Reviewerly/exHarmony huggingface dataset:
Monitoring and Analysis of Physical Exercise Effects Based on Multisensor Information Fusion

In this paper, multiple sensors are used to track human physiological parameters during physical exercise, and data information fusion technology is used to extract useful information for monitoring and analyzing the effects of physical exercise. This paper explores the interaction and developmental dynamics of multisensor information fusion technology and physical exercise data monitoring based on the interrelationship and interpenetration between the two. The design ideas and principles that should be followed for the software designed in this study are discussed from the perspective of the portable design of measurement instruments and the perspective of multisensor information fusion, and then, the overall architecture and each functional module are studied to propose a scientific and reasonable design model. The general methodological model to be followed for the development of this resource is designed, and the basic development process of the model is explained and discussed, especially the requirement analysis and structural design, and how to build the development environment are explained in detail; secondly, based on the course unit development process in this model, we clarify the limitations of the system through meticulous analysis of the measurement results, which provides a solid foundation for the next step of system optimization. Finally, with a focus on future development, we elaborate on the potential possible role and development trend of multisensor information fusion in the future period. In this paper, we propose to apply the multisensor data fusion algorithm to the monitoring, analysis, and evaluation of the effect of physical exercise, by collecting multiple human physiological parameters during physical exercise through multiple sensors and performing data fusion processing on the collected physiological parameters to finally evaluate the effect of physical exercise.
Improving Chi-Square Feature Selection using a Bernoulli Model for Multi-label Classification of Indonesian-Translated Hadith

Hadith is the foundational knowledge in Islam that must be studied and practiced by Muslims.In the Hadith, several types of teachings are beneficial to Muslims and all of mankind.Some Hadith serve as advice, while others contain prohibitions that Muslims should adhere to.There are yet others that do not belong to these categories and serve only as information.This study focuses on increasing the performance of Chi-Square feature selection to obtain relevant features for multilabel classification of Indonesian-translated Bukhari Hadith data.This study proposes a Chi-Square-based Bernoulli model to improve Chi-Square feature selection which is appropriate for short-text data such as Hadith.The findings of this study show that the proposed method can select relevant features based on data classes; thereby improving Hadith classification performance with an error value of 9.38% compared to that (9.91%) obtained using the basic Chi-Square feature selection.
Evaluation of heavy metals pollution in the soil of Helwan, Cairo, Egypt using magnetic and chemical analysis techniques

In the present study, we have applied magnetics and geochemical analysis in order to investigate the heavy metal contamination in Helwan area, Cairo, Egypt.We have collected 52 soil samples from Industry zones and urban areas.We characterized the spatial pollution pattern using ICP-MS and found enrichments in seven potentially toxic heavy metals; Cu, Zn, Pb, As, Sb, Cd, and Hg, located near industrial zones, with high pollution levels.The bulk magnetic susceptibility values (k) measured on samples range between a maximum of 3560 × 10 -6 SI near the Industry Zone area and 168 × 10 -6 SI locate in the eastern part of the study area (sandy soil), which is not fairly affected by pollution (un-urbanized).The mass-specific magnetic susceptibility (χ) measured on samples ranges between 0.058 × 10 -6 and 2.83 × 10 -6 m 3 /kg.Thermomagnetic runs reveal that magnetite and Ti-rich titanomagnetite control the magnetic signal.The spatial distribution of the mass-specific magnetic susceptibility (χ) and Pollution Load Index (PLI) shows the highest values in industrial areas.
Watermarking of a 3D Model based on Wavelet Transform

In the last decade, 3D models gained interest in many applications, such as games, the medical field, and manufacture. It is necessary to protect these models from unauthorized copying, distribution, and editing. Digital watermarking is the best way to solve this problem. This paper introduces a robust watermarking method by embedding the watermark in the low-frequency domain, then selecting the coarsest level for embedding the watermark based on the strength factor. The invisibility of the watermark for the proposed algorithm is tested by using different measurements, such as HD and PSNR. The robustness was tested by using different types of attacks; the correlation coefficient was applied for the evaluation of the extracted watermark. From the results, it is clear that the proposed algorithm achieved good invisibility and robustness against the geometrical attack and the added noise.
Didactic Teaching Aids: Functions, Typology, Features of Use

The article discusses didactic teaching tools used in the field of education. In the course of the research, the study and analysis of scientific and pedagogical materials were carried out, in which the problem of the use of material support in the teaching process is considered. Classifications of didactic tools were developed and the features of their application were considered. The result of the analysis of scientific and pedagogical materials was the information that the identified types of didactic teaching tools allow teacher not only to interest students in the educational process, but also to increase the effectiveness of teaching, as well as reduce the cost of resources. The data obtained can be used by teachers in educational institutions.
Mahasiswa dan Agen Perubahan pada Puisi “Sajak Pertemuan Mahasiswa” karya WS. Rendra

This article discusses the poem "Sajak Pertemuan Mahasiswa" by W.S. Rendra. The discussion of this poem is motivated by efforts and efforts to apply the concept of poetry structure put forward by Lévi-Strauss. The description of the structure of this poem aims to display the structure of the poem consisting of the outer structure (surface structure) and the inner structure (deep structure). The outer structures that will be discussed are forms of text, diction, and sound, while the inner structures that will be discussed are imagery, compounds, and themes. The method used in this study is descriptive of analytics with a type of qualitative research, namely placing the text of poetry as the object and research data. Data collection techniques are done by identifying, classifying, and recording. Data anlysis is done by meaning direct and directed content. Analysis is done by discussing the structure of poetry consisting of text, diction, and sound, imagery, compounds, and themes. The results showed that the structure of Lévi-Strauss poetry can determine the overall meaning and message of poetry. The use of blank verse, simple diction, dominance of vocal sounds /a/, thought imagery, and metaphorical-rhetorical majas refers to the theme of "openness". This theme can be interpreted very broadly, namely openness to justice, education, employment, income, and livelihood. This openness is tried by students as agents of change and a bridge between the government and the people.
CorefDPR: A Joint Model for Coreference Resolution and Dropped Pronoun Recovery in Chinese Conversations

In this work, we present that coreference resolution and dropped pronoun recovery are two strongly related tasks in Chinese conversations, as recovering the dropped pronoun needs to explore the referent of the pronoun at first. Meanwhile, the omitted entity mention should be recovered before its coreferences are resolved. This motivates us to propose CorefDPR, a novel model to jointly resolve these two tasks and make them enhance each other. CorefDPR firstly utilizes a pre-trained language model to encode tokens in the conversation snippet. Then, the coreference resolution layer detects all entity mentions from the candidate text spans and groups them as coreferent mention clusters based on the contextualized token states. Furthermore, the pronoun recovery layer explores the referent of each dropped pronoun from the coreferent mention clusters and predicts the probability distribution over pronoun category for each token. Finally, a general conditional random fields (GCRF) is employed to globally optimize the pronoun recovery sequence of the snippet by modeling both intra-utterance and cross-utterance pronoun dependencies, and the recovered pronouns are further linked back to corresponding mention clusters to complete them. Experimental results on the benchmark demonstrate that our proposed model outperformed the state-of-the-art baselines of both these two tasks, and the exploratory experiments also demonstrate that these two tasks mutually benefit each other.
Cross Domain Edge Detection Based Label Decoupling Salient Object Detection

The edge of salient object plays an important role in salient object detection. In this paper, a cross domain edge detection based label decoupling salient object detection network (CDENet) is proposed to improve the accuracy of saliency detection and make more adequate use of the edge information in an image. CDENet decouples the original salient object detection labels, and is trained with original saliency maps and the decoupled labels. In order to make better use of edge information, a cross-domain edge detection module (CDED) is proposed, which fuses the features of RGB domain and HSV domain. In addition, we also design a cross self-supervised module (CSSM) for label decoupling network. CSSM uses the implicit association between body prediction and edge prediction to strengthen the information interaction between different branches of the neural network. Comprehensive experiments on 5 widely used data sets in salient object detection show that CDENet is superior to 6 state-of-the-art algorithms in several objective indicators. Experiment results show that the proposed approach is an effective saliency detection method. CDED and CSSM can accurately fuse features of different branches and improve the salient object detection accuracy.
Mining Of Deep Web Interfaces Using Multi Stage Web Crawler

As deep web develops at an exceptionally high speed, there has been expanded interest in procedures that help productively find deep-web interfaces. Nonetheless, because of the huge volume of web assets and the dynamic idea of deep web, accomplishing wide inclusion and high proficiency is a difficult issue. In this venture propose a three-stage framework, for proficient reaping deep web interfaces. In the main stage, web crawler performs website based looking for focus pages with the assistance of web indexes, trying not to visit an enormous number of pages. To accomplish more exact outcomes for an engaged slither, Web Crawler positions websites to organize profoundly applicable ones for a given subject. In the second stage the proposed framework opens the web pages inside in application with the assistance of Jsoup API and preprocess it. At that point it plays out the word include of inquiry in web pages. In the third stage the proposed framework performs recurrence investigation dependent on TF and IDF. It additionally utilizes a blend of TF*IDF for positioning web pages. To kill inclination on visiting some exceptionally applicable connections in shrouded web registries, In this paper we propose plan a connection tree information structure to accomplish more extensive inclusion for a website. Venture trial results on a bunch of delegate areas show the deftness and exactness of our proposed crawler framework, which proficiently recovers deep-web interfaces from enormous scope destinations and accomplishes higher reap rates than different crawlers utilizing gullible Bayes calculation.
Efficient Resources Allocation and Energy Reduction with Virtual Machines for Cloud Computing

Cloud computing is an internet provisioned method for sharing the resources on demand by network management, storage, services, applications and the serves that necessitate management optimal effort. VMM (virtual machine migration) plays a major role in enhancing the resource utilization, application isolation, processing nodes, fault tolerance in VMs for enhancing nodes portability and for maximizing the efficiency of physical server. For balancing the clouds with resources for the enhanced performance, varied users are served with application deployment in the cloud environment is considered as the major task. The user can rent or request the resources when it becomes significant. The emphasis of this paper is on different energy VM energy efficient module as per machine learning methods. While allocating the VMs to the host machines, MBFD (Modified Best Fit Decreasing) is considered and the classification of host machine capability such as overloaded, normal loaded and underloaded is executed according to SVM (Support vector machine). SVM is utilized as a classifier for analyzing the MBFD algorithm and for the classification of the host as per the job properties. In this procedure, the numbers of jobs that are not allocated are examined via simulation which is computed by means of time consumption, energy consumption and a total number of migrations.
Development of Gaiting Analysis using Footwear

An intelligent hybrid project is developed for assisting the partially impaired and physically challenged people. The Fall Detection is proposed to ensure the safety of these people. Force Sensing Resistor (FSR) sensors are attached to the footwear. For each in a pair of shoes four FSR sensors are attached at the precise locations in the shoe. In this method, the user’s pressure from the specified points is calculated in real time using an integrated force sensory system, which comprises FSR sensors in the shoe. When the pressure from these specified points cross the boundary limit of the specified safety value, i.e., the maximum pressure, it is assessed that the user is going to fall down. The data retrieved from these FSR sensors is used for computing the gaiting analysis. We have used Arduino microcontrollers for data collection from the pair of shoes incorporated with sensors. The data gathered here is transmitted via Bluetooth protocol to the core FPGA where the main Gaiting Analysis is performed and fall detection is achieved here. The novelty in this paper is we have used the FPGA board for Fall Detection because it has high performance, less power consumption and parallel processing support. So using FPGA is the best option in this project. This project can be broadly applied across Healthcare and Military applications. The detected fall can be further improved and it can be prevented by Fall Prevention prototypes. In this study, we have successfully achieved the desired results, and all the proposed methods were finally verified through simulations and experiments.
Personalized Virtual Campus Journey Adaptation to User Controlled Experience

Increasingly, educational institutes are migrating into mobile platforms and mobile app technology to communicate, advertise, and dissimilate education-related information to their stakeholders using virtual campus journey mobile apps. Campus journey mobile apps generally provide standardized generic customization to their user base, incorporating a list of favorite touchpoints based on the users’ frequent behaviors. In the literature, personalization, and customization, definitions are mixed-up and inter-changed with no proper separation of these two concepts. In this research, the personalized virtual journey is examined according to the user’s preference. It includes creating and updating the personalized virtual campus journey path as an activity of the user and having it as an integral part of the personalized virtual campus journey application. This research presents the concept structure, design, implementation, and evaluation results of the personalized virtual campus journey mobile app development according to the user’s preferences with the user given the ability to control his or her own virtual journey experience.
Development Training of Android-Based Interactive Learning Application as Supporting Media in Online Learning

&lt;span class="ShortAbstract"&gt;&lt;em&gt;&lt;span&gt;In supporting the success of online learning, it is necessary to increase the creativity of educators in developing and using learning media applications. The purpose of this research is to provide training and assistance to lecturers, teachers and prospective teacher students in making Windows and Android-based learning applications. The structured training program is carried out in collaboration between lecturers from the Teacher Training and Education Faculty and partner teams from the Permata Bunda Educational Institution through the Kedaireka Matching Fund program. The training was carried out in a hybrid manner with details of 10 participants participating offline and 60 participants participating online. The training was carried out for one day by dividing into 3 types of activities, namely the introduction of learning applications, direct practice of making learning applications and as a closing activity a questionnaire was given as an evaluation of the success of the activity. Based on the evaluation results obtained information that 14.26% of participants have been able to develop learning applications up to the publication stage, 71.43% of participants have been able to develop learning application content and 10% of participants are able to complete the learning media framework design stage, while 4 ,26% are still at the stage of program installation. In addition, at the end of the activity, participants also filled out a satisfaction questionnaire with the ongoing training activities. Based on the response analysis of participants' satisfaction above 85% which is included in the very good category. This shows that participants are very satisfied with the training they have received&lt;/span&gt;&lt;/em&gt;&lt;/span&gt;
Virtual Reality Technologies in Health Care: A Literature Review of Theoretical Foundations

The digitization of health care promises an improvement of medical care through the adoption of virtual reality (VR) related technologies.Although most undergoing mechanisms of clinical effectiveness are yet not defined theoretically, research approaches have already taken place in several empirical settings.To structure current and upcoming scientific work in this field, we conducted a literature review with regard to theoretical implications of both IS-related and healthcare-related research.We found several theoretical bases to build upon in the field of psychology, but expressed a need for enrichment of theoretical foundations in the field of IS research.We therefore plead for a theoretical foundation enriched by synergetic concepts of clinically effective VR related technologies.Finally, we conclude that VR related technologies appear as a promising approach worth further theoretical and empirical research in order to improve medical care.
Specificity of working with gifted primary school students in China

In China, a Key National Strategy has been developed. According to the Strategy, education is refocused on high quality, the development of a holistic approach to the harmonious development of pupils' personalities, which should be imbued with the spirit of innovation, business skills and develop moral, intellectual, physical, and aesthetic aspects. The Ministry of Education of China also supported the position of an individual approach to each pupil and considering his physical and psychological development. The reform aims to create a favorable educational environment for the development of each pupil, active research training, increasing the flexibility and diversity of courses. Among all the subjects of the educational program, literacy is defined as the most fundamental for the successful assimilation of other educational areas and the holistic development of children's personalities, an indispensable strategy for preserving cultural heritage, national unity, and creativity. The program defined the principles of training. They are: to involve all pupils in the educational process and develop their intellectual abilities, to understand the peculiarities of language and literature education, to promote independent learning, joint learning, and learning based on pupils' requests, to take into account their differences and learning needs, to create curricula based on openness and viability. Such positive changes in pedagogical practice create a favorable educational environment that adapts to different rates of pupil learning. The program aims to develop a wide range of pupils' skills, such as literary literacy, financial literacy, mathematical literacy, and scientific literacy. The program provides an inclusive educational environment that can engage gifted pupils in terms of pedagogical strategies (e.g., individualized learning, research learning, and independent learning), enriched reading materials, and dynamic, multiple assessments). The main problems of implementing the new literacy program are: insufficient teacher competence, the difficulty of transforming deep traditions from the pedagogy of transmission and collectivism to the pedagogy of constructivism, the large class size in China leads to overload of teachers.
Handwritten English Alphabet Recognition

Abstract: Character recognition is a process by which a computer recognizes letters, figures, or symbols and turns them into a digital form that a computer can use. In moment’s terrain character recognition has gained a lot of attention in the field of pattern recognition. Handwritten character recognition is useful in cheque processing in banks, form recycling systems, and numerous further. Character recognition is one of the well- liked and grueling areas of exploration. In the unborn character recognition produce a paperless terrain. In this paper, we describe the detailed study of the being system for handwritten character recognition. We give a literature review on colorful ways used in offline English character recognition. Keywords: Character; Character recognition; Preprocessing; Segmentation; Point birth; Bracket; neural network; Convolution neural network.
A Neural Graph-based Local Coherence Model

Entity grids and entity graphs are two frameworks for modeling local coherence.These frameworks represent entity relations between sentences and then extract features from such representations to encode coherence.The benefits of convolutional neural models for extracting informative features from entity grids have been recently studied.In this work, we study the benefits of Relational Graph Convolutional Networks (RGCN) to encode entity graphs for measuring local coherence.We evaluate our neural graph-based model for two benchmark coherence evaluation tasks: sentence ordering (SO) and summary coherence rating (SCR).The results show that our neural graph-based model consistently outperforms the neural grid-based model for both tasks.Our model performs competitively with a strong baseline coherence model, while our model uses 50% fewer parameters.Our work defines a new, efficient, and effective baseline for local coherence modeling 1 .