Reviewerly/exHarmony huggingface dataset:
A Real-Time Subway Driver Action Sensoring and Detection Based on Lightweight ShuffleNetV2 Network

The driving operations of the subway system are of great significance in ensuring the safety of trains. There are several hand actions defined in the driving instructions that the driver must strictly execute while operating the train. The actions directly indicate whether equipment is normally operating. Therefore, it is important to automatically sense the region of the driver and detect the actions of the driver from surveillance cameras to determine whether they are carrying out the corresponding actions correctly or not. In this paper, a lightweight two-stage model for subway driver action sensoring and detection is proposed, consisting of a driver detection network to sense the region of the driver and an action recognition network to recognize the category of an action. The driver detection network adopts the pretrained MobileNetV2-SSDLite. The action recognition network employs an improved ShuffleNetV2, which incorporates a spatial enhanced module (SEM), improved shuffle units (ISUs), and shuffle attention modules (SAMs). SEM is used to enhance the feature maps after convolutional downsampling. ISU introduces a new branch to expand the receptive field of the network. SAM enables the model to focus on important channels and key spatial locations. Experimental results show that the proposed model outperforms 3D MobileNetV1, 3D MobileNetV3, SlowFast, SlowOnly, and SE-STAD models. Furthermore, a subway driver action sensoring and detection system based on a surveillance camera is built, which is composed of a video-reading module, main operation module, and result-displaying module. The system can perform action sensoring and detection from surveillance cameras directly. According to the runtime analysis, the system meets the requirements for real-time detection.
Machine Learning Approach for New COVID-19 Cases Using Recurrent Neural Networks and Long-Short Term Memory

This research aims to predict new COVID-19 cases in Bandung, Indonesia. The system implemented two types of deep learning methods to predict this. They were the recurrent neural networks (RNN) and long-short-term memory (LSTM) algorithms. The data used in this study were the numbers of confirmed COVID-19 cases in Bandung from March 2020 to December 2020. Pre-processing of the data was carried out, namely data splitting and scaling, to get optimal results. During model training, the hyperparameter tuning stage was carried out on the sequence length and the number of layers. The results showed that RNN gave a better performance. The test used the RMSE, MAE, and R2 evaluation methods, with the best numbers being 0.66975075, 0.47075, 0.29616625, and 0.7644 on the test data.
STUDENT'S LANGUAGE POLITENESS IN LEARNING AT SMAN 1 KOTA PASURUAN

This study aims to obtain an objective picture of students' language politeness in learning at SMAN 1 Pasuruan City. The type of research is descriptive qualitative research. The data of this research is in the form of students' language politeness and its context. The data source in this research is the students' speech in learning at SMAN 1 Pasuruan City. The technique of data collection is simak catat technique. The main instrument in this research is the researcher himself. The analysis technique is done through (1) data reduction, (2) data presentation, (3) verification of data analysis results, and (4) conclusion drawing. The results of this study show that the communication made by students in learning at SMAN 1 Pasuruan still maintains language politeness which is shown in the use of tact maxim, generosity maxim, approbation maxim, and agreement maxim. However, students of SMAN 1 Pasuruan also still violate the politeness of approbation maxim and diction errors. The more dominant use of the politeness maxim shows that the students' character in language politeness is still well maintained as a form of maintaining cultural norms.
Comparison of Error Rate Prediction in CART for Imbalanced Data

CART is one of the tree based classification algorithms. CART is a tree consisting of root nodes, internal nodes, and terminal nodes. The accuracy of the model in CART can be calculated by measuring prediction errors in the model. One common method used to predict error rates is cross-validation. There are three cross-validation algorithms, namely leave one out, hold out, and k-fold cross-validation. These methods have different performance in dividing data into training data and testing data, so there are advantages and disadvantages to each method. Every algorithm has its shortcomings; hold out cannot guarantee that the training set represents the entire dataset, leave one out is very time-consuming and requires significant computation because it has to train the model as many times as there are data points, and k-fold provides longer computation time because the training algorithm must be run k times. In reality, the data often encountered is imbalanced. Imbalanced data refers to data with a different number of observations in each class. In CART, imbalanced data affects the prediction results. This research focuses on comparing error rate prediction methods in the CART model with imbalanced data. The study uses three types of data: univariate, bivariate, and multivariate, obtained from differences in population means and correlations between independent variables. The results obtained indicate that the k-fold algorithm is the most suitable error rate prediction algorithm applied to CART with imbalanced data.
Classification of Nutrition Problems for Indonesian Toddler With Decision Tree Algorithm C4.5

Indonesia continues to encounter numerous challenges, particularly in the health and economic sectors. As the future of the nation, the quality of human resources is crucial for Indonesia's development. The development of Indonesia is key to improving the quality of life of its people, and a focus on this development can positively impact the health and economy of the community. A healthy and educated generation is fundamental for the country's expected progress, as nutritional status is one of the factors significantly affecting the quality of human resources. Nutritional problems can cause serious impacts, such as improper physical growth, decreased IQ quality, and even death. The goal is to analyze the factors affecting the nutritional status of toddlers by classifying each variable using a decision tree. A decision tree is a flow chart that resembles a branching tree structure. The C4.5 algorithm was utilized in this study. It can process both numeric and categorical data, handle missing attribute values, and generate easy-to-interpret rules. After conducting the analysis, it was found that there are 392 districts/cities in Indonesia where the prevalence of stunted toddler nutritional status is less than 20%. The model created using the C4.5 algorithm was evaluated and achieved an accuracy of 99.8% and a kappa value close to 1. This indicates that the model can accurately classify toddler nutrition problems in Indonesia.
Sistem Informasi Pengolahan Data Karyawan di Perusahaan pada Dinas Tenaga Kerja dan Transmigrasi Kabupaten Ogan Komering Ulu Selata

&#x0D; The Personnel Information System is an integrated system, which includes employee data collection, data processing, procedures, rank, employment status, and computer technology to produce fast, complete and accurate information in order to support personnel administration activities.&#x0D; Personnel storage is the main thing in staffing issues, especially in finding or changing staffing data to then change it according to needs. This Personnel Information System Application was made using descriptive research methods with the type of case study research at the Office of Manpower and Transmigration, Ogan Komering Ulu Setalan Regency , where the data collection techniques used include observation, interviews, and literature studies. For the analysis and design methods used in making the system using the ERD method as a data model and DFD as a process model. Using codeigniter 3 programming and action scripts in making visual code programs.&#x0D; Based on the test results, it was concluded that it allows delays in the data manipulation process and delays in each data display that appears. Meanwhile, based on observations concluded that the system was considered good enough, learnable, easy to use. The system created is in accordance with the needs of the Ogan Komering Ulu Setalan Manpower and Transmigration Office, and this system has been tested using the Black Box testing technique.
Sistem Informasi Pelayanan Gangguan Berbasis Web di PT. Telkom Kabupaten Ogan Komering Ulu

Telkom Indonesia is a State-Owned Enterprise (BUMN) in the field of IT services, ICT and telecommunications networks in Indonesia. PT. Telkom Indonesia has a service that can be used for individuals or companies, namely Indihome. Indihome is a network package, an Indihome Fiber package that provides internet services, interactive TV and home telephones. It takes a design data system for troubleshooting services that can be accessed by customers and suitable technicians to support the smooth running of the process, processing of complaint information and corrective action. &#x0D; By using the proposed system correctly, it may be easier to monitor the distribution of complaint information, so as to speed up the repair process. This can also help speed up the presentation of reports that are accurate, timely and relevant so that they can assist management in making decisions. This is intended so that technicians do not face the main obstacles in recording information and monitoring the length of time working on field problems. In order to overcome this problem, we need a design data system service for electrical problems that can be accessed by the appropriate admin to support the smooth running of the process of processing complaint information and corrective action.&#x0D;
Pengembangan Video Animasi Toonly Pada Mata Kuliah Bunpo Shochukyu Bab 41-43

This study aims to develop an appropriate animated video form for the Bunpo Shochukyu course chapters 41-43 at the Japanese Language Education Study Program UNDIKSHA and analyze the feasibility level of the animated video being developed. The model used is the Multimedia Development Life Cycle (MDLC) which was introduced by Luther Sutopo and modified by Binanto (2010). The development of this learning video was carried out based on the results of semi-structured interviews by supporting lecturer 1 and supporting lecturer 2 to find out the obstacles to the Bunpo Shochukyu course in the UNDIKSHA Japanese Language Education Study Program. The distribution of questionnaires to students was also carried out to determine the readiness, strengths and weaknesses of the learning media used and the distribution of a second questionnaire to determine the characteristics of the desired learning media. The results of this study are 3 animation-based shochukyu bunpo learning videos which are equipped with backsound and have passed the feasibility test stage by alpha test and beta test. The first assessment by media experts obtained an average of 57 and the assessment by material experts obtained an average of 53. The second assessment by media experts obtained an average of 59 and the assessment by material obtained an average of 52. The beta test was carried out by 6 students and obtained an average of 98.5. The results of the assessment on the alpha test and beta test stated that the learning video developed was feasible to use.
Analisis Fungsi Fukujoshi Dalam Anime Captain Tsubasa 2018

Fukujoshi is a particle that connects the previous words with the words in the next section. Fukujoshi can be found in various media, for example in textbooks, manga, drama, and anime. Fukujoshi has various functions and meanings. In one sentence fukujoshi there are several different functions. Researchers assess the importance of research that analizis fukujoshi in anime, so that there is no misunderstanding of students. The purpose of this research is to find out what are the functions of fukujoshi in the anime Captain Tsubasa 2018.The research design used is a qualitative research with descriptive method. The data in this study are sentences that use the fukujoshi function in the anime Captain Tsubasa 2018 based on the theory of Chandra (2009). The source of data in this study is the anime Captain Tsubasa 2018. The instrument of this research is the researcher himself. Based on the results of the research conducted, the researchers found 2 functions of fukujoshi bakari, 2 functions of fukujoshi dake, and 2 functions of fukujoshi hodo in the anime Captain Tsubasa 2018 by Youichi Takahashi based on Chandra's theory (2009).
A unified multi-view multi-person tracking framework

Abstract Despite significant developments in 3D multi-view multi-person (3D MM) tracking, current frameworks separately target footprint tracking, or pose tracking. Frameworks designed for the former cannot be used for the latter, because they directly obtain 3D positions on the ground plane via a homography projection, which is inapplicable to 3D poses above the ground. In contrast, frameworks designed for pose tracking generally isolate multi-view and multi-frame associations and may not be sufficiently robust for footprint tracking, which utilizes fewer key points than pose tracking, weakening multi-view association cues in a single frame. This study presents a unified multi-view multi-person tracking framework to bridge the gap between footprint tracking and pose tracking. Without additional modifications, the framework can adopt monocular 2D bounding boxes and 2D poses as its input to produce robust 3D trajectories for multiple persons. Importantly, multi-frame and multi-view information are jointly employed to improve association and triangulation. Our framework is shown to provide state-of-the-art performance on the Campus and Shelf datasets for 3D pose tracking, with comparable results on the WILDTRACK and MMPTRACK datasets for 3D footprint tracking.
Harnessing Predictive Modeling and Software Analytics in the Age of LLM-Powered Software Development (Invited Talk)

In the rapidly evolving landscape of software development, Large Language Models (LLM) have emerged as powerful tools that can significantly impact the way software code is written, reviewed, and optimized, making them invaluable resources for programmers. They offer developers the ability to leverage pre-trained knowledge and tap into vast code repositories, enabling faster development cycles and reducing the time spent on repetitive or mundane coding tasks. However, while these models offer substantial benefits, their adoption also presents multiple challenges. For example, they might generate code snippets that are syntactically correct but functionally flawed, requiring human review and validation. Moreover, the ethical considerations surrounding these models, such as biases in the training data, should be carefully addressed to ensure fair and inclusive software development practices. This talk will provide an overview and reflection on some of these challenges, present some preliminary solutions, and discuss opportunities for predictive models and data analytics.
Development of a microcontroller and resistive touchscreen-based speed monitoring and control system for DC motor

&lt;span&gt;Speed control is a key requirement in direct current (DC) motor applications where accuracy, reliability, flexibility, and safety are of high importance. In this study, a microcontroller and resistive touchscreen-based DC motor speed monitoring and control system were developed. The core components employed in the development of the system include Arduino ATMega328P microcontroller, thin film technology (TFT) resistive touch screen, L293D motor driver, and infrared (IR) sensor module. ATMega328P microcontroller is the brain of the system around which the overall circuit design was modeled. TFT resistive touch screen displays the motor speed and also, enables the users to set a desired speed. L293D motor driver regulates the voltage and current supplied to the motor, and a feedback loop comprising an IR sensor module ensures the maintenance of the motor speed at the desired level. A performance test was conducted on the developed system to ascertain its correct functionality. The developed speed monitoring and control system operated satisfactorily during testing; achieving a speed control in the range of 800 to 3000 rpm. The developed device is useful and can be scaled up for various domestic and industrial applications.&lt;/span&gt;
Automatic Resource Scaling Management System on Electric Power Supercomputing Clouds

With the advent of the big data era, the explosive growth of data volume has put enormous computing and storage pressure on electric power companies. As a powerful technical means, supercomputing clouds are widely used in data processing, storage, and online services. However, critical services in supercomputing clouds are often deployed with overprovisioned resources to ensure the quality of service for users, resulting in significant energy consumption and additional costs. At the same time, insufficient resources for service provisioning can lead to performance degradation and service violations. To this end, this paper proposes an automatic resource scaling management system on electric power supercomputing clouds. Specifically, the proposed system is based on Transformer’s long-sequence prediction model to predict the future load intensity of the service and calculate the number of instances required by the service in the future through the runtime service requirement estimation component, thus automatically scaling resources and minimizing resource costs. Experimental results show that our system achieves the best scaling behavior based on performance metrics and the lowest cost overhead compared to strong baselines.
Short-Term Sequential Pattern for Temporal Knowledge Graph Reasoning

Temporal knowledge graphs (TKGs) have been widely used in various fields, and predicting missing knowledge graph inference has been widely explored. However, the task of reasoning about potential future facts on TKGs is more challenging and has attracted the attention of researchers. As the unknowability of future events complicates inference, a thorough study of the characteristics of historical facts becomes crucial. The study of the concurrency of historical events and the underlying common patterns of relation-ships facilitates reasoning about future facts. In this paper, we propose a novel representation learning model based on Short-Term Sequential Patterns for TKG reasoning, namely STSP. By modeling TKG sequences recurrently and learning representations of entities and relations. Specifically, the STSP encoder uses three main modules. Concurrent facts for each timestamp are modeled using a convolution-based relation-aware GCN. The entity-aware attention module is used to integrate the entity representation of the current timestamp and the previous timestamp. The sliding window mechanism is used to learn different relations sequentially. The entity and relation representations are then handed over to a translation-based decoder for final reasoning. We use four benchmark datasets to evaluate the proposed approach. The experimental results show that STSP out-performs state-of-the-art TKG reasoning methods and obtains substantial performance.
Access Control

Application security is essential to safeguard your data and business processes. The practice consists of several techniques, like authentication, authorization, vulnerability scanning, etc. Authentication is the process of confirming that a user is who they say they are. Typically, this can be accomplished by requiring the user to provide a username and password. Post authentication, the application must authorize if the user has the permissions needed for access. Authorization can also be based on the domain logic. The application needs to perform the preceding checks before serving every user action. Asking the user to submit this information every time leads to a poor user experience. Thus, the application authentication flow validates the user credentials and supports means of fetching the information again. Traditional applications used to offer this by saving the user details in the session associated with the user login. Modern applications do not store this information and instead encrypt the information in a JSON web token passed with each request.
ANALISIS EFISIENSI SALURAN DAN EFEKTIVITAS DAERAH IRIGASI OONGAN PADA DAERAH ALIRAN SUNGAI (DAS) TUKAD AYUNG

The Oongan Irrigation Area, which is located in East Denpasar as part of the Tukad Ayung River Basin, has a potential land area of 1781 Ha, but only 617 Ha has been utilized. In March 2019, the average discharge reached 680 l/second, while the Oongan intake gate required a discharge of 880.7 l/s for the rice growing period. Based on these data, there is a need for in-depth research in the Oongan Irrigation Area regarding the efficiency and effectiveness of irrigation canals.. The research method used is the survey method. Based on the results of the analysis of the efficiency of the primary and secondary canals and the effectiveness of the Oongan Irrigation Area, the percentage results were 80.47%, 94.1% and 35% which showed that the primary canal was not efficient and irrigation that was not effective was below the standard value, namely 90% and 55%. There needs to be an improvement both in the water management system and the physical improvement of irrigation infrastructure. Meanwhile, secondary canals get an average yield with a percentage of 94.1% which is included in the efficient category so that it needs to be maintained and carried out maintenance through operational and maintenance activities on irrigation canals.
Optimization of Data chain entry strategy based on suppression of interference

In order to improve the network performance and anti-jamming ability of the data link and enhance the testing level of the equipment, it is necessary to simulate the network access process under the special magnetic field environment. In the background of this application, this paper takes link-16 data link system as the research object, analyzes the process of coarse synchronization and fine synchronization in network synchronization, and puts forward a new network integration strategy and optimizes the analysis. On the other hand, the abstract transformation of engineering problem is carried out by using the three-point quadratic interpolation algorithm, the mathematical formula of the time of entry is designed, and the simulation experiment is carried out. Through the mechanism analysis and simulation experiment, the rationality and feasibility of the strategy optimization is verified.
An Analysis of Students’ Difficulties in Reading Comprehension at Eleventh Grade of Senior High School 8 Jambi City

The aim of this research is to identify the difficulties in reading comprehension faced by the eleventh-grade students at SMAN 8 Jambi City. This was qualitative research with a phenomenological approach. The subject of the research was the eleventh-grade students at Senior High School 8 Jambi City in Academic Year 2022/2023. The technique of collecting the data was a Reading test. The research used the technique of analyzing the data reduction, data display, and conclusion drawing/verification. The result of the research shows that there are 34,1% of students have difficulties understanding to find the main idea of the text with a total of incorrect is 68. Moreover, there are 21,1% of students have difficulties knowing the meaning of several vocabulary words in the text with a total of incorrect is 42. There are 18,6% of students who have difficulties understanding and making inferences from the text with a total of incorrect is 37. Lastly, there are 26,1% of students have difficulties in understanding to look inferring the meanings of particular words from the text.