Reviewerly/exHarmony huggingface dataset:
Point of Sales (POS) System Design using Design Thinking Framework for Motorcycle Workshop

The challenge of MSME business is the change of transaction systems from conventional to digital processes to minimize human error. This research offers an idea to identify problems, classify digital innovation ideas, analyze needs, and design management information systems Point of Sales (POS) modules in CV. Renaldi Motor. The method used in designing the management information system of the POS module of the OmO Jaya Workshop application is design thinking. The stages in this research consist of empathizing, defining, ideating, prototyping, and testing. The results of this study show that the problem faced by Micro, Small, and Medium Enterprises (MSMEs) workshop businesses is the process of recording transactions that still use conventional methods. Through the digital innovation of the POS module management information system in the form of the OmO Jaya workshop application. Through the application, a CV. Renaldi Motor is expected to optimize the digital transaction recording system and improve business performance. Thus, the risk of business losses due to human error can be minimized. In addition, the Blackbox test results show that the test results in each process have been successful and as expected. This study concluded that the Point of Sales (POS) System can improve CV. Renaldi Motor's business performance through digitizing the sales transaction recording process.
Elevating 'TutorYuk' Online Peer Tutoring Services Through Information System: A Design Thinking Framework Approach

Through applications supporting informal education services, the development of information technology during the fourth industrial revolution has promoted the growth of the education industry in Indonesia. This study uses the Design Thinking framework to design and analyze information systems for the online peer tutoring services known as TutorYuk. The stages in system design are as follows: empathize stage, namely the process of identifying user needs and classifying practical problems related to requirements in the informal education sector; define the location, analyze the priority of user needs and issues that need to be solved through information systems; the idea stage, the process of networking aspirations related to solutions to problems in the education sector that shows the urgency of system design; prototype stage, design system flow and function and visualization of application interfaces; Testing stage, testing system performance. The findings of this study demonstrate that TutorYuk is built to suit student needs for learning help based on course names and instructor qualities chosen by users. The duration and experience of the instructor are considered when providing tutoring services so that they can be tailored to the client's needs. Consequently, by offering digital media to service suppliers (tutors) and service recipients, TutorYuk can become a commercially successful application (tutees).
Analysis and Design of Information System for Parking Management “ParkHere” Using Design Thinking Method

Finding a secure and comfortable parking space is one of the needs of automobile owners when they visit retail centers. The ability of parking lot managers to provide information on sufficient capacity and access presents a barrier to delivering parking facilities in retail centers. Due to this, a website-based parking facility management information system is required, allowing drivers to make reservations and payments online based on when they plan to visit the shopping center. The method used in the design of ParkHere information systems is design thinking which consists of empathizing, defining, ideating, prototyping, and testing stages. The output of this study shows that at the empathize stage, motorists need information about the capacity of parking lots in various shopping centers before visiting the location. At the defined stage, this shows the need for motorists for website-based and real-time parking facility information. At the idea stage, it is necessary to design an information system based on the website that provides information on the capacity of parking facilities based on the operational time of the shopping center. At the prototype and testing stages, the ParkHere information system can answer the user's needs in choosing a place and location for vehicle parking based on the specified time.
Forensic Speaker Recognition: Review of Methods and Evaluations

Abstract: Forensic voice comparison is quite challenging because the quality of speech recordings in casework is often poor and there is often mismatch between the recording conditions of the known- and questioned-speaker recordings. Further, the field of forensic voice comparison is characterized by a large diversity of methods, procedures and evaluation of comparison results. The present paper aims to provide a concise review of various comparison methods and assessment of comparison results. The need of an extensive amount of experimental studies and statistical evaluation on a large database is highlighted to use the likelihoodratio framework for reporting conclusions that has been now firmly established as a theoretical framework for any forensic discipline.
Application of Machine Learning Algorithm in Predicting Axillary Lymph Node Metastasis from Breast Cancer on Preoperative Chest CT

Axillary lymph node (ALN) status is one of the most critical prognostic factors in patients with breast cancer. However, ALN evaluation with contrast-enhanced CT (CECT) has been challenging. Machine learning (ML) is known to show excellent performance in image recognition tasks. The purpose of our study was to evaluate the performance of the ML algorithm for predicting ALN metastasis by combining preoperative CECT features of both ALN and primary tumor. This was a retrospective single-institutional study of a total of 266 patients with breast cancer who underwent preoperative chest CECT. Random forest (RF), extreme gradient boosting (XGBoost), and neural network (NN) algorithms were used. Statistical analysis and recursive feature elimination (RFE) were adopted as feature selection for ML. The best ML-based ALN prediction model for breast cancer was NN with RFE, which achieved an AUROC of 0.76 ± 0.11 and an accuracy of 0.74 ± 0.12. By comparing NN with RFE model performance with and without ALN features from CECT, NN with RFE model with ALN features showed better performance at all performance evaluations, which indicated the effect of ALN features. Through our study, we were able to demonstrate that the ML algorithm could effectively predict the final diagnosis of ALN metastases from CECT images of the primary tumor and ALN. This suggests that ML has the potential to differentiate between benign and malignant ALNs.
Current Intensity Conductivity, Resistance and EMF in Electrical Circuit Systems Using Geogebra

The COVID 19 pandemic spread worldwide, having considerable consequences in education, interrupting learning and increasing the great challenge that exists in the educational culture of building learning and teaching experiences that impact the learning results of the students more educated, vulnerable and disadvantaged. To do this, it seeks to link theoretical learning with real life through the use of digital tools that solve problems in science learning. The method used is simulation, it has been chosen to simulate electrical conductivity, the flow of electrical charges from electrical resistance, resistivity and Ohm's law through GeoGebra and the link it has with JavaScript to perform a simulation totally dynamic. As a result, it was possible to build a simple and combined electrical circuit to obtain the passage of the flow of electrical charges through the circuit and the intensity of the electrical current; Combinations of resistivity and resistance connected to a power source that is fed with a potential difference. Demonstrating that the electric charges move generating the intensity of continuous electric current and that they vary due to the variation of the electric potential generated by the battery. Arriving at the discussion in front of other research works by simulation that with GeoGebra it is also possible to obtain good results, which contributes to understanding the difference in electrical potential through simulations that promote quality education, equitable and that produces relevant learning results to improve theoretical and practical knowledge that promotes sustainable development.
A Blockchain-based secure Internet of Medical Things framework for smart healthcare

&lt;p class="MDPI17abstract"&gt;The Internet of Medical Things (IoMT) industry has grown lightning during the estimated time frame. Privacy and security are essential concerns given the scale and widespread use of IoMT networks. Blending healthcare data in one place storage device to prepare an effective predictive model, on the other hand, increases more severe security and privacy issues, ownership, and regulation. An advanced system is required to enhance information utilization while restricting privacy issues. This research presents a new architecture to address security and privacy challenges in e-healthcare services in Healthcare 5.0. This paper integrates the distributed ledger technology (DLT) with IoMT. This integration has become necessary due to the growing demand for e-health-related technologies and services. The proposed solution in this research is based on a Blockchain framework to enhance medical data privacy and integrity. This research evidence found that Blockchain can overcome IoMT privacy and data protection. The proposed framework has far-reaching consequences for the medical field. The proposed method has reduced Commit time by up to 10 seconds, and Commutative probability has been gained to 88%. It has advantages, including faster data exchange, less duplication of effort, and more secure patient information.&lt;/p&gt;
Interdisciplinarity in Knowledge Management

We take a tour of what a valid and useful knowledge management would look like. To do so we distinguish between the two major types of organizational knowledge – human and technological – and show how rationality is to be differently understood in these two types. Human or personal reason is distinguished by a nonseparability of its concepts owing to the intuitive manner in which they are unified in the act of understanding, so that knowledge is a risky matter of interpretation and judgment. Technology instead requires a separability of concepts essential for reducing them to measurement, where they must stand visibly apart. We go on to show the paradoxical but essential need of these two knowledge types for one another, shown in the duality between unobservable and observable concepts necessary for judging an organization’s value. We find that fact and counterfact, actual and possible, observable and unobservable are brought together in organizational knowledge.
Penerapan Model Pembelajaran Two Stay Two Stray Untuk Meningkatkan Keterampilan Diskusi Siswa Kelas X-IPA1 di SMAN 5 Kota Ternate Tahun pelajaran 2021-2022

This study aims to improve the discussion skills of class X-IPA1 students of SMA Negeri 5 Kota Ternate, through the two stay two stray learning model. The research focused on problems related to the low skills of students in discussion activities including aspects of courage and fluency in speaking. This type of research is classroom action research with three cycles, each cycle consisting of: planning, implementing, observing and reflecting. Data collection techniques and tools in this study used descriptive analysis techniques for data in the form of observation sheets, interviews and observations. This research was conducted in July-August 2022. The subjects in this study were students in class X-IPA 1 at SMA Negeri 5 Kota Ternate. Data were analyzed with qualitative descriptive techniques. The results showed that the discussion skills in the aspects of courage and fluency increased in each cycle. In cycle 1 the average value on the aspect of courage is 28.13. The pre-cycle value was 77.50 in cycle I, increased to 80.63 in cycle II, and increased to 98.13 in cycle III. The aspect of speaking fluency increased with the pre-cycle average value of 25.63 to 68.75 in cycle I. The average value of cycle I increased to 80.00 in cycle II and the average value of cycle II increased to 96.25 in cycle III. Students are more skilled in discussions by applying the Two Stay two Stray learning model to the material of the text of the observation report.
PEMEROLEHAN BAHASA BAKU SHABIRA ALULA DALAM VIDEO TIKTOK SHABIRA ALULA DAN AYAH

With the rise of language development in the era of globalization, this research is expected to be useful for others by adding knowledge and insight and being able to teach good language to children, by teaching good language to children can influence the development of children's attitudes and character. This research focuses on the acquisition of standard language on Shabira Alula and Ayah's TikTok, namely in the acquisition of language in the fields of phonology, syntax, and semantics. This study aims to explain and describe language acquisition in the fields of phonology, syntax, and semantics in Shabira Alula. This study uses a qualitative descriptive design with data collection techniques using observation techniques and also note taking. The results of this study indicate that Shabira Alula has been able to pronounce standard words as a whole or only in the form of words, and has been able to pronounce vowel and consonant phonemes quite well, but Shabira Alula is not perfect in pronouncing phonemes [r]. In acquiring syntax, Shabira Alula has been able to obtain sentences from one-word, two-word and multi-word utterances. Apart from that, Shabira Alula's declarative, interrogative, imperative and interjection sentences have also been able to do this, although there are still many advantages and disadvantages in the perfection of her pronunciation. In semantic acquisition, Shabira Alula has been able to determine the context of the conversation so that the resulting response does not deviate from the conversation.
Review and Prospect of English Activity-based Approach in China (2018-2022)

Using a combination of quantitative and qualitative research methods, this paper collects the literature on English activity-based approach published in 15 Chinese educational journals from 2018-2022, and has a review from four dimensions: development trend, research object, research content and research methods. The results show that: the number of studies is generally on an upward trend; the research objects are concentrated at the high school level, followed by middle school and the least at elementary school; the main contents of studies include conceptual research and design philosophy at the macro level, lesson design research, teaching strategies and literacy development at the micro level; the research methods are mainly non-empirical studies, and the empirical studies are mainly qualitative studies. This paper then analyzes the problems of the existing research and presents an outlook on the future development of English learning activity perspective research.
Collaborative-Based Dynamic Trust Model for Bring-Your-Own-Device Access Control Management in Cloud Environment

The operation of bring-your-own-device (BYOD) in the cloud has not only opened cloud servers to more threats but inflicted additional costs on cloud security as it would have to monitor employee devices and their operations. Many organizations therefore have adopted zero trust scheme for BYOD access control management in cloud environment. However, zero trust model introduces extra cost and hostility against internal employees, who have a certain level of trust, as against outsiders. This paper posits that trust quantification for BYOD access control management should be determined by cloud service providers and employers in a dynamic and continuous manner based on session and information values. The paper therefore presents a collaborative-based dynamic trust model that fuses the perspectives of BYOD employer and cloud service provider agents (trustees) for BYOD Nodes (trustors) access control management. The trustees provide prior evidences about the BYOD requests from which plausible inferences are drawn. Three framing of trusts including employee, device and program trusts are formulated based on reliable trust metrics. Dempster-Shafer Belief Function is used to evaluate the belief scores of the trustors’ requests from the probabilities assigned by the trustees. The model is applied to two BYOD nodes, with varying session and information values. The outcomes reveal that the collaborative-based dynamic trust model ensures reduced cost and improved usability compared to zero trust model.
Long-term performance of roof-top GCPV systems in central Viet Nam

In pursuit of the objective of achieving "net zero emissions," many countries worldwide, including Viet Nam, have prioritized the utilization of photovoltaic technology for energy conversion. Specifically, the implementation of roof-top grid-connected photovoltaic systems (GCPV) has emerged as a highly efficient solution in urban areas. These systems offer several advantages, such as minimizing land usage, lowering monthly electricity expenses, preventing building heat, generating income for households, and reducing transmission and distribution costs. This article focuses on a comprehensive long-term analysis conducted on 51 roof-top GCPV systems in the tropical monsoon climate of Hue City, Viet Nam, during the period from 2019 to 2023. The analysis findings reveal that roof-top GCPV systems with a capacity of 3-6 kW are well-suited for households in the central region of Viet Nam, characterized by a tropical monsoon climate. These systems exhibit an average sizing ratio of 1.03. The annual average daily final yield peaked at 3.28 kWh/kWp/day in 2021 and reached its lowest point at 2.97 kWh/kWp/day in 2022. Notably, the typical slope of the yield gradually increases with the installed capacity and the studied year. Furthermore, the monthly average daily final yield demonstrates a seasonal pattern, with higher yields observed from March to August and lower yields from September to January, aligning with the climate of the study area. As the years progress, the capacity factor and performance ratio of roof-top GCPV systems display a declining trend. Throughout the entire study period, these systems successfully mitigated 664 metric tons of CO2 emissions. The evaluation of long-term yield data offers valuable insights for photovoltaic installers, operators, and system owners, aiding in system maintenance and optimizing load utilization across different time periods. Long-term performance can be used by energy managers and owners of roof-top GCPV systems to identify supply shortfalls and initiate countermeasures.
Enhancing Adversarial Training via Reweighting Optimization Trajectory

Despite the fact that adversarial training has become the de facto method for improving the robustness of deep neural networks, it is well-known that vanilla adversarial training suffers from daunting robust overfitting, resulting in unsatisfactory robust generalization. A number of approaches have been proposed to address these drawbacks such as extra regularization, adversarial weights perturbation, and training with more data over the last few years. However, the robust generalization improvement is yet far from satisfactory. In this paper, we approach this challenge with a brand new perspective – refining historical optimization trajectories. We propose a new method named Weighted Optimization Trajectories (WOT) that leverages the optimization trajectories of adversarial training in time. We have conducted extensive experiments to demonstrate the effectiveness of WOT under various state-of-the-art adversarial attacks. Our results show that WOT integrates seamlessly with the existing adversarial training methods and consistently overcomes the robust overfitting issue, resulting in better adversarial robustness. For example, WOT boosts the robust accuracy of AT-PGD under AA- $$L_{\infty }$$ attack by 1.53%–6.11% and meanwhile increases the clean accuracy by 0.55%–5.47% across SVHN, CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets.
A Vlogger-augmented Graph Neural Network Model for Micro-video Recommendation

Existing micro-video recommendation models exploit the interactions between users and micro-videos and/or multi-modal information of micro-videos to predict the next micro-video a user will watch, ignoring the information related to vloggers, i.e., the producers of micro-videos. However, in micro-video scenarios, vloggers play a significant role in user-video interactions, since vloggers generally focus on specific topics and users tend to follow the vloggers they are interested in. Therefore, in the paper, we propose a vlogger-augmented graph neural network model VA-GNN, which takes the effect of vloggers into consideration. Specifically, we construct a tripartite graph with users, micro-videos, and vloggers as nodes, capturing user preferences from different views, i.e., the video-view and the vlogger-view. Moreover, we conduct cross-view contrastive learning to keep the consistency between node embeddings from the two different views. Besides, when predicting the next user-video interaction, we adaptively combine the user preferences for a video itself and its vlogger. We conduct extensive experiments on two real-world datasets. The experimental results show that VA-GNN outperforms multiple existing GNN-based recommendation models.
Ex-ThaiHate: A Generative Multi-task Framework for Sentiment and Emotion Aware Hate Speech Detection with Explanation in Thai

Social media platforms have both positive and negative impacts on users in diverse societies. One of the adverse effects of social media platforms is the usage of hate and offensive language, which not only fosters prejudice but also harms the vulnerable. Additionally, a person’s sentiment and emotional state heavily influence the intended content of any social media post. Despite extensive research being conducted to detect online hate speech in English, there is a lack of similar studies on low-resource languages such as Thai. The recent enactment of laws like the “right to explanations” in the General Data Protection Regulation has stimulated the development of interpretable models rather than solely focusing on performance. Motivated by this, we created the first benchmark hate speech corpus, called Ex-ThaiHate, in the Thai language. Each post is annotated with four labels, namely hate, sentiment, emotion, and rationales (explainability), which specify the phrases that are responsible for annotating the post as hate. In order to investigate the effect of sentiment and emotional information on detecting hate speech posts, we propose a unified generative framework called GenX, which redefines this multi-task problem as a text-to-text generation task to simultaneously solve four tasks: hate-speech identification, rationale detection, sentiment, and emotion detection. Our extensive experiments demonstrate that GenX significantly outperforms all baselines and state-of-the-art models, thereby highlighting its effectiveness in detecting hate speech and identifying the rationales in low-resource languages. The code and dataset are available at https://github.com/dsmlr/Ex-ThaiHate . Disclaimer: The article contains offensive text and profanity. This is due to the nature of the work and does not reflect any opinion or stance of the authors.