Reviewerly/exHarmony huggingface dataset:
Study on the Effects of Educational Intervention Programs for Improving Basic Academic Skills of vulnerable elementary school students: Focus on Weekly Participation Frequency and Grade

Objectives This study aimed to analyze the variations in the impact of an educational intervention program de-signed to enhance basic academic skills among vulnerable elementary school students. The analysis considered factors such as weekly participation frequency and grade. The primary goal was to gather evidence for assessing performance and enhancing the program, ultimately improving participation outcomes.&#x0D; Methods Using the BASA test results (reading, writing, and mathematics) of 173 elementary school students who participated in the basic academic intervention program at the City Dream Start Center A in Gyeonggi Province from 2016 to 2022, we conducted a pre-post test difference analysis (t-test), a covariance analysis based on weekly participation frequency (1 and 2 sessions), and Analysis of Covariance(ANCOVA) based on grade level (1st-2nd grade, 3rd-4th grade, and 5th-6th grade).&#x0D; Results Children who participated in the basic academic intervention program showed the following results: Firstly, based on weekly participation frequency, higher effects were observed in reading and mathematics when participating twice a week compared to participating once a week. Secondly, based on grade level, reading showed higher effects in 3rd-4th, and 5th- 6th grades compared to 1st-2nd grades, while mathematics showed higher effects in 1st-2nd grades compared to 3rd-4th grades. However, there were no significant differences in writing based on participation frequency and grade level.&#x0D; Conclusions According to these findings, educational intervention programs conducted on a one-on-one basis for vulnerable elementary students need to consider individualized tailored lessons. Therefore, it is necessary to differentiate between short-term intensive interventions and long-term systematic interventions, considering the students' weak subjects and grade levels. Also, the development of diagnostic methods and the restructuring of the curriculum are required to enhance participation outcomes.
What’s in the research toolbox for a team of academic Liaison Librarians

This paper highlights how the library uses technology and information to connect with people in the University Library. Multimedia content production, webinars, self-guided webpages, and cross-team collaboration have all enabled the Academic Liaison team to reach new audiences and provide accessible and targeted research support. From one-on-one consultations to workshops with over 400 attendees – the team combines online and in-person services to be the conduit between key research stakeholders and the library.
Improving Ranking Using Hybrid Custom Embedding Models on Persian Web

Ranking plays a crucial role in information retrieval systems, especially in the context of web search engines. This article presents a new ranking approach that utilizes semantic vectors and embedding models to enhance the accuracy of web document ranking, particularly in languages with complex structures like Persian. The article utilizes two real-world datasets, one obtained through web crawling to collect a large-scale Persian web corpus, and the other consisting of real user queries and web documents labeled with a relevancy score. The datasets are used to train embedding models using a combination of static Word2Vec and dynamic BERT algorithms. The proposed hybrid ranking formula incorporates these semantic vectors and presents a novel approach to document ranking called HybridMaxSim. Experiments conducted indicate that the HybridMaxSim formula is effective in enhancing the precision of web document ranking up to 0.87 according to the nDCG criterion.
Gratisnet: the power of the network

No Library is an island with a collection that stretches to provide everything a client might want. GratisNet to the rescue! Whilst never a replacement for collections, an efficient resource sharing system is a means to acquire those papers from infrequently required journals that are now so discoverable.
Conceptualizing the Academic Management and Lecturer-Student Relationship for University Students’ Achievement Motivation in China

China has attached great importance to education. In 2019, since the higher education gross enrolment rate achieved 51.6%, Chinese higher education entered the stage of popularization, which makes China face new problems and challenges. In the stage of popularization, there are some adjustments in academic management and lecturer-student relationship, which have influences on the students’ achievement motivation. In order to further clarify the connotation and relationship of academic management, lecturer-student relationship and achievement motivation, and then take better measures to stimulate students' achievement motivation, this paper introduces the basic situation and systematically reviews the relevant literature, then designs the research methodology. The paper has a more profound and clean understanding of the three concepts (academic management, lecturer-student relationship, achievement motivation), and then puts forward the conceptual framework of the research, which can make some contributions to the improvement of the quality of university personnel training.
Web-Based Design of Central Buton Tourism System

Central Buton is an area located in the Buton archipelago, Southeast Sulawesi which is a former heritage area of the Sultanate of Buton where the area has many natural and cultural tourist charms that must be preserved. This study aims to design and implement web-based tourism in Buton Tengah. The method used in the development of this application is Waterfall in which there are five namely Analysis, Design, Coding, Testing, and Implementation of Maintenance Programs, with the PHP programming language and Sublime Text 3. The result of the research is that the Central Buton Tourism system is website-based and has a service function, namely the function of providing tourist information, booking tickets, so that the sales system can be improved for the better. Testing this system using the Black Box testing method, so that it is able to answer and show that the system created is in accordance with the objectives of the researcher and is able to meet user needs.
Analisis Kepuasan Pelanggan Menggunakan Metode Algoritma C4.5 Berdasarkan E-Survey Kejaksaan Negeri Minahasa

Customers in the Minahasa State Prosecutor’s Office are the main thing in service. A customer supports experiencing various levels of satisfaction, namely if the service is not appropriate after being received, then the customer will feel dissatisfied. On the other hand, if the service is in line with expectations, the customer will feel satisfied that one day the customer will use the service. The calcutation method use is the C4.5 algorithm. The software development method used is Prototype. Data collection methods use are observation, literature and interview. Based on the first and second tests, customer satisfaction data on administrative, case, legal, and technical service with 80 training data and 20 testing data for the first test and 90 training data and 10 data testing data for the second test obtained an accuracy result of 90% for administrative service satisfaction, 90% accuracy for case service satisfaction, 90% accuracy for legal service satisfaction and 90% accuracy for technical service satisfaction. So that it can be satisfaction with service based on the e-survey of the minahasa state prosecutor’s office.
PENGEMBANGAN SISTEM REPOSITORI SKRIPSI DI TEKNIK INFORMATIKA UNIVERSITAS NEGERI MANADO BERBASIS WEB

Based on observations made by researchers at Informatics Engineering Universitas Negeri Manado, there is no Thesis Repository that functions to accommodate data on Thesis Titles of Informatics Engineering Study Program Students. Based on this, researchers plan to develop a web-based Thesis Repository System at Universitas Negeri Manado Informatics using the Agile Software Development method. After the web-based Thesis Repository System in Informatics Engineering Universitas Negeri Manado was successfully developed using the Agile Software Development method, testing carried out using the black box testing method obtained good results, where 12 test points were successfully achieved and the developed system could help the study program in recording thesis data from students.
AN ANALYSIS OF STUDENTS’ PERCEPTION USING KAHOOT GAME IN VOCABULARY LEARNING PROCESS

In general, teaching and learning activities are mostly supported by books, the presentation of the materials by the teacher, and written or oral exercises. This type of learning method is monotonous. With technological developments and the internet as an educator, it is reasonable to utilize technology-based online learning applications, such as Kahoot. This research aims to find out the students' perceptions using Kahoot game in the vocabulary learning process. The participants of this research are XI Bahasa class students at MAN 1 BONE who have experienced using Kahoot. This qualitative research used questionnaire, interview, and documentation as instruments, purposive sampling technique was used to select participants to get data through interview. The results showed that Kahoot provides many positive impacts on students' learning. It was found that students perceive Kahoot as an online learning platform that increases interest, improves their vocabulary knowledge, and increases their motivation and enthusiasm which leads to active learning through flexible learning process. In addition, this research found difficulties that students experienced when using Kahoot, such as poor internet connection.
ONE-ON-ONE LEARNING METHOD IN SOLVING ENGLISH SPEAKING PROBLEMS: A STUDY ON EFL LEARNERS

The goal of the current study was to provide more information on one-on-one instruction as a learning approach for EFL learners' English speaking difficulties. The study's main interests were the EFL students' perceptions of one-on-one instruction and the speaking skills they acquired from it. This study employed a qualitative research design. Semi-structured interviews, documentation and speaking activity observation were used as data gathering methods. The researcher included EFL students who take speaking-focused courses at an English course in Makassar in the data collection process. There were ten EFL students available for an interview. After gathering the data, the researcher examined the data using three steps of data analysis; data reduction, data display and conclusion drawing/verification. The findings of this study showed that EFL students had difference opinions about one-on-one instruction. One-on-one instruction, according to them, is good, great, effective, efficient, fun, adaptable, and peaceful. Then, there were certain speaking exercises that EFL learners typically pick up along the learning process to hone their speaking skills. These speaking exercises included role plays, retelling movies, describing images, speaking games like dominos and speaking boards, question and answer sessions, telling daily activities, sharing experiences, outlining preferences, and talking about planning.
Hardhat-wearing detection based on YOLOv5 in Internet-of-Things

&lt;p&gt;Worker safety is paramount in many industries. An essential component of industrial safety protocols involves the proper use of hardhats. However, due to lax safety awareness, many workers neglect to wear hardhats correctly, leading to frequent on-site accidents in China. Traditional detection methods, such as manual inspection and video surveillance, are inefficient and costly. Real-time monitoring of hardhat use is vital to boost compliance with hardhat usage and decrease accident rates. Recently, the advancement of the Internet of Things (IoT) and edge computing has provided an opportunity to improve these methods. In this study, two detection models based on You Only Look Once (YOLO) v5, hardhat-YOLOv5s and hardhat-YOLOv5n, were designed, validated, and implemented, tailored for hardhat detection. First, a public hardhat dataset was enriched to bolster the detection model’s robustness. Then, hardhat detection models were trained using the YOLOv5s and YOLOv5n, each catering to edge computing terminals with varying performance capacities. Finally, the models were validated using image and video data. The experimental results indicated that both models provided high detection precision and satisfied practical application needs. On the augmented public dataset, the hardhat-YOLOv5s and hardhat-YOLOv5n models have a Mean Average Precision (mAP) of 87.9% and 85.5%, respectively, for all six classes. Compared with the hardhat-YOLOv5s model, Parameters and Giga Floating-point Operations (GFLOPs) of the hardhat-YOLOv5n model decrease by 74.8% and 73.4%, respectively, and Frame per Second (FPS) increases by 30.5% on the validation dataset, which is more suitable for low-cost edge computing terminals with less computational power.&lt;/p&gt;
COGNITIVE IMPAIRMENT, FALLS, AND DEMENTIA: NOVEL APPROACHES FOR EARLIER DETECTION AND MORE EFFECTIVE INTERVENTION

Abstract This symposium will discuss novel digital approaches for the remote assessment of cognitive status and detection of fall risk in older adults. We will then describe several emerging digital exercise, ballroom dancing, and other non-invasive intervention strategies targeted for vulnerable older populations at increased risk for falling, or developing cognitive impairment and dementia. Collectively, we posit that these innovative detection and intervention strategies hold significant potential as more effective and scalable approaches for identifying cognitive impairment, preventing falls, and mitigating cognitive decline in high-risk older adults. In presentation 1, Roque et al. examines the utility of ecological momentary cognitive assessment and its potential as a digital biomarker of cognitive impairment in community-dwelling older adults. In presentation 2, Mahoney et al. discusses the development and clinical potential of a novel digital health app, CatchU®, that remotely captures visual-somatosensory integration performance to detect fall propensity in older adults. In presentation 3, Bajdek et al. describes the feasibility of a 12-week digitally delivered home-based fall prevention exercise program for older adults with elevated fall risk. In presentation 4, Blumen et al. report preliminary efficacy and mechanistic findings from a randomized trial of social ballroom dancing in older adults at increased risk for dementia. In presentation 5, Reid et al. describes the feasibility of a real-world community-level physical activity intervention for older adults with motoric cognitive risk syndrome. The discussant, Dr. Verghese, will integrate the key findings from these studies, discuss their theoretical and methodological contributions, and consider future research directions.
Korelasi antara Dukungan Orang Tua dengan Motivasi Belajar Anak Selama Menerima Pembelajaran Daring di SD Kota Bengkulu

This study aims to determine the correlation between parental support and children's learning motivation while receiving online learning during covid-19 in Bengkulu City. This study uses a quantitative approach to the type of correlation. The population of this study were students at SD Kota Bengkulu. The sample that the researchers used was 360 respondents. The research instrument used was a questionnaire. The steps in this study are data tabulation, validity test, reliability test, data description, normality test, hypothesis testing and conclusions. In this study concluded that: there is a positive and significant relationship between parental support learning motivation is based on the calculation of SPSS version 16 is equal to 0.501 and the results correlation rvaluecount is less than 0.05 then H0 is rejected and Ha accepted so that there is a correlation between parental support and student motivation. The correlation of 0.501 lies between 0.40-0.599, it can be concluded that there is a moderate correlation between parental support and student motivation while receiving online learning at SD Kota Bengkulu. Keywords: parental support, children's learning motivation.
Investigating Deep Neural Network Architecture and Feature Extraction Designs for Sensor-based Human Activity Recognition

The extensive ubiquitous availability of sensors in smart devices and the Internet of Things (IoT) has opened up the possibilities for implementing sensor-based activity recognition. As opposed to traditional sensor time-series processing and hand-engineered feature extraction, in light of deep learning’s proven effectiveness across various domains, numerous deep methods have been explored to tackle the challenges in activity recognition, outperforming the traditional signal processing and traditional machine learning approaches. In this work, by performing extensive experimental studies on two human activity recognition datasets, we investigate the performance of common deep learning and machine learning approaches as well as different training mechanisms (such as contrastive learning), and various feature representations extracted from the sensor time-series data and measure their effectiveness for the human activity recognition task.
A Marketing Risk Inspection Model for Electric Power Enterprises Based on Capsule Network under Regulatory Background

Marketing audit is a whole process of power marketing supervision, review work. The establishment of an information system which can monitor the power grid in real time has become the urgent task of the current power grid development. This paper uses Java language, SSH framework, data warehouse, AJAX and ORACLE database for design. A risk assessment method of power grid marketing audit based on capsule network algorithm is established. The steps and problems to be considered in applying capsule network algorithm to market inspection and supervision are discussed through case analysis. The results show that the index system is reasonable and scientific. The quick evaluation results of marketing audit based on capsule network algorithm are effective and feasible.
NTTFusion: Efficient Number Theoretic Transform Acceleration on GPUs

Fully homomorphic encryption (FHE) holds great promise as an encryption technology for safeguarding privacy by enabling computations directly on encrypted data. However, FHE encounters significant performance bottlenecks due to the extensive utilization of number theoretic transform (NTT) and its inverse (INTT). Therefore, it is crucial to accelerate NTT to enhance the efficiency of FHE. Conventional NTT implementations rely on mandatory synchronization to maintain data consistency, which leads to two critical problems: excessive synchronizations and unexplored synchronization switching points. This paper presents NTTFusion, an efficient GPU-based NTT acceleration design that focuses on boosting the performance of NTT. (i) To reduce the number of synchronizations, we propose two types of stage fusion methods specifically designed for different polynomial lengths. For small polynomial lengths, we employ the butterfly decomposition approach, while for large polynomial lengths, we leverage the thread aggregation method. (ii) To explore the optimal synchronization switching point, we propose an SM-aware synchronization combination strategy to balance synchronization overhead and hardware utilization. Finally, we conduct experiments on a realistic NVIDIA GPU server and demonstrate that the butterfly decomposition method achieves up to 1.37× speedup compared to the state-of-the-art implementation. Furthermore, the thread aggregation method can yield up to 1.32× speedup for larger polynomial lengths. The optimal synchronization switching point-based NTT, which incorporates thread aggregation, can produce a maximum 1.6× performance boost under a typical large polynomial length.
STAG: Enabling Low Latency and Low Staleness of GNN-based Services with Dynamic Graphs

Many emerging user-facing services adopt Graph Neural Networks (GNNs) to improve serving accuracy. When the graph used by a GNN model changes, representations (embedding) of nodes in the graph should be updated accordingly. However, the node representation update is too slow, resulting in either long response latency of user queries (inference is performed after update completes) or high staleness problem (inference is performed based on stale data).Our in-depth analysis shows that the slow update is mainly due to neighbor explosion problem in graphs and duplicated computation. Based on such findings, we propose STAG, a GNN serving framework that enables low latency and low staleness of GNN-based services. It comprises a collaborative serving mechanism and an additivity-based incremental propagation strategy. With collaborative serving mechanism, only part of node representations are updated during the update phase, and the final representations are calculated in the inference phase. It alleviates the neighbor explosion problem. The additivity-based incremental propagation strategy reuses intermediate data during update phase, eliminating duplicated computation. Experimental results show that STAG greatly reduces staleness time with a slight increase in response latency, and support 2.7~27x workload compared to existing approaches.
Vision Attentive Robot for Elderly Room

With people’s busy schedules, elderly people have to stay alone in their houses in the daytime. There are a large number of accidents have happened to elderly people when they are alone at home. It is crucial to have a monitoring system to identify the potential hazards for the protection of elders to address this risk. In this research, we propose a method to identify postural behaviors, walking abnormalities, and falling situations using the skeleton data obtained from the Microsoft Kinect camera. In this paper, we discuss the identification of the falling of an older person. For that, we used an LSTM model, and the features of the model are velocities of angles and joints of the skeleton. This system achieved a validation accuracy of 88.34%, and it offers a promising solution for keeping an eye on and recognizing potential dangers for elderly people.
PANG: A Pattern-Aware GCN Accelerator for Universal Graphs

Graph convolutional neural network (GCN) extends deep learning to process graph data and demonstrates superior performance. However, due to the irregularity, graphs show inconsistent patterns across different regions, which leads to distinctions in data reusability and edge processing activity, and consequently poses impacts on hardware efficiency and resource utility. Prior accelerators seldom explore the distinct patterns across graph regions and adopt a fixed strategy for the whole graph without consideration for region-specific characteristics. In this paper, we identify the inconsistent patterns of graphs and characterize the distinctions between graph regions. Then, we propose an adaptive dataflow to adapt the region-specific patterns. Third, we implement PANG, a pattern-aware accelerator that can dynamically adjust the dataflow to exploit the reusability and alleviate the frequent destination switching. Evaluated on real-world datasets, PANG achieves significant performance improvement.
ABE for Circuits with poly (λ) -sized Keys from LWE

We present a key-policy attribute-based encryption (ABE) scheme for circuits based on the Learning With Errors (LWE) assumption whose key size is independent of the circuit depth. Our result constitutes the first improvement for ABE for circuits from LWE in almost a decade, given by Gorbunov, Vaikuntanathan, and Wee (STOC 2013) and Boneh, et al. (EUROCRYPT 2014) – we reduce the key size in the latter from poly(depth $,\lambda)$ to poly $(\lambda)$. The starting point of our construction is a recent ABE scheme of Li, Lin, and Luo (TCC 2022), which achieves poly $(\lambda)$ key size but requires pairings and generic bilinear groups in addition to LWE; we introduce new lattice techniques to eliminate the additional requirements.