Reviewerly/exHarmony huggingface dataset:
The Effects of Extract Function Refactoring On High Cyclomatic Complexity Code Smell and Maintainability Index

Software that operates actively needs to be adequately managed and maintained to avoid future problems. One of the problems in the software is the existence of code smells. A code smell indicates that a problem will, in turn, occur in the software's source code. OpenSID is open-source software used to create village administration websites. The detection carried out in this study shows that the OpenSID source code contains 32 code smells of High Cyclomatic Complexity type, which, if left untreated, can cause problems in the future. One of the problems is the reduced level of maintainability of the software. This research performs refactoring using the Extract Function method on a High Cyclomatic Complexity code smell to analyze what effect refactoring has on the code smell and its impact on the value of the OpenSID Maintainability Index. The research used the following tools: PHPMD, PHPDepend, and PhpStorm®. The results showed that after refactoring, the High Cyclomatic Complexity code smells were gone in some functions, and the average value of the OpenSID Maintainability Index increased from 37.0337 to 42.0912. Even though, in the end, the maintainability of this software is still in the category of difficult to maintain, this Maintainability Index increases to 13.66% has shown that the refactoring Extract Function method in code smells High Cyclomatic Complexity can increase the maintainability of this software.
Human Activity Recognition Based on Wearable Devices and Feedforward Neural Networks

Human activity recognition (HAR) using wearable sensors has garnered significant attention in recent years. This technology can continuously track human activities which is essential for remote health monitoring, elderly care, and rehabilitation. In this study, we propose a simple feedforward neural network (FFNN) model equipped with four fully connected layers to enhance the accuracy of HAR. Experimental results conducted on the UCI-HAR (UCI Human Activity Recognition) dataset show that the proposed FFNN model achieve an accuracy and F1 score of 0.96. It outperforms other existing algorithms, which are K-Nearest Neighbors (KNN) and Support Vector Machines (SVM), highlighting the effectiveness of a simplified neural network architecture in enhancing HAR performance.
Toward Delay-Conscious Federated Learning: A Systems Perspective

Federated Learning (FL) was introduced to mitigate centralized Machine Learning (ML) data privacy legislation, which restricts the diversity and performance of ML applications. FL allows training data on its source device, and only model updates can leave the device for learning aggregation at the Cloud/Edge. Despite FL advantages, it is challenging for it to realize a training performance that is comparable to traditional ML, especially in terms of training speed. In this article, we first introduce an original analysis of the systems factors contributing to FL training speed. We then define current FL limitations associated with these factors, propose novel and efficient solution strategies to over-come these limitations, and assess the impact of these strategies through preliminary evaluation. Finally, We identify challenges and future research directions that need to be addressed to realize the benefits of pushing FL limits.
Algorithm Analysis of Decision Tree, Gradient Boosting Decision Tree, and Random Forest for Classification (Case Study: West Java House of Representatives Election 2019)

In an increasingly modern era, machine learning-based technologies offer solutions to everyday problems. Machine learning enables us to build models that process data and train them to provide predictive results. We call predictions with categorical labels ‘classifications’. We commonly use several basic models for classification, including Decision Trees, Support Vector Machines, Linear Regression, and so on. We can combine these basic models to create an ensemble model, consisting of a boosting algorithm and a bagging model. We will test these two types of algorithms on the profile data of the candidates for the HoR West Java in the 2019 legislative elections. In this paper, we compare the Random Forest bagging algorithm and the Gradient Boosting Decision Tree boosting algorithm. The Decision Tree algorithm is also compared as a baseline. Prior to the comparison, we conduct data pre-processing and data exploration to identify the data features that influence the winning label for feature selection. Furthermore, we address the data imbalance by using Borderline-SMOTE before training the model. We compare the test results based on the F1 score. Our findings reveal that the use of selected features and the Borderline-SMOTE1 sampler significantly improved the F1 score. Additionally, the best-performing model for predicting victory with the given data is the Gradient Boosting Decision Tree, with parameter tuning and using feature selection results, which achieved an F1 score of 0.7445
Learning Motivation of Students

"Learning motivation is the main segment of success in what we call the educational process. The motivation for learning is influenced and determined by the intrinsic factors, as well as the extrinsic factors. Learning motivation can be described by a simplistic idea in which we observe a set of internal elements of conduct such as values, motives, intentions and aspirations that organize, challenge and guide learning efforts. Throughout the research we carried out, a series of essential elements had been investigated that refer to the studentsʼ motivation of learning. The method of investigating the issue of studentsʼ motivation in learning was the survey, conducted using the questionnaire as a tool. In this respect, the article presents a number of essential elements such as learning motivation, the reason for accumulating knowledge, the reason for obtaining a diploma, the reason for self-accomplishment, but also professional reasons of the students from the study program Pedagogy of Primary and Preschool Education. "
Individualization, Differentiation and Interactivity - Paradigms of Effective Learning

Centering on the student in the context of the instructional-educational action is achieved by generating both interactive/collaborative and differentiated/individualized learning situations, all the more so as the appropriate technological resources can be exploited to provide the feedback necessary for adjustment in learning. The present study presents an investigative analysis to decipher the understanding of the concepts of didactic and exploitative individualization, differentiation and interactivity, with the aim of identifying the contribution of differentiation/individualization and interactivity in achieving effective learning. The study revealed a common perception of the concepts of individualization, differentiation and interactivity at the level of teachers for preschool, primary and secondary education. Also, the academic performance of students in pre-university education (preschool, primary and secondary school) was influenced by interactive strategies and learning tasks based on individualization and differentiation to a comparative extent with their contribution to the development of the degree of socialization and positive interaction, of increasing the level social-emotional skills.
Unveiling the factors shaping consumer acceptance of AI assistant services in the hotel industry: a behavioral reasoning perspective

Technological advancements in automation, artificial intelligence (AI), and robotics have completely changed the service industries, including the hospitality sector. This study aims to investigate the fundamental elements that impact the acceptance (or rejection) of artificial intelligence assistants' services. This research employed semi-structured interviews to gather insights from both individuals who use AI services in hotels and those who do not. Based on Behavioral Reasoning Theory, this study is empirically tested by interviews with intended respondents. This study examines the "reasons for" and "reasons against" using AI assistant services in the hotel. Technology anxiety, privacy concern, prior experience, and task complexity are "reasons against," while performance expectancy, effort expectancy, social influence, facilitating conditions, humanness, social presence, perceived interaction enjoyment, cuteness, and trust are "reasons for." This study found that "reasons against" negatively affect attitude and adoption intention, while "reasons for" positively affect them. Behavioral Reasoning Theory is used for the first time to evaluate Bangladeshi hotel consumers' AI service assistant adoption intentions. As a new technology, AI assistants provide a better understanding of user usage. Furthermore, this study offers essential facts regarding the utilization of technology in the hospitality sector.
A Glove-Wearing Detection Algorithm Based on Improved YOLOv8

Wearing gloves during machinery operation in workshops is essential for preventing accidental injuries, such as mechanical damage and burns. Ensuring that workers are wearing gloves is a key strategy for accident prevention. Consequently, this study proposes a glove detection algorithm called YOLOv8-AFPN-M-C2f based on YOLOv8, offering swifter detection speeds, lower computational demands, and enhanced accuracy for workshop scenarios. This research innovates by substituting the head of YOLOv8 with the AFPN-M-C2f network, amplifying the pathways for feature vector propagation, and mitigating semantic discrepancies between non-adjacent feature layers. Additionally, the introduction of a superficial feature layer enriches surface feature information, augmenting the model's sensitivity to smaller objects. To assess the performance of the YOLOv8-AFPN-M-C2f model, this study conducted multiple experiments using a factory glove detection dataset compiled for this study. The results indicate that the enhanced YOLOv8 model surpasses other network models. Compared to the baseline YOLOv8 model, the refined version shows a 2.6% increase in mAP@50%, a 63.8% rise in FPS, and a 13% reduction in the number of parameters. This research contributes an effective solution for the detection of glove adherence.
Using secret sharing to improve FIDO attack resistance for multi-device credentials

Public key cryptography-based authentication methods such as FIDO/WebAuthn can provide a number of security improvements over passwords. However, the need to register every device the user wishes to use reduces its usability for consumer applications. In 2022, the FIDO Alliance introduced a multi-device credential model, which claims to offer numerous convenience and usability enhancements for end users; however, the fact that the WebAuthn credential can leave the protection of a hardware security module undermines the security guarantees previously provided by the FIDO/WebAuthn framework. This limits FIDO’s usefulness for applications which require more rigorous protection. Furthermore, FIDO does not specify key management aspects, instead, end users and relying parties are left responsible for credential management. These trade-offs force a difficult compromise between overall security and convenience. In this paper, we propose a protocol for a split-key FIDO mechanism, in which each user device maintains a portion of the user’s credential instead of a fully-usable private key. By proving access to multiple devices, the user can use a cloud provider to reassemble their private key in an HSM-protected environment, then use the cloud provider to authenticate on their behalf. Service providers can preserve many of the benefits of multi-device credentials, while still providing relatively strong private key protections. In addition, our proposal adds a key management overlay to the existing FIDO/WebAuthn framework. Our approach uses a user’s existing cloud provider to reduce access to a user’s private key, thus reducing the potential for credential theft, even if one of the user’s devices is fully compromised.
Image Deblurring Based on Convex Non-Convex Sparse Regularization and Plug-and-Play Algorithm

Image deblurring based on sparse regularization has garnered significant attention, but there are still certain limitations that need to be addressed. For instance, convex sparse regularization tends to exhibit biased estimation, which can adversely impact the deblurring performance, while non-convex sparse regularization poses challenges in terms of solving techniques. Furthermore, the performance of the traditional iterative algorithm also needs to be improved. In this paper, we propose an image deblurring method based on convex non-convex (CNC) sparse regularization and a plug-and-play (PnP) algorithm. The utilization of CNC sparse regularization not only mitigates estimation bias but also guarantees the overall convexity of the image deblurring model. The PnP algorithm is an advanced learning-based optimization algorithm that surpasses traditional optimization algorithms in terms of efficiency and performance by utilizing the state-of-the-art denoiser to replace the proximal operator. Numerical experiments verify the performance of our proposed algorithm in image deblurring.
A Deep Learning Approach for Biped Robot Locomotion Interface Using a Single Inertial Sensor

In this study, we introduce a novel framework that combines human motion parameterization from a single inertial sensor, motion synthesis from these parameters, and biped robot motion control using the synthesized motion. This framework applies advanced deep learning methods to data obtained from an IMU attached to a human subject's pelvis. This minimalistic sensor setup simplifies the data collection process, overcoming price and complexity challenges related to multi-sensor systems. We employed a Bi-LSTM encoder to estimate key human motion parameters: walking velocity and gait phase from the IMU sensor. This step is followed by a feedforward motion generator-decoder network that accurately produces lower limb joint angles and displacement corresponding to these parameters. Additionally, our method also introduces a Fourier series-based approach to generate these key motion parameters solely from user commands, specifically walking speed and gait period. Hence, the decoder can receive inputs either from the encoder or directly from the Fourier series parameter generator. The output of the decoder network is then utilized as a reference motion for the walking control of a biped robot, employing a constraint-consistent inverse dynamics control algorithm. This framework facilitates biped robot motion planning based on data from either a single inertial sensor or two user commands. The proposed method was validated through robot simulations in the MuJoco physics engine environment. The motion controller achieved an error of ≤5° in tracking the joint angles demonstrating the effectiveness of the proposed framework. This was accomplished using minimal sensor data or few user commands, marking a promising foundation for robotic control and human-robot interaction.
The Generalized Degrees-of-Freedom Region of the Two-User MIMO Broadcast Channel with Delayed CSIT

In this paper, we characterize the generalized degrees-of-freedom (GDoF) region of the two-user ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) multiple-input multiple-output (MIMO) broadcast channel with delayed channel state information at the transmitter (CSIT), where there are one transmitter with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> antennas and two receivers with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> antennas, respectively. Under delayed CSIT, different from the existing converse approaches in the multiple-input single-output (MISO) GDoF and MIMO degrees-of-freedom (DoF) models, we incorporate new components into traditional approaches for this MIMO GDoF converse. For the achievability, we generalize the existing MISO achievable scheme. Our result reveals how the channel strength and antenna configuration impact the GDoF region of the two-user MIMO broadcast channel with delayed CSIT. Furthermore, the extension of our converse to a GDoF outer region of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -user MIMO broadcast channel with delayed CSIT is also provided.
Be Cloud Smart with an Effective Cost Management Strategy for Cloud Services: A Case Study

As digital globalizations going in full swing across the world, organizations increasingly migrate their operations to the cloud. So, effective cost management of cloud services becomes paramount. This master-level article delves into the intricacies of cost management in cloud computing through a detailed case study. The case study explores a real-world scenario where a company transitions its IT infrastructure to the cloud, aiming to achieve operational efficiency while mitigating costs. The article analyzes the challenges faced during this migration and presents a comprehensive cost management strategy tailored to the specific needs of the organization.
Improving Clustering Accuracy of K-Means and Random Swap by an Evolutionary Technique Based on Careful Seeding

K-Means is a “de facto” standard clustering algorithm due to its simplicity and efficiency. K-Means, though, strongly depends on the initialization of the centroids (seeding method) and often gets stuck in a local sub-optimal solution. K-Means, in fact, mainly acts as a local refiner of the centroids, and it is unable to move centroids all over the data space. Random Swap was defined to go beyond K-Means, and its modus operandi integrates K-Means in a global strategy of centroids management, which can often generate a clustering solution close to the global optimum. This paper proposes an approach which extends both K-Means and Random Swap and improves the clustering accuracy through an evolutionary technique and careful seeding. Two new algorithms are proposed: the Population-Based K-Means (PB-KM) and the Population-Based Random Swap (PB-RS). Both algorithms consist of two steps: first, a population of J candidate solutions is built, and then the candidate centroids are repeatedly recombined toward a final accurate solution. The paper motivates the design of PB-KM and PB-RS, outlines their current implementation in Java based on parallel streams, and demonstrates the achievable clustering accuracy using both synthetic and real-world datasets.
Analysis of lightning vulnerability level in the City of Mataram and surrounding area using the simple additive weighting method

Mataram City, West Lombok and Central Lombok Regencies are areas that have a high level of infrastructure development. This high level of infrastructure development has indirectly increased the risk of lightning strikes in the city of Mataram and its surroundings. This study aims to determine the level of threat, susceptibility, and vulnerability to lightning strikes in the city of Mataram and its surroundings, and to find a correlation between the increase in land use as buildings and settlements with the increase in the number of lightning strikes. The lightning data was obtained from the monitoring results of the Mataram Geophysics Station in the 2017-2021 period which was processed using the Simple Additive Weighting method. From the processing results, the level of vulnerability to lightning in the very high category is in Selaparang and Mataram sub-districts. The results of the Pearson correlation calculation in an increase in the number of areas used as buildings and settlements to an increase in the number of lightning, obtained 14 districts with high Pearson correlation index values and 3 districts with very high Pearson correlation index values which included Batukliang, Sekotong, and Jonggat Districts.
Wireless Sensor Network (WSN) of a flood monitoring system based on the Internet of Things (IoT)

Indonesia records exceptionally high rainfall, particularly during the rainy season when almost all areas of the country are consistently showered with heavy rain. Vigilance is therefore crucial due to the risk of flooding from overflowing rivers or dams. It is essential to develop flood monitoring systems to mitigate the risk and impact of flooding. This study aimed to design and build a flood monitoring system with parameters that support flood warnings. These include measurement of the water level using an ultrasonic sensor and rainfall using a tipping bucket-based hall sensor. The flood detection system was installed at Pondok Aren, Tangerang Selatan, Banten. A website was developed to display information on water levels and rainfall measurements every 10 minutes, as well as cumulative rainfall over 24 hours, presented in values, tables, and graphs. The device design included a warning feature in the form of a strobe light that would activate if the water level exceeded the minimum threshold in addition to providing rainfall status notifications. The system performed well in trials, with data transmitted to the database every 10 minutes. Raingauge sensors exhibited a 0.86% error rate, while the ultrasonic sensor showed an average error rate of just 0.25%.
Unveiling the potential: preliminary result on an AI-based onsite earthquake early warning system deployment in Denpasar City

Due to Indonesia’s geographical location within the Pacific Ring of Fire, it is susceptible to earthquake occurrences. Therefore, the implementation of an Earthquake Early Warning (EEW) System is crucial to mitigate the impact of earthquakes. This study focused on deploying an on-site EEW system on Bali Island, an Indonesian region known for its elevated seismic activity. The study evaluated the system’s performance over five months in 2023. Throughout this observation period, the AI-based on-site EEW system successfully detected the earthquake’s P-wave before the occurrence of stronger seismic waves, such as the peak ground acceleration (PGA). It provided a warning time of up to 75 seconds and accurately predicted the intensity of the impending earthquake. These outcomes meet the fundamental requirements for an effective EEW system. The results obtained from the deployed AI-based on-site EEW system demonstrate the potential for wider implementation of such systems across Indonesia.