for domain adaptation.Usually, classification error is used in the fitness function to evaluate the goodness of subsets of features.In this paper, we propose to employ complexity metrics instead, as we assume that reducing the complexity of the problem will lead to good results while being less computationally demanding and independent from the classifier used for testing.We found out that our method is indeed faster and selects fewer features, obtaining competitive classification accuracy results.
An empirical comparison of generators in replay-based continual learning

This study is in the context of continual learning (CL) with DNNs.It compares several types of generators when performing replay, i.e., the generation of previously seen samples, to avoid catastrophic forgetting.Principal generators are generative adversarial networks (GANs) and variational autoencoders (VAEs).We evaluate these generators in various flavors (conditional, Wasserstein etc.) w.r.t.CL performance on a variety of CL tasks generated from the MNIST benchmark.Concerning generators, we find that VAEs are generally more compatible with CL than GANs.More generally, we find that replay-based CL faces counterintuitive issues for seemingly simple problems: first, that performance degrades more strongly as less information is added, and, furthermore, that performance degrades even when only known information is added.
Industrial Solution in Fashion-domain Recommendation by an Efficient Pipeline using GNN and Lightgbm

In this paper, we present our 1st place solution for the ACM RecSys 2022 Challenge. The annual challenge focus on the nuances of fashion domain recommendation this year. Dressipi, the organizer, is the fashion-AI expert, providing products and outfit recommendations to leading global retailers. Putting forward the challenge of accurately predicting which fashion item will be bought at the end of the session, given a dataset of user sessions, purchase data, and content data about items. As the public dataset contains millions of pretty short sessions, both accuracy and scalability should be taken into account. Our solution is a fast prediction pipeline consisting of feature extraction, retrieval and rank. Specifically, the critical factor of our high score submission is the combination of collaborative filtering varieties and pre-trained embedding from GNN, which are carefully designed for the challenge dataset. Finally, we illustrate our experiments on different hyper-parameters and loss functions.
Modeling the School of the Future: A Study of Existing School Practices by the Positive Changes Factory

The share of private schools in the total number of educational institutions has increased almost 2.5-fold over the past two decades, according to a study by the Institute of Education of the National Research University Higher School of Economics. In many ways, this was a response to the market demand, with the parents becoming increasingly aware of the limitations and shortcomings of an education based on the average public school model — especially during the COVID-19 pandemic, when schooling moved home. Numerous new educational approaches that are actively offered, primarily by private schools, — especially those that classify as “alternative” schools — are another response to this demand.
Research on 24-Hour Dense Crowd Counting and Object Detection System Based on Multimodal Image Optimization Feature Fusion

Motivation. In the environment of day and night video surveillance, in order to improve the accuracy of machine vision dense crowd counting and target detection, this paper designs a day and night dual-purpose crowd counting and crowd detection network based on multimode image fusion. Methods. Two sub-models, RGBD-Net and RGBT-Net, are designed in this paper. The depth image features and thermal imaging features are effectively fused with the features of visible light images, so that the model has stronger anti-interference characteristics and robustness to the light noise interference caused by the sudden fall of light at night. The above models use density map regression-guided detection method to complete population counting and detection. Results. The model completed daytime training and testing on MICC dataset. Through verification, the average absolute error of the model was 1.025, the mean square error was 1.521, and the recall rate of target detection was 97.11%. Night vision training and testing were completed on the RGBT-CC dataset. After verification, the average absolute error of the network was 18.16, the mean square error was 32.14, and the recall rate of target detection was 97.65%. By verifying the effectiveness of the multimode medium-term fusion network, it is found to exceed the current most advanced bimodal fusion method. Conclusion. The experimental results show that the proposed multimodal fusion network can solve the counting and detection problem in the video surveillance environment during day and night. The ablation experiment further proves the effectiveness of the parameters of the two models.
Application of Reinforcement Learning in Multiagent Intelligent Decision-Making

The combination of deep neural networks and reinforcement learning had received more and more attention in recent years, and the attention of reinforcement learning of single agent was slowly getting transferred to multiagent. Regret minimization was a new concept in the theory of gaming. In some game issues that Nash equilibrium was not the optimal solution, the regret minimization had better performance. Herein, we introduce the regret minimization into multiagent reinforcement learning and propose a multiagent regret minimum algorithm. This chapter first introduces the Nash Q-learning algorithm and uses the overall framework of Nash Q-learning to minimize regrets into the multiagent reinforcement learning and then verify the effectiveness of the algorithm in the experiment.
Design of AI System for National Fitness Sports Competition Action Based on Association Rules Algorithm

In information system construction, online data migration is a very important link. At present, in different fields, people provide protection for online data migration through the way of project management to ensure the speed and efficiency of online migration. However, some problems may occur in the process of online data migration. In the development of contemporary sports, competitive sports, as the high-end stage of sports development, are constantly pursued by ordinary sports enthusiasts. Therefore, in the national fitness activities, how to combine the national fitness and competitive sports data to provide a more professional storage platform is a focus of research but also a problem to be solved in the process of online data migration. Because the data mining ID3 algorithm only supports querying and retrieving RowKey indexes, it does not support non-RowKey column indexing. Therefore, if you want to query non-RowKey indexes, the data mining ID3 algorithm will search the form in the overall scan, but the performance of this method is low. In order to improve the query speed of non-RowKey columns, this paper designs a secondary index function based on HBase. The sports competition action system can retrieve data from the secondary index of the query state, to avoid scanning the whole world and improve the search speed. In this paper, ID3 algorithm is used to combine national fitness and competitive sports data, which provides a guarantee for the migration of competitive sports data in the national fitness system.
Application of Multi-Feature Fusion Based on Deep Learning in Pedestrian Re-Recognition Method

A system known as pedestrian recognition makes use of several cameras to identify the surrounding area and quickly identify and match the target demographic. Based on pedestrian recognition, the picture model, pedestrian features, and other information, the features are developed to have a high degree of generalizability, distinctiveness, and accuracy. The application approach for pedestrian re-recognition based on deep learning for numerous features is proposed in this paper. The suggested approach successfully preserves high-level semantic information, which helps network members extract all of the pedestrian properties. As external material and semantic information were combined horizontally and vertically, environmental interference was decreased, and people’s ability to create networks was enhanced. The voice channel of the speech system was introduced in order to fully utilize the global information network, and the connection between the channels was carefully addressed in order to enhance the global information network’s capacity for expression. The null convolution reduced the operational continuity of the identification information. To increase the consistency of the data, the multi-level spatial convolution structure was merged with the entire image in this paper. After numerous experiments, the three groups were 89.5%, 89.5%, and 89.1%, respectively, compared to 1501, DukeMTMC-reID, CUHK03, and other medial groups, and the experimental results were 85% and 89.5%, respectively. The multimode feedback MP3 module was taken from the MP3 module in order to gain richer and denser multimode feature information. Comparing the module’s initial response level (RANK1) with the various cycles yields the average accuracy for each cycle (catalog). The experiment demonstrates that the two mixed pile groups can enhance the modulus of the mixed pile group and get better results. The multi-level multi-scale pole function effectively combines the characteristics of pedestrians in various scales, and the addition of the ASP module enhanced the network context information’s overall ability to be represented, aided in this chapter’s research method’s ability to more thoroughly analyze scene structure, and increased the precision of pedestrian re-recognition.
Plow: A Novel Approach to Interlinking Modular Ontologies Based on Software Package Management

Ontology development offers many challenges, with some of the most prominent being modularization and evolution of ontologies over time. Based on lessons learned from popular programming language package managers, we present a novel approach to package management of OWL ontologies. Most prominently we integrate a dependency resolution algorithm based on the popular SemVer versioning scheme with tooling support for dependency locking, which allows for decoupling publication and consumption of ontologies, reducing the need for coordination in ontology evolution. To complete our unified approach, we additionally provide an integrated registry, which serves as a domain-agnostic repository for ontologies (https://registry.field33.com).
Illegal Activity Detection on Bitcoin Transaction using Deep Learning

Abstract Forensic investigations increasingly leverage artificial intelligence to identify illegal activities on Bitcoin. Bitcoin transactions have an original graph (network) structure, which is sophisticated and yet informative. However, machine learning applications on Bitcoin have given limited attention to developing end to end deep learning frameworks that are modeled to exploit the Bitcoin graph structure. To identify illegal transactions on Bitcoin, the current paper extracts nineteen features from the bitcoin network and proposes a deep learning based graph neural network model using spectral graph convolutions and transaction features. The proposed model is compared with two state of the art techniques an Ensemble of Decision Trees, and a Decision trees trained on Convoluted features for classification of illegal transactions on Bitcoin. To understand the efficacy of the proposed model, a dataset is collected consisting of 13310125 transactions of 2059 entities having 3152202 Bitcoin account addresses and belonging to 28 categories of users. Two sets of experiments are performed on the datasets: labeling transactions as legal or illegal (binary classification) and identifying the originator of the transaction to one of the twenty-eight types of entities (multi-class classification). For fast and accurate decisions, binary classification is appropriate, and for pinpointing the category of bitcoin users, a multi-class classifier is suitable. On both the tasks, the proposed models achieved a maximum of 92\% accuracy, validating the methodology and suitability of the model for real-world deployment.
Theoretical and Methodological Bases of Organization and Implementation of Educational Work in Educational Institutions of Higher Education

Introduction to the problem. Appeal to necessity of development of bases of educational work organization in university becomes the urgent task of development of modern higher education. Despite the fact that effective, meaningful traditions of upbringing are being preserved in the activity of universities, the existing practice has serious difficulties in the integral definition of theoretical and methodological foundations of educational work in the organizations of higher education. Objective of this article. Identify and substantiate the constants in the theory and methods of organization and implementation of educational work in educational institutions of higher education. The research methodology consists of systemic, environmental, socio-cultural, personal activity, and multisubject approaches as well as complex methods of theoretical research which was applied to a wide range of researches in this field as well as to descriptions of practices (self-descriptions, information from university websites). Results of the study. On the basis of the identified theoretical positions which require conceptual clarification (the level of understanding and implementation of education in high school, goals and objectives of education, the content, the methodological support of the upbringing process) the invariant theoretical and methodological foundations of the organization of educational work in the university were determined. Methodological invariant defining initial bases for organization of upbringing work in university. Account peculiarities of growing up of the contemporary youth. Reliance on basic research in the field of education. Integration of upbringing, self-education, and professional education. Combination of national, professional and personal values of subjects of education Initiation and maintenance of the saturated life activity and personal-professional communication in the university space Transformation of the university environment and space into educational environment and educational space. Conclusion. Present active attention to upbringing and educational work in institutions of higher education and corresponding state-social expectations can encourage renovation and development of corresponding educational systems and practices only if clearly outlined and clear theoretical and methodological grounds.
Human-Robot Interactions in the Workplace – Key Challenges and Concerns

Theoretical background: The use of robots/AI in the workplace has grown rapidly in the last years. There is observed enlargement not only of the numbers of robots but also the quality of their functions and applications. Therefore, many questions of practical, scientific and moral nature have arisen. The flowering use of robots has drawn scientists’ attention to interactions between humans and robots. As a result, a new multidisciplinary research area – Human-Robot Interactions (HRI) – is growing. Representatives of HRI try to answer the questions like: How anthropomorphic features of robots may affect interactions between robots and employees? How are robots supposed to look and behave to make interactions more pleasant for employees? Can human cooperation with humanoid robots lead to the formation of socio-mechanical bonds? Purpose of the article: The paper aims to identify determinants of human-robot interactions in the workplace and identify key research problems in this area. Research methods: The method of a systematic review of the literature fulfiled the above-mentioned purpose. The Web of Science was chosen as the basic database. The list of publications from the Web of Science was supplemented with some other publications which were related to the topic. Main findings: There are several factors that determine the perception and quality of HRI in the workplace. Especially trust, anthropomorphic features of the robot, and organizational assignment may decide about the human acceptance of the use of a non-human agent and HRI. The concept of social interaction with robots is at an initial stage yet. An adopted research paradigm also plays an important role. It seems that the classical assumptions of organizational sociology will not stand the test of time. Researchers and practitioners are facing new challenges. Especially there are some ontological questions that are not easy to be answered unanimously. Can we treat a robot as a mechanical device or rather as a member of a newly created community?
A Graph Neural Network Recommendation Method Based on Social Relationship Embedding

There is a considerable amount of research in online social networks, most of which focuses on the structural analysis of social graphs. The interpersonal relationships of social networks, especially friend circle, can solve the cold start and sparsity problems, and through the relationship between social networks can effectively recommend users' favorite items (items), such as music , videos, brands/products, preferred tags, location, services, etc. User relationships in social networks are diverse and there are many different perspectives on different social networks. Associations among users can form multi-layered composite networks, and multi-layered social networks present new challenges and opportunities. Different relationships can influence users' preferences to different degrees, which in turn affects their behavior. Therefore, fusing multiple social networks is an effective way to improve recommendation. Although some studies have started to address multiple social network recommendations, simple linear superposition cannot reflect the coupling and nonlinear association between multiple social networks. In this paper, we propose a graph neural network recommendation model under social relationships based on this background. We first propose to compute the 2nd order collaborative signals and their intensities directly from the neighboring matrix for updating the node embedding of the graph convolution layer. Secondly by embedding historical evaluations, various social networks constituting different dimensions, the attention integration of user preferences by different social networks is achieved, and its effectiveness and scalability are demonstrated in theoretical derivation and experimental validation. The theoretical derivation and experimental validation demonstrate its effectiveness and scalability.
Literature Review of Moving Object Detection Using Machine Learning

Abstract: In order to answer three different research questions, this paper conducts a literature review on the subject of machine learning in object detection security. The work done covers more than 34 research papers that are relevant to this topic. Charts, tables, and statistics are used to summarize the data and provide the reader with an easily readable summary of the papers that are relevant to this topic.
Intensive Reading and Task- Based Approaches: A Relevant Viewpoint in Improving Students’ Reading and Writing Proficiency

The gap that exists between reading and writing proficiency in English continues to grow. Moreover, the poor performance of the students is alarming when it comes to reading and writing. The kind of approach, strategy, and method has a big impact and factor which would affect the students’ performance. The purpose of this study is to determine the significant difference of Intensive reading approach in reading proficiency in terms of word recognition, comprehension, and speed. Also, to determine the significant difference of Task- based approach in the writing proficiency of the students in terms of sentence structure, vocabulary, cohesion, and transition device. Consequently, the intensive reading approach with the components of Know, want, and learn (KWL) Chart, Vocabulary Self Selection strategy (VSS), and Story maps used during the execution of the lessons. However, in the task – based approach which has the components of Listing, Ordering/sorting, Comparing/contrasting was used as part of the writing test. The findings show that there was significant difference in intensive reading approach in the reading proficiency of the respondents in terms of word recognition and comprehension. There were constant changes every time the treatment was being used. However, in speed, the changes were being manifested. It implies that the longer the time you use the treatment, the higher the chance that the speed of the students will be improved. The length of time of the intervention was another factor that may have caused these non-significant results. In the same manner, there was significant difference in task- based approach in the writing proficiency of the respondents in terms of sentence structure, vocabulary, cohesion, and transitional devices. The writing proficiency was all improved across the three post tests using the intervention. The findings show that there was improvement in the three consecutive posttests of Grade 8 respondents in reading and writing proficiency after the exposure to the intensive reading and task-based approaches. The use of the said approaches shows positive results in enhancing reading proficiency in terms of word recognition, comprehension, and speed. Moreso, in writing proficiency in terms of sentence structure, vocabulary, cohesion, and transitional devices.&#x0D; Keywords: Intensive Reading Approach, Task- based Approach, Reading Proficiency, Writing Proficiency.