Reviewerly/exHarmony huggingface dataset:
PARTISIPASI CITIZEN JOURNALIST DALAM MENULIS BERITA DI KOMPASIANA.COM

Technology increasingly developing in addition to making it easier for humans to live their lives also encourages changes in society. So far, people who have only been media consumers can now produce messages or act as journalists. Usually they are referred to as citizen journalists or citizen journalists. This study discusses Citizen Journalist Participation in News Writing at Kompasiana.com. The purpose of this study is to understand the participation of citizen journalists in writing news on Kompasiana.com. This study uses a qualitative approach and the type of descriptive research. The method used is a case study. The results of this study indicate that the participation of citizen journalists in writing news at Kompasiana.com is driven by their desire to voice their thoughts and continue to train their writing skills as an effort to self-actualize. The factors behind their participation in writing news on Kompasiana.com are their passion or interest in writing, experience factors and other supporting factors that cause people to participate, namely the existence of k-werds or gifts for writers in Kompasiana.com. The implications that are felt when involved in citizen journalism are wider knowledge and sharper and more critical analytical blades in looking at an issue, as well as being able to improve people's literacy and writing skills.
Task-Specific Dependency-based Word Embedding Methods

Two task-specific dependency-based word embedding methods are proposed for text classification in this work. In contrast with universal word embedding methods that work for generic tasks, we design task-specific word embedding methods to offer better performance in a specific task. Our methods follow the PPMI matrix factorization framework and derive word contexts from the dependency parse tree. The first one, called the dependency-based word embedding (DWE), chooses keywords and neighbor words of a target word in the dependency parse tree as contexts to build the word-context matrix. The second method, named class-enhanced dependency-based word embedding (CEDWE), learns from word-context as well as word-class co-occurrence statistics. DWE and CEDWE are evaluated on popular text classification datasets to demonstrate their effectiveness. It is shown by experimental results they outperform several state-of-the-art word embedding methods.
ANDROID-BASED LEARNING MEDIA ON STUDENTS' LEARNING OUTCOMES ON THERMOCYMIA MATERIALS

The purpose of this study was to determine: (1) the effectiveness of Android-based learning media on student learning outcomes in thermochemical material; (2) Student responses to the use of Android-based learning media on thermochemical material; (3) The learning outcomes of students who are taught using Android-based chemistry learning media are the same as the KKM value. This research was conducted at Public High School 5 Medan with a total sample of 68 people. The research instrument consisted of standardized tests and questionnaires. The analytical techniques used are: (1) Two-party t test (Independent Samples T test); and (2) One Sample Test. From the results of this study, it was found that: (1) Anroid-based chemistry learning media was effective on student learning outcomes on thermochemical material, with a t-test of 7.063 which was greater than t-table of 2.00. Then based on the Gain Score, the t-test of 6.843 was obtained, which was greater than the t-table of 2.00; (2) Student responses "Very Interesting" with percentages of 92.27%, 93.93% and 92.12%, respectively, on the use of anroid-based learning media in thermochemical material; (3) The learning outcomes of students who are taught using android-based chemistry learning media are not the same as the KKM value, where , tcount (2.126) &gt; ttable (2.032), means that it has a positive effect on KKM
The Effects of Mobile-assisted Focus on Form Instruction through Listening Activity on the Development of Grammar Knowledge and Listening Skills

This study aims to investigate the effects of focus on form instruction in a mobile learning environment on the development of grammar knowledge and listening skills and students’ perception. For this study, 104 Korean university students participated, and they were divided into three groups: the control group and two experimental groups. Two experimental groups were though in the mobile learning environment, but only the experimental group had focus on form instruction. 2 Data were collected through grammar pre- and post-tests, listening pre- and post-tests, and questionnaires. The major findings were as followings. First, experimental group 2 showed higher test scores than experimental group 1 and the control group on a post-grammar test. Second, two experimental groups were more effective than the control group on listening tests. Finally, learners in experimental group 2 had positive perspectives on mobile-assisted focus on form instruction giving a positive opinion about mobile learning and input enhancement in the video. Suggestions and implications were provided for mobile-assisted focus on form instruction.
Granular Learning with Deep Generative Models using Highly Contaminated Data

An approach to utilize recent advances in deep generative models for anomaly detection in a granular (continuous) sense on a real-world image dataset with quality issues is detailed using recent normalizing flow models, with implications in many other applications/domains/data types. The approach is completely unsupervised (no annotations available) but qualitatively shown to provide accurate semantic labeling for images via heatmaps of the scaled log-likelihood overlaid on the images. When sorted based on the median values per image, clear trends in quality are observed. Furthermore, downstream classification is shown to be possible and effective via a weakly supervised approach using the log-likelihood output from a normalizing flow model as a training signal for a feature-extracting convolutional neural network. The pre-linear dense layer outputs on the CNN are shown to disentangle high level representations and efficiently cluster various quality issues. Thus, an entirely non-annotated (fully unsupervised) approach is shown possible for accurate estimation and classification of quality issues..
Speech Production on Special Needs Children (Down Syndrome): a Psycholinguistic Study at SLB Mekar Sari 1 Cibinong

Children with mental retardation are those who experience disturbances in cognitive aspects. This will affect the process of acquisition and production of speech. This study aims to describe the forms of words and sentences that can be uttered by mentally retarded children, especially Down Syndrome, and the influence of the family environment on the development of their speech production. This research uses descriptive qualitative method. The focus of this research is how Down Syndrome children produce words and sentences. In addition, this study also looks at family environmental factor that can affect the process of producing words and sentences. This research was conducted on children with Down Syndrome aged 8 years at SLB Mekar Sari 1 Cibinong. Data collection was carried out through observation, interviews and literature studies. Then, the data obtained are subjected to a reduction, presentation, and verification process. This study produced several findings; First, children with Down Syndrome at the grade 1 level (8 years) have not been able to produce many complete words. Second, they still need stimulation so that they are able to say simple words or sentences with the SPO (Subject, Predicate and Object) pattern.
PROFESSIONAL DEVELOPMENT OF PRIMARY SCHOOL TEACHERS IN THE SYSTEM OF METHODICAL WORK OF INSTITUTIONS OF GENERAL SECONDARY EDUCATION

The article theoretically substantiates the foundations of the professional development of primary school teachers in the system of methodical work of institutions of general secondary education, in particular, the definition of the concept «professional development of primary school teachers in the system of methodical work of institutions of general secondary education» is specified, which we understand as a managed dynamic process, which is inherently aimed at achieving a high level of professional development and develops according to scientific principles and approaches in specific pedagogical conditions, with the use of developed and adapted learning models in accordance with the requirements of the digital society, the concept of the New Ukrainian School, updating the system of methodical work and crisis conditions. Recommendations are given for the purpose of positive dynamics of professional development of primary school teachers in the methodical work system of general secondary education institutions.
A Novel Ensemble Model to Summarize Kannada Texts

Automatic text summarization is the task of producing a smaller piece of text containing important sentences and all relevant important information from the original document. With the existence of abundant digital data, this technique helps in gaining quick access to the required data in native languages. In this work, we present a technique for an efficient extractive summarization of Kannada documents and articles. In the proposed novel ensemble model, each of the sentences in the input text are assigned a ‘Weighted Terms value’. This is computed by leveraging the concepts of term frequency—inverse document frequency (TF–IDF) algorithm, Galavotti Sebastiani Simi (GSS) coefficients and positional ranking of sentences. An additional mathematical function is also devised to compute weights for sentences as a whole, based on their positions in the text. The final summary is curated by coherently picking the sentences whose ‘weighted terms value’ exceeds the threshold which is set based on the size of the required summary.
Expanding Access to Open Environmental Data: Advancements and Next Steps

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Corresponding author: Denis S. Willett, denis_willett@ncsu.edu
A Study on Basic Human Activity Recognition Based on Sensor Jacket for Interaction in Metaverse Environment

Metaverse is attracting attention as activities in various fields online have increased recently. On the metaverse platform, users express their feelings and communicate with other users using gestures, which are nonverbal communication, as well as voice and text messages. In this study, we constructed a basic dataset by utilizing the behavior used in the metaverse platform. Previously, motion capture systems were used to obtain accurate motion data. However, there is a problem that accessibility and convenience are poor due to the complexity of expensive equipment and use. To compensate for this problem, we have created a sensor jacket for wearing and fast motion recognition. Therefore, to verify recognition accuracy for sensor jacket use in metaverse environments, motion data from motion capture systems and sensor jacket systems are utilized by extreme learning machine algorithms and motion-sphere to compare and analyze recognition and matching rates. In addition, an easy-to-use motion recognition sensor jacket is proposed to communicate smoothly in a metaverse environment.
A Validated Privacy-Utility Preserving Recommendation System with Local Differential Privacy

This paper proposes a new recommendation system preserving both privacy and utility. It relies on the local differential privacy (LDP) for the browsing user to transmit his noisy preference profile, as perturbed Bloom filters, to the service provider. The originality of the approach is multifold. First, as far as we know, the approach is the first one including at the user side two perturbation rounds - PRR (Permanent Randomized Response) and IRR (Instantaneous Randomized Response) - over a complete user profile. Second, a full validation experimentation chain is set up, with a machine learning decoding algorithm based on neural network or XGBoost for decoding the perturbed Bloom filters and the clustering Kmeans tool for clustering users. Third, extensive experiments show that our method achieves good utility-privacy trade-off, i.e. a 90$\%$ clustering success rate, resp. 80.3$\%$ for a value of LDP $\epsilon = 0.8$, resp. $\epsilon = 2$. Fourth, an experimental and theoretical analysis gives concrete results on the resistance of our approach to the plausible deniability and resistance against averaging attacks.
use of flashcard media in English learning topic knowing numbers in class 1c students at SDITA El Mamur school

Background: English subjects at the SD/MI level emphasize the introduction of vocabulary as a foundation or basis in learning English. English ability can be seen from the wealth of vocabulary that a person has. One of the creative efforts in making learning interesting is using media flash cards.&#x0D; Purpose: This study aims to determine the application of flashcards media about numbers in English in grade 1C at SDITA EL MAMUR.&#x0D; Design and methods: The researcher collected data using a qualitative approach, the type of research was descriptive narrative, and the data collection technique was interviews with informants, namely the homeroom teacher of class 1C. at SDITA EL MAMUR.&#x0D; Results: The results of this study indicate that the use of flashcard media can assist students in pronouncing vocabulary numbers in English because the use of flashcard media is easy to understand and attractive to students.
LIMEADE: From AI Explanations to Advice Taking

Research in human-centered AI has shown the benefits of systems that can explain their predictions. Methods that allow an AI to take advice from humans in response to explanations are similarly useful. While both capabilities are well-developed for transparent learning models (e.g., linear models and GA$^2$Ms), and recent techniques (e.g., LIME and SHAP) can generate explanations for opaque models, little attention has been given to advice methods for opaque models. This paper introduces LIMEADE, the first general framework that translates both positive and negative advice (expressed using high-level vocabulary such as that employed by post-hoc explanations) into an update to an arbitrary, underlying opaque model. We demonstrate the generality of our approach with case studies on seventy real-world models across two broad domains: image classification and text recommendation. We show our method improves accuracy compared to a rigorous baseline on the image classification domains. For the text modality, we apply our framework to a neural recommender system for scientific papers on a public website; our user study shows that our framework leads to significantly higher perceived user control, trust, and satisfaction.
Bookshelf – A Document Categorization for Library using Text Mining

Categorizing books and other archaic paper sources to a course reference or syllabus is a challenge in library science. The traditional way of categorization is manually done by professionals and the process of seeking and retrieving information can be frustrating. It needs intellectual tasks and conceptual analysis of a human effort to recognize similarities of items in determining the subject to the correct category. Unlike the traditional categorization process, the author implemented the concept of automatic document categorization for libraries using text mining. The project involves the creation of a web app and mobile app. This can be accomplished through the use of a supervised machine learning classification model using the Support Vector Machine algorithm that can predict the given category of data from the book or other archaic paper sources to the course syllabus they belong to.
Counting Objects by Diffused Index: geometry-free and training-free approach

Counting objects is a fundamental but challenging problem. In this paper, we propose diffusion-based, geometry-free, and learning-free methodologies to count the number of objects in images. The main idea is to represent each object by a unique index value regardless of its intensity or size, and to simply count the number of index values. First, we place different vectors, refer to as seed vectors, uniformly throughout the mask image. The mask image has boundary information of the objects to be counted. Secondly, the seeds are diffused using an edge-weighted harmonic variational optimization model within each object. We propose an efficient algorithm based on an operator splitting approach and alternating direction minimization method, and theoretical analysis of this algorithm is given. An optimal solution of the model is obtained when the distributed seeds are completely diffused such that there is a unique intensity within each object, which we refer to as an index. For computational efficiency, we stop the diffusion process before a full convergence, and propose to cluster these diffused index values. We refer to this approach as Counting Objects by Diffused Index (CODI). We explore scalar and multi-dimensional seed vectors. For Scalar seeds, we use Gaussian fitting in histogram to count, while for vector seeds, we exploit a high-dimensional clustering method for the final step of counting via clustering. The proposed method is flexible even if the boundary of the object is not clear nor fully enclosed. We present counting results in various applications such as biological cells, agriculture, concert crowd, and transportation. Some comparisons with existing methods are presented.
Semantics of Conjectures

This paper aims to expand and detail the notion of formal semantics of Conjectures by applying a theoretic-model approach. After a short introduction to the concepts and basics of Conjectures, we will start from the notion of Simple Interpretation of RDF, applying and extending the semantic rules and conditions to fully cover the concepts and features of Conjectures.
CLASSIFICATION OF LARGE DOCUMENTS USING MACHINE LEARNING TECHNIQUES

Document classification is a growing interest in the research of text mining. Correctly identifying the documents into particular category is still presenting challenge because of large and vast amount of features in the dataset. In regards to the existing classifying approaches, Naive Bayes is potentially good at serving as a document classification model due to its simplicity. The aim of this paper is to highlight the performance of employing Naive Bayes in document classification. Results show that Naive Bayes is the best classifiers against several common classifiers (such as decision tree, neural network, and support vector machines) in term of accuracy and computational efficiency.
The admissibility of electronic evidence in modern criminal proceedings

With the advent of the phenomenon of electronic evidence in the science of criminal procedure, a new stage in the development of the theory of evidence and proof was marked. Particular attention was paid to issues related to the evaluation of electronic evidence – the applicability of existing criteria or the need to develop new unique criteria. The solution of the urgent problem of determining the criteria for evaluating electronic evidence requires conceptually new views, one can even say the need to develop a modern theory of evaluating electronic evidence, which would encompass the entire spectrum of available knowledge about this phenomenon and would allow solving the tasks of criminal proceedings more effectively and efficiently.&#x0D; Within the framework of this article, the author attempts to determine the applicability of the admissibility criterion in the evaluation of electronic evidence. The belief has been formed that the criterion of the admissibility of electronic evidence is a legal requirement, compliance with which means the possibility of using specific electronic evidence in proving a criminal case. In order to understand the content and specifics of the criterion for the admissibility of electronic evidence, this article analyzes the judicial and investigative practice, as well as the analysis of the opinions of representatives of the scientific community, defines specific requirements for the admissibility of electronic evidence, and also offers a unique definition of the criterion for the admissibility of electronic evidence.
Formation of employees’ devotion, loyalty and trust in the system of psychological prevention of staff turnover in educational organizations of the MIA of Russia

In modern conditions, one of the key tasks of personnel management is the formation of a stable able-bodied team of the organization, ensuring its sustainable functioning and development.&#x0D; The purpose of the study: to determine the features of the formation of devotion, loyalty and trust in the organization among employees in the system of psychological prevention of staff turnover in educational organizations of the Ministry of Internal Affairs of Russia.&#x0D; Object of study: the system of psychological prevention of staff turnover in educational organizations of the Ministry of Internal Affairs of Russia.&#x0D; Subject of study: the process of formation of devotion, loyalty and trust of the organization among employees in the system of psychological prevention of staff turnover of educational organizations of the Ministry of Internal Affairs of Russia.&#x0D; Methodology and theory of research. Foreign (Rusbalt [36], Farrell [25]) and domestic scientists (M. I. Magura [9], V. I. Dominyak, A. N. Pastushenya [14]) emphasize that staff turnover among newly hired employees, which should include students of educational organizations of the Ministry of Internal Affairs of Russia, can be caused by a low degree of professional devotion, which, according to M. Meskon, ensures that the subject of professional activity resists the impact of negative professional factors.&#x0D; This article presents an analysis of the three leading characteristics of an employee that ensure his professional constancy in the organization: «devotion», «loyalty», «trust in the organization» in behavioral, attitudinal, normative, positive and affective approaches. The classification of factors that determine the formation of professional loyalty of employees is highlighted. It has been proved that the formation of professional devotion, loyalty and trust of the organization among employees should be the core of the program of psychological prevention of employee turnover in an educational organization.