Reviewerly/exHarmony huggingface dataset:
Research of Digital Characteristics of the Step Cycle in Forensic Medicine

Authors:
raw_author_name: Dmitry Zybin
raw_affiliation_string: Federal State Institution Research Institute of Information Technologies of the Federal Penitentiary Service of Russia, Tver, 170100, Russian Federation

Abstract: This article presents the results of a study devoted to the analysis of the characteristics of the human step cycle as an indicator that allows you to identify a person on video by the signs of gait. Video recordings of volunteers walking on an electric treadmill were used as the research material. A software package has been developed that allows calculating the parameters of the step cycle used for processing the material. The average values of such characteristics of the step cycle were obtained and analyzed, such as the duration of the transition period and double support as a percentage of the step cycle and seconds, the length of the step cycle in seconds, walking frequency, walking pace separately for men, women and for the entire sample as a whole. The change of the listed indicators depending on the walking speed is investigated, the change in the frequency of steps with increasing speed is analyzed. A method of identifying a person in a video based on the characteristics of a step cycle is proposed, which allows comparing the digital characteristics of steps and making a reasonable conclusion about their similarity or difference. A software package has been developed to determine the anthropometric parameters of an individual in a video based on the characteristics of the step cycle. The obtained results can be used in forensic medicine for conducting expertise on identification of a person by signs of gait.
Construction of Talent Quality Evaluation Index System in Hydropower Industry Under the Background of Digital Transformation

Authors:
raw_author_name: Lihong Hao
raw_affiliation_string: Dadu River-hydropower Development Co. Ltd, 610000, Chengdu, China
raw_author_name: Tao Liu
raw_affiliation_string: Dadu River-hydropower Development Co. Ltd, 610000, Chengdu, China

Abstract: Digital transformation and upgrading pose new requirements for talent quality. Taking human resources positions as an example, this article constructs a “3+X” talent quality evaluation index system that includes knowledge, ability, attitude, and professional skills. Based on the data characteristics of 55 employees in human resources positions, a combination weighting model, entropy method, and BP neural network model are used, and an evaluation index system and weight evaluation index system suitable for the quality characteristics of talents in China’s hydropower industry have been constructed. The research results show that employee competence is an important indicator for driving digital transformation, with knowledge and professional skills playing a relatively central role, and attitudes being less differentiated. Finally, the research significance and shortcomings of the study are summarized, and the prospects for future development are provided.
Machine Learning Driven Strategies for Safeguarding India’s Digital Environment Against Cyberstalking

Authors:
raw_author_name: Chintan Singh
raw_affiliation_string: Amity Institute of Forensic Sciences, Amity University, Noida, Sector 125, U.P., India
raw_author_name: Himanshu Khajuria
raw_affiliation_string: Amity Institute of Forensic Sciences, Amity University, Noida, Sector 125, U.P., India
raw_author_name: Biswa Prakash Nayak
raw_affiliation_string: Amity Institute of Forensic Sciences, Amity University, Noida, Sector 125, U.P., India

Abstract: The ubiquity of the internet and mobile phones has drastically shifted the physical world into a virtual realm, encompassing individuals of all age groups, from schoolchildren to seniors. People now utilize social media and various internet-based platforms to connect and invest their time. However, within this virtual environment, the ability to conceal or impersonate one’s identity presents an opportunity for criminal activity to go unnoticed. Despite India’s progression towards becoming a digital nation, there exists a shortage of regulations and technological resources to address the challenges stemming from this digital paradigm shift. Cybercrime refers to any illicit behavior involving the use of a computer as a tool, target, or weapon. One subset of cybercrime is cyberstalking, which encompasses actions where a perpetrator employs a computer or associated technologies to induce fear in their victim. This chapter aims to delve into the definition, typology, detection, prevention, and the imperative need for cybercrime education. Its primary objective is to investigate the surge in internet stalking cases in India, exploring the definition, categories, and underlying causes of cyberstalking. Additionally, this article scrutinizes the Indian legal framework and judicial precedents related to these issues.
A Discussion of the Pedagogical Benefits of Nodal Knowledge in University Physics

Authors:
raw_author_name: Fengmei Chen
raw_affiliation_string: School of Intelligent Manufacturing, Nanning University, Nanning, China

Abstract: Focusing on nodal knowledge in university physics, this paper describes the definition and categorisation of nodal knowledge, revealing its characteristics and its connection to core knowledge. Through in-depth analyses, it is found that nodal knowledge is an important link in the physics subject that promotes and extends core knowledge, embodying the characteristics of fundamentality, selectivity, transition and diversity. In addition, this paper also highlights the superiority of using nodal knowledge for teaching, which not only helps students reduce the burden of memorisation, but also promotes the construction of a clear structure in the knowledge network and the understanding of the ins and outs of knowledge. Crucially, nodal knowledge makes it easier for students to understand and derive subject knowledge, thus helping them to master it more effectively.
Analysis of EFL Learners' Writing Challenges in the Digital Era

Authors:
raw_author_name: Noor Sahid Kusuma Hadi Manggolo
raw_author_name: M. Fatkhu Arifin
raw_author_name: Lilia Indriani
raw_author_name: Nurul Ngaini Khusniyati

Abstract: The Writing Intensive Course emphasizes fundamental English writing elements, providing a prime chance for students to improve written communication. Educators face challenges in enhancing expressiveness, but the large millennial class size, familiar with digital technology, presents a potential advantage. Millennials, as digital natives, show enthusiasm for digital-context learning, offering an opportunity for effective instruction in writing. The primary objective of this research is to find out the problems or challenges students undergo while learning to write English in the digital era. This quantitative research examined 174 EFL students to give particular responses that work with brief reviews of their challenges in English writing. Unable to express (10.9%), improve skills (9.8%), and develop ideas (5.2%) in writing stand strong as the biggest three difficulties in English writing. The diverse challenges, collected from participant samples, reveal consistent issues for both active and nonactive students. The study emphasizes the value of digital-assisted courses, offering hope for enhanced English writing and communication skills among SL/FL learners in a more accessible and effective manner. In brief, this study highlights internal factors as the primary challenges in writing class, with students struggling with self-expression, doubts about improvement, and generating ideas. Various challenges, including optimizing roles and feedback, underscore the importance of addressing obstacles for enhanced writing proficiency in digital-assisted courses
Emotional Regulation Ability on Test Results: Factors Affecting Motivation and Learning Achievement

Authors:
raw_author_name: I Ketut Wardana
raw_author_name: Anak Agung Putu Arsana

Abstract: The ability to regulate emotions in the acceptance of test results can impact both the increase and decrease of motivation. Unfortunately, there has not been much research explaining this phenomenon. Therefore, this study aimed to examine the influence of emotions on the acceptance of test results on students' learning motivation. This research used an ex post facto approach with a cause-and-effect correlation design. The population consisted of 300 students at SMPN 2 Kuta Selatan. Using purposive sampling, 30 students were involved as research samples. The research instrument consisted of 21 statements from the DAS questionnaire about test results, 20 statements about student motivation, and a formative test. Data were analyzed descriptively, using correlation statistics and paired sample t-tests. The research results showed that there was a significant relationship between the ability to manage depression, anxiety, and stress in the acceptance of test results. In addition, students with better emotional management skills tended to have positive motivation if their test results were disappointing. Meanwhile, students with low emotional abilities also had low motivation. For these affecting factors on students’ achievements, this study suggests a more holistic pedagogical approach and intervention as essential components in efforts to improve their learning achievement
The development of blockchain technology in manufacturing: A bibliometric analysis

Authors:
raw_author_name: Huma Sikandar
raw_author_name: Nohman Khan

Abstract: Many manufacturing organizations have adapted their governance systems, strategies, business models, and strategic processes in the "New Normal" and technologies like Blockchains have irreversibly transformed how people live and work. Despite extensive scholarly research, analysing emerging trends in Blockchain research remains critical. Unfortunately, the use of bibliometric analysis to study Blockchain has been limited, with little understanding of how its various research areas are progressing. Bibliometric analysis, a useful method for examining the development of research, has not been widely used in the context of blockchain. The progression of its numerous research areas is also yet unknown. This study seeks to fill these gaps by employing bibliometric analysis to assess scientific progress and research focus on Blockchains in manufacturing. We used Vos Viewer to conduct this bibliometric analysis to evaluate 265 papers published from 2017 to 2022 using co-authorship and co-occurrence of keywords analysis. We have identified the most productive authors, institutions, countries, and top research fields, highlighting the essential advancements in the Blockchain field. The findings demonstrate how keywords related to Blockchain have changed over time and describe the research hotspots. The originality of this research lies in its application of bibliometric analysis to examine the development and emerging trends in Blockchain research within the manufacturing domain while offering novel insights into the productivity of authors, institutions, and countries, as well as identifying research hotspots and future research directions. This study contributes to the inadequate body of literature on bibliometric analysis in Blockchain research and provides useful guidance for researchers and practitioners in the field.
Protecting ownership rights of ML models using watermarking in the light of adversarial attacks

Authors:
raw_author_name: Katarzyna Kapusta
raw_affiliation_string: Thales SIX GTS France, Gennevilliers, France
raw_author_name: Mohammed Lansari
raw_affiliation_string: Thales SIX GTS France, Gennevilliers, France

Abstract: In this paper, we present and analyze two novel—and seemingly distant—research trends in Machine Learning: ML watermarking and adversarial patches. First, we show how ML watermarking uses specially crafted inputs to provide a proof of model ownership. Second, we demonstrate how an attacker can craft adversarial samples in order to trigger an abnormal behavior in a model and thus perform an ambiguity attack on ML watermarking. Finally, we describe three countermeasures that could be applied in order to prevent ambiguity attacks. We illustrate our works using the example of a binary classification model for welding inspection.
Application of glasses‐free augmented reality localization in precision neurosurgery and literature review

Authors:
raw_author_name: Yahui Cui
raw_affiliation_string: Department of Neurosurgery Hangzhou Xixi Hospital  Hangzhou China
raw_author_name: Yupeng Zhou
raw_affiliation_string: Department of Neurosurgery Hangzhou Xixi Hospital  Hangzhou China
raw_author_name: Haipeng Zhang
raw_affiliation_string: Department of Neurosurgery Hangzhou Xixi Hospital  Hangzhou China

Abstract: Abstract To summarize the characteristics and therapeutic effects of glasses‐free augmented reality (AR) in neurosurgery, a case series of seven patients undergoing resection of intracranial lesions at the Neurosurgery Department of Hangzhou Xixi Hospital from November 2019 to October 2021 was collected. During the surgeries, glasses‐free AR was used for multi‐angle and multi‐modal image localization. The intraoperative positioning errors of all seven patients who underwent body surface localization with glasses‐free AR, in addition to routine surgical procedures, were less than 10 mm. These findings suggest that glasses‐free AR holds great promise for precision neurosurgery.
Pedagogical Response to the Needs of Preschool Children in a Disadvantaged Situation

Authors:
raw_author_name: Dejana Bouillet

Abstract: The aim of this article is to analyse the pedagogical response of Croatian ECEC teachers to the needs of preschool children in a disadvantaged situation. The quality of pedagogical practise to prevent unfavourable developmental trajectories in children in a disadvantaged situation, particularly in the Croatian system, is poorly researched. A vignette describing an imaginary child in a disadvantaged situation was used and 35 preschool teachers from 15 ECEC institutions described in writing in an online application how they would support the child in his or her development. The qualitative thematic analysis was conducted according to the five components of the strengths-based approach (relationships, empowerment, collaboration, strengths and focus on change). This approach assumes that through reciprocal relationships between children, parents and teachers, previously agreed goals are achieved and personal resources, motivation and belief in the possibility of achieving sustainable change are unleashed. The results show that ECEC teachers are predominantly oriented towards individual components of the strengths-based approach (quality of relationships, focus on children's strengths), while the implementation of the entire strengths-based approach in its entirety is lacking. It is emphasised that the strengths-based approach needs to be more strongly affirmed in Croatian ECEC system so that its principles are recognisable in the pedagogical practise of all ECEC teachers.
Breast cancer classification based on hybrid machine learning model

Authors:
raw_author_name: Zhe Lin
raw_affiliation_string: Shanghai University

Abstract: This study proposes a hybrid model that combines K-Means clustering and Random Forest classification as an approach for breast cancer classification. The objective is to exploit the advantages of unsupervised clustering and supervised classification techniques to enhance the accuracy and robustness of classification models. The dataset underwent preprocessing procedures encompassing the handling of missing values, feature normalization, and feature selection. Missing values were addressed through appropriate methods, and features were scaled and selected based on variance threshold or correlation analysis. Subsequently, K-Means clustering was applied to the preprocessed data to assign cluster labels to each sample. The study then proceeded to train a Random Forest classifier by incorporating both the cluster labels and the raw gene eigenvalues as mixed features. This integration of gene expression values and cluster labels provides supplementary information to the classifier, enabling the capture of more intricate patterns within the data. The Random Forest classifier was trained using optimized parameters determined through parameter tuning, including the number of trees, maximum depth, and minimum number of split samples. Extensive experiments and evaluations conducted in this study revealed that the hybrid model outperformed the standalone Random Forest classification. The incorporation of K-Means clustering facilitated the discovery of underlying data structures and patterns, ultimately enhancing the classifier's discriminatory ability. The hybrid model exhibited superior accuracy, precision, recall, and F1 scores, demonstrating its efficacy in accurately classifying breast cancer samples.
Large language models capsule: A research analysis of In-Context Learning (ICL) and Parameter-Efficient Fine-Tuning (PEFT) methods

Authors:
raw_author_name: Haokun Wu
raw_affiliation_string: Carnegie Mellon University

Abstract: In the context of natural language processing (NLP), this paper addresses the growing need for efficient adaptation techniques for pre-trained language models. It begins by summarizing the current landscape of NLP, highlighting the challenges associated with fine-tuning large language models like BERT and Transformer. The paper then introduces and analyzes three categories of parameter-efficient fine-tuning (PEFT) approaches, namely, In-Context Learning (ICL)-inspired Fine-Tuning, Low-Rank Adaptation PEFTs (LoRA), and Activation-based PEFTs. Within these categories, it explores techniques such as prefix-tuning, prompt tuning, (IA)3, and LoRA, shedding light on their advantages and applications. Through a comprehensive examination, this paper concludes by emphasizing the interplay between performance, parameter efficiency, and adaptability in the context of NLP models. It also provides insights into the future prospects of these techniques in advancing the field of NLP. To summarize, this paper offers a detailed analysis of PEFT methods and their potential to democratize access to cutting-edge NLP capabilities, paving the way for more efficient model adaptation in various applications.
Review of collaborative filtering recommendation systems

Authors:
raw_author_name: Xueyan Wu
raw_affiliation_string: East China University of Science and Technology

Abstract: In the era of information overload, recommender systems develop rapidly. And because the needs of information consumers are full of diversity and the information data provided by information producers is too large, to enhance the efficiency and quality of recommendations, the research community has introduced numerous approaches to optimize recommendation systems. As collaborative filtering stands as a time-tested technique in recommendation systems, This paper facilitates a swift comprehension of recent advances in collaborative filtering. It does so by examining the techniques presented across the entire collaborative filtering recommendation systems research field in recent years, especially its development in the domain of deep learning, and have a solid understanding of the field of study.
Transformers: Statistical interpretation, architectures and applications

Authors:
raw_author_name: Fanfei Meng
raw_affiliation_string: Northwestern University
raw_author_name: Yuxin Wang
raw_affiliation_string: Northwestern University

Abstract: Transformers have been widely recognized as powerful tools to analyze multiple tasks due to its state-of art multi-head attention spaces, such as Natural Language Processing (NLP), Computer Vision (CV) and Speech Recognition (SR). Inspired by its abundant designs and strong functions on analyzing input data, we would like to start from the various architectures, further proceed to the investigation on its statistical mechanism and inference and then introduce its applications on dominant tasks. The underlying statistical mechanisms arouse our interests and intrigue us to investigate it in a higher level, and this surveys will focus on its mathematical foundations and then use the principles to try to analyze the reasons for its excellent performance on many recognition scenarios.
Soft and Hard Skills to Be Developed By a Pre-Service Teacher under Distance Learning: Case Study

Authors:
raw_author_name: Ivonne Daniela Orjuela Romero
raw_affiliation_string: Universidad Santo Tomas

Abstract: This article reflects on the development of soft and hard skills and their importance within the academic and educational course of students who study a degree in English as a foreign language under the distance modality. At the center of this study, the opinions of 46 students from various semesters underscore the fundamental role that the cultivation of these competencies plays in distance education. The qualitative approach and NVIVO software were used in a multiple case study to carry out axial coding. In this way, the information was classified into 6 categories, supported by the opinions provided by the survey participants: Personal Growth, Assertive Communication, Professional Enrichment, Digital Management, Pedagogical Confrontation, and English Proficiency. By analyzing the perspectives of these students, the research concludes that pre-service teachers have a valuable perspective on the development of competencies and recognize the integration of these competencies in their distance learning path, promoting personal and professional growth for their future roles.
Portable Referee System for Volleyball Game Based on Pressure Monitoring and Self-Powering Communication

Authors:
raw_author_name: Jun WU

Abstract: Sports refereeing has traditionally relied on human judgement, which can be prone to errors. However, the development of automatic referee systems has the potential to address this issue, improve training, and promote sports in the future. In this study, we present a novel and portable referee system specifically designed for volleyball games, which utilizes piezoelectric sensors to detect pressure and simultaneously harvest energy for powering the system. The system consists of two parts: a wearable glove and a device mounted on the volleyball. The glove part has five sensors fixed on the fingertips and the volleyball has eight sensors evenly distributed on its surface. The collected contact data from the sensors is wirelessly transmitted to a personal computer. Our experimental results demonstrate that the system can accurately capture a three-second contact and detect foul actions in a volleyball game. These findings highlight the potential of the system for sports and other monitoring applications.
ANALYSIS OF NEEDS FOR DEVELOPING TEACHING MATERIALS FOR WRITING ADVERTISING TEXTS BASED ON ANDROID PLATFORM APPLICATIONS FOR CLASS VIII STUDENTS

Authors:
raw_author_name: Gusti Yarmi

Abstract: This research is motivated by the low ability of students to write attractive advertising texts. This is because the teaching materials available in schools are not yet able to facilitate students being able to write text advertisements well. The research carried out in this study was descriptive qualitative research, namely to describe students' needs for teaching materials that suit the students' needs