Reviewerly/exHarmony huggingface dataset:
ANALYSIS OF USER RATINGS ON THE BUBBLE LYSN APPLICATION USING THE SYSTEM USABILITY SCALE (SUS) METHOD

ABSTRACT&#x0D; Artists from the agency. In 2020 Dear U launched a bubble feature that can send messages between fans and artists in the form of text, video, photos, or voice notes with a live broadcast system where these messages will reach users who subscribe and vote for the artist they like. This feature also has an attractive appearance with two-way communication where users can also reply to messages sent by the artist. So this feature allows users and artists to feel closer to each other. This study aims to analyze the usability of the bubble lysn application based on the user's experience.
Design Patterns Understanding and Use in the Automotive Industry: An Interview Study

Automotive software is increasing in complexity, leading to new challenges for designers and developers. Design patterns, which offer reusable solutions to common design problems, are a potential way to deal with this complexity. Although design patterns have received much focus in academic publications, it is not clear how they are used in practice. This paper presents an interview-based study that explores the use of design patterns in the automotive industry. The study findings reveal how automotive practitioners view and use design patterns in their software designs. Our study revealed that industry experts have a view of design patterns which often differs from the academic views. They use design patterns in combination with architecture guidelines, principles, and frameworks. Instead of the academic focus on the design patterns, industry professionals focus on the design, architectural tactics, and standards. Such findings highlight the need for a more nuanced understanding of the concept and practical applications of design patterns within the context of industrial software engineering practices.
How the bundengan string material affects its musical sound

Bundengan is a traditional musical instrument from Wonosobo Regency, Central Java, Indonesia. Bundengan consists of several main components, namely the kowangan resonator, a set of loaded strings, and a set of bamboo bars. Bundengan can imitate the sound of the percussive elements in gamelan lengger Wonosobo, such as gong, kempul, kenong and kethuk. This research aims to analyze the impact of the choice of bundengan string material on the frequency spectrum and temporal dynamics of the sound produced by the bundengan. Quantitative data is obtained by measuring the acoustical signal produced by each bundle string that is tested. Our data indicates that the natural gut badminton string is the most suitable string for the sound of gong, kempul and kethuk, with errors of 1.87 cents, 23.88 cents, and 0.88 cents, for the sound of gong, kempul, and kethuk, respectively. Meanwhile, the solid core badminton string with single wrap is the most suitable string for kenong, with an error of 3.29 cents. The data also shows that the natural string gut has the most suitable damping factor to produce the sound of the gong, with a damping factor of 0.036.
APLIKASI SISTEM MANAJEMEN GUDANG SPAREPART TRUK DENGAN TEKNOLOGI OCR MENGGUNAKAN FRAMEWORK LARAVEL

The Spare Parts Warehouse Management System application is software that helps companies or organizations manage the stock and distribution of spare parts or spare parts needed to maintain the smooth running of their production or operations. This system includes the process of procuring, organizing and sending spare parts needed in company operations. The scientific method uses qualitative, collecting data descriptively through interviews, observation and case studies. The development framework used is a prototype model, involving a series of steps starting from communication, Quick Plan Modeling, Quick Design, Construction of Prototype, Deployment Delivery and Feedback needs analysis, initial evaluation by users, prototype improvements, to implementation. In its implementation, the final result of this research was a Spare Parts Warehouse Management System Application with Web-Based OCR Technology Using the Laravel Framework PT Rajawali Inti Probolinggo, with a final respondent score of 84.8% through external testing from a total of 10 respondents.
Research on Precision Teaching Management Methods in Universities in the Era of Big Data Based on Entropy Weight Method

The widespread adoption and advancement of big data technology have led to its integration across various industries, offering convenience and efficiency in various tasks. The field of education is no exception, as the integration of modern technology with education becomes increasingly common. Traditional experiential teaching methods are gradually being replaced by data-driven teaching approaches. The establishment of data-driven platforms enables the collection and analysis of teaching materials, injecting new energy into precision teaching. This article focuses on modeling and evaluating the management method of precision teaching in universities during the era of big data, utilizing the entropy weight method for analysis. Specific analyses are conducted to provide a comprehensive understanding of the subject matter.
A Recommendation Model Fusing Knowledge Graph Convolutional Networks and Social Relationships

The knowledge graph’s rich semantic information can solve some of the issues with conventional recommendation. Most current knowledge graph-based recommendation models solely take into account the prospective characteristics of items, ignoring the supporting function of user potential features for recommendation systems, despite approaches like data sparsity and cold start. We suggest a recommendation model that incorporates social relationships and knowledge graph convolutional networks to solve this issue. Once more, the KGCN model is utilized to get the item feature matrix. Next, the multilayer perceptron is fed with the user feature vector, user embedding representation, and item feature vector to determine projected scores and provide user recommendations. The proposed recommendation model outperforms other benchmark models, according to experimental results that were done on the publicly accessible datasets CiaoDVD, Epinions, and FilmTrust to verify the model performance.
Research on Autonomous Robot Navigation Algorithm in Indoor Environment

With the development of the times, in order to make robots more autonomous, this has led to research on autonomous navigation and other aspects. In the research of autonomous navigation, it is generally divided into two aspects. Firstly, the construction of the map requires the combination of sensors and algorithms to construct the map. During the construction process, many factors need to be considered to apply different algorithms in different scenarios and robot hardware facilities. When not applicable, it is easy to experience map misalignment and other phenomena. After building the map, the robot needs to plan its path based on the current environment to reach the desired location. For path planning, Ant colony optimization algorithms with high robustness is used to better adapt to the environment. Compared with other algorithms, Ant colony optimization algorithms has better effect in indoor small environment. On the basis of the traditional algorithm, Ant colony optimization algorithms is improved by adjusting the volatilization coefficient of Pheromone, which makes the path planning of Ant colony optimization algorithms more reasonable.
SGC-GCN: Constructing Stronger Feature Fusion Ability Graph Convolution for Skeleton-Based Action Recognition

Skeleton-based action recognition algorithms have made extensive use of graph topologies to model the connections between human skeletal joints. Constructing graph topology models with greater representational power is key to obtaining powerful feature extractors. However, existing methods are not as effective in achieving stronger modelling of associations between physically connected joints in the human skeleton. To tackle these concerns, we put forward an innovative approach involving a distinct graph topology - a graph convolutional neural network (SGC-GCN) that amalgamates a dynamic, refined graph topology with a static, shared graph topology. The dynamic partial feature extractor (CTR-GC) and the static partial feature extractor (SGC-GC) are used in conjunction with each other to obtain a stronger feature aggregation capability in the form of static reinforcement of dynamic weaker features and to achieve the objective of strengthening the correlation modelling between interconnected joints within the human skeletal structure. The combined use of the two also introduces only a small number of additional parameters, ensuring that the refined features are not affected by the noise of statically shared features. Combining SGC-GC with the temporal modelling module has resulted in the development of SGC-GCN, a graphical convolutional network with even greater feature aggregation capability. Our network demonstrates a remarkable performance surpassing existing advanced methods on the dataset(NTU RGB+D), yielding substantial advancements in action recognition accuracy.
Peer review declaration

All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Single-blind / Double-blind / Triple-blind / Open / Other (please describe) 1 st phase, abstracts: double-blind peer review (2 scientific committee members and discussion by programming committee). 2 nd phase, papers: single-blind peer review (1-2 peer reviewers plus editors). • Conference submission management system: ConfTool • Number of submissions received: 1 st phase, abstracts: 501 2 nd phase, papers: 305 • Number of submissions sent for review: 1 st phase, abstracts: 500 2 nd phase, papers: 305 • Number of submissions accepted: 1 st phase, abstracts: 419 2 nd phase, papers: 285 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received × 100): 1 st phase, abstracts: 83.6% 2 nd phase, papers: 93.4% • Average number of reviews per paper: 1.1 (not counting associate editors’ often thorough reviewing) • Total number of reviewers involved: 136 • Any additional info on review process: Authors were given the possibility to revise papers up to two times. Much effort went into finding reviewers with expertise in the topic discussed. Editors carefully checked the papers, added their requests to the reviewers’ where necessary and verified corrections. • Contact person for queries: Name : Barbara Smith, Coordinating Editor, barbara.smith@epfl.ch Affiliation: EPFL Smart Living Lab
PENGEMBANGAN MEDIA SCRAPBOOK DENGAN DESAIN “KATA TANYA” UNTUK MELATIH KEMAMPUAN MEMBACA LANJUTAN PESERTA DIDIK DI KELAS IV SEKOLAH DASAR

This research aims to develop a Scrapbook learning media product with a "question word" design to train advanced reading skills in Theme 2 Subtheme 1 Learning 1 class IV elementary school material. The place of research was carried out at Sentul 4 Serang State Elementary School. The subjects of this development research is grade IV. The research model used is the Research &amp; Development model which has been modified with 6 steps, i.e, analyzing potential and problems, collecting data, designing product designs, validating product designs, revising product designs, and product trials (limited trials). Researchers collected data using preliminary study techniques, interviews, observation, questionnaire collection and documentation. This scrapbook media has passed the validation stage by six experts by getting an overall score of 86.6 in the very worthy category. The overall score was obtained from an average accumulation of 84% for media experts, 84% for language experts and 92% for material experts with a very decent category from the three expert assessments. The trial was carried out on class IV students by collecting questionnaires and getting a satisfaction score of 97.8% in the very good category. It can be concluded that the development of scrapbook media with a "question word" design to train advanced reading skills is declared feasible for development and use by fourth grade elementary school students.
Shedding Light on the Techniques for Building Bayesian Networks in Software Engineering

Context Bayesian networks (BNs) have been used to tackle several software engineering (SE) problems, such as risk management and effort estimation. They enable reasoning under uncertainty and have the advantage of incorporating expert knowledge to build more accurate models when sufficient historical data are not available. Software practitioners often encounter a lack of substantial evidence concerning the usability, limitations, risks, and benefits of BNs, as is the case with many other topics in the SE literature. Therefore, there is a need to organize and systematize the existing knowledge in this area. Objective This paper aims to provide researchers and practitioners with an overview of the techniques for building BNs in SE. Method We conducted a tertiary study following the guidelines available in the SE literature. Results We examined six secondary studies. Our findings revealed that expert knowledge emerges as the predominant technique for structure learning and, in conjunction with learning from data using automated tools, is widely employed for parameter learning in BNs. Conclusion Despite the attention given to data-driven approaches in SE, it is worth acknowledging the significant value that expert knowledge continues to hold in constructing more accurate and robust models. This observation underscores potential opportunities for developing expert-driven solutions to enhance model building and foster the adoption of BNs in the software industry.
Klasifikasi Nasabah Potensial menggunakan Algoritma Ensemble Least Square Support Vector Machine dengan AdaBoost

In the era of business and economics that are interconnected with each other and competition between companies in seeking market share so that there will be an increase, especially in the number of customers, especially deposit customers, financial institutions and other companies are increasingly realizing the importance of understanding and identifying potential customers correctly to get potential customers. customers subscribe to deposits. Potential customer classification is a strategic approach that allows financial institutions to identify potential customers who have the potential to subscribe to deposits. With a deeper understanding of the characteristics and needs of potential customers, financial institutions can direct marketing resources more effectively, increase marketing efforts, and increase the conversion of potential customers to active customers. The aim of this research is to develop and test the Ensemble Least Square Support Vector Machine model with AdaBoost in classifying potential customers which can increase accuracy in identifying potential customers who have the potential to subscribe to deposits. The research results showed that this method achieved an accuracy of 95.15%, a sensitivity of 92.93%, and a specificity of 97.61%. In comparison with single Support Vector Machine and Least Squares Support Vector Machine models, the Ensemble Least Squares Support Vector Machine outperforms both in terms of accuracy.
Analisis Perbandingan Metode Fuzzy Logic Dan Metode SAW Dalam Pemilihan Keluarga Penerima Bantuan Sosial

Ensuring and fulfilling the needs of the community isa form of government responsibility to reduce existing socialinequalities. One of the efforts that the government has made isto provide social assistance through the Non-Cash FoodAssistance program. However, the process of selecting recipientsof social assistance is often not on target. For this reason, it isnecessary to build a system that is able to support in determiningdecisions for the selection of families receiving social assistance.To help the selection process of social assistance recipients, ofcourse, it must use the right and appropriate method so that theselection process produces social assistance recipients who reallydeserve assistance. The selection process in this study uses twodecision support methods, namely Fuzzy Logic and SimpleAdditive Weighting (SAW) and has conducted accuracy tests onboth methods against the suitability of recipient eligibility data,so that it can be seen which method has the highest level ofaccuracy in the selection of social assistance recipients. Theresults of the accuracy test carried out in this study are that bothmethods produce the same high level of accuracy in thesuitability of prospective recipient eligibility results, namely100%, this means that both methods can be used in determiningrecipients of social assistanc.
An adjustment distributed method for Route Planning of Space Exploration Task

Aerospace missions are usually accomplished with limited resources and time, and important to use time and resources wisely. With limited resources and time, it is very complicated to complete aerospace exploration missions, such as target classification missions, object tracking missions, target collection, space junk cleanup. Reasonable route planning is the basis for completing aerospace exploration missions. We are unable to complete aerospace exploration missions at once, and need two or more times to complete, or the total cost is too large, which will waste a lot of time and cost. Heuristic optimization algorithms are often used to solve path planning problems, while generated inefficient route, and require more time and costs. Therefore, in this paper, we propose an adjustment distributed algorithm (ADM) to address the limitations of heuristic optimization algorithms and save cost of time and cost, and obtain more reasonable routes. Experimental results show that our algorithm has better route planning capability with lower total cost.
Knowledge Retrieval Mechanism for Smart Buildings Based on IoT Devices Data

The use of smart building solutions can bring advantages to both a building and its users. The main motivation of this work is to propose a solution based on internet of things devices to retrieve knowledge from historic data and, using the knowledge gained, contribute to the comfort of the user and the building’s sustainability. The use of internet of things devices enables the easy deployment of the proposed system in today’s buildings, where this kind of devices are commonly found. The knowledge regarding the role of each device and how they interact or impact each other will be studied by the proposed solutions that will deploy a continuous mechanism. The designed software architecture was developed in python, to allow easy implementation of different features. To test the proposed solution, it was used real data from a research building where several types of sensors were correlated to assess relations among data. The results show significant correlations between celling light consumption and the room’s light intensity sensor, and between the operation of air conditioning units and the room’s temperature, for example, data from 2 months, regarding air conditioner unit in room N102, demonstrates a − 0.85 correlation with the room temperature, when operating in cooling mode.
Over-the-Air Federated Averaging with Limited Power and Privacy Budgets

This paper develops an optimal design for device scheduling, alignment coefficient, and aggregation rounds within a differentially private over-the-air federated averaging (DP-OTA-FedAvg) system considering a constrained sum power budget. In DP-OTA-FedAvg, gradients are aligned using an alignment coefficient and then aggregated over the air, utilizing channel noise to ensure participant privacy. This study highlights two critical tradeoffs in aligned over-the-air federated learning (OTA-FL) systems with limited power and privacy budgets. Firstly, it reveals the tradeoff between the number of scheduled devices and the alignment coefficient. Secondly, it investigates the balance between aggregation distortion and local training error while adhering to the sum power constraint. Specifically, we measure privacy using differential privacy (DP) and perform convergence analyses for both convex and non-convex loss functions. These analyses provide insights into how device scheduling, the alignment coefficient, and the number of global aggregations affect both privacy preservation and the learning process. Building on these analytical results, we formulate an optimization problem aimed at minimizing the optimality gap of DP-OTA-FedAvg under power and privacy constraints. By specifying the number of aggregation rounds, we derive a closed-form expression describing the relationship between the alignment coefficient and the number of scheduled devices. We then tackle the problem through iterative optimization of scheduling and aggregation rounds. The effectiveness of the proposed policies is verified through simulations, and the performance advantage is particularly pronounced in scenarios where devices have poor channel conditions and limited sum-power budgets.
Differentially Private Federated Learning With Importance Client Sampling

As numerous consumer electronics applications like smartphones and wearables generate lots of distributed data daily, consumer desire to safely and efficiently tackle private and isolated data. Federated learning (FL) is hopeful to satisfy the above requirement due to strong data security and applicability to large-scale scenarios. But diverse clients inevitably cause non-independent and identically distributed (non-iid) data among clients, which severely hinders performance analysis. Besides, affected by non-iid data, the participating clients are typically heterogeneous, which induces the client sampling problem. More importantly, albeit FL can enhance privacy via data localization, for highly secret data like physiological data from wearables, FL should possess stronger security to prevent third-party attacks. For data heterogeneity, client sampling, and privacy security, we propose differential privacy (DP) enabled and importance-aware FL algorithm DPFLICS to jointly handle these problems. Specifically, we utilize the truncated concentrated DP to tightly track the end-to-end privacy loss. To attain better sampling, the server selects partial clients with the probability derived from our importance client sampling. Moreover, to further improve performance, we also leverage the adaptive YOGI optimizer on the server side, which is an adaptive gradient method improved from the widely-used ADAM optimization. Finally, the multiple experiments exhibit the effectiveness of our method.