Reviewerly/exHarmony huggingface dataset:
Improvement of raft consensus algorithm based on credit values on electronic medical record (EMR) blockchain

With the improvement of medical information security and sharing requirements, the blockchain Electronic Medical Record (EMR) platform has become an effective choice to solve the problem of securely storing and sharing medical information. The consensus mechanism is the key to EMR blockchain technology. Raft consensus is the most widely used non-Byzantine fault-tolerant consensus algorithm, which has the disadvantage that election disputes occur easily, and it cannot adapt to malicious nodes. Therefore, this paper proposes a raft consensus algorithm based on a credit evaluation model (CO-Raft) with the OC-SVM algorithm to filter out Byzantine nodes in the chain and build a dynamic node evaluation model based on the credit value to improve the speed of leader selection. Experiments show that the CO-Raft consensus mechanism in this paper provides better security and higher throughput in the EMR blockchain.
DMPFrame: A Conceptual Metadata Framework for Data Management Plans

AbstractResearch Data Management (RDM) is a complex process and to ensure good RDM practice, one of the ways is to associate or create Data Management Plans (DMPs) with every research project. Interestingly this opens up other challenges such as finding the corresponding DMP for a research dataset. It is sometimes difficult or impossible to find a particular DMP associated with a dataset, which undermines the actual value of DMPs. This is the result of poor metadata management of DMPs throughout their creation using a DMP tool to their archiving. Implementing adequate metadata management for the description and organization of DMPs might solve this issue. We have examined 12 open-source DMP tools, in particular, to evaluate the metadata adopted by these tools. The current study spots and highlights the gaps in the DMP metadata management in DMP tools and suggests DMPFrame as a conceptual framework addressing such gaps to improve the existing tools’ DMP metadata management practices. Based on the examined DMP tool’s metadata elements analysis and mapping, DMPFrame manages DMP metadata under 6 categories, namely, contributors, project, funding, organization, DMP, and output. The current study also suggests a systematic workflow that DMP tools could incorporate for metadata creation for DMPs.Keywords: DMP metadataresearch data managementdata management planDMPFrameDMPDMP toolRDM Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 VIVO: https://wiki.lyrasis.org/display/VIVO/VIVO+ontology+design+principles2 CERIF: https://eurocris.org/eurocris_archive/cerifsupport.org/cerif-in-brief/index.html
Classification and keyword extraction of online harassment text in Thai social network

Online harassment in social network services (SNS) is a type of cyberbullying issue that needs to be addressed and required preventive measures. In this paper, we develop a detection of cyberbullying regarding harassment textual posts in Thai on the Facebook SNS. We collect public posts and ask experts to label the post as positive or negative regarding harassment posts or not. The annotated data are trained for binary classification considering words in the centre as features to predict malicious intent to insult and threaten other users. The information gain score obtained in generating a prediction model is ranked for the top 20 words with the highest score as significant words involving online harassment. From experiments, the results show that the detection performance obtained a 0.78 f1 score on average. The result analysis indicated that the word surface approach helps detect insulting post decently, but some posts with metaphor to tone down the malicious intent may not be detected as harmful semantic intent are hidden behind word form. Top-20 significant words for bullying showed that bullying posts were body-shaming and lower social status.
Jumping particle swarm optimization algorithm framework for content-based image retrieval system

Content-based image retrieval (CBIR) has been studied well in the last decades in numerous research fields such as medicine, journalism, and private life. Applications of CBIR have been widely employed in medical images due to their direct impact on human life. With continues growing of digital libraries, there is a need for an efficient method to retrieve images from large datasets. In this paper, a new method was developed for CBIR based on the jumping particle swarm optimization (JPSO) algorithm. The proposed algorithm represents a developed instant of particle swarm optimization (PSO). However, JPSO the approach does not consider the velocity components to guide particle movements in the problem space. Instead of relying on inertia and velocity, intermittently random jumps (moves) occur from one solution to another within the discrete search space. To test the performance of the proposed algorithm, three types of medical image databases were used in the experiment which are the endoscopy 100, dental 100, and 50 skull image databases. The results show that the proposed algorithm could achieve high accuracy in image extraction and retrieve the accurate image category compared with other research works.
Police Training for Managing Crime: With Special Reference to the Crime Data Management System of Bangladesh Police

Initiations of the Crime Data Management System (CDMS) have led e-policing to a higher level of accuracy. The purpose of this study is to explore the existing training on CDMS, assess the training needed on CDMS, and recommend some ways forward. This study follows the mixed methodology where the primary data were collected by using surveys, key informant interviews (KIIs), and FGDs. This study reveals that since its initiation in 2005, CDMS has been working to depict case information, investigation reports, and crime analysis. In 2010, this system introduced online medium, and the Police Headquarters took over total control from the Crime Investigation Department (CID). Gradually in 2015, the Dhaka Metropolitan Police (DMP) introduced this system and in 2016, this system has been used at all police stations throughout the country. Moreover, this study also finds that from 2009 to 2020 more than half of the police officials did not receive any training on CDMS. The training receivers express their dissatisfaction with its short duration, and the aged and newly promoted officers remain vulnerable due to the partial training system. Additionally, police officials working in local thanas remain largely untrained and they also need to use this software on a regular basis. Social Science Review, Vol. 40(1), Jun 2023 Page 73-88
The Perception of Deepfake Technology Among University Students in Malaysia

It was alarming to know that a new technology (Deepfake) is paving the way to social media by demoralising popular people (celebrities) and others. It has badly affected reputation of many people without their knowledge and consent. It was decided to know about Deepfake technology at length and its extent in harming and hurting the people. A group of university students were randomly selected and administered through a questionnaire. The questionnaire comprised questions pertaining to awareness of deepfake and its consequences. The objectives of the study were to examine the emergence of Deepfake technology, the impact of Deepfake technology utility, and understanding the strategies to mitigate irresponsible usage of Deepfake technology among social media users. The descriptive data was analyzed using numbers and percentages. The major findings included its existence, impact on social media and strategies to mitigate irresponsible usage of Deepfake technology. A wider and larger diverse community using social media extensively will provide better results.
Management of Tracking in Real Time on a Website-based Laundry Information System

Dewi Jaya Laundry provides a variety of laundry services. However, the administrative process and transactions with customers are still not computerized, so it can take more time and customers cannot do tracking of their clothes. To overcome these problems, a solution was built in the form of a website-based information system that has a Real Time Tracking feature. Primary data collection was obtained directly from the research site, namely Dewi Jaya Laundry located on Perumnas street by making direct observations at Dewi Jaya Laundry. Meanwhile, we gather secondary knowledge from scientific articles and books on database development for websites. It utilizes Waterfall research methodology and is built with PHP and MySQL databases. This system is built through the stages of analysis, design, development, and testing. The black box method is used for system testing, which emphasizes the functionality of the system. The system's ability to perform its intended functions has been verified by the test results. This research succeeded in developing a laundry information system that has a Real Time Tracking feature that functions to monitor customer laundry packages, so customers can know which stage of the washing or drying process is taking place, so they can know when clothes are ready to be taken.
Exploring English as a Foreign Language (EFL) Learning Media in Vocational Higher Education

Employing suitable English learning media have the potential to underpin students’ language acquisition. The use of internet technology assists students to boost their English language skills. Selecting suitable English language topics to the E-learning platform promote to the learners’ eagerness to study in the classroom. This study investigated the suitable topics used for English for Tourism and Hospitality (ETH) in vocational higher education. The research used descriptive qualitative research which explored the topics use to teach (ETH) in English as a Foreign Language (EFL) classroom. Several obvious lessons were immerged to the E-learning system, for example, taking a reservation by phone, welcoming a guest, room service: taking an order, taking massages for a guest.
Analisis dan Implementasi Honeypot Honeyd Sebagai Low Interaction Terhadap Serangan Distributed Denial Of Service (DDOS) dan Malware

Every computer device connected to a wide computer network is vulnerable to security risks. These threats encompass vulnerabilities to data, information, resources, and services within the system. These threats include intrusion, eavesdropping, theft of vital data, as well as damage to the network system. These actions are carried out by parties who are not accountable, commonly referred to as intruders or attackers. One method to prevent or anticipate these malicious actions is by utilizing the honeyd Honeypot technique. The honeyd Honeypot adopts a low-interaction approach, which involves indirect interaction with attackers. This Honeypot serves as a decoy or simulated server intentionally presented as a target for attacks. The purpose of this Honeypot is to detect and analyze ongoing attacks. In this research, the honeyd Honeypot is implemented as a simulated server resembling an authentic server. This server provides various services and opens several ports deliberately prepared as attack targets, such as Port 139, and Port 21.The results of this research unveil the existence of attacks. Signs of these attacks include a surge in network traffic, reaching up to 100 Megabits above the normal level. Another indicator is a sudden spike in CPU usage, reaching 100%. The activities of these attacks can be analyzed through the installed Wireshark application on the Honeypot server. Information obtained from this analysis encompasses details about the attacker's activities, enabling more effective preventive, anticipatory, and corrective measures. These steps encompass securing the server, network system, and existing services.
One Day One Article One Week One Chapter Program in Fostering Students’ Literacy: Implementation and Perception

Literacy has been a big concern for the English Education Department. As an effort to prepare the freshmen to be literate and get ready for academic life during their study in undergraduate program, the department enacted a literacy program namely, “One Day One Article One Week One Chapter”. The present research aims to portray the implementation and investigate the students’ perception of the program. This research is qualitative by using observation and questionnaire distribution. The observation was carried out during the implementation of the program. A closed-ended questionnaire, which was developed to determine the students’ perception, was administered at the end of the program to the thirty first-semester students. Based on the observation, it was revealed that the program’s implementation is seen to be applicable as a regular program in the department. Based on the questionnaire, the students’ reading frequency was in the sometimes category; the topic preference was mostly social; the reading source was from online and printed sources; no significant number of students did post-reading activities; and the students read anywhere. The students’ perception of the program was positive in terms of reading importance and reading advantage.
Efektivitas Pembelajaran Berdiferensiasi Model Complex Instruction dan CIRC (Cooperative Integrated Reading And Composition) Terhadap Kemampuan Membaca Pemahaman Untuk Siswa Sekolah Dasar Kelas IV

The purpose of this study was to determine differences in the effectiveness of reading comprehension skills based on the differentiated learning methods of complex instruction and CIRC models in fourth grade elementary school students, as well as to determine the higher level of effectiveness between differentiated learning of Complex instruction and CIRC models on reading comprehension abilities in elementary school students. Class IV. This experimental research uses the Non Equivalent Control Group Design design which will use two classes as research subjects, namely the control class and the experimental class. The results showed that when teaching using differentiation was used for fourth grade elementary school students, the CIRC (Cooperative Integrated Reading and Composition) model and the Complex Instruction model provided diverse learning outcomes for students. The experimental class (SD Negeri Suruh 01 Class IV A) got an average score of 97.25 on the post test, compared to the average score of the control class (SD Negeri Suruh 01 Class IV B) which was only 79.35. In the experimental class, students were more involved in group discussions and were better prepared in communicating the results of group work, whereas in the control class, there were still some students who refused to work together in completing assignments given by the teacher. The difference in the application of the learning model to learning outcomes is based on the behavior targeted at students when working in groups. The experimental class, seen from the grades and completeness achieved by each student, has a greater value than the control class.
Peningkatan Minat Belajar Anak melalui Bimbingan Belajar dengan Metode Pembelajaran AKSI (Aktif, Kreatif, Santai dan Inovatif) di Desa Sumber Blimbing tahun 2023

The aim of the Teaching Village Program is to provide learning guidance to students at SDN 03 Purwodadi and children aged 6-12 years in Purwodadi Village to provide learning. The results of this activity are learning guidance provided by PMBP IKIP Budi Utomo Malang students using the AKSI method to increase children's interest in learning. ACTION (active, creative, relaxed and innovative) is a strategy in teaching that makes the atmosphere in the teaching and learning process safe and comfortable so that the learning carried out by students is focused. The learning process carried out by teachers currently tends to be teacher-dominated activities. This teaching village program uses the classroom action research method. The existence of a teaching program can help children in the learning process, such as providing assistance and guidance in the learning process which aims to increase students' enthusiasm and interest in learning
Clinical outcome prediction using observational supervision with electronic health records and audit logs

Audit logs in electronic health record (EHR) systems capture interactions of providers with clinical data. We determine if machine learning (ML) models trained using audit logs in conjunction with clinical data (“observational supervision”) outperform ML models trained using clinical data alone in clinical outcome prediction tasks, and whether they are more robust to temporal distribution shifts in the data. Using clinical and audit log data from Stanford Healthcare, we trained and evaluated various ML models including logistic regression, support vector machine (SVM) classifiers, neural networks, random forests, and gradient boosted machines (GBMs) on clinical EHR data, with and without audit logs for two clinical outcome prediction tasks: major adverse kidney events within 120 days of ICU admission (MAKE-120) in acute kidney injury (AKI) patients and 30-day readmission in acute stroke patients. We further tested the best performing models using patient data acquired during different time-intervals to evaluate the impact of temporal distribution shifts on model performance. Performance generally improved for all models when trained with clinical EHR data and audit log data compared with those trained with only clinical EHR data, with GBMs tending to have the overall best performance. GBMs trained with clinical EHR data and audit logs outperformed GBMs trained without audit logs in both clinical outcome prediction tasks: AUROC 0.88 (95% CI: 0.85–0.91) vs. 0.79 (95% CI: 0.77–0.81), respectively, for MAKE-120 prediction in AKI patients, and AUROC 0.74 (95% CI: 0.71–0.77) vs. 0.63 (95% CI: 0.62–0.64), respectively, for 30-day readmission prediction in acute stroke patients. The performance of GBM models trained using audit log and clinical data degraded less in later time-intervals than models trained using only clinical data. Observational supervision with audit logs improved the performance of ML models trained to predict important clinical outcomes in patients with AKI and acute stroke, and improved robustness to temporal distribution shifts.
Designing a Decision Support System for Educational Resource Allocation

The effective allocation of educational resources is crucial for enhancing the quality of education. This study explores the development and application of a decision support system (DSS) using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. Various entities are evaluated based on a set of critical criteria. Using the TOPSIS approach, entities were systematically ranked, offering a clear directive for optimal resource allocation. This study provides a significant tool for resource management, offering a transparent, objective, and data-driven process. However, DSS should complement other qualitative and contextual considerations in the decision-making process. Future research directions include the integration of temporal data, broadening the set of criteria, and the inclusion of machine learning for dynamic decision-making. Ultimately, this study underscores the value of systematic and data-driven processes in resource allocation, contributing to the improvement of the quality of education
Utilizing Educational Media in the Field of Education Fosters Active Learning Within the Classroom

This study examines the use of learning media in the world of education to create active learning in the classroom. This study uses a qualitative approach, which describes and describes the use of instructional media in the world of education. The data collection technique used in this research is library research, where library research is the activity of collecting research-related materials from scientific journals, literature, and authors. The data in this study are based on books and journals that are relevant to the author's research. The data analysis technique used in this study is qualitative data analysis where in this study the opinions expressed by experts regarding instructional media, these data are used as a basis for strengthening the author's arguments in analyzing the effect of using instructional media. The results of this study indicate that learning media is needed in the teaching and learning process, the role of learning media is very important in the world of education where learning media can support the teaching and learning process of students and teachers. The influence of the use of learning media on learning makes teaching and learning easier for students and teachers, which can increase student motivation because teaching materials are used in the form of media. Learning attracts students' attention. The meaning of teaching materials becomes clearer, so that students understand more and learn to master the objectives of teaching materials.
Expert System for Diagnosing Metabolic Syndrome at The Tembilahan Regional General Hospital

Metabolic syndrome is a disease that is considered trivial and common in society. When sufferers feel the effects of the disease, it will make treatment and healing difficult. To detect this disease early, an expert system application with the forward chaining method is needed so that this application makes it easier to detect disease early. The method for developing this system uses the waterfall method. System analysis and design using UML. System testing uses a black box where system test results are received. Application user testing, using statistical tests with Beta testing with a 95% confidence level, the system can be accepted and trusted. Based on these results, this system can be a solution for early detection of metabolic syndrome.
Classical half-adder using trapped-ion quantum bits: Toward energy-efficient computation

Reversible computation has been proposed as a future paradigm for energy efficient computation, but so far few implementations have been realized in practice. Quantum circuits, running on quantum computers, are one construct known to be reversible. In this work, we provide a proof-of-principle of classical logical gates running on quantum technologies. In particular, we propose and realize experimentally, Toffoli and Half-Adder circuits suitable for classical computation, using radio frequency-controlled 171Yb+ ions in a macroscopic linear Paul-trap as qubits. We analyze the energy required to operate the logic gates, both theoretically and experimentally, with a focus on the control energy. We identify bottlenecks and possible improvements in future platforms for energetically efficient computation, e.g., trap chips with integrated antennas and cavity qed. Our experimentally verified energetic model also fills a gap in the literature of the energetics of quantum information and outlines the path for its detailed study, as well as its potential applications to classical computing.