Reviewerly/exHarmony huggingface dataset:
Optimization of Pose Recognition Algorithms in Smart Wearable Devices

With the surge in popularity of smart wearable devices, the necessity for pose recognition has become paramount, especially in fields like motion analysis, health monitoring, and virtual reality. Yet, achieving efficient and precise pose recognition on devices with limited resources poses significant hurdles. Addressing this, we introduce an innovative optimization method for pose recognition algorithms, harnessing the power of the C++ compiler. Our approach uniquely integrates traditional machine learning paradigms with model simplification techniques. Furthermore, by exploiting C++ functionalities such as inline assembly, template metaprogramming, and compile-time optimization, we craft an optimization tailored explicitly for wearable hardware. Evaluations conducted on various devices testify to our method's superiority, marking a 10% surge in accuracy, 30% boost in efficiency, and a 15% dip in energy consumption relative to contemporary methods. Apart from achieving stellar accuracy and real-time performance, our method distinguishes itself in resource efficiency and scalability. This article offers an in-depth exploration of our design philosophies, optimization tactics, experimentation methodologies, and seminal discoveries, signposting the future trajectory of pose recognition in smart wearables and highlighting its commercial potential.
Two-Stage Multi-Instance Multi-Label Learning Model for Video Social Relationship Recognition

Recognizing social relationships among characters from videos facilitates multimedia content analysis and helps viewers understand the storyline quickly. However, treating the information at each moment in the video equally will affect the extraction of information related to social relationships. Most existing methods only consider the social relationships between any two characters in the video, ignoring the potential correlations of social relationships among multiple characters. To address these issues, we propose a Two-Stage Multi-Instance Multi-Label learning model (TSMIML) for video social relationship recognition. In the first stage, we focus on obtaining effective social relationship features and mining which moments' information reflects the social relationships between characters better by generating multiple instances. In the second stage, we focus on discovering which social relationships exist in the video, exploring potential correlations among social relationships to promote social relationship recognition in the video. Compared with the state-of-the-arts methods, extensive validation on the large-scale dataset has demonstrated the effectiveness of our proposed model.
Geological mapping using remote sensing, GIS, field studies and laboratory data of Wadi Hammamat area, Central Eastern Desert, Egypt

In the present study, the Operational Land Imager (OLI) images have been used with several processing approaches for delineating the different rock units of the Wadi Hammamat area. To validate the remote sensing data, a geological map of the Wadi Hammamat area at a scale of 1:20,000 was produced using a Geographic Information System (GIS) and field research. The geological map of the research area has been improved and modified using several a variety of image processing methods, such as False color composite (FCC), Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Band Ratio (BR). The best band combination of FCC 761,762 and 751 RGB. The result of PCA is PCA1, PCA2 and PCA3 show well lithological differentiation, while the best result of PCA is PC5, PC2, and PC3. In which it can distinguish between the different types of Hammamat molasse sediments; Hammamat graywacke, Hammamat siltstone, and Hammamat conglomerate. A new proposed FCC band ratio (7/5,5/3&3/1) has been developed as a best lithological discrimination.
EsaNet: Environment Semantics Enabled Physical Layer Authentication

Wireless networks are vulnerable to physical layer spoofing attacks due to the wireless broadcast nature, thus, integrating communications and security (ICAS) is urgently needed for 6G endogenous security.In this letter, we propose an environment semantics enabled physical layer authentication network based on deep learning, namely EsaNet, to authenticate the spoofing from the underlying wireless protocol.Specifically, the frequency independent wireless channel fingerprint (FiFP) is extracted from the channel state information (CSI) of a massive multi-input multi-output (MIMO) system based on environment semantics knowledge.Then, we transform the received signal into a two-dimensional red green blue (RGB) image and apply the you only look once (YOLO), a single-stage object detection network, to quickly capture the FiFP.Next, a lightweight classification network is designed to distinguish the legitimate user from the illegitimate user.Finally, the experimental results show that the proposed EsaNet can effectively detect a physical layer spoofing attack and is robust in time-varying wireless environments.
Dense Material Segmentation with Context-Aware Network

The dense material segmentation task aims at recognising the material for every pixel in daily images. It is beneficial to applications such as robot manipulation and spatial audio synthesis. Modern deep-learning methods combine material features with contextual features. Material features can generalise to unseen images regardless of appearance properties such as material shape and colour. Contextual features can reduce the segmentation uncertainty by providing extra global or semi-global information about the image. Recent studies proposed to crop the images into patches, which forces the network to learn material features from local visual clues. Typical contextual information includes extracted feature maps from networks targeting object and place related tasks. However, due to the lack of contextual labels, existing methods use pre-trained networks to provide contextual features. As a consequence, the trained networks do not give a promising performance. Their accuracy is below 70%, and the predicted segments have coarse boundaries. Considering this problem, this chapter introduces the Context-Aware Material Segmentation Network (CAM-SegNet). The CAM-SegNet is a hybrid network architecture to simultaneously learn from contextual and material features jointly with labelled materials. The effectiveness of the CAM-SegNet is demonstrated by training the network to learn boundary-related contextual features. Since the existing material datasets are sparsely labelled, a self-training approach is adopted to fill in the unlabelled pixels. Experiments show that CAM-SegNet can identify materials correctly, even with similar appearances. The network improves the pixel accuracy by 3–20% and raises the Mean IoU by 6–28%.
SELF-ESTEEM LEVELS AMONG SECONDARY SCHOOL STUDENTS IN DIFFERENT CATEGORIES OF SCHOOLS. CASE OF PUBLIC SECONDARY SCHOOLS IN NYANDARUA WEST SUB-COUNTY, KENYA

The major purpose of the study was to investigate whether students’ level of self-esteem can be influenced by the category of school a learner attends. To achieve this objective, schools were categorized into three categories which are day schools, day-boarding schools, and boarding schools. Students’ self-esteem was measured and the data obtained was analyzed. The aim was to find out whether there was any significant difference in students’ level of self-esteem in different school categories. The research was conducted using an ex post facto research design. Categories of schools were seen as a naturally occurring independent variable, which could have an effect on students’ level of self-esteem. The population of the study comprised students in public secondary schools in Nyandarua West Sub County. The Sub County had 8 public secondary schools when the research was conducted; with a student population of 3067. Data relevant to the study was collected using questionnaires, and a self-esteem test prepared by the researcher. The questionnaires were administered to a sample of 240 students, selected using simple random sampling from the 1320 form two students in the Sub County. Data was analysed using mean calculations, percentages, and a one-way ANOVA test. It was found out that there was a significant difference in students’ total-mean self-esteem in different categories of schools. Students in boarding schools had a higher level of self-esteem in general compared to students in day-boarding and day schools; whose self-esteem levels were average. Probably, boarding schools have a more child-friendly environment, hence, more opportunities and different facilities which help learners to achieve more and hence acquire positive growth in self-esteem. However, in day schools where opportunities could have been less adequate students recorded a low level of self-esteem. It is therefore recommended that MOEST should finance seminars to sensitize secondary school administrators on adolescents’ needs so that every Principal would endeavor to provide students with age-appropriate resources and activities. To achieve this, the government should supply adequate facilities to schools that do not have them. Further studies should be conducted to find out the environments in boarding schools that facilitated self-esteem growth among students, additionally, whether similar results would be obtained in other Sub Counties in Kenya.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0044/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Hierarchical remote preparation of an arbitrary m-qudit state with multiple agents

Based on Jin et al.’s hierarchical remote preparation scheme of an arbitrary [Formula: see text]-qudit state with three agents, we propose one scheme for multiple agents by utilizing the symmetry of cluster states. To save entanglement resource, we then put forward the other improved scheme. There exists a hierarchy among the agents in terms of their abilities which are quantified by nonconditioned fidelity. The upper-grade agent needs the assistance of all the remaining upper-grade agents and any one of the lower-grade agents to recover the target state, while the lower-grade agent requires all the other agents’ collaboration. It is important to emphasize that the recovery operation is derived by a general expression clearly revealing the relationship with the measurement results.
Organizational and Pedagogical Conditions for Transition to Project Management of a Preschool Educational Organization

The Russian education system is currently undergoing a process of digital transformation at all levels. These changes are due to socio-economic development, demographic processes, needs of society, peculiarities of production, where human potential is replaced by artificial intelligence and other environmental factors that have a significant impact on the microenvironment of an educational organization. All these processes require a change in the management model, which could meet the trends in the development of society, quickly transform and modify. The article proposes a project management model, flexible, situational, as well as organizational and pedagogical conditions that contribute to the implementation and use of the developed management model in preschool educational organizations (PEO), regardless of the form of ownership (municipal, state or private). One of the most important conditions for the transition to digital management of PEO is the professional development of teachers in the field of digital education, the practical use of digital equipment and interactive platforms in the classroom, it is also very important to select management personnel, change the scheme for appointing managers who must have competencies in the field of management , marketing, economics, risk management and personnel, and not just have a set of pedagogical knowledge, skills and abilities, as it is now. However, the implementation of the project management model at the moment turns out to be difficult, the transition to it should be phased, as the first stage, the authors of the article propose the implementation of the organizational and pedagogical conditions described in the work.
Retracted: Evaluation Model of Music Therapy’s Auxiliary Effect on Mental Health Based on Artificial Intelligence Technology

[This retracts the article DOI: 10.1155/2022/9960589.].
Retracted: Development and Teaching Application of Interactive Virtual Algorithm Animation of C Language Program

[This retracts the article DOI: 10.1155/2022/7082914.].
Retracted: Evaluation of Football Teaching Quality Based on Big Data

[This retracts the article DOI: 10.1155/2022/7174246.].
Stages of Research and Development Model Research and Development (R&amp;D)

One of the efforts that every educational person can make to improve the quality of education in Indonesia is research, especially educational research. Through research, problems in education can be identified and then solutions can be found. New, more innovative things in education can also be developed and applied from research. There are several types of research that can be carried out, one of which is research and development (R&amp;D). Initially, research and development was applied to the industrial world, and was the spearhead of an industry in producing new products needed by the market. Almost 4% of costs are used for this research, even for certain fields such as computers and pharmaceuticals the cost allocation can exceed 4% (Borg and Gall, 1989). Meanwhile, in the social and educational fields, the role of research and development is still very small, namely less than 1% of overall education costs. This is considered to be one of the main reasons why progress in education lags somewhat behind when compared to other fields. As stated by Borg and Gall (1989), unfortunately, R &amp; D still plays a minor role in education. Less than one percent of education expenditures are for this purpose. This is probably one of the main reasons why progress in education has been logged for behind progress in other fields.
Application of Augmented Reality Module for Alkane Derivatives to Improve Students' Spatial Ability and Mastery of Concepts

Chemistry learning, which includes three levels of representation (macroscopic, sub-microscopic, and symbolic), can shape students' mental models of chemistry. The augmented reality module for alkane derivatives (ARMAD) was developed and implemented to improve students' spatial abilities and mastery of concepts through understanding the three levels of chemical representation. The one-group pre-test and post-test design was implemented with 60 students as participants in a senior high school in Sukabumi, West Java. Spatial ability was measured using five multiple-choice questions and three essay questions for visual-spatial, spatial orientation, and spatial relationships. Concept mastery data was collected using 20 multiple-choice questions. Improved spatial ability and concept mastery were calculated using the normalized-gain test and the Wilcoxon test. The mean score of N-gain spatial ability is in the high category with a score of 0.87 and in the medium category for mastering the concept of alkanes with a score of 0.59. The results of the Wilcoxon test on spatial ability data and mastery of concepts showed that there is a significant difference
To represent and serve photonics

PhotonicsViewsVolume 20, Issue 5 p. 1-1 EditorialFree Access To represent and serve photonics Partnership agreement between EOS and PhotonicsViews signed Patricia Segonds, Patricia Segonds President, European Optical Society, Professor, Institut Néel – CNRS / Université Grenoble Alpes FranceSearch for more papers by this author Patricia Segonds, Patricia Segonds President, European Optical Society, Professor, Institut Néel – CNRS / Université Grenoble Alpes FranceSearch for more papers by this author First published: 17 October 2023 https://doi.org/10.1002/phvs.202370501AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume20, Issue5October/November 2023Pages 1-1 RelatedInformation
User Satisfaction on Academic Information System in UIN KH Abdurrahman Wahid Pekalongan

The aim of this research is to evaluate the satisfaction level of students, lecturers, and educational staff towards the integrated academic information system, Sistem Informasi Akademik Terpadu (SIKADU), at UIN KH Abdurrahman Wahid Pekalongan, and to test whether there are any differences in satisfaction levels among these three groups. Additionally, this study aims to identify any issues encountered by users while utilizing it. The population for this research consists of users of SIKADU in UIN KH Abdurrahman Wahid Pekalongan. A convenience sampling technique was employed, and 162 respondents were selected as the sample. The data was analyzed using descriptive statistics, Kruskal Wallis and Cronbach's alpha test. The findings revealed that (1) there are significant differences in satisfaction levels among these three groups (2) 61.8% of the respondents were satisfied with the performance of SIKADU (3) only 17.3% of respondents accessed SIKADU on a daily basis (4) the majority of respondents (67.3%) accessed SIKADU through their mobile phones while at home (5) all dimensions of satisfaction measurement were deemed valid and reliable (6) the primary obstacles faced by the academic community were related to inputting data during course input and downloading scoring forms using SIKADU.
PENINGKATAN PERILAKU PROSOSIAL SANTRI MELALUI CINEMATHERAPY

Prosocial behavior is a form of behavior that must be owned by every individual because it is good. In fact, there are still many students whose prosocial behavior is still at a low level. One of the things that can be done is through the application of cinematherapy. The aims of this study were (i) to find out how the level of prosocial behavior of students at the Uswatun Hasanah Pagaden Islamic Boarding School was before and after being given cinematherapy, and (ii) to find out whether cinematherapy was able to increase the prosocial behavior of students at the Uswatun Hasanah Pagaden Islamic Boarding School. This research is a quantitative research using an experimental approach. The experimental design used in this study was a pre-experimental design with the type of one group pretest posttest control design. The sample in this study were seven students. The data in this study were analyzed quantitatively. The results showed that (i) the research sample had a low level of prosocial behavior during the pretest whereas after the treatment there was a significant increase in prosocial behavior in, and (ii) cinematherapy was effective in increasing prosocial behavior in students at the Cadre Uswatun Hasanah Pagaden Islamic Boarding School.
Community Aware Recommendation System with Explicit and Implicit Link Prediction

Recommendation systems are essential tools that help users discover content they may be interested in, amidst the vast amount of information available online. However, current methods, such as using historical user-item interactions and collaborative filtering, have limitations in accurately predicting user preferences. Our research aims to address these challenges and improve the performance of recommendation systems. In this article, we propose a new approach to recommendation systems using a method called Probabilistic Matrix Factorization (PMF). We transform the standard PMF method into a community-based PMF that takes into account implicit relationships between users and items. To achieve this, we use a machine learning technique called Reduced Kernel Extreme Learning Machine (RKELM). Our proposed framework is designed to integrate these implicit relationships and identify communities of users with similar preferences based on PMF. We conducted a comparative analysis of our newly developed model against existing methods, using two well-known datasets. Various performance metrics, such as prediction errors, were employed to evaluate the effectiveness of our proposed community-based PMF approach with RKELM. Our model demonstrates improved performance, achieving a 7% improvement for the Douban dataset and a 4% improvement for the Last.fm dataset. Despite the improvements demonstrated by our model, potential limitations and challenges may still exist, such as scalability to larger datasets or adaptability to different domains. Future work could explore these aspects and investigate further enhancements to our approach.
Application of K-Means Clustering for Detection Downy Mildew at Madura Corn Plant Using Digital Image Processing

The development and cultivation of corn is necessary in line with the increasing consumption of food ingredients and industrial needs, especially food products made from corn. In the development of maize in Indonesia, the main obstacle is the disturbance of Plant Pest Organisms (OPT), especially diseases, one of which is downy mildew. This disease can be identified by a change in color, so we need a way to find out the difference between the color of healthy leaves and the color of leaves that have changed due to downy mildew. One solution that can be used is image processing. Therefore the aim of this study was to detect downy mildew based on leaf color in corn plants based on digital image processing, to produce precise and objective results. The algorithm used is the K-Means Clustering algorithm. This study uses 50 images of training data and 25 images of test data. Based on the simulation of downy mildew disease identification using K-Means Clustering it achieves an accuracy rate of 85%.
THE SOCIAL ASPECTS AND VALUES OF CHARACTER EDUCATION IN PIPIET SENJA NOVEL SKETSA RASA: STUDY OF LITERATURE SOCIOLOGY

This study aims to describe the social aspects and values of character education studied by the literature sociology according to Ian Watt in the novel Sketch of Taste by Pipiet Senja. The present research applied a qualitative descriptive method. The data were collected using a non-interaction technique, that is, reviewing documents and archives. This research was conducted in 6 months, from July to December. The present study found out the social aspects in the form of custom, family, work and education and the values of character education in the form of religious values, moral values, independent values, complex work values, creative values, democratic values, communicative values, and curiosity values. It is recommended that the Indonesian language teachers, students and other researchers examine the social and character education values in literary works.&#x0D; &#x0D;