Reviewerly/exHarmony huggingface dataset:
A szótagzáró lvariabilitása szlovákiai magyar nyelvjárási hangoskönyvek szövegeiben

With the help of the dialectological software Bihalbocs, regional dialect texts combined with sound were published under the title Hungarian Dialectal Talking Book. The collection, which has become a series, currently comprises 16 parts. The advantage of the software is that the texts are linked to the audio files, i.e. it synchronizes them to the millisecond. By clicking anywhere in the text, we can listen to the dialect data and any part of the text at any time, without having to look up the original file and search for the desired part. This technique was used to create the Slovakia Hungarian Dialectal Talking Book I-III. which publishes dialect texts from the 3 main areas of Hungarian dialect regions extending into Slovakia (the Central Transdanubian – Little Hungarian Plain dialect, the Palóc dialect and the Northeastern dialect). The paper firstly recounts the design and creation of this multimedia database, followed by a brief description of certain parts as well as the structure of the audiobooks. The next part of the study discusses the mapping possibilities based on the quantitative analysis of the audiobooks. Such computational dialectological studies tend to employ two different methods: firstly, using the automated search function without any prior grouping by the researchers, and secondly, using the search function once the data is annotated. Computational methods not only reduce the amount of effort required but also provide plenty of opportunities for more granulated dialectological analyses. The quantitative methods applied concentrate on the distribution of certain phenomena affecting the system, prevalence, and realization of phonemes. The paper describes the regional characteristics for each of these phenomena based on data from the audiobooks. The study analyzes the presence, omission, and reduction of syllable-final l in 34 dialect texts at 27 research points, depending on the phonological situation and the duration of the vowel before l. This paper presents the maps created with the Bihalbocs program to illustrate the regional variability of syllable-final l based on the analyzed regional Hungarian dialect texts in Slovakia.
Identification E-SIM For Motorcycle Security Using Atmega 8 Microcontroller

Motorcycle theft is getting more disturbing, so it encourages the author to make security devices on motorbikes. This study has created a security system that can secure motorcycles using an e-SIM based on the Atmega-8 Microcontroller. Where the e-SIM has a chip, the chip itself has 7 bytes. In this case, the authors take advantage of the 7-byte chip in the e-SIM. The e-SIM will replace the motorcycle ignition key. Not only that, but the e-SIM will also give orders to the motor starter to start the motorcycle. Thus, only the owner of the e-SIM who already has a sim can give the motorcycle ON and OFF orders. The research method used is direct observation of the selected object, namely the author's home environment, and conducting literature studies related to the Atmega-8 microcontroller. This study aims to create a security system for motorcycle vehicles to avoid theft and the use of motorcycles for children without driving licenses
Formation of speech and consciousness in anthropogenesis: evolutionary drivers and socio-psychological mechanisms

&lt;p&gt;The work is aimed at the theoretical reconstruction of the genesis of articulate speech and main abilities of consciousness based on evolutionary regularities and socio-psychological mechanisms. The basic concepts of the conceptual apparatus of reconstruction are presented: &amp;ldquo;abilities&amp;rdquo;, &amp;ldquo;attitudes&amp;rdquo;, &amp;ldquo;interiorization&amp;rdquo;, &amp;ldquo;interactive ritual&amp;rdquo;, &amp;ldquo;niches&amp;rdquo;, &amp;ldquo;social orders&amp;rdquo;, group and individual &amp;ldquo;concerns&amp;rdquo;, &amp;ldquo;communicative concerns&amp;rdquo;, &amp;ldquo;supporting structures&amp;rdquo;, including "magic wands" with a special potential for flexibility and multifunctionality. In ontogeny, human attitudes and abilities are formed through the mechanisms of interiorization (according to Leo Vygotsky) and interactive ritual (according to E. Durkheim, E. Goffman, R. Collins). To reconstruct human traits folding in anthropogenesis is to represent a regular stepwise transformation of the most ancient hominids&amp;rsquo; initial ingredients, probably like the apes&amp;rsquo; features. Particular attention is paid to pre-rituals that form the internal and behavioral attitudes of apes, as well as their abilities for sign communication and learning new signs. It is shown what sequence of challenges and responses, new concerns and supporting structures led hominids to the formation of joint intentionality (M. Tomasello), self-domestication (D. K. Belyaev, R. Wrangham), normative rituals, the first group rules and internal normative attitudes (C. Lovejoy, D. Dor et al.). This complex phenomenon of normativity, the corresponding social orders and renewed communicative concerns became the main drivers of articulate speech development and closely associated with it consciousness abilities.&lt;/p&gt;
Data Processing and Persistence in Virtual Reality Systems

Data processing and persistence are key aspects of developing a Virtual Reality system. In this paper, an improvement is offered to the distance calculation algorithm of the Unity Engine. Additionally, data persistence mechanisms provided by the Unity Engine are reviewed, and File System is selected as an appropriate option. Storage of object coordinates to the File System is implemented. The results provide a baseline for developing a system for creating virtual stands for professional research.
Rancang Bangun Miniatur Control Room Pada Budidaya Jamur Tiram

This research focuses on the design and control room miniatures to reduce room temperature and increase humidity values in oyster mushroom cultivation. This miniature control room is designed to be able to maintain a temperature of 15-35°C and 50-80% humidity required by oyster mushrooms. In maintaining the temperature and humidity of the cultivation room, a tool is needed in the form of a fog maker and fan as well as a DHT 11 sensor. The DHT 11 sensor detects the temperature and humidity around the room and the cooling fan will be rotated by a DC motor according to the drive signal given by the fuzzy logic controller and the mist maker. functions to produce dew or increase the humidity of the room, the mist maker will be active if the actual value of humidity is below 85%, but if the actual value of humidity is above 85% then the mist maker itself will not be active. In the results of the temperature and humidity experiment for 2 hours, the initial temperature was 29°C and 81% humidity, for the initial temperature set point value was 26°C and 80% humidity.
Sistem Pakar Diagnosis Penyakit Pada Tanaman Jeruk Gerga Dengan Metode Forward Chaining Studi Kasus Balai Penyuluhan (BPP) Kecamatan Jangkat Kabupaten Merangin

Citrus fruit is one of the most popular types of fruits by the people of Indonesia because of its freshness and contains many types of vitamins. However, the population of citrus plants has experienced a high increase due to the limited knowledge of farmers in terms of proper citrus farming. Another obstacle that causes citrus fruit products in Indonesia to not meet expectations is the emergence of pests and diseases. Many losses are caused by plant diseases that are too late to diagnose, and have reached a severe stage causing crop failure. The purpose of the study was to build an Expert System for Diagnosis of Diseases in Citrus with the Forward Chaining Method Case Study at the Agricultural Extension Center (BPP) in Jangkat District, Merangin Regency, a system that can replace the temporary role of experts or experts in identifying citrus plant diseases quickly and accurately. An expert system for diagnosing citrus plant diseases is needed as a means of consultation and a means of learning for farmers, especially from the Extension Center. For this reason, the difficulties experienced by farmers can be overcome by providing a software (expert system) in the form of an application program to determine diseases that attack citrus or lime plants as well as proper handling and prevention methods. The input in this system is in the form of disease data, symptom data, and rule data. The output is a checking report. The result of this research is a system for diagnosing citrus plant diseases. Keywords— citrus plant diseases, Diagnosis, expert system, forward chaining.
Development of Physics E-book Based on Technological Pedagogical Content Knowledge (TPACK) on Thermodynamic Laws Topic

This research was developmental research with aims to develop, knowing validity and practicability of thermodynamics e-book based on TPACK. This research was using 4D models and did only as far as develop step. The result based on 4D model steps were (1) Define produces task analysis to fulfil 21st century skill, (2) Design produces 2 product which are teacher book and student book, (3) Develop step produces result of validation and the last product (e-book/flipbook for teacher and student). Expert validation showed the teaching instructional material is valid, learning planning is valid, Content Representation is valid, and e-book is valid. 89,5% students responded this e-book very interesting. This e-book can be said quite practically. Limited trial and small experiment were carried out in one of class in senior high school of one Ngoro Mojokerto and concluded that the increase in the stabilization of sub-topic introduction to Temperature and the Thermodynamics Zero Low is high through the activity of comparing (Let’s Compare). Students can learn thermodynamics by keeping up with science and technology developments by this e-book. The practical benefit is helping the success of learning process carried out at school and home because e-books are provided in 2 forms, teacher book and student book.
Method for Identifying Business Goals and Tasks for AI Service System

In this study, we considered projects in which enterprise service systems are developed using artificial intelligence (AI) technologies. Many enterprises have started applying AI technologies to their business functions. When effectively introducing AI technologies, it is important to identify suitable business domains in the enterprise before planning the projects. For this purpose, we propose a GQM+Strategies-based method that divides the top business goal into strategies as part of a goals-strategies decomposition tree, and identifythe business goals and tasks for AI service systems. In addition, we propose a method for assessing the applicability of an AI service system for the identified business tasks. We confirm the effectiveness of the proposed methods through a real-world business analysis and a use case analysis.
Model Pemberian Remedial Teaching terhadap Hasil Belajar Matematika Siswa Kelas IV SDN 1 Mataram

Learning and instructional are one of the most important elements in supporting student output. In general, if the learning is effective, the results will also be better and vice versa. A student's learning difficulties at school are influenced by internal factors and external factors. The success of the process can be seen from the results of student learning. The purpose of this study was to determine the effectiveness of implementing remedial teaching in mathematics and to determine student completeness after remedial was carried out. The method used in this research is experimental research with the Pre-Experimental Designs approach. This research was conducted in class IV SDN 1 Mataram. The sample in this study was 20 grade IV students at SDN 1 Mataram. The instrument used was the test instrument. The final results of this study obtained t count &gt; t table or 6.65 &gt; 2.09, Ho was rejected and Ha was accepted, meaning that learning mathematics with Remedial Teaching is effective in improving student learning outcomes in mathematics class IV SDN 1 Mataram Academic Year 2022/2023
Pengaruh Penggunaan Media Big Book terhadap Kemampuan Membaca dan Menulis Permulaan Siswa Kelas I SDN 35 Pontianak Selatan

This study aims to determine the effect of using big book media on the ability to read and write in class I SDN 35 Pontianak Selatan. Specifically, the aims of this study were to find out: (1) Data on the initial reading and writing abilities of students in class I who were not taught using big book media; (2) Data on the initial reading and writing abilities of students in grade I who were taught using the big book media; (3) The effect of using big book media on the ability to read and write beginning in first grade students at SDN 35 Pontianak Selatan. The research method used is the experimental method with the Quasy Experimental form. The sample in this study were 30 students from 87 students selected by purposive sampling technique. The data collection technique for this research is measurement and the data collection tool in this research is a test in the form of a reading and writing test. Data analysis techniques in this study used the average (mean) and t-test. The results of the study can be concluded that: (1) The average reading and writing abilities of students in grade I who are not taught using big book media are 69.20 and 72.96; (2) The average initial reading and writing abilities of students in class I who were not taught using the big book media were 76.53 and 75.6; (3) There is an influence on students' initial reading and writing abilities after being taught using big book media in class I students of SDN 35 Pontianak Selatan.
PERBANDINGAN METODE MULTIPLE LINEAR REGRESSION (MLR) DAN REGRESSION KRIGING (RK) DALAM PEMETAAN KETEBALAN TANAH DIGITAL

Soil thickness has a significant influence on many of earth surface processes, and it can be mapped using various methods. Digital soil mapping can be used to estimate the spatial distribution of soil thickness and can estimate the uncertainty of the soil prediction map. Digital soil mapping using regression methods such as Multiple Linear Regression (MLR) and Regression Krigging (RK) was used to estimate soil thickness of the slope of Bener Reservoir. Bener Dam is a national strategic project which is built for irrigation to improve farming quantity. The aim of this research was to determine the spatial variation of the soil thickness at the slope of Bener Reservoir. The accuracy of MLR and RK were compared to select the best soil thickness prediction. There were 212 and 53 soil thickness samples from fieldwork which were used for data training and testing, respectively. There were 5 environmental variables such as elevation, distance from river, slope, plan curvature, and topographic wetness index. R programming language with gstat, krige, and stats Packages was employed for MLR and RK prediction. The result showed that KR is more accurate than MLR.
5G Resource Sharing Scheme and Consensus Algorithm Based on Blockchain

One of the outstanding features of 5G is the powerful resource sharing ability to cope with the increasing content demand and data usage, especially in 5G IoT scenarios. However, due to data attacks, sharing resources in mobile networks is extremely vulnerable to serious data leakage risks and security threats. Blockchain has the characteristics of decentralization, security, non-tamperability, and incentive mechanism, which enables this emerging disruptive technology to provide innovative solutions to the challenges in 5G networks. This paper combines resource sharing in 5G with blockchain technology, proposes a blockchain-based 5G resource sharing architecture, provides incentives for resource sharing between nodes, and considers the latency issue in 5G, and proposes BFT-PoS consensus algorithm, which combined PBFT and PoS, improves the efficiency of the consensus algorithm and is verified by simulation.
Automated Data Mapping Based on FastText and LSTM for Business Systems

With the continuous development of information technology, massive information processing has become an important problem in business systems. However, the metadata information from different business systems lacks a unified and standardized description method. Mapping data by the manual way greatly reduces the efficiency. Therefore, an automated data mapping method is very necessary. In this paper, we regard data mapping as a text classification problem based on the following reasons: 1) the text classification technology has become more and more mature in the field of the natural language processing (NLP), which is very suitable for processing massive data; 2) a large number of heterogeneous mapping data can be treated as text. In order to implement automated data mapping, in this paper, we propose a classification model based on FastText and long-short term memory (LSTM) for data mapping in business systems. By observing the characteristics of mapping data in business systems, we firstly use FastText to learn word representation containing semantic information, and then adopt the LSTM model to extract features for text classification automatically. Experimental results show that the proposed method can automatically classify mapping data in business systems with common quality.
Temporal Anomaly Forged Scene Detection by Referring Video Discontinuity Features

Today we are living in the techno-social world, where we are seeing news and social media. We are believing published videos and images are the sources of trustworthiness. Due to the high-end easily available media editors believing and trusting published media is a challenge. CC Tv introduced to capture live videos and images for security purposes till due to the limitation of storage, managing cc tv footage and identifying anomalous forge scenes in huge media footage sources is a current proposal of our research work. In this paper, we are proposing forged anomalous scene detection by referring to discontinuity features using open-source deep learning and recurrent and transfer learning approaches. We propose to learn a classification model to identify the frames that are anomalies in nature. After that, the input video is separated into frames, and we propose to check each frame for its forged anomaly scene. The frames that are classified as forged anomaly scenes are then arranged temporally to maintain the continuity of the video. This makes sure that the rendered scene maintains its temporal continuity. We propose to detect the doctoring in the video assuming the sudden change in the scene as a case-specific doctoring. We propose to learn the flow vectors for both continuity and discontinuity. In a test video, we propose to generate the flow vectors using the Lucas Kanade technique and then test the flow vectors for their pattern recognition with the trained model. The decision obtained on flow vectors is then superimposed on the input video.
Annotation of Procedural Questions in Standard Arabic Using Syntactic Grammars

Most question answering systems have been designed to answer short questions (straight answers such as dates, locations), but there are only a few pieces of research about complex questions. In this paper, we present a method for analyzing complex questions at the syntactic and the morphological levels with a pattern-based structure. These linguistic patterns allow us to annotate the question and its semantic features for extracting the focus and topic. We start with the implementation of the rules which identify and annotate the various medical named entities. Our Named Entity Recognizer (NER) is able to sift through references to people, places and organizations, diseases, viruses, as targets to extract the correct answer from the user. The NER is embedded in our question answering system. The task of our system is divided in four phases each of which plays a critical role in the general performance: question analysis, segmentation and passage retrieval, answer validation and, finally, answer extraction. We use the NooJ platform which represents a valuable linguistic development environment. The first evaluations show that the actual results are encouraging and could be deployed for further question types.
Deep Learning-based Point Cloud Joint Geometry and Color Coding: Designing a Perceptually-Driven Differentiable Training Distortion Metric

Deep learning (DL)-based coding has recently become very popular for multimedia data, notably images and point clouds (PCs). Training a DL coding model using the backpropagation algorithm requires a differentiable loss function. Thus, for PC joint geometry and color coding, both the PC geometry and color distortion metrics must be differentiable. Since the distortion/quality metrics commonly used for the final PC quality assessment do not meet this criterion, new PC distortion metrics have to be designed for DL-based training purposes. Moreover, for PC joint geometry and color coding, it is critical to define the balance between the geometry and color distortions in a meaningful way, ideally driven by the human perception and subjective quality assessment. In this context, this paper proposes a perceptually-driven design for a differentiable PC joint geometry and color distortion metric to be used for training purposes in DL-based coding, notably to define the relative weights for the geometry and color distortions. The obtained perceptually-driven weights achieve a rate reduction of around 3 % regarding the default balanced weights at no complexity cost. This is the first proposal in the literature with this purpose and this perceptual approach.
Multi-granularity Feature Attention Fusion Network for Image-Text Sentiment Analysis

Multi-modal sentiment analysis of images and texts in social media has surpassed traditional text-based analysis and attracted more and more attention from researchers. Existing studies on multi-modal sentiment analysis of texts and images focus on learning each modal feature independently, which ignores the correlation between images and texts. In the field of social media, such correlation is often multi-granularity, that is, image areas are often associated with text (words, phrases, sentences) with multiple granularity. In this paper, a multi-granularity feature attention fusion network is proposed to model the correlation between image content and multi-granularity text content for multi-modal sentiment analysis. Specifically, the model proposed in this paper includes feature learning layer, interactive information fusion layer and classification layer. Image features and text features of multi-granularity can be learned in feature learning layer. In the interactive information fusion layer, multi-granularity text features and image features are interacted and fused, and the last classification layer uses the features learned last time to complete classification. The proposed model is validated on two public multimodal data sets of graphs and texts, and the experimental results show that the model is effective.
Joint Matrix Factorization and Structure Preserving for Domain Adaptation

Domain adaptation aims to learn robust classifiers for the target domain by transferring knowledge from the labeled source domain. However, most of the existing studies emphasize learning domain-invariant feature representations by employing distribution alignment on the feature space, which ignores the influence of data noise and structure knowledge. To address these issues, we propose a new domain adaptation approach, which can effectively reduce the impact of data noise and simultaneously thoroughly exploit the manifold data structures to transfer discriminative knowledge. Specifically, we jointly model matrix factorization, maximum entropy, distribution alignment in a unified framework, which can effectively alleviate the negative transfer of the outliers. Furthermore, we devise graph dual regularization to thoroughly explore the intrinsic manifold data structures, which can significantly reduce structure discrepancy across domains. Experimental results on various domain adaptation tasks demonstrate the superiority of the proposed method.