Reviewerly/exHarmony huggingface dataset:
Development of Physical Education Learning Media for Football Material forJunior High School Class VII Students on Smartphones Based on AndroidApplications

Authors:
raw_author_name: Ujang Nurdin
raw_author_name: Afeb Chesa Arianto

Abstract: This study aims to (1) produce a product development of Physical Education Sports And Health learning media for soccer material on android application-based smartphones for Junior High School students, (2) determine the feasibility of Physical Education Sports And Health learning media development products for soccer material on android application-based smartphones for Junior High School students, and (3) determine the level of practicality Physical Education Sports And Health learning development products for soccer material on android application-based smartphones for Junior High School students. This research is a development research with the ADDIE approach model, namely: analyse, design, develop, implement, and evaluate. The subjects of this study were a small-scale trial of 7 students in class VII SMP N 2 Sewon, a large-scale trial subject of 30 students in class VII SMP N 2 Bantul, expert validation including material experts and media experts. The results showed that Physical Education Sports And Health learning media product of soccer material on smartphones based on android applications had a very good / very feasible level of feasibility. The level of product feasibility based on material expert validation reached an average of 90% with a very feasible category, media expert validation reached 86.6% with a very feasible category, small-scale trials reached an average of 92.3% with a very good / very feasible category, large-scale trials reached an average of 90.2% with a very good / very feasible category.
Sensing Data Concealment in NFTs: A Steganographic Model for Confidential Cross-Border Information Exchange

Authors:
raw_author_name: Ghassan Al-Sumaidaee
raw_affiliation_string: Department of Electrical and Computer Engineering, McGill University, Montréal, QC H3A 0G4, Canada
raw_author_name: Željko Žilić
raw_affiliation_string: Department of Electrical and Computer Engineering, McGill University, Montréal, QC H3A 0G4, Canada

Abstract: In an era dominated by rapid digitalization of sensed data, the secure exchange of sensitive information poses a critical challenge across various sectors. Established techniques, particularly in emerging technologies like the Internet of Things (IoT), grapple with inherent risks in ensuring data confidentiality, integrity, and vulnerabilities to evolving cyber threats. Blockchain technology, known for its decentralized and tamper-resistant characteristics, stands as a reliable solution for secure data exchange. However, the persistent challenge lies in protecting sensitive information amidst evolving digital landscapes. Among the burgeoning applications of blockchain technology, non-fungible tokens (NFTs) have emerged as digital certificates of ownership, securely recording various types of data on a distributed ledger. Unlike traditional data storage methods, NFTs offer several advantages for secure information exchange. Firstly, their tamperproof nature guarantees the authenticity and integrity of the data. Secondly, NFTs can hold both immutable and mutable data within the same token, simplifying management and access control. Moving beyond their conventional association with art and collectibles, this paper presents a novel approach that utilizes NFTs as dynamic carriers for sensitive information. Our solution leverages the immutable NFT data to serve as a secure data pointer, while the mutable NFT data holds sensitive information protected by steganography. Steganography embeds the data within the NFT, making them invisible to unauthorized eyes, while facilitating portability. This dual approach ensures both data integrity and authorized access, even in the face of evolving digital threats. A performance analysis confirms the approach's effectiveness, demonstrating its reliability, robustness, and resilience against attacks on hidden data. This paves the way for secure data transmission across diverse industries.
VisFormers—Combining Vision and Transformers for Enhanced Complex Document Classification

Authors:
raw_author_name: Subhayu Dutta
raw_affiliation_string: Department of Computer Science & Engineering, Dr. B.C. Roy Engineering College, Durgapur 713206, West Bengal, India
raw_author_name: Subhrangshu Adhikary
raw_affiliation_string: Department of Research & Development, Spiraldevs Automation Industries Pvt. Ltd., Raiganj 733123, West Bengal, India
raw_author_name: Ashutosh Dhar Dwivedi
raw_affiliation_string: Cyber Security Group, Aalborg University, DK-2000 Copenhagen, Denmark

Abstract: Complex documents have text, figures, tables, and other elements. The classification of scanned copies of different categories of complex documents like memos, newspapers, letters, and more is essential for rapid digitization. However, this task is very challenging as most scanned complex documents look similar. This is because all documents have similar colors of the page and letters, similar textures for all papers, and very few contrasting features. Several attempts have been made in the state of the art to classify complex documents; however, only a few of these works have addressed the classification of complex documents with similar features, and among these, the performances could be more satisfactory. To overcome this, this paper presents a method to use an optical character reader to extract the texts. It proposes a multi-headed model to combine vision-based transfer learning and natural-language-based Transformers within the same network for simultaneous training for different inputs and optimizers in specific parts of the network. A subset of the Ryers Vision Lab Complex Document Information Processing dataset containing 16 different document classes was used to evaluate the performances. The proposed multi-headed VisFormers network classified the documents with up to 94.2% accuracy, while a regular natural-language-processing-based Transformer network achieved 83%, and vision-based VGG19 transfer learning could achieve only up to 90% accuracy. The model deployment can help sort the scanned copies of various documents into different categories.
Predicting classroom activity index through multi-scale head posture classification network

Authors:
raw_author_name: Zheng Zhong
raw_affiliation_string: National Engineering Research Center for E-learning (NERCEL), Central China Normal University (CCNU), Wuhan, China
raw_author_name: Kunyan Li
raw_affiliation_string: National Engineering Research Center for E-learning (NERCEL), Central China Normal University (CCNU), Wuhan, China
raw_author_name: Xiaoyu Zeng
raw_affiliation_string: National Engineering Research Center for E-learning (NERCEL), Central China Normal University (CCNU), Wuhan, China

Abstract: The quantification of the interplay between student behavior data and classroom teaching effectiveness using quantitative metrics has perennially posed a challenge in the evaluation of classroom instruction. Classroom activity serves as a reflection of student engagement, emotional ambiance, and other pertinent aspects during the pedagogical process. This article presents a methodology for quantifying student head posture during classroom instruction utilizing AI-driven video analysis technology, notably the Classroom Activity Index (CAI). A Classroom Activity Analysis System (CAAS) was designed and developed, integrating a multi-scale classification network based on ECA-ResNet50 and ECA-ResNet18. This network discerns and categorizes various head regions of students situated in both the frontal and real rows of a lecture-style classroom, irrespective of their dimensions. The classification network attains exceptional performance, boasting F1 score of 0.91 and 0.92 for student head-up and head-nodding. Drawing on the live classroom instruction at a higher vocational college in Wuhan, Hubei Province, China, a comparative experiment was executed. The findings revealed that three factors: teacher-student verbal interaction, teacher body language, and utilization of digital resource, all exert an influence on CAI. Simultaneously, the degree of classroom activity as gauged by FIAS and manual analysis fundamentally aligns with the CAI indicators quantified by CAAS, validating the efficacy of CAI in the quantification of classroom activity. Consequently, the incorporation of CAAS in teaching, research, and oversight scenarios can augment the precision and scientific rigor of classroom teaching assessment.
Research of news text classification method based on hierarchical semantics and prior correction

Authors:
raw_author_name: Ping Sun
raw_affiliation_string: Wuhan Vocational College of Software and Engineering, Wuhan, Hubei, China
raw_author_name: LinLin Song
raw_affiliation_string: School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
raw_author_name: Ling Yuan
raw_affiliation_string: School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
raw_author_name: Haiping Yu
raw_affiliation_string: Wuhan Vocational College of Software and Engineering, Wuhan, Hubei, China
raw_author_name: Yinzhen Wei
raw_affiliation_string: Wuhan Vocational College of Software and Engineering, Wuhan, Hubei, China

Abstract: News text is an important branch of natural language processing. Compared to ordinary texts, news text has significant economic and scientific value. The characteristics of news text include structural hierarchy, diverse label categories, and limited high-quality annotation samples. Many machine learning and deep learning methods exist to analyze various forms of news text. However, due to label imbalance, hierarchical semantics, and confusing labels, current methods have limitations. Therefore, this paper proposes a news text classification framework based on hierarchical semantics and prior correction (HSPC). Firstly, data augmentation is used to enhance the diversity of the training set and adversarial learning is employed to improve the resistance of the model with its robustness. Then, a hierarchical feature extraction approach is employed to extract semantic features from different levels of news texts. Consequentially, a feature fusion method is designed to allow the model to focus on relevant hierarchical semantics for label classification. Finally, highly confusing label predictions are corrected to optimize the label prediction of the model and improve confidence. Multiple experiments are performed on four widely used public datasets. The experimental results indicate that HSPC achieves higher classification accuracy compared to other models. On the FCT, AGNews, THUCNews, and Ohsumed datasets, HSPC improves the accuracy by 1.03%, 1.38%, 2.55%, and 1.15%, respectively, compared to state-of-the-art methods. This validates the rationality and effectiveness of the designed mechanisms.
Exploring Effective Models of Leadership in the Age of AI within Private Higher Education Institution in England

Authors:
raw_author_name: Sarwar Khawaja
raw_affiliation_string: SK HUB LTD

Abstract: Artificial intelligence (AI) has gained significant interest and application in various fields and the impacts are evident in teaching, learning, and assessment. On one hand, AI can bring the much-needed revolution in higher education by assisting leaders and management to offer personalized teaching approaches that meet specific student needs, it can also enhance efficient feedback and the automation of administrative functions including assessment and grading. These capabilities make AI an important instrument in promoting efficiency in higher education and creating room for institutions to redirect their focus on other equally important aspects such as curriculum development. However, despite these advantages, AI comes with significant setbacks that adversely impact the quality of education. Particularly, various platforms allow students to do their assignments automatically thus negating their responsibility to research and learn. As such, institutions must put in place effective models of leadership and approaches that will ensure the use of AI in an ethical manner without compromising the quality of education.
Natural Language Processing Advancements: Breaking Barriers in Human-Computer Interaction

Authors:
raw_author_name: José Gabriel Carrasco Ramírez
raw_affiliation_string: Lawer graduated at Universidad Católica Andrés Bello. Caracas. Venezuela. / CEO, Quarks Advantage. Jersey City, United States. / Director at Goya Foods Corp., S.A. Caracas. Venezuela

Abstract: Natural Language Processing (NLP) advancements have revolutionized human-computer interaction, breaking barriers and opening new frontiers in technology. NLP techniques enable machines to understand, interpret, and generate human language, facilitating seamless communication between humans and computers. This paper explores recent advancements in NLP technology, highlighting their impact on various domains and discussing challenges and future directions in the field
Creating an atlas of normal tissue for pruning WSI patching through anomaly detection

Authors:
raw_author_name: Peyman Nejat
raw_affiliation_string: KIMIA Lab, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA
raw_author_name: Areej Alsaafin
raw_affiliation_string: KIMIA Lab, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA
raw_author_name: Dennis H. Murphree
raw_affiliation_string: Department of Dermatology, Mayo Clinic, Rochester, MN, USA
raw_author_name: Saba Yasir
raw_affiliation_string: Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
raw_author_name: Joaquin J. Garcia
raw_affiliation_string: Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
raw_author_name: H. R. Tizhoosh
raw_affiliation_string: KIMIA Lab, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA

Abstract: Patching whole slide images (WSIs) is an important task in computational pathology. While most of them are designed to classify or detect the presence of pathological lesions in a WSI, the confounding role and redundant nature of normal histology are generally overlooked. In this paper, we propose and validate the concept of an "atlas of normal tissue" solely using samples of WSIs obtained from normal biopsies. Such atlases can be employed to eliminate normal fragments of tissue samples and hence increase the representativeness of the remaining patches. We tested our proposed method by establishing a normal atlas using 107 normal skin WSIs and demonstrated how established search engines like Yottixel can be improved. We used 553 WSIs of cutaneous squamous cell carcinoma to demonstrate the advantage. We also validated our method applied to an external dataset of 451 breast WSIs. The number of selected WSI patches was reduced by 30% to 50% after utilizing the proposed normal atlas while maintaining the same indexing and search performance in leave-one-patient-out validation for both datasets. We show that the proposed concept of establishing and using a normal atlas shows promise for unsupervised selection of the most representative patches of the abnormal WSI patches.
Challenges in the Adoption of Mobile Money Services by Mobile Phone Users in Lusaka, Zambia.

Authors:
raw_author_name: Andrew Mweemba Nyika

Abstract: Purpose: This study investigates mobile money adoption in Lusaka, Zambia, utilizing a mixed-methods approach.&#x0D; Methodology: The study employs a mixed-methods approach to explore mobile money adoption in Lusaka, Zambia.&#x0D; Findings: The study uncovers several key findings regarding mobile money adoption in Lusaka, Zambia. Convenience, ease of use, and perceived security emerge as primary motivators for adoption. Conversely, lack of understanding, perceived risk, and a preference for traditional banking methods are identified as major deterrents. Interestingly, cultural factors, such as religious beliefs, play a minimal role in adoption decisions. Demographic factors, including age, education level, and income, exhibit a complex interplay with adoption rates, with younger, more educated, and higher-income individuals generally more inclined to use mobile money.&#x0D; Unique Contribution to Theory, Practice, and Policy: The study's findings contribute to both theory and practice in the field of mobile money adoption. They highlight the predominance of practical considerations over cultural factors in technology adoption, suggesting the need for enhanced adoption models that go beyond traditional socio-economic determinants. Additionally, the study identifies technological discomfort as a significant deterrent to adoption, indicating a need for improved digital literacy and user-friendly interfaces. Furthermore, an awareness-confidence paradox is revealed, wherein increased awareness of cyber scams negatively impacts trust in mobile money security. This suggests the importance of balancing risk education with strong security reassurances. Fee perception also emerges as a hurdle, with over 60% of respondents perceiving fees as unfair, underscoring the importance of educating users on the value proposition of mobile money services. Overall, the study offers valuable insights for strategic product and service development, regulatory and policy formulation, and initiatives aimed at advancing financial inclusion and promoting economic growth in similar urban and semi-rural settings.
Chatbot Service Quality in Banking : Analyzing Indian Banking Customer Perceptions and Influence on Customer Satisfaction and Value

Authors:
raw_author_name: Shivi Khanna
raw_affiliation_string: Associate Professor, CHRIST (Deemed to be University), Bennerghatta Main Road, Pai Layout, Hulimavu, Bengaluru - 560 076, Karnataka

Abstract: Purpose : The study has two objectives : first, to determine the quality of chatbot services provided by Indian banks; second, to assess the influence of chatbot service quality variables on customer satisfaction and customer value. Research Methodology : The study used a quantitative methodology, selecting individuals at random from a group of Indian banking clients. We used a questionnaire to collect data from the selected sample as part of a causal research investigation. We made use of SPSS and Python for this analysis. Customer satisfaction and value were taken into account as the dependent variables in our study. The seven elements of service quality—functionality, convenience, security, design, customization, enjoyment, and assurance—made up the independent variables. Findings : According to this study, client satisfaction and value were significantly shaped by the quality of the services provided. Customers’ value was significantly impacted by functionality and enjoyment, and their satisfaction was greatly influenced by assurance, design, and personalization. The unexpected negative impact assurance had on customer value is noteworthy and calls for more research. Practical Implications : In the highly competitive banking industry, this research has important ramifications for banks. It highlighted how important service quality is, which led banks to give priority to customer pleasure and think about making strategic changes. Banks could obtain a competitive advantage by improving the quality of their services, improving chatbot services, and implementing a customer-centric strategy by utilizing the research findings that have been presented. Our research helped banks evolve with the needs of their customers in mind, enabling them to gain credibility, repeat business, and long-term success in the ever-changing banking services market. Originality/Value : This study examined how consumers in Indian banks perceive the value and satisfaction of chatbot services and how they use them. The study provided useful recommendations and concepts to improve the general consumer experience.
Candidate Selection in Teams: Be the Best or Surround Yourself with the Best?

Authors:
raw_author_name: Jeremy B. Lill
raw_affiliation_string: The University of Kansas

Abstract: ABSTRACT This study investigates, via an experiment, how the decentralization of a firm’s selection process affects the caliber of the chosen candidate in a team-based environment. We predict and find that, when decision makers have comprehensive and unambiguous candidate-specific information regarding who is the best for the job, the quality of the selected candidates is lower under a decentralized versus centralized selection process. We also find that nonpecuniary status concerns drive the effect. Results of two boundary conditions reveal that, as the clarity of information regarding who is the best candidate for the job decreases (due to decision-makers having weaker or mixed signals about job candidates), the quality of selected candidates becomes no worse under a decentralized than under a centralized selection process. Overall, our results indicate that nonpecuniary status considerations and information environment can influence candidate selection decisions in organizations. Data Availability: Data are available from the authors upon request.
Blockchain in the Lens of Populism : A Short Communication of Research Evidence

Authors:
raw_author_name: Aarti Mehta Sharma

Abstract: Purpose : Blockchains are a sophisticated and extensively utilized technology that facilitates bulk data management operations and increases productivity for many companies, including governments. However, because of the high prices and complexity, not all large firms could take advantage of this disruptive weapon, which leads to populist ideas. The purpose of this brief message was to examine the new, popular ideas in blockchain technology that improve the global productive efficiencies of businesses. Methodology : The two-stage method that produced the insights in this communication was followed by a sentiment analysis, network map, theme map, and latent Dirichlet allocation. Findings : Blockchain technology has been widely adopted by the majority of the major services businesses. The industries that are currently scaling up, however, lack the viability to use this technology. The results also showed that neither academic research nor the corporate framework had examined the new populism situation. Practical Implications : The productive efficiency of industries would be improved if governments and policymakers used populistic settings in blockchain technology. Additionally, eliminating the cost-based inequalities would inspire populist ideas throughout all industries, generating combined revenues. Originality/Value : This brief message implied negative opinions about blockchain technology and was taken from a significant study on the impact of this technology on business success. This message is meant to benefit not only corporations but also governments and policymakers in facilitating regular access to technology for expanding industries that could strengthen economies.