Reviewerly/exHarmony huggingface dataset:
Chatbots to Support People With Dementia and Their Caregivers: Systematic Review of Functions and Quality (Preprint)

<sec> <title>BACKGROUND</title> Over the past decade, there has been an increase in the use of information technologies to educate and support people with dementia and their family caregivers. At the same time, chatbot technologies have become increasingly popular for use by the public and have been identified as having benefits for health care delivery. However, little is known about how chatbot technologies may benefit people with dementia and their caregivers. </sec> <sec> <title>OBJECTIVE</title> This study aims to identify the types of current commercially available chatbots that are designed for use by people with dementia and their caregivers and to assess their quality in terms of features and content. </sec> <sec> <title>METHODS</title> Chatbots were identified through a systematic search on Google Play Store, Apple App Store, Alexa Skills, and the internet. An evidence-based assessment tool was used to evaluate the features and content of the identified apps. The assessment was conducted through interrater agreement among 4 separate reviewers. </sec> <sec> <title>RESULTS</title> Of the 505 initial chatbots identified, 6 were included in the review. The chatbots assessed varied significantly in terms of content and scope. Although the chatbots were generally found to be easy to use, some limitations were noted regarding their performance and programmed content for dialog. </sec> <sec> <title>CONCLUSIONS</title> Although chatbot technologies are well established and commonly used by the public, their development for people with dementia and their caregivers is in its infancy. Given the successful use of chatbots in other health care settings and for other applications, there are opportunities to integrate this technology into dementia care. However, more evidence-based chatbots that have undergone end user evaluation are needed to evaluate their potential to adequately educate and support these populations. </sec>
Face Mask Detection with Deep Learning and Computer Vision to Fight COVID-19

Covid-19 is an airborne infectious disease caused by corona virus and was first detected in Wuhan, China. Covid-19 has spread throughout the world and has infected more than eight million people worldwide. According to the scientists globally, wearing a fabric mask and maintaining social distancing would prevent the spread of the virus. It is practically not possible to detect the violations done in public places, so with the help of face mask detection system, it becomes simpler to check if all the guidelines are followed. In most of the institutions, companies, hospitals, shopping malls, the face mask detection system at entry and exit points would be of great help in this pandemic and not just for Covid-19, this system can be used for any air-borne disease to reduce the spread of it. Our project uses OpenCV, Keras, TensorFlow with deep learning and computer vision concepts to detect face mask by giving still images as input and by real time video streaming.
School Information Management System (SIMS/EMIS) in Lebanon through the Lens of K-12 Public School Leaders: A Burden or an Opportunity?

The purpose of this paper was to investigate the importance of the Education Management Information System (EMIS) “known as School Information Management System (SIMS)” for the Lebanese public k-12 schools. It is a quantitative study that surveyed the whole population of the K-12 public schools in Lebanon (N=1237[1]). It aimed to determine if SIMS was considered an efficient tool for school principals to improve the quality of learning in their schools. It addressed the multiple technical, human, financial and managerial aspects of SIMS, the different challenges that Lebanese school principals confront while using SIMS. Additionally, it aimed to investigate the improvements school principals suggested and the alternatives they proposed. The study provides recommendations for the Lebanese Ministry of Education and Higher Education on how to improve this application to develop the education sector in Lebanon.
An Intuitive and Secured Framework for Sharing Health-care Data using Block-chain Techniques

Electronic Medical Records are now widely used by medical organizations as a replacement for physical manual records of the patients. These Electronic Medical Records (EMR) were effectively adopted as a result of the evolution in the field of Information technology supported by more innovative computer science engineering feats, as the EMR systems became more advanced it still had a drawback of being vulnerable to cyber attacks, which will eventually compromise the integrity and confidentiality. Hence the same EMR system is built along with the use of Block-chain technology on a cloud storage platform, this system will be integrated with various features compatible for the interoperability between the patients and medical service providers. The main objective of this project is to leverage maximum cyber protection to the EMR system.
The feature extracting method via partitioning the feature subspace and data fusion

The redundant information contained in feature can be reduced and the accuracy of data analysis is improved via extracting the features from the data set. The existing methods to extract the feature ignoring the information contained in the data vector of feature. In this paper, the similarity between data features is firstly calculated via multiple methods to form the similarity vector of feature. Then the adaptive weighted clustering ensemble is proposed to cluster the similarity vector of feature to partitioning the feature subspaces. Secondly, utilizing the characteristics of the data vector of feature, the weights of the features in the subspace are calculated, and then the effective features are extracted using the linear weighted method. In the experiments, the result shows that the proposed method can significantly improve the accuracy of the data analysis.
An overview of cyber-crimes and its impact on economy

The tremendous growth in the area of communication and digital world led to the potential challenges in the area of cyber law and its implications. Nothing is available without cost and drawbacks. Though it makes the life of people hassle free just on a click of button various things can be done at your ease but it led to other kinds of challenges also. Without technology especially computers everything comes to halt be it business, industries, and government functions and so on. Our economy as well as government various types of businesses are dependent on computers for smooth functioning of their businesses as also the economic progress, so do the criminals are active involving in various kinds of cybercrimes. Therefore, in the current paper a systematic understanding and overview of cybercrimes particularly computer related and their impacts over various areas are discussed.
Research on Improved FP-Tree Algorithm Based on Hadoop

With the development of science and technology, the Internet is more and more widely used in all aspects of society, and the research on the combination of data mining and big data platform has gradually become an urgent demand. This paper studies the association rules algorithm based on FP- Tree algorithm, and proposes the improvement of the algorithm. After combining with Hadoop platform, the algorithm is implemented and tested in big data environment, and the efficiency of the algorithm is improved to some extent.
Practitioner response: Reviewing the programme leadership strategy in an Australian university: Could it be adapted in the Greek context?

This chapter discusses the interesting for academia in Greece, since universities in Greece integrate a coordinated, university-wide, systems-based approach to academic professional learning or organisational development. In addition, the Network initiated expansive ways of thinking and co-facilitation practices that enabled context-specific learning and resulted in building a community of colleagues with collective efficacy. Universities in countries that have not yet articulated a comprehensive leadership strategy could adopt the BPL strategy as it is or some parts of it. In Greece, one of the primary actions to integrate leadership with a purpose across university departments within an institution would be to lead and facilitate the teaching team. The development of a Program Leaders' Network within Greek Universities could also strengthen leadership skills by building a community of trust where successful professional practices, challenges and expertise could be shared. An idea would be for this Network to take place online and be attended by all members of the Institution.
Brightness Contrast Using Convolution Neural Network

Enhancing low light image is considered a challenging task in computer vision. Since the images taken in conditions where there is minimal light, they are generally dark and unclear. This makes the further computer vision tasks complicated. This work aims at resulting in a process to detect those unlighted images and illuminate them to brighter images. To test the hypothesis, convolutional neural networks (CNNs) have been used to reduce/remove noise, image translation, grayscale brightening, contrast stretching, thresholding, edge detection, and convolution methods. This work also uses a module in OpenCV: dnn_superres that allows for upscaling images via convolutional neural networks. We also included popular super-resolution data as part of experimentation process. Different phases of CNN like convolutional layers, batch normalization, nonlinear mapping, reconstruction, activation functions, convolution, encoders, and decoders are included that help to improve the image quality in various phases as per the expectations. The results suggested that a maximum blurriness and noise can be removed, and images can be converted into more illuminated images. The work uses deep learning and computer vision algorithms for transforming dark and blurred images into illuminated images.
Practicing pedagogical skills according to Ho Chi Minh's style

On the basis of presenting the concept of Ho Chi Minh style, summarize Ho Chi Minh's style in the following aspects: thinking, working, leading, expressing, behaving, and living, generalizing legal issues basic theories of pedagogical training such as the concept of pedagogical training, the pedagogical training process and the classification of some pedagogical skills, the article proposes measures to train pedagogical skills according to the Ho Chi Minh style in teacher training in pedagogical schools today. The main content of the article presents three methods of training pedagogical skills, from generalization such as pedagogical training under the motto “Learning goes hand in hand with practice, theory with reality” to specific measures such as practicing speaking and writing skills, and practicing behavioral skills in pedagogical situations. To clarify the proposed measures, the article analyzes some examples.
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review : Single Anonymous • Conference submission management system : Easychair • Number of submissions received : 135 • Number of submissions sent for review : 135 • Number of submissions accepted : 99 • Acceptance Rate (Submissions Accepted / Submissions Received × 100) : 73.3 • Average number of reviews per paper : 6.75 • Total number of reviewers involved : 20 • Contact person for queries : Name : Chatarina Muryani Email : chatarinamuryani@staff.uns.ac.id Affiliation : Universitas Sebelas Maret
Mini-TKAGCN: a lightweight graph convolutional network via temporal kernel attention for skeleton-based action recognition

Skeleton-based human action recognition has achieved a great interest in recent years. The strong robustness of skeleton data to scene and camera interference makes the recognition algorithm follow with interest robust features of actions. Recent works has proved the effectiveness of skeleton modeling based on graph and learning spatio-temporal modes by Graph Convolutional Network (GCN). Although GCNs have excellent ability of neighborhood feature learning, it is not good at capturing long-distance dependence between joints. In particular, linear temporal skeleton sequences contain a great quantity of joints., which makes the process of learning advanced temporal cues unduly slow. In this paper, we propose a temporal feature enhancer using Temporal Kernel Attention (TKA). And guided by TKA, we design a performance-oriented network TKA-GCN and a lightweight network Mini-TKA-GCN for skeleton-based action recognition. Finally, on NTU-RGBD 60 and Kinetics-Skeleton 400 datasets, TKA-GCN and Mini-TKA-GCN proposed by this work, outperform most advanced works.
A Classification of Anomaly Explanation Methods

The usage of algorithms in real-world situations is strongly desired. But, in order to achieve that, final users need to be reassured that they can trust the outputs of algorithms. Building this trust requires algorithms not only to produce accurate results, but also to explain why they got those results. From this last problematic a new field has emerged: eXplainable Artificial Intelligence (XAI). Deep learning has greatly benefited from that field, especially for classification tasks. The considerable amount of works and surveys devoted to deep explanation methods can attest that. Other machine learning tasks, like anomaly detection, have received less attention when it comes to explaining the algorithms outputs. In this paper, we focus on anomaly explanation. Our contribution is a categorization of anomaly explanation methods and an analysis of the different forms anomaly explanations may take.
THE IMPLEMENTATION OF THE MIND MAPPING METHOD TO INCREASE STUDENTS' WRITING SKILLS AT THE TWELFTH GRADE OF SMA NEGERI 4 PEMATANGSIANTAR IN THE 2022 ACADEMIC YEAR

Writing is the most difficult skill because we know that by writing means we must be required to be able to compose language properly and correctly. To be able to write well, we must be able to think creatively and innovatively, to be able to produce a language that is worthy of writing. Writing is a skill that must be mastered by students because it has been explained earlier that writing is a difficult skill, so students want to be led to master it. In this study, researchers found that students of SMA Negeri 4 Pematangsiantar, especially grade 12 science, had problems writing an incident or explaining something by writing a description of the incident. This happened because they did not have detailed ideas when they were asked to explain the picture or the incident.&#x0D; They also feel lazy and bored when writing something, even if it's just a descriptive text. Many students lack the intention and motivation to write, and that's why most of them get bad grades in learning to write. They do not know what they should do in writing, choosing topics and problems and the order in paragraphs that they do not understand. And that causes the achievement obtained by students to below because they don't get much achievement. Because their vocabulary is very minimal and they also don't have ideas, innovation, and creativity in thinking so they are not able to write.&#x0D; The research was to find out whether there was effectiveness in the mind mapping method, especially for grade 12 students at SMA Negeri 4 Pematangsiantar, and also the researchers wanted to see if students were able to improve their writing using the mind mapping method. Based on the data generated from students' research in improving their writing skills through the mind mapping method, it can be concluded that the average of the pre-test and post-test obtained in the first cycle is 32.31 and 58.28 and a significant difference is found in the second cycle, which is 49.90 and 71.09. And it can be seen that the value after using the mind mapping method increased in cycle 2.
A Road Map of Optimization Problem Solving Techniques, Nomenclature and Algorithms

Optimization problems are different from other mathematical problems in that they are able to discover solutions which are ideal or near ideal in accordance to the goals. Problems are not solved in one step, but we follow different sequence of steps to reach the solution. The steps could be to define problems, construct and solve models and evaluate and implement solutions. This paper presents an overall outlook of how a problem of optimization type can be solved.
Artificial Intelligence Methods for Detecting Water Pollution

Abstract In the modern world, industrial development often negatively affects the environment, including the state of water bodies. Pollution of various types, from thermal to chemical (oil spills, industrial waste dumping and thermometric disturbances), have a detrimental effect on flora and fauna. Continuous monitoring of water areas allows timely detection of pollution. One of the tasks of analyzing the state of water resources is monitoring the water surface and monitoring the coastal zone. The aim of the study is to compare classical approaches based on the application of spectral characteristics and machine learning methods to the analysis of the state of water bodies. The studies show the disadvantages of classical methods of remote sensing in solving problems of autonomous monitoring, consisting in poor resistance to noise and the need for constant expert assessment. The paper presents solutions to the problem of detecting pollution of water bodies using machine learning methods.
University intelligent teaching evaluation management system based on 5G environment

As the Internet goes deeper and deeper into colleges and universities, the digital construction of the new era of colleges and universities is particularly important, especially the graduation design of college students to test the quality of learning is the most important. Therefore, under the catalysis of information technology, most schools have given up the traditional way of teaching evaluation, put the teaching quality evaluation on the platform, and use the intelligent classroom teaching evaluation management system. Intelligent classroom teaching evaluation management system is a management software developed for the school's teaching administrators, students and teachers to evaluate and manage teaching quality. Administrators act as educational administrators in reality, can achieve student management, teacher management, curriculum management, evaluation statistics summary and analysis and other functions. Students can evaluate the teachers in their own courses. Teachers can evaluate the peers through lectures, can also view the current course under which students haven't evaluation, have the effect of supervise the students, as well as the evaluation results, the summary of the peers in the students and teachers of every evaluation index score, and feedback, teachers get the feedback information, also can better improve their own teaching plan. Teaching supervision can check the teaching quality evaluation of the whole school, check the specific evaluation of each person on the course, and effectively supervise the teaching quality of teachers.
An Evaluation of Data Consistency Models in Geo-Replicated Cloud Storage

Cloud computing has become a major and most preferred technology for data storage across the world. This technology tremendously improved service delivery to clients as data and services are brought closer to the client. Bringing data and services closer to the client requires the implementation of a distribution architecture, that is, an architecture that spans across a wider geographical area. However, such architecture comes with its own challenges. It requires proper management of data and services across multiple data centres to maintain consistency, availability of data and services, and systems that are tolerant to failures.. In this study we focus on evaluating currently used methodologies to ensure data consistency performance levels for cloud storage and determine whether the proposed solutions are effective and feasible. From the surveyed data consistency models, we noted that it’s difficult to find one model that satisfies the three main challenges associated with data consistency across multiple geo-replicated data centres.
OntoHDClass: Ontology Driven Approach for High Dimensional Data Classification Integrating Semantic Measures and Recurrent Neural Network

Clustering the high-dimensional data has remained a burdensome job. Existing clustering algorithms developed are significantly inept. Frequently, in high dimensional data many noises and disturbances are formed and data is masked in existing clusters. Subsequently, we have presented an Ontology Driven Approach for High Dimensional Data Classification integrating Semantic Measures and Recurrent Neural Network to solve the above problem in this current Paper. The first phase OntoHDClass identifies the problem domain and formulate domain ontologies, accomplishes subspace relevance analysis by computing concept similarities. The second phase, after detection of dense regions elimination of outliers takes place and inliers are extracted. The extracted inliers then yield elite group by Cross entropy and Pearson’s correlation. The third phase involves classification of the space using Recurrent Neural Network. The Result are found to be high that the OntoHDClass system reaches an overall Precision, Accuracy, and False Negative Rate of 97.32%, 96.37%, and 0.05% respectively. Elimination of outliers advances the subspace clustering with more accuracy and the elite subspace that are produced is indicated by our result. OntoHDClass can learn the clusters in very subspaces with Lower fault rate besides higher accuracy.
Intelligent Library Management System Based on Augmented Reality and RFID Technology

In the scenario of large library, the current information service system can only provide regional information in the aspect of book location, but it can not support the navigation for users. In recent years, Augmented Reality (AR) technology has become more and more active in people’s vision, providing new ideas for the development of indoor navigation. This paper designs and implements an AR based intelligent library system, which not only has the basic functions of library information service, but also can provide AR navigation, so that users have a better navigation experience. The prototype system uses AR navigation into to provide integrated information services for users. In addition, considering that the position of books often changes in the library, this paper implements the method of book location based on neural network and Radio Frequency Identification (RFID) technology to track the position of books.
Administrasi Layanan Khusus

This article is written with the aim of conveying informations to the reader, so that it can be used as a reference to increase readers knowledge , and can have an academic impact on the author. This article is expected to be able to hone the ability of writings. This article was written use literature review techniques. This article contains informations about special service administration. Special service administrasion is needed so that the implementation of education can run smoothly and can complement efforts to achieve educational goals in schools.