Reviewerly/exHarmony huggingface dataset:
Development of Interactive Virtual Reality Application for Simulating Experimental Tensile Test

Information technology has significantly transformed traditional educational systems into adaptable forms of learning. Experimental tension test is a practical study included in the mechanical engineering curriculum, specifically as a part of Material and Metallurgy. The fundamental issue arises from the deficiency in comprehending the understanding of mechanical properties, which is a result of traditional educational methods. Virtual reality (VR) technology enhances the comprehension of the study of material properties for students studying mechanical engineering. This study presents a virtual reality tool, called VR experimental tensile test. It is intended to be utilized in flexible learning. Students as a user can investigate and examine the utilization of the program for learning and conducting safety lab orientation. The application evaluation was conducted using the User Experience Questionnaire (UEQ). This study suggests that using 3D model visualization in the virtual reality application enhances user experiences in learning tensile test and follow its lab orientation.
Collaboration of Progressive Web App (PWA) And Firebase Cloud Messaging (FCM) for Optimal Performance Mailing Software

The problem of correspondence cannot be separated from the ease, accuracy and speed of the processing process. In research from Riswandi Ishak and H. Trizaka, they proposed report management software as well as notification of disposition and monitoring of correspondence as a solution to correspondence problems. However, there are shortcomings in the notification process for the disposition of correspondence, which requires the software to be actively open. In research, P. Dwi proposed a notification solution with Firebase Cloud Messaging (FCM), so that it can send notifications as long as the browser is active and connected to the internet, even without opening the software. There is a problem currently when the software becomes unstable when the internet connection is bad or offline. Progressive Web Apps (PWA) offers the concept of web-based application development by implementing browser technology such as service workers and app manifests. PWA is capable of displaying pages offline but cannot save, change, or delete data in the database. The test results of this research used Lighthouse and showed an average score of 100 on the PWA criteria, 85 on the performance criteria, 97 on the accessibility criteria, and 100 on the best practices criteria. Additional results obtained by implementing PWA mean page loading times are 26.6% faster with cache and service workers. The PWA and FCM concepts provide the best experience in using Mailing Software even with minimal internet connection or offline. This strategy was chosen to still get a fast response when running the mail processing software.
Comparative Analysis Of Performance Levels Of Svm And Naïve Bayes Algorithm For Lifestyle Classification On Twitter Social Media

Lifestyle is how individuals express themselves through their activities and interests and utilize their financial resources and available time. Twitter is a social network platform that allows people to express opinions and directly criticize various topics, including the recently widely discussed lifestyle topics. Topic classification on Twitter is central in facilitating the search, recommendation, and management of relevant content for users. This research aims to analyze public sentiment regarding lifestyle using 11,000 pieces of data with the keywords "concert", "watching films", "smoking", and others related to lifestyle. Research data is labeled according to the sentiment of public opinion towards lifestyle. Negative polarity for data that has the context of "underestimating", "insulting", "sarcastic", and "feeling sad". Positive polarity for data that has the context of "grateful", "praying", "feeling happy", and "encouraging". Neutral polarity for data that has the contexts “ask”, “predict”, and “feel surprised”. Next, the data enters the pre-processing stage, which consists of case-folding, tokenization, stopword removal, and lemmatizing. The analysis continues by dividing the data into training and test data with a ratio of 70%:30%. Sentiment analysis uses an algorithm Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC). The analysis results show that the SVM algorithm provides better classification than NBC. In this case, the SVM algorithm produces accuracy, precision, recall, and value F1-Score the same, namely 61%.
Radiography Cervical Vertebrae with Suspect Spodylosis at the Prof. Dr Chairuddin Panusunan Lubis Educational Hospital University of North Sumatra Medan

The cervical vertebrae (neck bones) are vertebrae which consist of seven segments which have small segment bodies and large segment openings. Radiographic techniques are the science of studying procedures for photographing objects that are examined using X-rays. Cervical spondylosis is a common degenerative disease of the cervical spine. To show abnormalities in these organs, X-rays in the field of radiology are needed. Therefore, a radiological examination is needed to show whether there is spondylosis and its effect on the surrounding organs. with AP Axial and Lateral examination techniques. The AP Axial projection shows an image of the cervical spine from the AP position. Lateral projection shows an image of the cervical vertebrae from a lateral position, and spondylosis is clearly visible at C3 and C4. In collecting data, the author used interviews and field observations. The results of the diagnostic examination provided information about abnormalities in the cervical spine using Computer Radiography (CR), a cassette measuring 24 cm x 30 cm with a device used by Philips with a capacity of 500 mA.
Quantum radar and cognition: looking for a potential cross fertilization

The purpose of this chapter has been to capture, from the technical literature, points of tangency between the concepts of cognition and quantum physics. This might open the way to a potential hybridization between cognitive radar and quantum radar, a CQR. Being an initial vision, a lot of work is expected to be done to give more scientific fundamentals to this vision. We feel that the quantum remote sensing will be deployed on a longer time scale. We are in the stage of proving the concept and realizing even more powerful test beds. Open literature is witnessing this continuous upgrade. We believe that a cross-disciplinary approach between experts in different fields would be rewarding and would be essential to make progress in the field.
INTRODUCTION OF DIGITAL TECHNOLOGIES IN THE PROCESS OF TEACHING A FOREIGN LANGUAGE AT THE UNIVERSITY

Background. In this article the authors present a comprehensive review of selected empirical developments on artificial intelligence in education, research on some artificial intelligence technologies and applications, and analyze their potential benefits for the educational process. They also identify the need in the formation of new educational competencies among students, in particular linguo-digital, caused by the modern labor market requirements, and consider the application of the foreign universities experience in relation to the use of artificial intelligence tools in education.&#x0D; Purpose. The article aims to identify the potential of artificial intelligence in the formation of digital competencies among students in teaching a foreign language.&#x0D; Materials and methods. The research study is based on theoretical and experimental methods along with quantitative evaluation method, to conduct this study.&#x0D; Results. The authors focus on the term “linguo-digital competence” as a component of digital competence. Teaching experience demonstrates that multilingual education of students requires digitalization of professional training. It improves their skills in use of artificial technologies in studies of a foreign language, helping to develop professional as well as personal competences.&#x0D; Practical implications. The result of the study is recommendations on the methodology for introducing digital technologies into the educational process when teaching a foreign language at a university.
PENERAPAN MODEL MEMORIZATION BERBASIS GAMES DALAM MENINGKATKAN KETERAMPILAN BERPIKIR KRITIS PESERTA DIDIK DALAM PEMBELAJARAN IPS

The low interest of students in social studies subjects is possible because the teacher's efforts have not been optimal to increase the creative thinking of students' learning. Students' critical thinking skills are still low, seen during the current learning process, students are not encouraged to improve their thinking skills. The purpose of this study was to identify an increase in students' critical thinking skills after applying the games-based learning model of memorization. This study uses a quantitative method, this type of research uses a quasy experiment, the sample in this study is class VII students at SMPN 1 Tarogong Kidul, the instruments used for critical thinking skills are pretest and posttest, observation and documentation study. Based on the results of research and data management using the Mann Whitney test, the value of Sig.0.021 and α = 0.05 means Sig. &lt; α, then the game-based memorization learning model can improve students' critical thinking skills. So that students have the skills and abilities to think critically in the social studies learning process.Keywords: Memorization Model, Critical Thinking Ability, Social Studies Learning.
Investigation for Impact of Environmental Noise on Power Analysis Attacks

Power analysis attacks can easily infer the secret keys of commonly used cryptographic algorithms such as RSA and AES, which are currently standard in the internet. Statistical attacks employed in power analysis attacks capture the power consumption waveforms of processor using an oscilloscope and analyze it with a computer program to extract the secret keys. This paper focuses on environmental noise during the measurement of power consumption waveforms, aiming to investigate its impact on the accuracy of the attack. The contribution of this paper is to inform researchers proposing novel countermeasures against power analysis attacks that the accuracy of attack experiments can be significantly affected by environmental noise, which cannot be disregarded.
UI/UX Prototype Usability Analysis of E-Commerce Websites

User Interface (UI) and User Experience (UX) design have a very important role in the development of digital products and services. UX quality is determined by the extent to which the UI developed can meet the needs of users. There are several factors that influence the quality of UX, one of which is its usability. Usability is one of the important concepts in UI/UX design, which aims to create products that are easy to use, effective, and satisfying for users. Lova.ID, a company that sells women's fashion, requires an E-Commerce website system to increase marketing and sales. To find out whether the UI/UX prototype can be used according to user needs, it is necessary to carry out a usability testing and analysis. This study aims to test the UI/UX prototype that has been made. Tests based on effectiveness, efficiency, and user usability use the time based efficiency method and the System Usability Scale (SUS). The results of this study note that users can complete all tasks properly to determine the effectiveness of the prototype with a percentage of 100%. In addition, the average time needed to do all tasks is included in the fast classification. And, the usability of the prototype is at level B, which means it is well accepted by users.
Mitigating Message Passing Interference in Trusted Embedded Platforms

Message passing (MP) hardware usually uses a fixed-size message buffer, which removes the need for resource consuming connection state. However, buffers may overflow and stall further incoming messages, jamming the communication fabric, and possibly impeding communication on the whole platform. The prevention of communication channel interference is especially important for the isolation of applications in a distributed operating system environment. By jamming the inevitably shared communication fabric one application may be able to obstruct the progression of another application (cf. denial-of-service attack), thus breaking isolation. In this work we propose a reject-and-resend mechanism for embedded MP hardware, increasing the robustness of the communication fabric by preventing jamming. It drastically lowers communication interference, while keeping the low cost and low latency, connectionless communication of common MP hardware solutions.
Enhancing Aspect Detection Using BLSTM and Random Oversampling in Marketplace Application Review

Aspect-based sentiment Analysis (ABSA) has been contributing to a more nuanced understanding of sentiment-related issues and offering enhanced opportunities for sentiment analysis. The number of aspects in data sets are often unbalanced. This causes results that tend towards the majority class and ignore the minority class This research uses Bi-LSTM to classify the aspects and sentiments because Bi-LSTM is able to handle problems in LSTM. The primary objective of this research was to assess the efficacy of the Bidirectional Long Short-Term Memory method in the context of Aspect-Based Sentiment Analysis applied to reviews of Marketplace applications and common problems in machine learning in unbalanced datasets. The preprocessing of input data involved word embedding through Word2Vec Continuous Bag of Words (CBOW). Primarily, the training dataset encompassed 2250 instances, while the test dataset comprised 750. The evaluation enclosed a comparative analysis involving the proposed method (Bi-LSTM+CBOW+ROS), on Bi-LSTM+CBOW, Bi- LSTM+CBOW+RUS and Bi-LSTM+CBOW+SMOTE. The test results show that the proposed method is superior to all comparison methods. Bi-LSTM+CBOW+ROS produces the highest accuracy, namely the usability aspect of 92.40%, the maximum precision is 98.03%, the maximum recall is 100% and the maximum f1-score is 98.68%. The Usability aspect got the best results for all the methods tested.
Zoophobia Self-Therapy Application Using Graduated Exposure Therapy Method And Augmented Reality

Zoophobia is a severe anxiety disorder that develops when a person experiences anxiety and fear around specific animals. One method to control fear is Graduated Exposure. In its application, Graduated Exposure needs to be accompanied by a psychologist who provides exposure material as a stimulus for the sufferer's fear. The purpose of this research is to build an android-based application that implements exposure therapy as an alternative self-help therapy to control the fear of zoophobia patients. This application combines the use of augmented reality technology to replace real animals with 3D objects as a therapy stimulus, as well as a smart band as a user's heart rate measuring device during therapy. The results of testing the black-box method show that the functional system built is as expected. While beta testing, the results obtained 88.89% strongly agree that the utilization of zoophobia therapy using mobile applications can help reduce fear, 86.67% strongly agree that exposure therapy using mobile applications provides the same experience as direct exposure, 75.56% agree that the use of 3D objects can replace real animals as a fear stimulus, 75.56% agree that interacting with the intermediary of augmented reality technology provides the same fear.
The Design of Embedded Fuzzy Logic Controller for Autonomous Mobile Robots

In this paper, we propose an embedded fuzzy logic controller (EFLC) with a focus on addressing the challenges arising from the increasing number of fuzzy rules in autonomous mobile robots (AMRs). The Cortex-M0 is selected as the main processor, and the entire logic was implemented utilizing Verilog HDL. In order to evaluate performance, we analyzed the execution time difference between a software-only implementation of a fuzzy logic controller (FLC) on Cortex-M0 and a hardware-accelerated implementation with the proposed EFLC. The effectiveness and feasibility of the proposed design were demonstrated through simulations and field-programmable gate array (FPGA) implementation.
Image Radar Point Cloud Segmentation with Segment Anything Model

Radar point clouds are a rich source of information for various applications. However, clustering or classification of radar point clouds is challenging due to their sparsity, noise, and ambiguity. In this paper, we propose a novel approach that leverages the Segment Anything Model (SAM), a segmentation model introduced by Meta AI that can produce high-quality segment masks from 2D images, to predict 3D masks in image-radar point clouds. We extend SAM to handle 3D points indirectly, by associating point clouds with time-synchronized and calibrated image data, we first get masks from 2D images using SAM and then project the masks onto 3D points. Based on the masks, we can cluster radar point cloud and predict the remaining parameters of the objects by involving other radar point clouds attributes. This is also a convenient method to label radar point clouds for radar-only neural network development without supervision. Our approach is experimented on a self-made dataset and the results demonstrate reasonable qualitative accuracy without any further fine-tuning or training of SAM.
High-Performance BLS12-381 Pairing Engine on FPGA

As concerns for security are increasing, more and more applications require a more complicated algorithm that would allow more functionality in the cryptosystem. One of the core algorithms is the pairing-based cryptography (PBC), which relies on the property of pairing function. The computation speed of pairing would be important for realizing many systems in real-world scenarios, such as attribute-based encryption. To showcase the possible hardware acceleration of the pairing operation, we propose a high-performance accelerator, the pairing engine, which is implemented on FPGA. The architecture is adopted from previous ASIC implementation with the proposed new Fp2 modular multiplier and the fine modulo reduction that allow it to run at 180 MHz on FPGA. Our proposed pairing engine can compute the pairing operation on BLS12-381 within 62.2 µs, the fastest ever recorded, which is three times faster than the state-of-art in FPGA while also improving the Normalized Area-Time criteria by half.
Loop-Tiling Based Compiling Optimization for CNN Accelerators

With the rapid development of convolutional neural networks (CNNs), CNN accelerators have been a research focus and are continuously being developed. Loop tiling is a commonly deployed optimization method to guide hardware architecture design based on hardware resource constraints. However, the potential performance gain of loop tiling at the compiler phase is rarely considered. In this paper, a loop-tiling based compiling scheme is proposed, which could assist a large range of hardware architecture to improve the versatility and performance of inference. A network reorganization scheme is introduced which is customized to the target hardware architecture and reconstructed the network model in a more hardware-friendly manner. At the same time, the weight re-ordering is performed correspondingly to the newly generated network model to guarantee correct data access. Experimental results demonstrate that the proposed method could effectively enlarge the range of supported CNN models for CNN accelerators with various hardware configurations.
Scalable Highly Integrated Quantum Bit Error Correction System by Classical Electronics

In this presentation, I will introduce our project named "Development Scalable Highly Integrated Quantum Bit Error Correction System (Qubecs)." It belongs to one of the governmental-funded project called Moonshot Goal 6 "Realization of a fault-tolerant universal quantum computer that will revolutionize economy, industry, and security by 2050."
Using Networked Ontologies to support UX Evaluation in Immersive Context

Over the past few years, new interactive systems such as immersive technologies have gradually permeated our daily lives and found adoption across various fields. Immersive technologies provide users with immersive experiences. Assessing and modeling the quality of such experiences has become a trending topic in HCI, and UX is a key quality attribute in this context. When it comes to immersive experiences, evaluating UX is particularly challenging because the user should not be interrupted to provide feedback. In this paper, we propose using networked ontologies to support evaluating immersive experiences. We have explored using ontologies from an ontology network addressing the HCI domain to develop a tool that supports UX experts evaluating such experiences based on data recorded in interaction logs. We used the ontology-based tool to evaluate the UX of an immersive application that supports collaborative music composition. The tool extracted data from the application interaction logs applied UX metrics, and provided consolidated data and information in graphs and tables. We conducted a study and collected feedback from the tool developer and three UX experts who used the tool. Results showed that using networked ontologies to develop a tool to support UX evaluation is feasible and valuable. In summary, the ontologies helped at the conceptual level by offering a basis to define the system’s structural model and at the implementation level by assigning semantics to data to make inferences about UX. Based on the UX experts’ perceptions, the tool was considered a promising system, beneficial, helpful, and easy to use.
Preliminary Exploration on Node-To-Node Fault Tolerance Coordination in Distributed System

Internet of Things, edge computing devices, the widespread use of artificial intelligence and machine learning applications, and the extensive adoption of cloud computing pose significant challenges to maintaining fault tolerance in distributed systems. As the volume and complexity of systems continue to grow, the probability of system failures also grows, leading to downtime and poor performance. Fault tolerance is pivotal in modern computing systems by preventing downtime and service disruptions, even during failures. However, adopting fault tolerance comes with additional resource consumption, such as memory, CPU, disk, and network bandwidth usage, impacting system performance. Balancing fault tolerance with system performance is crucial to optimize resource utilization while minimizing the costs associated with detecting and recovering from faults. This paper introduces a node-to-node fault tolerance coordination mechanism for distributed systems to facilitate the tradeoff between fault tolerance and the cost of performance penalties and resource consumption. The proposed mechanism is preliminarily implemented on HDFS cluster. Our proposal enables opportunities to minimize the cost of the fault tolerance impact on performance and resource usage compared to the existing fault tolerance coordination mechanism.