Reviewerly/exHarmony huggingface dataset:
Influence of Vocalized Reading Practice on English Learning and Psychological Problems of Middle School Students

The purpose of this study is to improve the English learning anxiety and learning effect for middle school students. From the perspective of educational psychology, the influence of vocalized reading practice on the English learning of students is studied based on the self-efficacy theory and the schema theory. To encourage the students to practice English, the study might solve the problem of insufficient opportunities by applying the artificial intelligence (AI) chat system to the oral English practice of the students. Several research hypotheses are put forward, which concern the correlation between the English learning anxiety of the students with their self-efficacy, topic familiarity, and English grades under vocalized reading practice. Then, the hypotheses are verified through a controlled trial and a questionnaire survey (QS). Afterward, the experimental and QS data are statistically analyzed and tested with a regression model. The results show that the English grades, self-efficacy, and topic familiarity of the students have been significantly improved in the experimental group after the vocalized reading practice. The significance coefficient of the regression model is P = 0.000 < 0.05, which can be used to verify the proposed hypotheses. The English grades, self-efficacy, and topic familiarity can well-predict the English learning anxiety of the students. The computer simulation in educational communication (CSIEC) teaching system and AI can help create an interactive learning environment for the students to practice oral English by chatting with AI robots.
DETERMINAN PERILAKU MEROKOK PADA SISWA SMP SWASTA IMMANUEL KABANJAHE TAHUN 2020

SMOKING BEHAVIOR IN IMMANUEL KABANJAHE PRIVATE JUNIOR HIGH SCHOOL STUDENTS IN 2020&#x0D; There are many factors that background teenagers into smokers, including intrinsic factors that include gender factors, personality factors, occupational factors and confidence factors. Extrinsic factors include the influence of family and the environment, peer influence, climate influence, cigarette advertising, ease of obtaining cigarettes, absence of regulation, and attitude of health officials. This study aims to find out the factors that influence smoking behavior in private middle school students immanuel kabanjahe in 2020. The type of research used is qualitative research with phenomenological design. The type of research used is qualitative research with phenomenological design. Data collection in the form of in-depth interviews. Determination of informants using purposive sampling method and obtained informant as many as 5 people. The results of this study shows the factors influenced smoking behavior in Private Junior High School student Immanuel Kabanjahe are individual factors, family factors (parenting patterns), family history. Based on the results of the study it is recommended for students to form a community of anti-smoking peers in order to promote about smoking behavior and its dangers to health. For parents to pay more attention to their children. For schools to conduct UKS training activities to BK teachers related to cigarettes and their dangers to health by involving health centers or health services.
Towards Secure, Decentralised, and Privacy Friendly Forensic Analysis of Vehicular Data

The automotive industry has been transformed through technological progress during the past decade. Vehicles are equipped with multiple computing devices that offer safety, driving assistance, or multimedia services. Despite these advancements, when an incident occurs, such as a car crash, the involved parties often do not take advantage of the technological capabilities of modern vehicles and attempt to assign liability for the incident to a specific vehicle based upon witness statements. In this paper, we propose a secure, decentralized, blockchain-based platform that can be employed to store encrypted position and velocity values for vehicles in a smart city environment. Such data can be decrypted when the need arises, either through the vehicle driver's consent or through the consensus of different authorities. The proposed platform also offers an automated way to resolve disputes between involved parties. A simulation has been conducted upon a mobility traffic dataset for a typical day in the city of Cologne to assess the applicability of the proposed methodology to real-world scenarios and the infrastructure requirements that such an application would have.
Closed- and Open-world Reasoning in DL-Lite for Cloud Infrastructure Security

Infrastructure in the cloud is deployed through configuration files, which specify the resources to be created, their settings, and their connectivity. We aim to model infrastructure before deployment and reason about it so that potential vulnerabilities can be discovered and security best practices enforced. Description logics are a good match for such modeling efforts and allow for a succinct and natural description of cloud infrastructure. Their open-world assumption allows capturing the distributed nature of the cloud, where a newly deployed infrastructure could connect to pre-existing resources not necessarily owned by the same user. However, parts of the infrastructure that are fully known need closed-world reasoning, calling for the usage of expressive formalisms, which increase the computational complexity of reasoning. Here, we suggest an extension of DL-LiteF that is tailored for capturing such cloud infrastructure. Our logic allows combining a core part that is completely defined (closed-world) and interacts with a partially known environment (open-world). We show that this extension preserves the first-order rewritability of DL-LiteF for knowledge-base satisfiability and conjunctive query answering. Security properties combine universal and existential reasoning about infrastructure. Thus, we also consider the problem of conjunctive query satisfiability and show that it can be solved in logarithmic space in data complexity.
EKSPLORASI PERBEDAAN DAN INTERAKSI IMMERSION DAN FLOW DALAM VIRTUAL RUN EVENT [EXPLORATION OF THE DIFFERENCES AND INTERACTIONS BETWEEN IMMERSION AND FLOW IN VIRTUAL EVENT RUNS]

&lt;p&gt;This study aims to explore the differences between flow and immersion experiences and find the relationship between individual flow and immersion experiences in virtual run events. This research is qualitative research that uses narrative methods towards individuals by using interview for collecting information. The target of this study is the virtual run event participants from the period May–June 2021. This study involves informants who are sought through social media and personal relationships of the researchers. The search for informants was assisted by snowball sampling to be able to find informants who met the criteria. The results of this study found differences between flow and immersion and also the interaction between flow and immersion.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;BAHASA INDONESIA ABSTRAK&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Penelitian ini bertujuan untuk mengeksplorasi perbedaan pengalaman &lt;em&gt;flow&lt;/em&gt; dan &lt;em&gt;immersion&lt;/em&gt; dan juga mencari keterkaitan antara pengalaman &lt;em&gt;flow&lt;/em&gt; dan &lt;em&gt;immersion&lt;/em&gt; individu dalam &lt;em&gt;virtual run event&lt;/em&gt;. Penelitian ini merupakan penelitian kualitatif yang menggunakan metode riset naratif terhadap individunya dengan menggunakan teknik wawancara dalam menggali informasinya. Target dari studi ini adalah peserta &lt;em&gt;virtual run event&lt;/em&gt; dari periode Mei–Juni 2021. Studi ini melibatkan informan yang dicari melalui media sosial dan relasi pribadi dari peneliti. Pencarian informan dibantu dengan &lt;em&gt;snowball sampling&lt;/em&gt; untuk dapat menemukan informan yang memenuhi kriteria. Dari hasil studi ini ditemukan perbedaan antara &lt;em&gt;flow&lt;/em&gt; dan &lt;em&gt;immersion&lt;/em&gt; dan juga interaksi antara &lt;em&gt;flow&lt;/em&gt; dan &lt;em&gt;immersion&lt;/em&gt;.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;br /&gt;&lt;/strong&gt;&lt;/p&gt;
Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings

Dominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models.However, there is little attempt to combine these two types of models, which inituitively possess complementary advantages.In this paper, we propose a novel sentence ordering framework which introduces two classifiers to make better use of pairwise orderings for graph-based sentence ordering (Yin et al., 2019(Yin et al., , 2021)).Specially, given an initial sentence-entity graph, we first introduce a graph-based classifier to predict pairwise orderings between linked sentences.Then, in an iterative manner, based on the graph updated by previously predicted highconfident pairwise orderings, another classifier is used to predict the remaining uncertain pairwise orderings.At last, we adapt a GRN-based sentence ordering model (Yin et al., 2019(Yin et al., , 2021) ) on the basis of final graph.Experiments on five commonly-used datasets demonstrate the effectiveness and generality of our model.Particularly, when equipped with BERT (Devlin et al., 2019) and FHDecoder (Yin et al., 2020), our model achieves state-of-the-art performance.Our code is available at https:// github.com/DeepLearnXMU/IRSEG.
A HYBRID MODEL USING THE PRETRAINED BERT AND DEEP NEURAL NETWORKS WITH RICH FEATURE FOR EXTRACTIVE TEXT SUMMARIZATION

Deep neural networks have been applied successfully to extractive text summarization tasks with the accompany of large training datasets. However, when the training dataset is not large enough, these models reveal certain limitations that affect the quality of the system’s summary. In this paper, we propose an extractive summarization system basing on a Convolutional Neural Network and a Fully Connected network for sentence selection. The pretrained BERT multilingual model is used to generate embeddings vectors from the input text. These vectors are combined with TF-IDF values to produce the input of the text summarization system. Redundant sentences from the output summary are eliminated by the Maximal Marginal Relevance method. Our system is evaluated with both English and Vietnamese languages using CNN and Baomoi datasets, respectively. Experimental results show that our system achieves better results comparing to existing works using the same dataset. It confirms that our approach can be effectively applied to summarize both English and Vietnamese languages.
Applied convolutional neural network framework for tagging healthcare systems in crowd protest environment

Healthcare systems constitute a significant portion of smart cities infrastructure. The aim of smart healthcare is two folds. The internal healthcare system has a sole focus on monitoring vital parameters of patients. The external systems provide proactive health care measures by the surveillance mechanism. This system utilizes the surveillance mechanism giving impetus to healthcare tagging requirements on the general public. The work exclusively deals with the mass gatherings and crowded places scenarios. Crowd gatherings and public places management is a vital challenge in any smart city environment. Protests and dissent are commonly observed crowd behavior. This behavior has the inherent capacity to transform into violent behavior. The paper explores a novel and deep learning-based method to provide an Internet of Things (IoT) environment-based decision support system for tagging healthcare systems for the people who are injured in crowd protests and violence. The proposed system is intelligent enough to classify protests into normal, medium and severe protest categories. The level of the protests is directly tagged to the nearest healthcare systems and generates the need for specialist healthcare professionals. The proposed system is an optimized solution for the people who are either participating in protests or stranded in such a protest environment. The proposed solution allows complete tagging of specialist healthcare professionals for all types of emergency response in specialized crowd gatherings. Experimental results are encouraging and have shown the proposed system has a fairly promising accuracy of more than eight one percent in classifying protest attributes and more than ninety percent accuracy for differentiating protests and violent actions. The numerical results are motivating enough for and it can be extended beyond proof of the concept into real time external surveillance and healthcare tagging.
About Artistry in Art

The phenomenon of artistry is investigated as a special expressive quality that manifests itself in different types and genres of art. The author comprehends the variations of the artistic principle as the most important ontological component of art, generating “meaning over meaning”, bringing a new dimension to the artistic content. The subject of the analysis is a variety of aesthetic profiles of artistry: from the dominance of virtuosity, playfulness, maestria, improvisation to the ability to paradoxically remove the antinomic tension of the elements of the art form. The value of the momentary artistic experience is studied simultaneously with the discovery of the facets of the inner, hidden artistry of the work. The peculiarities of the manifestation of artistry are investigated not only in ways of artistic expressiveness, but also in special forms of life creation.
Supporting Enrollment in Higher Education Through a Visual Recommendation System

Nowadays, most universities offer programmes and subjects online, specially in the case of fully online open/distance universities. Students have a higher degree of flexibility, which allows them to choose among an endless list of subjects for advancing within their degree. Although this can be seen as a positive result of enrollment flexibility policies, it may be also the source of one of the most well-known problems in open/distance education: high dropout rates, partly caused by inadequate enrollment. In this paper we propose a recommendation system that helps students to navigate through the list of available subjects using a visual metaphor, taking into account students’ preferences and previous enrollment data. Our system is based on a two-dimensional map (2D) where subjects that can be taken together appear close to each other, as neighboring regions.
Semi-Stable Semantics for Abstract Dialectical Frameworks

Abstract dialectical frameworks (ADFs) have been introduced as a formalism for modeling and evaluating argumentation allowing general logical satisfaction conditions. Different criteria that have been used to settle the acceptance of arguments are called semantics. However, the notion of semi-stable semantics as studied for abstract argumentation frameworks has received little attention for ADFs. In the current work, we present the concepts of semi-two-valued models and semi-stable models for ADFs. We show that these two notions satisfy a set of plausible properties required for semi-stable semantics of ADFs. Moreover, we show that semi-two-valued and semi-stable semantics of ADFs form a proper generalization of the semi-stable semantics of AFs, just like two-valued model and stable semantics for ADFs are generalizations of stable semantics for AFs.
Linguistic Archives and Language Communities Questionnaire

Digital language archives hold vast amounts of materials in endangered or marginalised languages. However, due to limitations in technical infrastructure and the design of these archives, the materials are usually not easily accessible to speakers of the languages represented or their descendants. With the goal to establish best practices for researchers archiving linguistic data, this paper presents a questionnaire designed to assess how archival materials can be made more readily available to language communities.
A Survey on Sports Video Annotation Frameworks

Video annotation technique delivers many additional video processing capabilities for several applications. Sports broadcast video content is unique in regard to wealth of information as compared to any other video. Sports video annotation is becoming popular among researchers in recent times because of wide range of applications and challenges it pose. The demand for optimized design of framework for sports video annotation is at peak. This paper surveys state-of-the-art in annotation framework design, particularly for sports applications and provides insight into future aspects. This survey may help researchers to further conceive and develop advanced universal frameworks applied to all sports.
Publisher Correction to: A Telecommunication Model to Teach Facilitators to Deliver Acceptance and Commitment Training

[This corrects the article DOI: 10.1007/s40617-021-00628-x.].
Research on Behavior Monitoring of Elderly Living Alone Based on Wearable Devices and Sensing Technology

Human behavior recognition and status monitoring are current research hotspots, especially in the fields of medical monitoring, smart home, and elderly care. With the development of sensor technology, low-power IC chips, and wireless body sensors, miniature sensor networks can be popularized and applied in daily life. Since the energy consumption of sensor networks is a bottleneck problem that limits its development, this paper designs a multimodal collaborative sensing method for the application scenarios of elderly people living alone to reduce energy consumption in the process of daily behavior perception of the elderly. This method subdivides behavior perception into behavior recognition and status monitoring, determines the optimal sensor combination for identifying monitoring different behaviors based on information theory, and then uses a behavior recognition model modeled by a multiclassifier and a status model modeled by a plurality of two classifiers that are used to perceive user behavior. A large number of experimental results show that compared with the traditional sensor network method, our proposed solution can achieve effective sensing while reducing the energy consumption in the process of data transmission and model calculation, thereby prolonging the working life of the sensing network and realizing long-term and reliable daily behavior perception.
Perkembangan Bahasa Fonetik dan Sintaksis Anak Usia Dini (Usia 3-4 Tahun)

This study aims to analyze the language development of children at an early age of 3-4 years, by describing the development of language in the phonetic and syntactic fields of children aged 3-4 years. This research uses descriptive qualitative research method. The sample of this study were two children aged 3-4 years in KB Bustanul Janah Sambong Batang. From the two samples, it is known that the child's aspects of phonetic and syntactic development experience different developments starting from the ability to receive language, listen to other people's words, express language, enjoy listening and retell simple stories in sequence. In the aspect of phonetic language, children have developmental differences in understanding the sounds of language or how a language sound is produced by the human speech apparatus. In the aspect of syntactic language, children also have developmental differences regarding speaking and telling stories. Researchers concluded that early childhood in the age range of 3-4 years experienced differences in the development of phonetic and syntactic language. So the researchers recommend teachers to understand these differences and stimulate students according to age levels so that their development continues to increase.
Upaya Meningkatkan Aktivitas Dan Hasil Belajar Bahasa Indonesia Materi Masalah, Fakta Dan Opini Sebuah Artikel Menggunakan Model Cooperative Learning Tipe STAD

This study aims to improve activities and learning outcomes through the application of the STAD type Cooperative Learning model. This study uses classroom action research which has stages of planning, implementation, observation, and reflection, carried out in two cycles, each cycle carried out in two meetings. The research subjects were 36 students. The data needed was obtained through observation, documentation and tests. The validity of the data is done by triangulation technique. The data collected were analyzed using qualitative and quantitative analysis techniques. The results of the study increased learning activities in the initial conditions by 50% or 18 students, the first cycle was 80.56% or 29 students, increased to 94.44% or 34 students, while the student learning outcomes in the initial conditions were 63.06, the first cycle is 74.17, cycle II is 87.50 with learning completeness from 13 students or 36.11% to 58.33%, or 21 students and 88.89% or 32 students in the last cycle.
Empirical Study of the Relationship between Financial Development and Economic Growth Based on Intelligent Algorithms Based on Wireless Network Communication

In order to discuss a series of issues surrounding financial development and economic growth, it makes it more meaningful to discuss regional financial development and economic growth. This article aims to study the empirical study of the relationship between financial development and economic growth based on intelligent algorithms based on wireless network communication. In this article, a two-side positioning model is established, and the optimal solution is found for the feasible region through the particle swarm optimization algorithm. After two anchor nodes are realized, the position of the unknown node can be calculated. In addition, this article uses the Shadowing model to calculate the distance between wireless network nodes. This model is composed of path loss and occlusion factors, which can fully consider the impact of environmental factors on the signal. Experimental research data show that the improved algorithm has a positioning coverage close to 100% when the anchor node density is 30%. The improved algorithm in this article speeds up the convergence speed than positioning algorithm 1 and reduces the time by about 35%. Although the running time of the algorithm in this article is longer than that of positioning algorithm 2, the overall positioning accuracy is higher than that of the positioning algorithm, which is about 20% higher.
Konseptualisasi Peran Strategis pada Pendidikan Literasi Keuangan Syariah Anak Melalui Pendekatan Systematic Review di TK RA Al-Mu’min Gunung Putri

Children have good potential so that they can develop through interaction activities with the surrounding environment. Children can also determine Islamic financial products and services according to their needs, understand the benefits and risks, understand their rights and obligations, and believe that the selected service products can improve children's welfare in accordance with Halal principles and are profitable. Sharia law. Islamic financial literacy, meaning knowledge, skills and beliefs that can influence and improve the quality of financial management decisions to achieve prosperity. with faith, morality, and sharia. This study aims to comprehensively analyze the appropriate role of the relevant parties. The research method conceptually explores the strategic role of various related parties in the development of Islamic financial literacy education among early ages. Sources of information varied with interviews and utilized additional information as logical examination articles, books and applicable reports. The examination is calculated by the systematic review method. Children need Islamic financial literacy education because they need support and direction from each of the four conceptual strategic roles in order to achieve Islamic financial literacy from an early age.