Reviewerly/exHarmony huggingface dataset:
AdaBoost based hybrid concept drift detection approach for mental health prediction

One of the most crucial components of reducing the risk of serious mental disease is mental health prediction. Meanwhile, the department of public health may apply mental health prediction as a theoretical foundation to develop plans for behavioral therapies for health care workers. This study uses machine learning to make predictions about mental health. Conventional machine learning techniques, however, have difficulty providing reliable high accuracy values and generate a lot of false alarms. This is because the presence of concept drift in the continuous data. Concept drift is the term used to explain unforeseen changes in data features over time. Therefore, novel technique is needed to handle the presence of concept drift. In this study, we presented the Fixed Sliding Windowing (FSW) approach and studied its effects. In order to predict mental health, we employed the AdaBoost classifier, and we began retraining the model based on statistical tests. AdaBoost has been found to have a higher accuracy ratio after applying the proposed method.
Resúmenes al Español

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Resúmenes al Español. Intellect Dev Disabil 1 October 2023; 61 (5): 442–443. doi: https://doi.org/10.1352/1934-9556-61.5.442 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest Search
A Tool for Security Risk Assessment for APT Attacks: using Scenarios, Security Requirements, and Evidence

Advanced Persistent Threat (APT) attacks are complex, employing diverse attack elements and increasingly intelligent techniques. This paper introduces a tool for security risk assessment specifically designed for these attacks. This tool assists security teams in systematically analyzing APT attacks to derive adaptive security requirements for mission-critical target systems. Additionally, the tool facilitates the assessment of security risks, providing a comprehensive understanding of their impact on target systems. By leveraging this tool, security teams can enhance defense strategies, mitigating potential threats and ensuring the security of target systems.
Weight thresholded regularized robust coding for vision sensing-based hand gesture detection in collaborative robotics

Abstract Vision sensing-based hand gesture recognition is considered an important contemporary research problem of collaborative robotics in a human-robot coexisting environment. The problem becomes more complex in challenging environments, where a robot needs to correctly recognize a human gesture to perform navigation or some other designated job, even in the presence of poor illumination conditions, occlusion, etc. This work proposes a novel approach of successfully utilizing regularized robust coding (RRC)-based models to solve such hand gesture detection problems in real-life, challenging situations. The RRC model is used for robust regression of a signal or image and is known as an improvement over the classical sparse representation-based classification (SRC) model. In this work, we propose three novel variants of weight thresholding mechanism in conjunction with RRC (named WTRRC algorithms) which essentially employ more error-tolerant concepts of logistic functions for weight update, when the dictionary is formed by real-world, photometrically irregular hand gesture images. Extensive case studies in real-world environments (i) with poor illumination, and (ii) with both poor illumination and occlusions firmly establish superior performances of the proposed WTRRC variants compared to other state-of-the-art algorithms in such collaborative robotics problems.
Software Description of the AR Mobile Application

Abstract Before using Augmented Reality (AR) and its application in engineering graphics, the answer to the primary question should be received: “What is the need for augmented reality in technical drawing?”. The following reasons should be considered to answer this question precisely.
Anomaly Detection for Industrial Robot Prognostics and Health Management

Abstract Industrial robots have continued to play important roles in automation for smart manufacturing. Effective maintenance strategies are essential for successful robot operation. However, maintenance can be costly which has led manufacturers to explore different ways to monitor and evaluate the health of their robot workcell during operation. Successful methods to assess robot health rely on capturing and analyzing significant amounts of continuously generated data from the workcell. Various data streams relevant to a robot workcell may be available such as robot controller data, programmable logic controller data, maintenance logs, and part quality data. Although these data streams can reveal information about the workcell, the large volume and complexity of the data can prove difficult to analyze. Researchers at the National Institute of Standards and Technology (NIST) have developed a test method and accompanying unsupervised learning anomaly detection procedure to assess the health of robot workcells. The approach involves defining a distance metric which is then passed to a robust outlier detection step. Performance of the approach demonstrated on several types of simulated robot failures and compared against different popular anomaly detection algorithms.
Measures of Success

While setting objectives, prioritizing tasks, and crafting your documentation, crucial questions emerge: How can you assess your success? How can you evaluate the tangible and intangible outcomes of your efforts? How can you determine the quality and effectiveness of your documentation? This chapter explores various metrics that can bolster your efforts toward enhancing your developer documentation.
Variable-length Differential Evolution for Numerical and Discrete Association Rule Mining

Abstract This paper proposes a variable-length Differential Evolution for Association Rule Mining. The proposed algorithm includes a novel representation of individuals, which can encode both numerical and discrete attributes in their original or absolute complement of the original intervals. The fitness function used is comprised of a weighted sum of Support and Confidence Association Rule Mining metrics. The proposed algorithm was tested on fourteen publicly available, and commonly used datasets from the UC Irvine Machine Learning Repository. It is also compared to the nature inspired algorithms taken from the NiaARM framework, providing superior results. The implementation of the proposed algorithm follows the principles of Green Artificial Intelligence, where a smaller computational load is required for obtaining promising results, and thus lowering the carbon footprint.
Archives Management in the Public Relations and Community Development Division At PT Indonesia Power Suralaya Power Generation Unit Cilegon

The Public Relations and Community Development Division of PT Indonesia Power Suralaya PGU in carrying out their duties cannot be separated from the existence of documents that will later be archived for further use as evidence of the activities that the Public Relations and Comdev Division have carried out. The problems found in this study are the lack of coordination between employees, archive storage that is not well organized, and incomplete archives. This research was conducted aiming to find out how archive management is done. This study used a qualitative descriptive method using a purposive sampling technique. Then, in collecting research data, the authors used the methods of observation, participation, documentation and interviews. Based on the research conducted, it can be seen that archive management in the Division of Public Relations and Community Development at PT Indonesia Power Suralaya PGU, with the initial stage of planning, determines the storage period for archives. The next stage is to mobilize by archiving in the form of softcopy and hardcopy. Then, there is an organizing stage which is carried out by giving authority to employees in charge of data and documents. Finally, for the supervision stage, monitoring is carried out for all employees. From the results of the study it was concluded that the management carried out had gone through the stages of planning, actuating, organizing, and supervising as stated by Wardoyo regarding the focus of management.
Thinking on Construction of Intelligent Auxiliary Physical Exercise Mode Under National Fitness Plan

In recent years, with the increase of work intensity and life pressure, many people are suffering from unhealthy conditions. In this case, it is very necessary for those who care about health to maintain and enjoy an active and healthy life. Therefore, many people tend to improve their health status through nationwide fitness and exercise. With the development of electronic technology, more and more people use smart devices to monitor their movement. In view of the difficulties and challenges people are facing, this paper will propose a multi-user behavior monitoring algorithm based on machine learning, which can improve the frequency and enthusiasm of physical exercise in people's daily life, thus improving the overall health of society. The research on action recognition responds to the call of the World Health Organization and adapts to social development, which has great practical significance.
Meme politics

The Elgar Encyclopedia of Technology and Politics is a landmark resource that offers a comprehensive overview of the ways in which technological development is reshaping politics. Providing an unparalleled starting point for research, it addresses all the major contemporary aspects of the field, comprising entries written by over 90 scholars from 33 different countries on 5 continents.
SHARD: Safety and Human Performance Analysis for Requirements in Detection

Automated driving requires reliable perception of the environment to ensure the safety of the driving task. One common perception task is 3D object detection, which aims at perceiving location and attributes of dynamic objects. This task is typically evaluated on different benchmark datasets, which each propose different metrics. However, these different metrics generally lack consistency and bear no relation to safety. Most notably, there is a lack of consistent definitions of pass/fail criteria for any given detection metric. In this work, the issue is addressed by systematically considering safety and human performance across different aspects of the object detection task. This approach yields interpretable detection metrics as well as thresholds for pass/fail criteria. Furthermore, a validation approach leveraging a prediction network is introduced and successfully applied to the requirements. A comparison of existing detectors shows that current perception algorithms exhibit failures for a majority of objects on the nuScenes dataset. Therefore, the results indicate the necessity of explicit safety consideration in the development of perception algorithms for the automated driving task.
Pengabdian Masyarakat Membuat Aplikasi Iuran Keuangan

Along with the rapid development of the times, in all sectors of course it is necessary to follow these developments in order to achieve mutual comfort and safety. The Housing Complex also does not escape to be able to keep up with the times. Every housing estate certainly has activities to collect dues to be able to manage their living environment in a clean, safe and comfortable condition. The financial management of contributions carried out still applies a manual system, so that there is a human error between financial records and the actual evidence is not the same. In addition, with a manual system like this, misunderstandings can arise and the truth is quite difficult to prove due to manual recording. Using the Rapid Application Development method to design a financial management mobile app that has an incremental nature with the intention that the processing time can be done in a short time but still optimally. The data needed will be integrated using Firebase which has a real-time nature so that records can be recorded automatically and existing data is always updated according to existing events. Application design using the JavaScript programming language in the Visual Studio program and integrated with Firebase as a place to process data. Based on testing the system has been designed and can be used properly
Real-Time Pedestrian Detection with YOLOv7 and Intel MiDaS: A Qualitative Study

Abstract: In the realm of Advanced Driving Assistance Systems (ADAS), the accurate assessment of pedestrian proximity is of paramount importance. This paper introduces a qualitative methodology that integrates YOLOv7-pose for object detection and pose estimation with the MiDaS (Monocular Depth Estimation in Real-Time with Deep Learning) model for monocular depth estimation. The main objective is to qualitatively assess pedestrian proximity to the camera within the ADAS framework. This procedure involves classifying pedestrians as "near" or "far" based on an inverse depth threshold that has been predetermined. In addition, the paper performs a qualitative comparative analysis of the results produced by the MiDaS Small, Hybrid, and Large variants to learn more about the performance of depth estimation in these contexts, particularly in relation to the presence of pedestrians. The evaluation emphasises this approach's qualitative potential for achieving situationally appropriate and context-aware pedestrian proximity assessment. The safety and adaptability of ADAS systems can be improved with the help of such insights, which have numerous applications in robotics, surveillance, and autonomous vehicles.
Double graph correlation encryption based on hyperchaos

Preventing unauthorized access to sensitive data has always been one of the main concerns in the field of information security. Accordingly, various solutions have been proposed to meet this requirement, among which encryption can be considered as one of the first and most effective solutions. The continuous increase in the computational power of computers and the rapid development of artificial intelligence techniques have made many previous encryption solutions not secure enough to protect data. Therefore, there is always a need to provide new and more efficient strategies for encrypting information. In this article, a two-way approach for information encryption based on chaos theory is presented. To this end, a new chaos model is first proposed. This model, in addition to having a larger key space and high sensitivity to slight key changes, can demonstrate a higher level of chaotic behavior compared to previous models. In the proposed method, first, the input is converted to a vector of bytes and first diffusion is applied on it. Then, the permutation order of chaotic sequence is used for diffusing bytes of data. In the next step, the chaotic sequence is used for applying second diffusion on confused data. Finally, to further reduce the data correlation, an iterative reversible rule-based model is used to apply final diffusion on data. The performance of the proposed method in encrypting image, text, and audio data was evaluated. The analysis of the test results showed that the proposed encryption strategy can demonstrate a pattern close to a random state by reducing data correlation at least 28.57% compared to previous works. Also, the data encrypted by proposed method, show at least 14.15% and 1.79% increment in terms of MSE and BER, respectively. In addition, key sensitivity of 10 −28 and average entropy of 7.9993 in the proposed model, indicate its high resistance to brute-force, statistical, plaintext and differential attacks.
Revisiting traditional educational practices in the age of digitalization

During the period of lockdown both students and teachers experienced immersion in the digital environment to the full, what allowed actualizing the so-called “dark” sides of organization of the educational process; those metamorphoses, which traditional roles of a student and a teacher are currently getting exposed to. Post-pandemic realities of education within the contour of Timothy Morton’s dark ecology theory are considered in the present paper. The authors make use of a “hyperobject” concept while studying the introduction of digitalization in the field of education, submitting to extrapolate Morton’s optics to comprehension of educational practices, mediated by digital environment. It is shown in the study, that absorption of traditional educational practices proves to be a limited discourse, where the technological component of digitalization is being merely actualized and the essence of educational process is being simultaneously discredited.
Parents Perceptions on School Readiness: Descriptive Study

The purpose of this study is to explore the parents’ perceptions on Children’s School Readiness. The open-ended question was administered to 116 parents in Padang, Padang Panjang, Bukittinggi, and Sawahlunto, West Sumatera. Content analyses were used to categorize all the response from participants. Overall this study found that 84% participants’ think that school readiness are very important for children to enter elementary school, 15% said that it’s important, and 1% quite important. Most of the reasons are because school readiness helps children to be success in elementary school. Looking more closely about their perception on school readiness, participants think that school readiness is related to cognitive ability (27%), social skill (17%), emotional competence (14%), sufficient age (11%), physical development (11%). Another response is related to financial readiness and motivation. This finding indicated that many parents considered that cognitive ability such reading skill as one of significant factor for children to enter elementary school.
Blockchain Technologies and Smart Contracts in Smart Cities

Smart Cities are gaining attention due to the ever-increasing population. The increasing population and expanding urbanization resulted in increased congestion and numerous environmental problems. Due to COVID-19, more efficient urbanization increased the need for smart cities. To address the societal and efficient urban management challenges of these Smart Cities, advanced technologies like Blockchains can play vital roles. Smart Contracts are blockchain-based applications. Smart contracts are used to automate agreements without the involvement of a middleman. Smart contracts are executed by a network of computers once predefined conditions are met. The execution action can be in the form of the release of funds to parties, vehicle registration, sending notifications, and issuing a ticket. The information on the transactions completed is updated on the blockchain. This chapter provides insight into how blockchain technology works for smart contracts, which deliver numerous services in Smart cities ecosystems' in more reliable, data secured, and beneficial for the population in Smart cities. The chapter will contribute to the planning of Smart cities planners, developers, architects, and thinkers for using smart contracts to deliver various services in the smart city’s governance.
Penerapan Model Project Based Learning untuk Meningkatkan Motivasi Belajar Matematika

This research aims to determine the increase in mathematics learning motivation of class VA students at SDN 7 Cakranegara using the project based learning model. This research includes collaborative PTK research with a descriptive quantitative approach. This research was carried out in stages in two cycles. The subjects of this research are all VA class students at SDN 7 Cakranegara for the 2022/2023 academic year. The data collection methods used were observation and questionnaires. Data from motivation to learn mathematics in the form of pretest and posttest, then calculations were carried out on the results of the questionnaire. The learning motivation data was then analyzed to see improvements in the pre-cycle, cycle I and cycle II. Increased learning motivation from the results of the pretest or pre-cycle with 17 children in the low category, 5 children each in the moderate and high categories. Then it increased until cycle II, namely that there were no students with low learning motivation. Minimum students have motivation to learn in the sufficient category of 6 children, 12 children in the high category, and 9 children in the very high category. This shows that the application of the project based learning model can increase the mathematics learning motivation of class VA students at SDN 7 Cakranegara.
Penggunaan Media Video Pembelajaran terhadap Hasil Belajar Siswa Kelas III SDN 33 Mataram

Currently, teachers rarely use media to expand learning material. Teachers only focus on the text so that students find it difficult to understand abstract material. This research aims to analyze the use of learning video media on student learning outcomes. This method uses a quantitative experimental method of the Action Reaction (RA) type. The population in this study used 3rd grade students at SDN 33 Mataram with a sample of 20 students. Data collection techniques use test techniques. The data was analyzed using quantitative descriptive techniques. The results of this research show that there is an increase in learning outcomes through the use of learning video media in class 3 of SDN 33 Mataram. This is evidenced by the increase in student learning outcomes with an average student learning score in the pre-cycle of 65 with 9 students, a percentage of 45%, an increase in the first cycle with an average of 79 with 13 students, a percentage of 65% and in the second cycle the average was 92 with 19 students with a 95% completion percentage.
Google Classroom Learning Cloud Environment in the Modern Information and Digital Society

The purpose of the article is to analyse the Google Classroom learning cloud environment, to identify its advantages and disadvantages in the modern information and digital society, as well as to achieve this goal, the methods of analysis, synthesis, deduction, and induction were used, and also a survey that allowed to evaluate and to establish the advantages and disadvantages of using Google Classroom in the educational process was conducted. The results focus on the peculiarities of this learning platform the functioning and practical assessments of its potential. The author emphasises the peculiarities of organising video meetings, creating and editing training courses, publishing announcements, grades, and establishing feedback from teachers. Google Classroom boasts significant features, such as seamless integration with other company services, a robust security policy, and widespread accessibility across iOS and Android devices. Nonetheless, the realm of digital learning technologies is continuously evolving at a rapid pace. Consequently, it is only a matter of time before the next advancement in cloud-based learning environments emerges. According to survey findings, Google Classroom's ability to personalize students' educational paths is rated relatively modestly. As a result, future improvements in this aspect are likely to be necessary to enhance its efficacy further.