Reviewerly/exHarmony huggingface dataset:
IMPLIKATUR PERCAKAPAN DALAM DEBAT PASANGAN CALON BUPATI DAN WAKIL BUPATI SAMBAS TAHUN 2020

AbstractThis study aims to describe the forms and types of conversational implicatures in the 2020 Sambas Regent and Deputy Regent Candidate Debate. The research method used is descriptive with a qualitative research form. The data from this study are declarative sentences, imperative sentences, introgative sentences, general and specific conversational implicature in the 2020 Sambas Regent and Deputy Regent Candidate Pair Debate. The source of data was taking from the 2020 Sambas Regent and Deputy Regent candidate pair debate which is broadcast through the Sambas Regency Comunications and Information Agency youtube page which was broadcast live on November 21, 2020, lasted 3 hours 26 minutes 25 detik. The data collection technique in this research is a documentary study technique. Based on the results of the analysis in the 2020 Sambas regent and Deputy Regent Candidate Debate, it can be concluded that there are 23 forms af declarative implicature sentences, 21 forms of interrogative implicature sentences, 41 forms of imperative implicature sentences, 7 forms of general conversational implicature sentences, and 78 specific conversational implicature sentences. In addition, this research can also be used as teaching material in learning Indonesian for grade X second semester in senior high school level as the debate material.Keywords: Conversational Implicature, Debate, Implicatur
A Neural-Symbolic Approach to Computer Vision

The paper presents a general computer vision model based on neural-symbolic artificial intelligence capable of performing semantic analysis of a video stream. The proposed approach integrates artificial neural networks (ANN) with the knowledge base on the basis of an ontological approach. In this case, the knowledge base interacts with the neural networks as with agents and the results of their functioning are used for further semantic analysis. The problems of object detection and recognition of images, as well as emotions, are considered as tasks of computer vision. The features, advantages and prospects of using this model are described. Its implementation is considered on the example of an intelligent module that includes the FaceNet and eXnet neural network models for face identification and emotion recognition in the conversational modeling system.
Legal anthropocentrism between nature and technology: the new vulnerability of human beings.

Reflection on the possible reform of the Italian Civil Code requires an analysis of the evolution of the relationship between subject and object of law in the legal systems of the Western area. From this perspective, we note the thrusts toward a recognition of legal subjectivity to nature, animals, but especially to devices endowed with AI. At the same time, on another side, strong tendencies toward objectification/reification of human beings (and animals) emerge, from their origin to the different stages of their development. Anthropocentrism implodes, heading toward a transhumanist drift that only an inclusive humanistic vision of every living being could stem.
Improve Critical Thinking Skills with Informatics Educational Games

Facing the challenges of the 21st century Critical thinking is one of the main abilities that students must have. The problem of low critical thinking skills is caused by factors in students and learning that does not focus on developing critical thinking skills. This study aims to develop an Informatics educational game with higher order thinking skill (HOTS) questions based on the Berbras competition to improve students' critical thinking skills. The research design used by the ADDIE model consists of the stages of analysis, design, develop, implementation, evaluation. Test the effectiveness of educational games using experimental research with Noneequivalent Control Group Design. The research subjects were 32 students in class VIIA and 33 students in class VIIB. Collecting data using a questionnaire instrument and an essay test. The research data were analyzed using paired samples test, with the data requirements being normally distributed and homogeneous. The results of the normality and homogeneity test of the data, obtained a significance level greater than 0.05 so that the critical thinking ability data is normally distributed and homogeneous. The increase in critical thinking skills from the N-Gain analysis of the experimental class was 0.63 in the medium category and the control class was 0.43 in the medium category. Based on the results of the paired samples test, the value of Sig. (2-tailed) of 0.000 &lt;0.05, it can be concluded that the use of informatics educational games has a significant effect on increasing students' critical thinking skills.
Differential privacy preservation for graph auto-encoders: A novel anonymous graph publishing model

In recent years, the number of users in social networks has grown substantially, and more data-intensive applications have been developed. This creates a demand for the ability to mine large-scale graph data more efficiently, so that the information mined can be maximized (e.g., mining social relationships between people). However, the direct publication of the original graphs leads to potential leakage of users' privacy. Therefore, graph anonymization techniques are often utilized to process the original graphs. A key challenge of it lies in the balance between anonymity and usability. In this paper, we introduced the idea of graph auto-encoder, a fundamental element in graph neural networks, and proposed the Differential Privacy Deep Graph Auto-Encoder (DP-DGAE). Our main idea is to convert the anonymous graph publishing problem into a privacy-preserving problem for generative models, and optimize the models in terms of both privacy and usability using a multi-task learning approach. Theoretical analysis and experimental evaluations show that the DP-DGAE achieves anonymity while ensuring usability.
BARRIERS TO THE EFFECTIVENESS OF TELEWORKING IN PUBLIC ADMINISTRATION

Purpose: Teleworking under Covid-19 conditions is, for many organizations, a fundamental requirement to ensure continuity of operation.However, it is not a common way of organizing work in public administration entities.In particular, very few studies have examined the effectiveness of teleworking in local administration.The purpose of this paper is to investigate barriers to the effectiveness of teleworking in local administration in Poland.Design/methodology/approach: Literature analysis in the paper was prepared using VOSviewer software.Empirical research allowing us to achieve our objective was conducted using the CAWI method among 136 communes and cities with poviat rights from the Mazowieckie Voivodeship.Findings: We have established that the main obstacle in remote work was technological barriers and lack of appropriate technology in the surveyed entities.As a result of our research, we identified challenges related to insufficient digitalization of services in local administration as the main barrier for teleworking.Research limitations/implications: The paper uses quantitative research, carried out using the CAWI method, on local government entities in the Mazowieckie Voivodeship, on a research sample of 136 entities.Practical implications: The results of this research provide important guidance for practitioners and managers of public administration.The results indicate the most significant barriers to implementing remote working in public administration.The benefits of remote working for both citizens and public administration employees are also presented.Social implications: An analysis of the main challenges of remote working enables adaptation to the ongoing changes.Considering the difficulties of remote administration can help improve the functioning of the administration and, as a result, help to achieve widespread benefits for citizens.Originality/value: The research presented here explores the introduction of remote working to an unprecedented extent in the wake of the COVID19 virus pandemic.
A quantitative evaluation of explainable AI methods using the depth of decision tree

It is necessary to develop an explainable model to clarify how and why a medical model makes a particular decision.Local posthoc explainable AI (XAI) techniques, such as SHAP and LIME, interpret classification system predictions by displaying the most important features and rules underlying any prediction locally.Therefore, in order to compare two or more XAI methods, they must first be evaluated qualitatively or quantitatively.This paper proposes quantitative XAI evaluation metrics that are not based on biased and subjective human judgment.On the other hand, it is dependent on the depth of the decision tree (DT) to automatically and effectively measure the complexity of XAI methods.Our study introduces a novel XAI strategy that measures the complexity of any XAI method by using a characteristic of another model as a proxy.The output of XAI methods, specifically feature importance scores from SHAP and LIME, is fed into the DT in our proposal.The DT will then draw a full tree based on the feature importance score decisions.As a result, we developed two main metrics that can be used to assess the DT's complexity and thus the associated XAI method: the total depth of the tree (TDT) and the average of the weighted class depth (ACD).The results show that SHAP outperforms LIME and is thus less complex.Furthermore, in terms of the number of documents and features, SHAP is more scalable.These results can indicate whether a specific XAI method is suitable for dealing with different document scales.Furthermore, they can demonstrate which features can be used to improve the performance of the black-box model, in this case, a feedforward neural network (FNN).
Real-time multiple face mask and fever detection using YOLOv3 and TensorFlow lite platforms

COVID-19 has caused disruptions to many aspects of everyday life. To reduce the impact of this pandemic, its spreading must be controlled via face mask wearing. Manually mask-checking for everybody is embarrassing and uncontrollable. Hence, the proposed technique is used to help for automatic mask-checking based on deep learning platforms with real-time surveillance live infra-red (IR) camera. In this paper, two recent object detection platforms, named, you only look once version 3 (YOLOv3) and TensorFlow lite are adopted to accomplish this task. The two models are trained with a dataset consisting of images of persons with/without masks. This work is simulated with Google Colab then tested in real-time on an embedded device mated with fast GPU called Raspberry Pi 4 model B, 8 GB RAM. A comparison is made between the two models to verify their performance in relation to their precision rate and processing time. The work of this paper is also succeeded to realize multiple face masks real-time detection up to 10 facemasks in a single scene with high inference speed. Temperature is also measured using IR touchless sensor for each person with sound alarming to alert fever. The presented detector is cheap, light, small, and fast, with 99% accuracy rate during training and testing.
Text to Speech System for Lambani - A Zero Resource, Tribal Language of India

A Text to Speech (TTS) system empowers illiterate people by speaking, in their native language, the information available in electronic media. Speech data and corresponding transcript is essential for the development of a TTS system. Most tribal languages in India neither have script nor written literature. In this paper, we present our effort to build a TTS system for Lambani, a tribal language spoken by a group of nomadic people living in several regions of India. We generated a text corpus of about 3000 Lambani sentences, written in the script of Kannada language. These sentences were read by a literate Lambani speaker in a recording studio. The Lambani TTS system was developed by adapting the Nvidia’s Tacotron2 model, pretrained with English speech, following the transfer learning approach. We implemented two versions of the Lambani TTS system. The first version uses HiFi-GAN vocoder and the second uses the WaveGlow vocoder. We evaluated the two versions of the TTS system using both objective and subjective measures. Both Perceptual Evaluation of Speech Quality score and Mean Opinion Score were higher for the Lambani TTS system that used WaveGlow vocoder.
Analisis Penggunaan Media Pembelajaran Papan Pecahan Kelas Tinggi di SD Negeri Periuk Jaya Permai Tangerang

Learning is a process of interaction between students or students with educators or teachers and learning resources in a learning environment that includes teachers and students exchanging information. In learning activities, of course there are other components to support these learning activities, namely learning media. In learning Mathematics, planting and understanding the concept of a material must attract students' interest and motivate students to understand more about the material and there are no misconceptions. This study aims to determine the use of high grade fractional board media at Periuk Jaya Permai Elementary School. The type of this research is observation, interview guide and researcher as instrument and key. The source of the data for this research is the results of interviews with teachers at the SDN. The resource person explained that students would be more enthusiastic and motivated if the teacher brought the fraction board media because it was more fun and less rigid in learning activities in class. For this reason, the teacher uses the fraction board learning media to attract students' interest. The conclusion obtained from this research is that by bringing the fraction board media the teacher feels that the media facilitates the learning process carried out. This will affect the implementation and success in learning the appropriate, correct, and correct fractions of mathematics material, and students do not experience misconceptions.
A Robust Coverless Image Steganography Based on an End-to-End Hash Generation Model

Recently, coverless steganography algorithms have attracted increased research attention due to their ability to completely resist steganalysis algorithms. However, the existing algorithms do not attain the same robust balance against geometric and non-geometric attacks. In addition, most of the existing methods need to transmit some auxiliary information along with the stego-images, which increases the cost of the hidden information. In this paper, a robust coverless image steganography algorithm based on a hash generation model is proposed. Different from the existing methods, the hash sequences are generated by an end-to-end CNN model, where the input is the original images, and the output is the corresponding hash sequences. Therefore, no auxiliary information needs to be transmitted when hiding the secret information. Moreover, the attention mechanism and adversarial training are introduced to improve the robustness of the model. The loss function is redesigned to accommodate these operations. Finally, an index structure is built to enhance the mapping efficiency. The experimental results show that the proposed method possesses better robustness and security compared with the state-of-the-art coverless image steganography algorithms.
THE EFFECT OF VLOG CONTENT YOUTUBE MEDIA ON LEARNING ENGLISH

The proliferation of mobile devices and online storage space has led to an explosion of learning resources, one of which is the video-sharing site YOUTUBE. There are several types of content/videos in this application, one of which is English-based Daily Vlog content. The aims of this study are (1) to find out to what extent the content of YouTube vlogs affects English mastery, and (2) to identify the types of content that have the greatest impact on English language development. For what purpose do you watch YouTube vlogs? 3. Quantitative methods are used in this study. Quantitative studies involve the collection and analysis of numerical data to characterize phenomena, identify relationships, or test hypotheses. One of these tools is a student questionnaire. To calculate the final results, researchers use SPPS (Statistical Package for the social sciences). Research shows that vlog content on the video-sharing site YouTube has a considerable impact on student's ability to understand English.
Sentiment Analysis of COVID-19 Public Activity Restriction (PPKM) Impact using BERT Method

Covid-19 has grown rapidly in all parts of the world and is considered an international disaster because of its wide-reaching impact. The impact of Covid-19 has spread to Indonesia, especially in the slowdown in economic growth. This was influenced by the implementation of Community Activity Restrictions (PPKM) which limited community economic activities. This study aims to analyze the mapping of public sentiment towards PPKM policies in Indonesia during the pandemic based on Twitter data. Knowing the mapping of public sentiment regarding PPKM is expected to help stakeholders in the policy evaluation process for each region. The method used is BERT with IndoBERT specific model. The results showed the evaluation value of IndoBERT f-1 score reached 84%, precision 86%, and recall 84%. Meanwhile for the evaluation of the use of SVM f-1 score 70%, 72% precision, and 70% recall. Multinominal Na\"ive Bayes evaluation shows f-1 score 83%, precision 78%, and recall 80%. As a conclusion, BERT method with the IndoBERT model is proven to be higher than classical methods such as SVM and Multinominal Na\"ive Bayes.
Violence Detection Approach based on Cloud Data and Neutrosophic Cognitive Maps

Abstract Violence has remained a momentous problem since time immemorial. Various scientific studies are conducted in the recent past to identify the stimuli causing violent behavior among the masses and to achieve the target of cloud data protection. Given the inherent ambiguity or indeterminacy in human behaviour, this study in the area of violence detection appears to be effective, as it finds a variety of stimuli and character qualities that contribute to violent conduct among masses. This uncertainty of traits causing violence can easily be seen in surveillance data present over the cloud and also from the data collected using academic research. Therefore, for the purpose of identifying violent behavior we have considered the factors (data) from existing research and from data over clouds. The factors that lead to violent behavior and are identified by algorithms running over clouds are termed as determinate or certain factors. The factors that were not considered and least identified by the cloud algorithms and given less importance are termed indeterminate factors or uncertain factors. The indeterminate factors are also considered based on the expert’s opinion where the experts are not in a condition to provide a clear stance or when they are neutral in their opinion. Tests are performed using Neutrosophic Cognitive Maps (NCMs) to model the violent behavior taking into consideration both determinate and indeterminate factors. Earlier these tests were performed using Fuzzy Cognitive Maps (FCMs) where indeterminate or uncertain factors were not considered. Therefore, we provide a brief comparison between NCMs and FCMs and show how effective NCMs are when we need to consider the uncertainty of concepts while carrying out tests for identifying violent behavior. Later results are obtained by forming a Neutrosophic adjacency matrix which is evaluated using the concepts of linear algebra. The obtained results in the form of 1 ∗ n vector (1 I I I I 1 I 1 I I I I I I I I I I I ) clearly shows the presence of indeterminate factor ‘ I ’ in the vector which was absent in earlier models when designed using FCMs. This shows how these indeterminate or uncertain factors play a significant role in cultivating violent behavior which was not shown in the previous study. The study is significant since it takes into account factors from cloud data, experts’ opinions, and also from literature, and shows how these factors are taken into consideration at the data level itself so that they will not impact the modeling stage, and machine learning algorithms will perform well because uncertain and indeterminate information is taken care of at training phase itself. Hence uncertainty could be reduced in machine learning algorithms and in the overall recognition of violent behavior.
The Effects of the COVID-19 Pandemic on the Digital Competence of Educators

The COVID-19 pandemic is having an undeniable impact on all aspects of society [...]
Lymphatic Filariasis Detection Using Image Analysis

Elephantiasis is generally detected through microscopic examination of blood.Until now, this has been difficult because microfilariae only appear in the blood at night for a few hours (nocturnal periodicity).The lack of trained microscopy technicians is a serious problem.Due to the repetitive and tedious nature of diagnosis and the fact that there are few positive cases in a population of thousands.This is a contributing factor to increased detection errors.The main problem encountered is the high degree of difficulty and precision and the long time it takes to perform laboratory examinations.Image analysis method can be used as a way to identify Lymphatic Filariasis worms in the blood.Based on the description above, it can be said that the detection of Lymphatic Filariasis worms can be done with digital image analysis.This research will use the feature extraction method and Convolutional Neural Network to identify object features in the form of worms that cause elephantiasis (Lymphatic Filariasis) in digital images recorded by Trinocular digital microscope cameras.This study aims to determine the performance of image analysis methods used in the identification process of Lymphatic Filariasis worms using digital images recorded by a Compound Trinocular microscope.
A Knowledge Graph Convolutional Networks Method for Countryside Ecological Patterns Recommendation by Mining Geographical Features

The recommendation system is one of the hotspots in the field of artificial intelligence that can be applied to recommend suitable ecological patterns for the countryside. Countryside ecological patterns mean advanced patterns that can be recommended to those developing areas which have similar geographical features, which provides huge benefits for countryside development. However, current recommendation methods have low recommendation accuracy due to some limitations, such as data-sparse and ‘cold start’, since they do not consider the complex geographical features. To address the above issues, we propose a geographical Knowledge Graph Convolutional Networks method for Countryside Ecological Patterns Recommendation (KGCN4CEPR). Specifically, a geographical knowledge graph of countryside ecological patterns is established first, which makes up for the sparsity of countryside ecological pattern data. Then, a convolutional network for mining the geographical similarity of ecological patterns is designed among adjacent countryside, which effectively solves the ‘cold start’ problem in the existing recommended methods. The experimental results show that our KGCN4CEPR method is suitable for recommending countryside ecological patterns. Moreover, the proposed KGCN4CEPR method achieves the best recommendation accuracy (60%), which is 9% higher than the MKR method and 6% higher than the RippleNet method.