Reviewerly/exHarmony huggingface dataset:
Penerimaan Teknologi ChatGPT Di Kalangan Mahasiswa: Studi Deskriptif Model TAM Pada Mahasiswa Program Studi Teknik Informatika Universitas Negeri Padang

This research examines the acceptance of ChatGPT technology among students of the 2020 Informatics Engineering study program, Faculty of Engineering, Padang State University using the TAM model. This research aims to describe the acceptance of ChatGPT among students. The type of research used is quantitative research with a case study approach. The method used is descriptive research method. Data collection techniques through observation, interviews and questionnaires. Sampling in this research was by using saturated samples. Research data was obtained from the results of a questionnaire distributed to 89 samples who were informatics engineering students class of 2020. Based on the research results, it can be concluded that there is acceptance of ChatGPT technology among students of the Informatics Engineering Study Program Class of 2020, Faculty of Engineering, Padang State University, with five indicators which are obtained an average value of 2.61. The indicators include (1) Perception of ease of use, obtaining an average value of 2.64 whose interval scale is positive or categorized as good, meaning that the ease of use of ChatGPT so that it does not require special skills can be accepted by students of the Informatics Engineering Study Program Class of 2020. ( 2) Perception of user usefulness, obtained an average value of 2.65 whose interval scale is positive or categorized as good, meaning that ChatGPT provides benefits in helping user performance so that the indicator can be accepted by students of the 2020 Informatics Engineering Study Program. (3) Intention to use , obtained an average score of 2.57, which is a positive interval scale or categorized as good, meaning that the use of ChatGPT which will be reused over a longer period of time can be accepted by students of the 2020 Informatics Engineering Study Program. (4) Real conditions for using the system obtained an average score of 2.60 on a positive interval scale or categorized as good, meaning that users use ChatGPT truly to help user performance so that it can be accepted by students of the Informatics Engineering Study Program Class of 2020. (5) Attitude towards use, gain The average value is 2.61, the interval scale is positive or can be categorized as good, meaning that based on the evaluation of ChatGPT users, the impact of using ChatGPT can be felt so that this indicator can be accepted by students of the 2020 Informatics Engineering Study Program. Even though it is categorized as good, the use of ChatGPT can has a negative impact on students, such as dependence on ChatGPT which will affect students' critical thinking abilities in doing assignments and a lack of social interaction such as discussions because they rely too much on ChatGPT when doing assignments.&#x0D; &#x0D;
[Call attention to the overall benefit of digital pathology and promote its development].

The successful operation of the complete digital pathology(CDP) in foreign countries indicates that the full digital pathology process has entered the full implementation stage. Digital pathology in China started late and progressed slowly, so far there has not been a truly meaningful CDP. The pathologist's understanding of digital pathology is not comprehensive enough, and there are still doubts about the time efficiency and cost effectiveness of digital pathology processes. Therefore,a comprehensive analysis of the process, overall advantages and cost-effectiveness of CDP was made by drawing on international successful experience and hands-on practice experience, in order to promote the construction and development of the CDP in our country.国外全数字化病理科的成功运行，标志着全数字病理流程已进入全面实施阶段。国内数字病理起步晚、进展慢，至今尚未有真正意义的全数字病理科出现。业界对数字病理的认识还不够全面深入，对数字病理流程的时间效率和成本效益还存有疑虑。因此，作者借鉴国际上的成功经验和实践感受对全数字化病理的流程、整体优势和成本效益等进行了全面的解析，以期促进我国的数字化病理科建设和发展。.
21st Century Learning Trends : What Educators Need to Know

In the 21st century, a paramount emphasis lies on the education of the 21st century and the cultivation of multifaceted skills that bolster students in acclimating to dynamic transformations. A pivotal trajectory in educational paradigms manifests through the amalgamation of technology and the adoption of digital pedagogical methodologies. The transition towards this technological integration is propelled by the imperative to assimilate 21st-century proficiencies, fortifying students for an ever-evolving technological milieu. Concurrently, there is an accentuation on honing students' faculties of critical reasoning and amplifying scientific acumen. Character education assumes a centrality, underscoring its resonance with the perspectives of students navigating the complexities of the 21st century. Moreover, 21st-century education encompasses the advocacy for innovative aptitudes and entrepreneurial prowess. Global educational trends steer towards STEM-oriented frameworks, augmenting students' adeptness in science, technology, engineering, and mathematics. In summation, education in the 21st century is intricately entwined with technology, globalization, and the imperative for students to foster competencies germane to an expeditiously evolving world.
Impact of Corporate Social Responsibility Initiatives on Women’s Empowerment in India: A Comprehensive Analysis

Corporate Social Responsibility (CSR) initiatives have gained prominence globally as a means to address social issues, including gender equality and women’s empowerment. In India, where gender disparities persist across various socio-economic indicators, CSR initiatives hold significant potential to contribute to the empowerment of women. This research article presents a comprehensive analysis of the impact of CSR initiatives on women’s empowerment in India. Through a systematic review of existing literature, case studies, and empirical data, this study evaluates the effectiveness of CSR programs in promoting gender equality, enhancing economic opportunities, and advancing social inclusion for women. The article also explores the challenges and opportunities encountered in the implementation of CSR initiatives aimed at women’s empowerment, considering diverse sectors and geographical contexts within India. Furthermore, the article discusses the role of government policies, institutional frameworks, and stakeholder collaborations in facilitating meaningful CSR interventions for women’s empowerment. By critically assessing the outcomes and limitations of CSR efforts, this research contributes to a nuanced understanding of the potential of corporate engagement in advancing gender equality and women’s empowerment in India.
Engaging Community College Students in Computer Engineering Research through Design and Implementation of a Versatile Gesture Control Interface

Abstract Given the important role of community colleges in undergraduate education, in order to increase the recruitment of STEM students and improve undergraduate STEM education, it is crucial to develop strategies to inspire community college students' interest in STEM. With support from the Department of Education Minority Science and Engineering Improvement Program (MSEIP), a cooperative internship program between a community college and a public comprehensive university was developed to engage community college students in leading-edge engineering research. In summer 2016, five sophomore students from the community college participated in a ten-week computer engineering research internship project in a research lab at the four-year university. This internship project aimed to develop an intelligent electromyography (EMG)-based gesture control interface which deciphers EMG signals collected from forearm muscles to identify users' intended hand and arm movements. The gesture control interface has great potential to provide natural human-machine interaction in a variety of applications, from assistive devices through rehabilitation therapy to virtual reality (VR). The developed interface provides easy connection with a commercial EMG-based armband Myo and a modular software engine for customizable gesture recognition as well as a special pipeline for converting recognition decisions into control commands for external applications. The students also conducted usability testing of the gesture control interface on human subjects by using it to control a first-person shooter (FPS) virtual reality game which was developed by another community college student intern. The project provided a great opportunity for the student interns to gain research experience and learn valuable knowledge in human-machine interfaces, EMG signal processing, and gesture recognition. It also helped them improve their skills in experimental design, data analysis, scientific writing and presentation, as well as teamwork and time management. The outcome of this project indicated that the internship program was an effective method for inspiring community college students' interest in computer engineering research and strengthening their confidence and capability in pursuing an engineering profession.
What do we know about cryptocurrency investment? An empirical study of its adoption among Indian retail investors

Purpose This study aims to examine the cryptocurrency adoption (CA) level among Indian retail investors who use cryptocurrency as an investment and mode of transaction. Design/methodology/approach Through self-administered survey questionnaires, data is collected from 397 retail investors of Haryana (India). This study adopted a quantitative method using partial least squares structural equation modeling (PLS-SEM). Findings This paper offered a robust model with a high explanatory value for CA in which four of the five proposed factors of diffusion of innovation theory (trialability, compatibility, complexity and observability) and one of the two proposed factors of consumer behavioral theory (perceived value) significantly influences CA. More specifically, the absence of regulatory support is a barrier to the broad adoption of cryptocurrencies, as its regulations are necessary to mitigate or minimize uncertain outcomes. Research limitations/implications This research primarily focuses on CA in India. Thus, it can be extended to cover diverse other countries for more precise results. Practical implications The results provide insights to the government to design the policies, better regulate and make investment strategies that can ultimately enhance CA. In addition, the study’s results also inform financial educators, policymakers, employers and academicians about the significance of several variables affecting CA in India. Social implications From a social standpoint, this study is an advance that directs central banks and governments to develop, regulate and manage digital currencies and implement a digital currency ecosystem. Moreover, the results assist in understanding investors’ perceptions and decision-making perspectives toward cryptocurrencies through the country’s digitalization. Originality/value This paper fills the study gap to assist policymakers and cryptocurrency experts in broadening their knowledge base and recognizing prioritized intentions. Additionally, this study provides a theoretical model with the latent variable for a present and pertinent matter.
Transformation of Crimes (Cybercrimes) in Digital Age

The cybercrime thrives, understanding its evolution is critical for security and policy interventions. This research profiles cyber threat trends since the 1950s alongside legal developments. Background examines early hacking cultures, antivirus innovations, and seminal cases that set precedents. The objectives encompass analyzing major technical and legal inflection points over recent decades regarding cybercrimes. The study's aim is validating escalating cyber risks that demand urgent attention. Doctrinal methodology leverages scholarly journals, case law, and industry data. Key findings reveal sophisticated social engineering tactics, gaps in legal deterrence frameworks, and infrastructure vulnerabilities that underscore needs for global cooperation. Recommendations center on proactive reforms to cybersecurity statutes, public awareness, and cross-border collaboration to mitigate emerging threats. Tracing cybercrime's timeline provides vital context to catalyze preemptive actions against potential attacks in an increasingly interconnected online ecosystem.
Alteration Mapping for Porphyry Cu Targeting in the Western Chagai Belt, Pakistan, Using ZY1-02D Spaceborne Hyperspectral Data

Abstract The Chagai porphyry Cu belt in Pakistan is an important metallogenic terrain extending approximately 400 km in an east-west direction. Most of the known porphyry Cu deposits, such as the world-class Reko Diq deposit and Saindak deposit, are located in the western Chagai belt. In this study, the ZY1-02D hyperspectral data acquired by a recently launched spaceborne imaging spectrometer with 166 bands within a 0.4- to 2.5-μm spectral region were used to map mineral information over 8,000 km2 for exploring potential targets of porphyry Cu mineralization in the western Chagai belt. False color composite, spectral angle mapper, and wavelength position mapping methods were used in this research leading to the identification of a series of alteration minerals (including muscovite [sericite], kaolinite, alunite, epidote, chlorite, and calcite) from ZY1-02D data. The alteration mineral maps derived from ZY1-02D data match well with the known deposits and field inspections. Twenty-three new targets were identified as potential porphyry Cu mineralization targets for further exploration in the study area. Three targets, north of Saindak, Koh-i-Sultan, and Durban Chah, and six alteration sites in the southwest of Durban Chah, were inspected in the field, and Cu-Au mineralization was confirmed in all these inspected areas. As the ZY1-02D hyperspectral data covers most of the land area of the earth, this study provides new insights for mineral exploration and lithologic mapping in remote regions.
Cross-scale multi-instance learning for pathological image diagnosis

Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). However, such processing is typically performed at a single scale (e.g., 20× magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale relationships into a single MIL network for pathological image diagnosis. The contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL) algorithm that integrates the multi-scale information and the inter-scale relationships is proposed; (2) A toy dataset with scale-specific morphological features is created and released to examine and visualize differential cross-scale attention; (3) Superior performance on both in-house and public datasets is demonstrated by our simple cross-scale MIL strategy. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.
exKidneyBERT: a language model for kidney transplant pathology reports and the crucial role of extended vocabularies

Background Pathology reports contain key information about the patient’s diagnosis as well as important gross and microscopic findings. These information-rich clinical reports offer an invaluable resource for clinical studies, but data extraction and analysis from such unstructured texts is often manual and tedious. While neural information retrieval systems (typically implemented as deep learning methods for natural language processing) are automatic and flexible, they typically require a large domain-specific text corpus for training, making them infeasible for many medical subdomains. Thus, an automated data extraction method for pathology reports that does not require a large training corpus would be of significant value and utility. Objective To develop a language model-based neural information retrieval system that can be trained on small datasets and validate it by training it on renal transplant-pathology reports to extract relevant information for two predefined questions: (1) “What kind of rejection does the patient show?”; (2) “What is the grade of interstitial fibrosis and tubular atrophy (IFTA)?” Methods Kidney BERT was developed by pre-training Clinical BERT on 3.4K renal transplant pathology reports and 1.5M words. Then, exKidneyBERT was developed by extending Clinical BERT’s tokenizer with six technical keywords and repeating the pre-training procedure. This extended the model’s vocabulary. All three models were fine-tuned with information retrieval heads. Results The model with extended vocabulary, exKidneyBERT, outperformed Clinical BERT and Kidney BERT in both questions. For rejection, exKidneyBERT achieved an 83.3% overlap ratio for antibody-mediated rejection (ABMR) and 79.2% for T-cell mediated rejection (TCMR). For IFTA, exKidneyBERT had a 95.8% exact match rate. Conclusion ExKidneyBERT is a high-performing model for extracting information from renal pathology reports. Additional pre-training of BERT language models on specialized small domains does not necessarily improve performance. Extending the BERT tokenizer’s vocabulary library is essential for specialized domains to improve performance, especially when pre-training on small corpora.
Human-AI collaboration patterns in AI-assisted academic writing

Artificial Intelligence (AI) has increasingly influenced higher education, notably in academic writing where AI-powered assisting tools offer both opportunities and challenges. Recently, the rapid growth of generative AI (GAI) has brought its impacts into sharper focus, yet the dynamics of its utilisation in academic writing remain largely unexplored. This paper focuses on examining the nature of human-AI interactions in academic writing, specifically investigating the strategies doctoral students employ when collaborating with a GAI-powered assisting tool. This study involves 626 recorded activities on how ten doctoral students interact with GAI-powered assisting tool during academic writing. AI-driven learning analytics approach was adopted for three layered analyses: (1) data pre-processing and analysis with quantitative content analysis, (2) sequence analysis with Hidden Markov Model (HMM) and hierarchical sequence clustering, and (3) pattern analysis with process mining. Findings indicate that doctoral students engaging in iterative, highly interactive processes with the GAI-powered assisting tool generally achieve better performance in the writing task. In contrast, those who use GAI merely as a supplementary information source, maintaining a linear writing approach, tend to get lower writing performance. This study points to the need for further investigations into human-AI collaboration in learning in higher education, with implications for tailored educational strategies and solutions.
Mine-first association rule mining: An integration of independent frequent patterns in distributed environments

Association rule mining is a widely used data mining technique in various domains. It enables the identification of trends, frequent patterns, and relationships among the data. This study introduced a new method for mining association rules independently from multiple data sources. It combined the frequent patterns obtained from each data source to discover frequent patterns applicable across the distributed environment. The model can also be extended to generate the rules with the specified target. The proposed method’s performance is compared to that of the traditional association rule mining method. The experimental results demonstrate that while the generated rules may not be identical to those produced by the traditional method, the proposed model offers better transparency and memory utilization in association rule generation. In addition, the model uncovers meaningful relationships, allowing decision-makers to access the frequent patterns for the individual data sources and the entire data across the environment.
Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm

Abstract The novelty of this article lies in introducing a novel stochastic technique named the Hippopotamus Optimization (HO) algorithm. The HO is conceived by drawing inspiration from the inherent behaviors observed in hippopotamuses, showcasing an innovative approach in metaheuristic methodology. The HO is conceptually defined using a trinary-phase model that incorporates their position updating in rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained the top rank in 115 out of 161 benchmark functions in finding optimal value, encompassing unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, as well as the CEC 2019 test suite and CEC 2014 test suite dimensions of 10, 30, 50, and 100 and Zigzag Pattern benchmark functions, this suggests that the HO demonstrates a noteworthy proficiency in both exploitation and exploration. Moreover, it effectively balances exploration and exploitation, supporting the search process. In light of the results from addressing four distinct engineering design challenges, the HO has effectively achieved the most efficient resolution while concurrently upholding adherence to the designated constraints. The performance evaluation of the HO algorithm encompasses various aspects, including a comparison with WOA, GWO, SSA, PSO, SCA, FA, GOA, TLBO, MFO, and IWO recognized as the most extensively researched metaheuristics, AOA as recently developed algorithms, and CMA-ES as high-performance optimizers acknowledged for their success in the IEEE CEC competition. According to the statistical post hoc analysis, the HO algorithm is determined to be significantly superior to the investigated algorithms. The source codes of the HO algorithm are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/160088-hippopotamus-optimization-algorithm-ho .
AI-Based Sports Highlight Generation for Social Media

Social media plays a significant role for sports organizations with millions of active fans, but publishing highlights is often a tedious manual operation. With the development of AI, new tools are available for content generation and personalization to engage audiences. We propose an AI-based multimedia production framework for the automatic publishing of soccer and ice hockey highlights on social media, and disseminate our experiences with developing dedicated pipelines for event detection and classification, player detection and tracking, highlight clipping, cropping, thumbnail generation, game summarization, caption generation, and social media sharing.