Reviewerly/exHarmony huggingface dataset:
Peningkatan Pemahaman Konsep Sistem Koordinat Melalui Metode Tutor Sebaya pada Siswa Kelas VI SDN Bendogerit 2 Kota Blitar

Mathematics as structured knowledge, where properties and theories are made deductively based on defined or undefined elements and based on proven axioms, properties, or theories. Mathematics is also defined as a language of symbols about various ideas using defined terms. carefully, clearly and accurately. In the UPT Education Unit of SDN Bendogerit 2, Blitar City, there is a lack of cooperation with peers, this causes a lack of improvement in learning outcomes for students who are less intelligent. It can be seen in the initial learning that group and individual results are obtained, in group results from 5 groups only 1 group is complete or 20%, while in individual results from 20 students only 7 students or 35% have not achieved mastery of the material according to the KKM 75%. For this reason, classroom action research is needed using appropriate learning methods. The method used is the Peer Tutor Method which is a learning strategy that utilizes students who have privileges, intelligence and skills in the classroom to help provide explanations, guidance and direction to other students whose intelligence is somewhat lacking or slow in receiving lessons of almost the same age or classmate.
PENERAPAN UJI MULTIKOLINIERITAS DALAM PENELITIAN MANAJEMEN SUMBER DAYA MANUSIA

This study aims to determine and ensure that the instruments used in a study are guaranteed to be free from multicollinearity problems and minimize the level of bias/deviation with the real conditions that occur with the research results. This study uses a questionnaire as an instrument to collect data. Questionnaires were distributed/distributed to the community/households. The data collection technique is random (random). The results of the analysis show that from the variables used, there is no multicollinearity problem between the independent variables. Given the importance of an instrument free from multicollinearity problems, the suggestion for researchers is to use the research instrument that is truly guaranteed to be free from multicollinearity disorders.
Sistem Informasi Bimbingan Tugas Akhir Berbasis Web Pada Universitas Adiwangsa Jambi

Adiwangsa University Jambi is one of the private educational institutions, wherein running its business is pursuing the role of information technology, such as final project guidance activities which are currently still carried out conventionally, namely face-to-face. This often causes problems, especially during the pandemic which has quite an impact on graduation or guidance. To overcome the existing problems, the author proposes the design of a web-based final project guidance information system in the form of a prototype that can be implemented in the future so that it can be a solution. The tools used in modeling this system are use case diagrams, activity diagrams, and class diagrams. The results of this study are in the form of a web-based final project guidance information system prototype that can help smooth the course of final project guidance activities at Adiwangsa University Jambi.
INTERSEMIOTICS IN CONTEMPORARY ADVERTISING. FROM SIGN TRANSLATION TO MEANING COHERENCEi

The very informed contemporary advertising-reluctant consumer asks for coherence and transparency from the creators of the advertising discourses, who, on the other hand, try to be relevant using local and contextual features even if the brand is international (Pepsi uses a Romanian song within Shazam and the international brand Danone is “Made in Romania”). This paper explores a series of theoretical concepts, from intersemiotic complementarity and cohesion to a contextual model of social semiotics, from translation and adaptation theories to multimodality and intermediality, in order to find some simple instruments for the construction and adaptation of the advertising messages to the consumers’ social, technological and cultural context, able to better target an audience and to maintain coherence all along a discourse that uses from conventional print media and TV to very innovative mobile apps and other digital endeavors.
Evaluasi Virtual Reality Menggunakan Technology Acceptance Model (TAM) Terkait Dunia Metaverse

Abstract - Digital disruption makes innovation and transformational sustainable in the digital world. The Industrial Revolution 4.0 has changed the face of the new digital era with a diverse foundation of new and advanced technologies in it. According to Geissbauer (2016) there are 10 technologies that are the foundation of Industrial Revolution 4.0 i.e. mobile devices, IoT platforms, location detection technologies, advanced human machine interfaces, automation &amp; Fraud Detection, 3D manufacturing, smart sensors, big data analytics &amp; advanced algorithms, Multilever customer interaction and customer profiling, Augmented Reality / wearables / immersive technologies, cloud computing. Virtual Reality as one of the technological foundations in the Industrial Revolution 4.0 has grown rapidly along with the term ‘Metaverse’ echoed by Mark Zuckerberg CEO of Facebook in October 2021. Metaverse itself is identified as a virtual world where all human activities can be done in it. Virtual Reality as one of the components of the Metaverse is an attraction for the industry to start this technology, especially in exhibition activities (Expo). Virtual Reality Expo for SME is one of the important foundations regarding user understanding regarding the introduction of Virtual Reality technology. Therefore, as an advanced technology that is currently rampant, the author conducts research on Virtual Reality Expo using the technology Acceptance Model (TAM).&#x0D; &#x0D; &#x0D;
Rancang Bangun Sistem Pengelolaan Ruangan Berbasis Website

Abstract— Currently the benefits of using information technology affect the human environment, one of which is organizational performance on campus, which has rooms for lectures, student activities and other parties. One of the problems faced in the university environment is the management of the room, when the room is being used or will be borrowed. Room management, which is still done manually, makes it difficult to borrow rooms and get information on the schedule for the use of the room used. Therefore we need a room management system that can make it easier to borrow according to the room you want to use, can schedule rooms outside the use of lectures according to what has been scheduled in the university environment. The purpose of this research is to create a website-based room management system on campus. In order to make it easier for related parties to help an organization so that they can provide information, management, and business processes that run in an organization. The methodology used in designing this software is the Rational Unified Process (RUP) with inception, elaboration, construction, and transition stages. The modeling used in software design is the Unified Modeling Language (UML). The results of the research that has been carried out is that a website-based room management system can borrow rooms, provide information regarding rooms whether they are being used or not, schedule room use outside of the use of lectures that have been scheduled in the campus environment.Keywords—Space, Information Systems, Rational Unified Process
AI chatbot responds to emotional cuing

Emotion has long been considered to distinguish humans from Artificial Intelligence (AI). Previously, AI's ability to interpret and express emotions was seen as mere text interpretation. In humans, emotions co-ordinate a suite of behavioral actions, e.g., under negative emotion being risk averse or under positive emotion being generous. So, we investigated such coordination to emotional cues in AI chatbots. We treated AI chatbots like human participants, prompting them with scenarios that prime positive emotions, negative emotions, or no emotions. Multiple OpenAI ChatGPT Plus accounts answered questions on investment decisions and prosocial tendencies. We found that ChatGPT-4 bots primed with positive emotions, negative emotions, and no emotions exhibited different risk-taking and prosocial actions. These effects were weaker among ChatGPT-3.5 bots. The ability to coordinate responses with emotional cues may have become stronger in large language models as they evolved. This highlights the potential of influencing AI using emotion and it suggests that complex AI possesses a necessary capacity for “having” emotion.
Rock-Paper-Scissors Image Classification Using Transfer Learning

Games are a genre of deliberate mental or physical activities which can be done alone or in groups for the purpose of enjoyment and recreation. Every game has its unique collection of participants, movements, and tactics, in addition to its own set of principles and guidelines. Rock-Paper-Scissors is probably the most well-known game for determining a winner in the world. The rules are pretty straightforward. Rock, paper, or scissors are displayed by each player simultaneously. The rock defeats the scissors but is defeated by the paper. Scissors defeat paper but lose to rock and paper defeats rock but loses to scissors. Using hand images, this study proposed identifying images of hands playing rock-paper-scissors. Transfer learning is used in the proposed model to recognize and classify images of rock-paper-scissors. The dataset for this study was acquired from the Kaggle. There are 2188 images in the dataset, including 712 rock images, 726 paper images, and 750 scissor images. The dataset is divided into two parts: 80% is utilized for training the model, and 20% is utilized for model validation. The RPSICTL model evaluation results good validation accuracy of 99.54%.
NeuralCCD: Integrating Multiple Features for Neural Coincidental Correctness Detection

Fault localization seeks to locate the suspicious statements possible for causing a program failure. Experimental evidence shows that fault localization effectiveness is affected adversely by the existence of coincidental correctness (CC) test cases, where a CC test case denotes the test case which executes a fault but no failure occurs. Even worse, CC test cases are prevailing in realistic testing and debugging, leading to a severe issue on fault localization effectiveness. Thus, it is indispensable to accurately detect CC test cases and alleviate their harmful effect on fault localization effectiveness.To address this problem, we propose NeuralCCD: a neural coincidental correctness detection approach by integrating multiple features. Specifically, NeuralCCD first leverages suspiciousness score, coverage ratio and similarity to define three CC detection features. Based on these CC detection features and CC labels, NeuralCCD utilizes multi-layer perceptron to learn a different feature-based model for a program, and finally combine the trained models of different programs as an ensemble system to detect CC test cases. To evaluate the effectiveness of NeuralCCD, we conduct large-scale experiments on 247 faulty version of five representative benchmarks and compare NeuralCCD with four state-of-the-art CC detection approaches. The experimental results show that NeuralCCD significantly improves the effectiveness of CC detection, e.g., NeuralCCD yields by at most 109.5%, 93% and 81.3% improvement of Top-1, Top-3 and Top-5 over Tech-I when utilized in Dstar formular.
Path Avoidance System of Intelligent Robot Based on Computer Vision

Abstract At present, obstacle avoidance technology is widely used in the military field, scientific detection, traffic control, industrial manufacturing, medical service, and many other fields, and various machines with autonomous navigation and obstacle avoidance capabilities have been used to varying degrees, thus replacing some human daily production activities and bringing many conveniences to people’s lives. The key to autonomous obstacle avoidance is to obtain the direction information of the obstacle in its direction of advance in real time, which is also the precondition of obstacle avoidance system in obstacle avoidance path planning. In this paper, an intelligent robot path avoidance system is designed based on computer vision (CV) theory. The binocular vision ranging experiment verifies that the system has a less ranging error and can effectively detect the location of obstacles within a certain range, and the success rate of intelligent robot avoidance of obstacles reaches more than 96%, achieving a good avoidance effect.
Spatial–Temporal Self-Attention Enhanced Graph Convolutional Networks for Fitness Yoga Action Recognition

Fitness yoga is now a popular form of national fitness and sportive physical therapy. At present, Microsoft Kinect, a depth sensor, and other applications are widely used to monitor and guide yoga performance, but they are inconvenient to use and still a little expensive. To solve these problems, we propose spatial–temporal self-attention enhanced graph convolutional networks (STSAE-GCNs) that can analyze RGB yoga video data captured by cameras or smartphones. In the STSAE-GCN, we build a spatial–temporal self-attention module (STSAM), which can effectively enhance the spatial–temporal expression ability of the model and improve the performance of the proposed model. The STSAM has the characteristics of plug-and-play so that it can be applied in other skeleton-based action recognition methods and improve their performance. To prove the effectiveness of the proposed model in recognizing fitness yoga actions, we collected 960 fitness yoga action video clips in 10 action classes and built the dataset Yoga10. The recognition accuracy of the model on Yoga10 achieves 93.83%, outperforming the state-of-the-art methods, which proves that this model can better recognize fitness yoga actions and help students learn fitness yoga independently.
A Wireless Device to Modular Robotized Instrument for Health Information

This article is referred to an innovative wireless device for ECG patient monitoring during minimally invasive surgery. Our aim is to create new type of laparoscopy instruments to improve healthcare. The work presents a wireless device for ECG (DECG) as part of a robotic modular laparoscopic instrument (RMLI). Thus, the device allows ECG analysis and monitoring of the patient to be carried out complexly in combination with other diagnostic and therapeutic (RMLI) mode of operation activities. The proposed device provides detection and rapid warning of abnormal heart rate during surgery. Innovative uMAC wireless network stack is designed for module-control block communication. Control computer program processes and monitors (remotely or directly) received information from wireless device that is connected to patient. The software of the device is developed in Tcl/Tk scripting language for operation under Windows. A shared reality upgrade for Android has also been developed for it. The novelty of the solution is related to the construction and connection of the ECG with the used RMLI robotic module. In the future the designed instrument will can work autonomous. The team has worked in the field of minimally invasive and laparoscopic surgery roboticized instrumentation and the presented development is a continuation of this work.
Ant Colony Optimization for Parallel Test Assembly

Ant Colony Optimization (ACO) algorithms have previously been used to compile single short scales of psychological constructs. In the present article, we showcase the versatility of the ACO to simultaneously construct multiple parallel short scales that adhere to several competing and interacting criteria. Based on an initial pool of 120 knowledge items, we assembled three 12-item tests that (a) adequately cover the construct at a domain level, (b) follow a unidimensional measurement model, (c) allow reliable and (d) precise measurement of fact knowledge, and (e) are gender fair. Moreover, we aligned the test characteristic and test information functions of the three tests to establish the equivalence of the tests. We cross-validated the assembled short scales and investigated their association with the full scale and covariates that were not included in the optimization procedure. Finally, we discuss potential extensions in metaheuristic test assembly and the equivalence of parallel knowledge tests in general.
A Garter Snake Optimization Algorithm for Constrained Optimization

Abstract The utilization of meta-heuristics has been widespread in resolving optimization problems, with constant development of new and effective algorithms. Thisresearch presents the Garter Snake Optimization Algorithm (GSO), which ismotivated by the mating behavior of garter snakes and leverages various techniques such as screening, grafting, and annual growth to conduct a productive andthorough search of the exploration space. The algorithm incorporates both socialand personal behaviors to accomplish a balance between exploration and exploitation of the best solutions. To assess the performance of the proposed algorithm,it is tested on four restricted benchmark problems and a frequently utilized realengineering problem, and the optimization results are compared with other algorithms. In many respects, the GSO outperforms other algorithms, demonstratingits superiority and potential in addressing constrained optimization problems.
Quantum federated learning in healthcare: The shift from development to deployment and from models to data

Abstract Healthcare organizations have a high volume of sensitive data and traditional technologies have limited storage capacity and computational resources. The prospect of sharing healthcare data for machine learning is more arduous due to firm regulations related to patient privacy. The balanced protection of confidentiality, integrity, and availability of healthcare data, has become a major concern beyond classical data security considerations. In recent years, federated learning offers a solution to accelerate distributed machine learning addressing concerns related to data privacy and governance. Currently, the blend of quantum computing and machine learning has experienced significant attention from academic institutions and research communities. Quantum computers have shown the potential to bring huge benefits to the healthcare sector through efficient distributed training across several quantum nodes. The ultimate objective of this work is to develop a quantum federated learning framework (QFL) to tackle the optimization, security, and privacy challenges in the healthcare and clinical industries for medical imaging tasks. In this work, we proposed federated quantum convolutional neural networks (QCNNs) with distributed training across edge devices. To demonstrate the feasibility of the proposed QFL framework, we performed extensive experiments on medical datasets (Pneumonia MNIST, and CT-kidney disease analysis), which are non-independently and non-identically partitioned among the healthcare institutions/clients. The quantum federated global model maintained a high classification testing accuracy and generalizability and outperformed the locally train clients regardless of how unbalanced the medical data is distributed among the clients. The global model achieved the best performance as compared to local clients in terms of the area under curve of the receiver operating characteristic curve (AUCROC) (0.953) and an average of (0.98) on all classes for predicting outcomes on pneumonia and CT-kidney datasets, respectively. Moreover, the client selection mechanism is proposed to reduce the computation overhead at each communication round, which effectively improves the convergence rate. The proposed quantum federated learning framework is validated and assessed via large-scale simulations. Based on our results from numerical simulations, the deployment of distributed and secure quantum machine learning algorithms for enabling scalable and privacy-preserving intelligent healthcare applications would be extremely valuable.
Perancangan Server Kantor Desa Tomuan Holbung Berbasis Client Server

Abstract− In the era of regional autonomy, the village is a very important part in determining the success of development and in direct contact with the community, especially in public services. Server is a medium that has an important role to provide internet access services for its users. So that the network can be used optimally, it is necessary to have a server. This study aims to design a server in the Tomuan Holbung Village Office, Asahan Regency by building an efficient, flexible server infrastructure and optimizing resource use, so that it can be adjusted to the workload so that hardware resources are optimal. This research was conducted to be a solution for the use of hardware resources provided by the Server machine that has not been used optimally, so efficient steps are needed to overcome this problem. The results of the research obtained are in the form of server design and optimizing the use of resources and managing servers efficiently in terms of time and cost.&#x0D;
SoK: Membership Inference is Harder Than Previously Thought

Membership Inference Attacks (MIAs) can be conducted based on specific settings/assumptions and experience different limitations. In this paper, first, we provide a systematization of knowledge for all representative MIAs found in the literature. Second, we empirically evaluate and compare the MIA success rates achieved on Machine Learning (ML) models trained with some of the most common generalization techniques. Third, we examine the contribution of potential data leaks to successful MIAs. Fourth, we examine if the depth of Artificial Neural Networks (ANNs) affects MIA success rate and to what extent. For the experimental analysis, we focus solely on well-generalizable target models (various architectures trained on multiple datasets), having only black-box access to them. Our results suggest the following: (a) MIAs on well-generalizable targets suffer from significant limitations which undermine their practicality, (b) common generalization techniques result in ML models which are comparably robust against MIAs, (c) data leaks, although effective for overfitted models, do not facilitate MIAs in case of well-generalizable targets, (d) deep ANN architectures are not more vulnerable to MIAs compared to shallower ones or the opposite, and (e) well-generalizable models can be robust against MIAs even when not achieving state-of-the-art performance.
How to Combine Membership-Inference Attacks on Multiple Updated Machine Learning Models

A large body of research has shown that machine learning models are vulnerable to membership inference (MI) attacks that violate the privacy of the participants in the training data. Most MI research focuses on the case of a single standalone model, while production machine-learning platforms often update models over time, on data that often shifts in distribution, giving the attacker more information. This paper proposes new attacks that take advantage of one or more model updates to improve MI. A key part of our approach is to leverage rich information from standalone MI attacks mounted separately against the original and updated models, and to combine this information in specific ways to improve attack effectiveness. We propose a set of combination functions and tuning methods for each, and present both analytical and quantitative justification for various options. Our results on four public datasets show that our attacks are effective at using update information to give the adversary a significant advantage over attacks on standalone models, but also compared to a prior MI attack that takes advantage of model updates in a related machine-unlearning setting. We perform the first measurements of the impact of distribution shift on MI attacks with model updates, and show that a more drastic distribution shift results in significantly higher MI risk than a gradual shift. We also show that our attacks are effective at auditing differentially private fine tuning. We make our code public on Github: https://github.com/stanleykywu/model-updates.