Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis Motorku X Using Applications Naive Bayes Classifier Method

The rapid development of technology has brought convenience to humans in their daily lives. The continuously evolving technology generates large amounts of data. Data can provide valuable information if processed effectively. The Motorku X application is one of the innovations created by Astra Motor to facilitate consumers or potential customers in servicing and purchasing motorcycles. The Motorku X application generates review data every day. These review data can be utilized for future application development. To make the most of the reviews, sentiment analysis is one of the techniques used to process the review data. Sentiment analysis is a method to measure consumer sentiments in terms of positive or negative reviews. The algorithm used in this research is the Naïve Bayes classifier. One of the advantages of Naïve Bayes is its ability to work quickly and efficiently in terms of computational time. The research consists of several stages: data collection, data labeling, pre-processing, data splitting, tf-idf weighting, implementation of Naïve Bayes classifier, and evaluation of the results. The data comprises 1000 reviews divided into two classes: positive class (number) and negative class (number). The research was conducted with three scenarios of training and testing data sharing: 90%:10%, 80%:20%, and 70%:30%. The best results were achieved with the 90%:10% ratio, with an accuracy of 76%, precision of 76%, and recall of 97%.
Hybrid Machine Learning Techniques for Comparative Opinion Mining

Comparative opinion mining has lately gained traction among individuals and businesses due to its growing range of applications in brand reputation monitoring and consumer decision making among others. Past research in sub-field of opinion mining have mostly explored single-entity opinion mining models and the mining of comparative sentences suing single classifiers. Most of these studies relied on a limited number of comparative opinion labels and datasets while applying the techniques in limited domains. Consequently, the reported performances of the techniques might not be optimal in some cases like working with big data. In this study, however, we developed four hybrid machine learning techniques, with which we performed multi-class based comparative opinion mining using three datasets from different domains. From our results, the best-performing hybrid machine learning technique for comparative opinion mining using a multi-layer perceptron as the base estimator was the Multilayer Perceptron + Random Forest (MLP + RF). This technique had an average accuracy of 93.0% and an F1-score of 93.0%. These results show that our hybrid machine learning techniques could reliably be used for comparative opinion mining to support business needs like brand reputation monitoring.
Data Sharing Technique for Electronic Health Record (EHR) Classification using Support Vector Machine Algorithm

The Electronic Health Record (EHR) integrates information about medical history in patients, complications, and history of drug use efficiently, which demands optimality and speed of service for efficiency and effectiveness of services, especially in determining outpatient and inpatient services on accurate patient history data. In efforts to improve data accuracy, this study combined the c, γ, and degree kernels in the Linear, Polynomial, and Radial Basis Function (RBF) kernels as well as data sharing techniques 10-fold cross-validation, k-medoids, and Hold- out (70 % 30%) resulted in superior K-Medoids data sharing techniques for each Polynomial kernel with an accuracy of 75.76% and a Radial Basis Function (RBF) kernel with an accuracy of 75.56% so that it can be said that the combination of K-Medoids and Polynominal kernel in the algorithm Support Vector Machine (SVM) can be used in this research case
Forecasting The Value of Indonesian Oil-Non-Oil and Gas Imported Using The Gated Recurrent Unit (GRU)

In Indonesia, various factors play a role in economic development. Oil-non-oil and gas imports are one of the main factors. However, the value of oil-non-oil and gas imports in Indonesia fluctuates monthly. Therefore, an appropriate method is required to monitor changes in the value of oil-non-oil and gas imports in Indonesia so that the government can make the right choices. This study uses the GRU method to estimate the amount of oil-non-oil and gas imports in Indonesia. The best model for forecasting over the next two years has an optimum structure of 32 GRU units, 16 batch sizes, and 100 epochs, with a dropout of 0.2 and uses 80% training data and 20% test data. The MAPE value obtained is 0.999955%, with an accuracy of 99.000044%. Forecast results suggest an improvement from June 2022 to July 2024.
PELATIHAN TEKNIK PENULISAN ARTIKEL ILMIAH BAGI MAHASISWA DI KOTA PEKANBARU

This study aims to determine the importance of publishing scientific papers for students. The background of this research is students who are not familiar with writing scientific articles. This service activity was carried out offline for 1 day by resource persons using the E-Module. The target group in this service activity is especially Economics and General Education Students from Universities in Pekanbaru with a total of 60 participants consisting of students from various Study Programs. The implementation of this training was carried out by a team of Lecturers of the Economic Education Study Program at the Faculty of Tarbiyah and Teacher Training, namely Ibuk Wardani Purnama Sari, M.Pd. E, Mrs. DR. Rohani, M.Pd, Mr. Manuscript, M.Pd. E, and Mrs. Mahdar Ernita, M.Ed. The resource persons in this training were Mr. Dr. Dicki Hartanto, MM and Mr. Arif Yasthopy, M.Sc. The method used in this service activity is presentation and practice as well as demonstrations. Based on the results of the community service activities, the scientific writing training ran smoothly, the material provided was understandable to the participants so that they could practice directly with examples of theses which were used as journal articles.
Penggunaan E-learning Sevima Edlink : Kajian Persepsi Siswa

This research aimed at analyzing student perceptions in using sevima edlink on Mol concept material. It was a descriptive quantitative research. Purposive sampling technique was used in this research. The samples of this research were 35 students of X IPA grade at Senior High School of Cendana Pekanbaru. Questionnaire and interview techniques were used for collecting the data. The results of this research showed that students’ perception of X IPA grade at Senior High School of Cendana Pekanbaru was in good category in using Sevima Edlink e-learning (77%). The implications of this research were using Sevima Edlink e-learning could provide benefits and additional e-learning for teachers and students, adding new insights related to e-learning for students, using Sevima Edlink e-learning could form good perceptions for students, and inputting for other researchers to use Sevima Edlink as an online learning media.Keywords: Students’ Perception, Sevima Edlink, Mol Concept
The November 4, 1856 Kanto, Japan, Earthquake did not Occur in the Tachikawa Fault Zone

It has been thought that the earthquake on November 4, 1856, had caused severe damage to Tokorozawa in the present Saitama Prefecture and Kumegawa and Tachikawa in the present Tokyo Metropolitan Area, Kanto, Japan. The seismic intensities in these areas are evaluated as 5-lower or 5-upper on the Japan Meteorological Agency (JMA) scale and the epicenter was estimated near the above area. However, the severe damage in these areas is relied on a short description in the book of municipal history which was published approximately 100 years after the 1856 event and there are no other historical records describing severe damage in these areas regardless of abundant historical materials in those days. Furthermore, there are several mistakes in the book and this short description is possibly made by a human error during the editing process. Since the short description is doubtful, we investigated whether the severe damage at these areas is recorded in contemporary historical materials that were written in and around the above areas or not. We confirmed that there were no descriptions on the severe damage in these areas in the contemporary historical documents and concluded that the 1856 event did not cause severe damage in these areas and the short description in the book of municipal history is a fake. By comparing the seismic intensity distribution for the 1856 event derived from historical materials with those for various earthquakes in the Kanto district during 1919-2022, the 1856 earthquake was inferred as an M 5.0~6.0 slab or interplate event beneath the area from northern Tokyo Bay to northeastern Saitama and southern Ibaraki Prefectures. The 1856 event is possibly a moderate-sized aftershock or triggered event following the 1855 Ansei-Edo earthquake, although the uncertainty is significantly large due to the sparse distribution of the estimated seismic intensities for the 1856 event.
A 0.13mJ/Prediction CIFAR-100 Fully Synthesizable Raster-Scan-Based Wired-Logic Processor in 16-nm FPGA

A 0.13mJ/prediction with 68.6% accuracy wired-logic deep neural network (DNN) processor is developed in a single 16-nm field-programmable gate array (FPGA) chip. Compared with conventional von-Neumann architecture DNN processors, the energy efficiency is greatly improved by eliminating DRAM/BRAM access. A technical challenge for conventional wired-logic processors is the large amount of hardware resources required for implementing large-scale neural networks. To implement a large-scale convolutional neural network (CNN) into a single FPGA chip, two technologies are introduced: (1) a sparse neural network known as a non-linear neural network (NNN), and (2) a newly developed raster-scan wired-logic architecture. Furthermore, a novel high-level synthesis (HLS) technique for wired-logic processor is proposed. The proposed HLS technique enables the automatic generation of two key components: (1) Verilog-hardware description language (HDL) code for a raster-scan-based wired-logic processor and (2) test bench code for conducting equivalence checking. The automated process significantly mitigates the time and effort required for implementation and debugging. Compared with the state-of-the-art FPGA-based processor, 238 times better energy efficiency is achieved with only a slight decrease in accuracy on the CIFAR-100 task. In addition, 7 times better energy efficiency is achieved compared with the state-of-the-art network-optimized application-specific integrated circuit (ASIC).
A denotationally-based program logic for higher-order store

Separation logic is used to reason locally about stateful programs. State of the art program logics for higher-order store are usually built on top of untyped operational semantics, in part because traditional denotational methods have struggled to simultaneously account for general references and parametric polymorphism. The recent discovery of simple denotational semantics for general references and polymorphism in synthetic guarded domain theory has enabled us to develop TULIP, a higher-order separation logic over the typed equational theory of higher-order store for a monadic version of System F{mu,ref}. The Tulip logic differs from operationally-based program logics in two ways: predicates range over the meanings of typed terms rather than over the raw code of untyped terms, and they are automatically invariant under the equational congruence of higher-order store, which applies even underneath a binder. As a result, "pure" proof steps that conventionally require focusing the Hoare triple on an operational redex are replaced by a simple equational rewrite in Tulip. We have evaluated Tulip against standard examples involving linked lists in the heap, comparing our abstract equational reasoning with more familiar operational-style reasoning. Our main result is the soundness of Tulip, which we establish by constructing a BI-hyperdoctrine over the denotational semantics of F{mu,ref} in an impredicative version of synthetic guarded domain theory.
IEEE Conference On Blockchain And Cryptocurrency(ICBC'23)

It is a great pleasure for us to write this brief report for the fifth IEEE Conference on Blockchain and Cryptocurrency (ICBC'23) on behalf of the entire Technical Program Committee of the conference, organizing committee, and the steering committee of the conference. The conference, held at Sofitel-Dubai between May 1st and May 5th of 2023, was very successful, with 120 participants and 694 authors from 37 countries. Dubai provided the perfect background for a conference on Block-chain, as it aims to become the first city to truly decentralize the governance through its Dubai Blockchain Strategy (https://www.digitaldubai.ae/initiatives/blockchain) and the vibrant blockchain startup ecosystem in the city.
Cyber Protection &amp; Awareness For Females in Bangladesh

Cybersecurity is the application of technologies to protect data from attack, damage, or unauthorized access, and is a major cyber threat for women due to the revolution in Information Technology. Women are facing a major cyber threat due to the revolution in Information Technology, which has caused them to lose their privacy. Alarmingly, young women and girls in Bangladesh have been victims of cybercrime. The privacy of young girls and women has been put at risk due to the accessibility of social media without knowledge of security measures. Bangladesh has fallen behind in raising youth awareness. The measurement of the present degree of cyber-security awareness is the main emphasis of this research. Moreover, the primary results are according to statistics. The authorities claim that 70% of women experience cyberbullying but do not report it. When the machine learning algorithms were applied to the cybersecurity dataset, the accuracy scores of the SVM, XGBoost, and Catboost methods were 93.6%, 93.7%, and 94.07%, respectively. Our CatBoost classifier produces the best outcome. The precision of CatBoost is 94.87%.
MODEL OF PEDAGOGICAL SUPPORT OF SPIRITUAL SELF-DETERMINATION OF A PERSON AS ONE OF THE BASIS FOR DESIGNING THE COURSE “FOUNDATIONS OF THE RUSSIAN STATEHOOD”

The need to introduce the course “Fundamentals of Russian Statehood” into the curricula of all areas of study at universities poses a number of tasks for the pedagogical community. They are related to the choice of methodological foundations, work on the content and the choice of methods and means of teaching. The article presents a model for the formation of a student’s spiritual self-determination, which is one of the goals of the course, based on psychological mechanisms of personality development: reflection, personalization and stereotyping.
Fixpoint constructions in focused orthogonality models of linear logic

Orthogonality is a notion based on the duality between programs and their environments used to determine when they can be safely combined. For instance, it is a powerful tool to establish termination properties in classical formal systems. It was given a general treatment with the concept of orthogonality category, of which numerous models of linear logic are instances, by Hyland and Schalk. This paper considers the subclass of focused orthogonalities. We develop a theory of fixpoint constructions in focused orthogonality categories. Central results are lifting theorems for initial algebras and final coalgebras. These crucially hinge on the insight that focused orthogonality categories are relational fibrations. The theory provides an axiomatic categorical framework for models of linear logic with least and greatest fixpoints of types. We further investigate domain-theoretic settings, showing how to lift bifree algebras, used to solve mixed-variance recursive type equations, to focused orthogonality categories.
Digital library evaluation using the Human-Organization-Technology Fit Model

Purpose. This research aims to evaluate the Rumah Ilmu website which is used as digital library media at Universitas Negeri Semarang using the Human-Organization-Technology-Fit (HOT-Fit) Model.Methodology. The method used is clause associative which functions to test the relationship between variables. The sampling technique used simple random sampling and the sample size was 62 respondents. The data collection technique uses a questionnaire distributed via Google Forms. The data analysis technique with correlation analysis.Results and discussion. The results of the validity and reliability tests show that all question items are declared valid. The results of the correlation test show that overall the relationship between the three elements in the HOT-Fit Model is in the strong and very strong categories.Conclusions. The research results concluded that the significance value of the HOT and net benefit shows a value of 0.00 5% with a correlation coefficient of 0.878. This indicated that the Rumah Ilmu website in supporting digital libraries was quite good. However, improving the quality of the system must continue to be carried out in the future so that it can provide wider benefits to users.
Keterampilan sosial pustakawan dalam memberikan layanan prima kepada pemustaka di era Information, Communication and Technology (ICT)

Purpose. The aim of this research is to provide knowledge to librarians that the importance of social skills is seen from attitudes and behavior in providing excellent service. Library services are an important indicator in determining user satisfaction. So librarians need to focus on user-oriented services.Methodology. The type of research used in this paper is library research. Data collection was obtained through reference books, scientific journals, relevant research related to research and other sources of information that support this research. The literature data taken comes from 2017-2022. The data analysis technique begins with the researcher obtaining library sources which the researcher collects first from various sources, then arranges them and classifies them, then analyzes them, classifies them and interprets them to obtain conclusions.Results and discussion. The results showed that the social skills that librarians should have in providing primary services to users using the principles of people-based service and service excellence are aimed at satisfying their libraries, increasing loyalty, and increasing the number of libraries visiting the library. Moreover, in terms of service, at least a librarian must have competence in the current era of information and information technology. The four categories that he must have are knowledge, skill, affective, and pragmatic model approaches. These four things are the basis for the librarian's activity, especially the section of service.Conclusions. The role of librarians in the era of information, communication, and technology (ICT) must be an agent of change for society. It aims to provide excellent service to users, thereby improving the quality of libraries in the eyes of the public with the hope that the library becoming the center of community activity in study life.
Destroke

Abstract: With the increasing prevalence of cyber threats and malicious activities, ensuring the security of personal and sensitive information has become paramount. Key loggers pose asignificant threat by capturing keystrokes and compromising user credentials.
Secondary Level Students’ Experiences of Cyberbullying through Facebook

ICT users face various challenges due to the lack of knowledge and awareness to use its tools properly. Along with the spread of Facebook users, the trend of cyberbullying is gradually increasing among teenagers including school students who have been victimised daily. This study explores the students’ experiences of cyberbullying through Facebook and the strategies they apply to cope with cyberbullying. I employed the qualitative research method in which the semi-structured interview was designed to collect data from 16 participants. The finding of this study depicts that cyberbullying is experienced by the participants in various ways like sharing memes on Facebook wall and Messenger, using nicknames, using unacceptable (vulgar) words, teasing, threatening, hacking Facebook passwords, and using, proposing for being close friends and joining photos and uploading, sending messages time and again and calling during night time. The study also found that cyberbullying makes victimised people feel insecure. It was also suggested to report with the family members and friends making strict laws, keeping in school level curriculum and launching mass awareness to cope with the cyberbullying activities found in the school level students of Nepal.
Transformer-Based Unified Segmentation: Simultaneous Semantic and Instance Tasks on Point Clouds

Traditionally, 3D segmentation tasks have operated in silos, focusing separately on semantic and instance segmentation. However, this disjointed approach lacks interoperability and fails to fully unleash the potential of a more integrated, multitask solution. To overcome this limitation, we introduce TUS-Net, an innovative transformer-based architecture meticulously crafted for both semantic and instance segmentation of point clouds. Our model introduces two pivotal advancements: First, it employs a superpoint-based pre-processing step that minimizes computational overhead without compromising on precision. Second, we leverage a dual-branch design within the transformer architecture, allowing it to adapt to the nuances of both segmentation tasks dynamically. Through extensive experimentation on the ScanNet dataset, our findings demonstrate that TUS-Net surpasses prevailing specialized models by a substantial margin and maintains remarkable computational efficiency. Notably, we achieve a 5.7% enhancement in mean Average Precision (mAP), for instance, segmentation, while striking an optimal balance between accuracy and runtime for semantic segmentation. These outcomes underscore the versatility, efficiency and high-performance attributes of TUS-Net, positioning it as an indispensable framework for robust 3D point cloud segmentation.
Machine learning versus manual earthquake location workflow: testing LOC-FLOW on an unusually productive microseismic sequence in northeastern Italy

It is an open question whether machine-learning (ML) methods can be trusted in areas where dense and localized seismic networks are in operation, and prompt and accurate detection and location of earthquakes are essential to guide decision-making processes that contribute to seismic-risk-mitigation-strategies, even for very low-magnitude events. To address these concerns, we compare the performance of a widely-used ML phase picker, PhaseNet, integrated with several popular earthquake location methods (included in LOC-FLOW), with the results obtained by the workflow adopted since 2012 by the Collalto Seismic Network, installed to monitor natural and potentially induced microearthquakes nearby an underground gas storage. The tested dataset concerns the most populated microseismic sequence observed so far (374 events, ML⩽2.5, August 2021, Refrontolo, NE-Italy), as its unusual productivity raised some criticalities in the combination of automatic routines, and time-consuming manual revision of phase picks adopted by the standard workflow. LOC-FLOW is able to detect the majority of the events listed in the manually revised catalog, demonstrating its ability to efficiently and accurately build earthquake catalogs from continuous seismic data. We highlight both the advantages and limitations of the ML-picker and recommend the use of template-matching-techniques in the final stage of processing to increase the number of events.