Reviewerly/exHarmony huggingface dataset:
EFEKTIVITAS PENGGUNAAN MEDIA PEMBELAJARAN PREZI TERHADAP MOTIVASI BELAJAR SISWA PADA MATA PELAJARAN SISTEM KOMPUTER (STUDI KASUS DI SMK IZZUL ISLAM SALEM)

This study aims to: (1) determine the application of desktop Prezi learning media on computer systems subjects. (2) Knowing the difference in learning motivation between students who are taught using desktop Prezi and students who are taught using conventional media. (3) Knowing the comparison of the effectiveness of desktop Prezi media with conventional media. This research is quantitative with experimental method. The research design used is the Noneequivalent Control Group Design. The subjects of this research are students of class X Multimedia at SMK Izzul Islam Salem in the Academic Year 2021/2022. Collecting data using observation sheets and student response questionnaires. Data were analyzed descriptively. Student learning motivation questionnaire data obtained from the pre-test and post-test scores with an average of 61.8% and 75.8% for the experimental class while in the control class 63.2% and 68.8%, respectively. Then the results of observations from observations in the classroom the average percentage of student learning motivation in the control class shows a figure of 53%. Meanwhile, the average percentage of students' learning motivation in the experimental class shows a figure of 65.5%. Calculation of the average gain score, the experimental class obtained an average gain score of 0.36 which was greater than the average gain score in the control class of 0.15. Thus, it can be concluded that learning using Prezi desktop is more effective than using conventional media.
Drone Tracking Based on the Fusion of Staring Radar and Camera Data: An Experimental Study

This paper presents an experimental study on tracking a small drone target with a high resolution camera and a staring radar. The objective is to assess the benefits of fusing the outputs of both sensors using real data collected during live drone trials. We examine the impact of losing the signal from one sensor, which often occurs in practice for various reasons such as occlusions, high background noise-clutter, target sharp maneuvers, etc. We demonstrate that fusion with filtering, namely employing interacting multiple models with unscented Kalman filter in modified spherical coordinates or a simple extended Kalman filter, can deliver improved overall target tracking performance under such degraded sensing conditions.
EFL STUDENTS' PERCEPTION ON ENGLISH LEARNING MEDIA: WORD BANK

AbstractLearning vocabulary is a crucial component of learning the English language. The four essential abilities in English for Second Language Learners will be easier to acquire through vocabulary mastery. Students' lower enthusiasm to learn a second language may have a background related to their lack of L2 vocabulary maintenance. Games are one the interactive media that can be applied in the classroom. This study was carried out to investigate how 13 students felt about the interactive game Word Bank when learning English in class. Senior High School Widyagama Malang grade 12 students served as the study's subjects. These details were gathered from teacher interviews and observations of English classes. With the aid of qualitative research techniques, the data was gathered. Students were given a survey in the form of a Google Form, which was used to collect the data. Data analysis revealed that students responded well to the questionnaire. According to the study's findings, using Word Banks in English learning is highly desirable and effective for use in the classroom. Boredom can be decreased by involving class XII Senior High School Widyagama Malang students. The findings of this study can help educators choose engaging learning tools with plenty of content.
Training on making Interesting Percentage Learning Media Using Canva

Purpose ­ Presentation of learning media that is interesting, creative, and informative is one of the capitals for creating lessons that are easily accepted by students. The purpose of community service is creativity and the creation of innovation, especially in making learning media, especially in percentage broadcast media. Methode - The method used is demonstration by practicing directly through the Zoom Meeting. The material provided includes creating an account, introducing features, using available templates to managing the use of existing elements to support the material that will be made into slides. Result and discussions - Community service activities are carried out through the delivery of material including creating accounts, introducing features, using available templates to setting up the use of existing elements to support material that will be made into slides. Conclusion - This training can add insight and knowledge to students, especially in adding references to how to deliver teaching materials, attractive percentages.
Keynote: Data Scarcity in Sensor-Based Human Activity Recognition

Sensor-based Human Activity Recognition (HAR) is a research field that has been widely studied in Pervasive Computing. Due to its many applications from healthcare to well-being, sensor-based HAR has been proposed to recognize high-level activities considering smart-homes environmental sensors as well as low-level activities using wearable sensors. The most effective solutions presented so far rely on supervised learning approaches, that have the potential of reaching high recognition rates. However, such approaches assume that large amounts of labeled data are available. Annotating human activities is costly, time-consuming, intrusive, and often prohibitive. Considering the high variability of activity execution among different subjects, methods that are capable of personalizing to each subject with limited labeled data are needed. In the last few years, several research groups are indeed working hard trying to mitigate this problem. However, data scarcity still remains an open challenge in sensor-based HAR. This talk presents latest research efforts on these topics. In particular, the talk will cover: a) purely knowledge-based approaches enhanced with active learning for smart-home sensor data, b) hybrid data-driven and knowledge-based approaches relying on context data to mitigate data scarcity in mobile settings, and c) semi-supervised federated learning approaches. We will discuss the above-mentioned challenges, report our experience and identify critical aspects which still need to be investigated.
UMUM 2023: Second Workshop on Ubiquitous and Multi-domain User Modeling - Welcome and Committees

This is 2nd workshop on Ubiquitous and Multi-domain User Modeling (UMUM-23), held in conjunction with PerCom 2023.This workshop aims to explore a new line of research to build ubiquitous and multi-domain user models and increase their applicability in real-world contexts, e.g., activity recognition and context/activity-aware services.In UMUM-23, 1 keynote talk, 1 opening talk and 7 oral presentations will be provided.We look forward to meeting you all.
TinyHAR: Benchmarking Human Activity Recognition Systems in Resource Constrained Devices

Advances in deep learning, especially Convolutional Neural Networks (CNNs) have revolutionized intelligent frame-works such as Human Activity Recognition (HAR) systems by effectively and efficiently inferring human activity from various modalities of data. However, the training and inference of CNNs are often resource-intensive. Recent research developments are focused on bringing the effectiveness of CNNs in resource con-strained edge devices through Tiny Machine Learning (TinyML). However, this is extremely hard to achieve due to the limitations in memory, compute power, and energy of resource constrained edge devices. This paper provides a benchmark to understand these trade-offs among variations of CNN network architectures, different training methodologies, and different modalities of data in the context of HAR, TinyML, and edge devices. We tested and reported the performance of CNN and Depthwise Separable CNN (DSCNN) models as well as two training methodologies: Quantization Aware Training (QAT) and Post-training Quantization (PTQ) on five commonly used benchmark datasets containing image and time-series data: UP-Fall, Fall Detection Dataset (FDD), PAMAP2, UCI-HAR, and WISDM. We also deployed and tested the performance of the model-based standalone applications on multiple commonly available resource constrained edge devices in terms of inference time and power consumption. The experimental results demonstrate the effectiveness and feasibility of Tiny ML for HAR in edge devices.
A Side-Channel Attack Against Classic McEliece When Loading the Goppa Polynomial

The NIST Post-Quantum Cryptography (PQC) standardization challenge was launched in December 2016 and recently, has released its first results. The whole process has given a considerable dynamic to the research in post-quantum cryptography, in particular to practical aspects, such as the study of the vulnerabilities of post-quantum algorithms to side-channel attacks. In this paper, we present a realistic template attack against the reference implementation of Classic McEliece which is a finalist of the 4th round of NIST PQC standardization. This profiled attack allowed us to accurately find the Hamming weight of each coefficient of the Goppa polynomial. With only one decryption, this result enables us first, to find directly the Goppa polynomial in the case of weak keys with the method of Loidreau and Sendrier (P. Loidreau and N. Sendrier, “Weak keys in the McEliece public-key cryptosystem”, IEEE Trans. Inf. Theory, 2001). Then, in the case of “slightly less weak keys”, we also find this polynomial with an exhaustive search with low complexity. Finally, we propose the best complexity reduction for exhaustive Goppa polynomial search on $$\mathbb {F}_{2^m}$$ . We attack the constant-time implementation of Classic McEliece proposed by Chen et al. This implementation, which follows the NIST specification, is realized on a stm32f4-Discovery microcontroller with a 32-bit ARM Cortex-M4.
LOGICAL-FORMING TASKS IN THE DEVELOPMENT OF LOGICAL COMPETENCE OF IT FOREIGN APPLICANTS

The article introduces the definition logical competence of the context of future IT professionals. The difference between existing approaches to the definition of logical competence and logical competences is considered. The importance of logical competence as a main competence on the formation and development of the professional competence of future IT specialists is presented. Difficulties in the process of formation and development of logical competence and ways to solve them are described. An overview of various educational systems of the world aimed at the formation of the logical competence of applicants for IT areas is presented. Also, the difficulties arising in the development of logical competence among foreign applicants of future IT specialists in Russian universities are considered. Despite the seemingly obvious problem of insufficient knowledge of the Russian language for learning, the main difficulty is the difference in programs and methods of learning in different countries. If the Russian educational system in the training of future IT specialists attaches great importance to the fundamental serious mathematical base, then even the best IT education systems in various countries focus on the applied part of disciplines. As you know, in the development of any kind of competencies, the foundation, the basic subjects on the basis of which the competencies are formed, is of paramount importance. The article describes tasks that form and develop the logical competence of applicants for future IT specialists. The proposed definition of logical competence, as well as the ways of its formation and development among applicants for IT areas, can be extended to other technical areas with appropriate refinement and recommended for work with applicants or undergraduate students.
Main Directions of Corrective and Developmental Work to Overcome Specific and Permanent Violations of Writing Skills in Dysgraphia of Younger School Age Children

The article examines the main directions of corrective and developmental work to overcome violations of writing skills in dysgraphia in children of primary school age. It has been proven that the organization of corrective work to overcome writing and reading disorders of primary school age children in logopoint is based on the implementation of general didactic and special principles. It is emphasized that the method proposed in the study is effective and can be successfully used in the practice of corrective and developmental work among elementary school children.
THE IMPLEMENTATION OF ENGLISH LEARNING BASED ON MODEL OF INTERCULTURAL COMMUNICATIVE COMPETENCE (ICC) FOR IMPROVING STUDENTS’ COMMUNICATION INDEPENDENCE

The purpose of this research is to determine the implementation of English learning based on the intercultural communicative competence (ICC) model to increase student independence in communication. Students can develop their talents, interests, knowledge, attitudes, awareness, behaviour, and skills when talking with people from different cultures (foreigners). The method approach in this research is qualitative-grounded theory using the techniques and data analysis of Miles and Huberman (1994), namely data collection, data reduction, data display, and conclusion drawing/verification. The number of participants in this research consisted of eleven participants in the fourth semester and eleven participants in the sixth semester, so total participants were twenty-two participants in this research, this sample was taken randomly. The results of this study show that students could not implement five dimensions, namely skills: interaction (11), knowledge: social (5), awareness: practice (5), attitude: 4 and behaviour: 3. Conclusion of this research, the students of the English Education Department, Faculty of Teacher Training and Education University of Muhammadiyah Mataram implemented three main dimensions encompasses skills: The ability to interact, knowledge: social knowledge, and awareness: awareness to do the practice.
Fire Management System Using AI Technology

Abstract: Firefighting is an important job but it is very dangerous occupation. Due to that, Robots are designed to find a fire, before it rages out of control. It could be used to work with fire fighters to reduce the risk of injury to victims. This paper presents the Fire Fighting Robot competition that purposely to simulate the real-world operation of an autonomous robot to rescue 10 victims and stop 5 fires in a house within three minutes. The robot development is consisting of three elements which is the hardware, electronic, and programming. The robot has three DC motor, two for driving system and another single DC motor for ball suction subsystem and the fire blowing subsystem. Various sensors are also interfaced with PIC16F877A the robot such as photoelectric sensors, fiber optic sensor and RGB color sensors. LCD display also gives the graphical information of the robot status to the user. For the programming part, C language is used
Real Estate Price Prediction Using Artificial Intelligence

The sales of houses are influenced by various factors such as location, area, population, and other relevant information. Predicting individual housing prices can be instrumental in estimating future real estate prices. This study employs the Random Forest algorithm, a machine learning technique, as the primary methodology for developing a housing price prediction model. By focusing on the Random Forest algorithm, this project aims to optimize prediction accuracy and consistency, considering it as the best model for price prediction. The implementation of this project involves using Python (AI/ML) for coding the Random Forest algorithm, while HTML, CSS, and JS are employed for designing the system's frontend. Ultimately, the House Price Prediction system proves to be a valuable tool for accurately assessing property prices and maintaining a record of price fluctuations, promoting transparency and discouraging fraudulent activities.
SYSTEMATIC LITERATURE REVIEW OF A SCIENTIFIC JOURNAL: IMPROVING LITERACY IN ONLINE CLASSROOM LEARNING

This research is a systematic literature review research that can provide a clear picture of what has been investigated and what gaps require further research, then aims to overcome this problem by identifying, critically evaluating, and integrating the findings of all studies, and aiming not to just to gather all the evidence on the research question; it is also intended to support the development of evidence-based guidelines for practitioners. This study uses ten main articles to find out how to improve literacy in online classrooms. This main article was taken because it is related to the main objective of this research, namely improving literacy in online classroom learning, which we all know that we are living in the era of the Covid 19 pandemic, which forces us to change all the order of life including in the world of education. The result of this research is that there are seven things that can improve literacy in online classroom. There are scientific literacy and literacy learning opportunities, digital tools and using technology, students' creativity and creating new applications, online environments and content knowledge, teachers and students hold positive attitudes, teaching philosophy and pedagogical knowledge, and texts and resource's positive identity work.
Implementation of TPACK in Microteaching of Mathematics Education Students Based on Online Learning Platforms

The purpose of this study was to determine the effectiveness of the implementation of TPACK in microteaching mathematics education students in terms of differences in online learning platforms. The type of research used in this study is Mixed Method with a Sequential Explanatory design by starting with quantitative data collection and then continuing with qualitative data collection.. The subjects in this study consisted of 12 mathematics students. Subjects were given a TPACK ability test and the opportunity to simulate learning using an online learning platform, then conducted in-depth interviews regarding the implementation results. The results showed that there were differences in the ability to implement student TPACK in microteaching consisting of PCK components including basic skills of explaining and asking, TPK components covering basic skills of explaining and providing reinforcement, TCK components covering basic skills of explaining and providing variations, and TPACK components covering basic skills of opening and closing as well as essential skills of managing small groups. Based on the two platforms used, Moodle is better when compared to Google Classroom in supporting and making it easier for students to implement TPACK when compared to using Google Classroom. Especially in terms of appearance, features, integration with other applications, and storage limits. It can be stated that Moodle is more effective when compared to Google Classroom in implementing TPACK in microteaching.
Moviebox: A Movie Recommendation System

Abstract: In the vast world of cinema, numerous critically acclaimed movies often go unnoticed, despite their significant artistic and cultural value. Recognizing this disparity, our research endeavors to bridge the gap by developing a comprehensive movie recommendation system that highlights these "Out of the box" films. In the initial phase, we meticulously collected a bespoke dataset by scraping data from IMDb, encompassing a wide range of movies from various genres and regions. In the subsequent phase, we constructed an advanced algorithm utilizing content-based filtering techniques. This algorithm analyzes both user behavior and movie features to provide personalized recommendations that align with users' unique preferences. By embracing this approach, our research aims to enhance the discoverability and appreciation of lesser-known yet meaningful movies, empowering users to explore a diverse array of cinematic experiences.
Research on Global Path Planning of Robot Based on Ant Colony Algorithm and Gaussian Sampling

In response to the issue of the traditional ant colony algorithm (T-ACO) with many iterations and slow convergence speed in robot global path planning, we propose an enhanced ant colony algorithm (S-IACO) that incorporates Gaussian sampling. Firstly, the initial pheromone concentration contained in the raster map is preprocessed. Gaussian distribution sampling is adopted, and the sampling median value is used as the initial pheromone concentration of the raster map. Secondly, the heuristic function of the ant colony algorithm was improved. The number of ant colony iterations and the current path length were dynamically introduced into the heuristic function of the algorithm as influencing factors. Finally, this work redefined the pheromone update rule and introduced the concept of loss function by considering the influence of initialization pheromone on the results. By comparing with other improved ant colony algorithms, as well as PSO and GA algorithms, the S-IACO algorithm proposed has fewer iterations and faster convergence speed.
Temporal Factors Associated With Visual Processing Bias in Peripersonal Space

The immediate space surrounding the body, reachable to the hands, is referred to as the Peripersonal Space (PPS). The PPS is characterized by anticipatory mechanisms to perform efficient goal-oriented or defensive actions towards objects in the environment on-time. Previous studies have shown visuo-spatial processing bias for stimuli presented in the PPS, reported in the form of faster Reaction Time, better Accuracy and enhanced Visual Sensitivity. However, recent studies show that the PPS-related effects are sensitive to temporal factors associated with the early anticipatory mechanisms in the PPS. The current manuscript highlights the temporal mechanisms underlying the PPS-related visual processing bias. Specifically, the PPS effect is conceptualized as comprising of an early anticipatory component that gives temporal allowance for the manifestation of a late component. The early component accommodates the previous studies that have explained the PPS effects to be perceptual in nature, whereas the late component accommodates the previous studies that have explained the PPS effects to be attentional in nature. To the best of our knowledge, no previous attempts have been made to conceptualize the PPS effects in terms of its time-separable components. The current conceptualization of the PPS-related effects seems more wholistic vis-à-vis the previous explanation that the effect is either perceptual or attentional in nature. Also, the current manuscript attempts to make theoretical integration between Time Perception and Peripersonal Space literature.
ANALYSIS OF LEARNING MOTIVATION BETWEEN MALE AND FEMALE STUDENTS OF ELEMENTARY SCHOOL X DURING ONLINE LEARNING DURING THE COVID-19 PANDEMIC

This paper discusses whether there is a difference between male and female students learning motivation during online learning amidst the COVID-19 pandemic or not. The research is quantitative research, which has 30 high-grade students at elementary school “X” in Tobelo. as the sample “ X ” in Tobelo. The results indicate that there are no significant difference s between male and female students learning motivation in which the mean score for male students' learning motivation was 46.13 and the mean score of female students’ learning motivation was 45.27 with a significance value of p &gt; 0.05 (0.342 &gt; 0.05). It implies that although the learning was conducted online during the COVID-19 pandemic, male and female students had similar learning motivation s.