Reviewerly/exHarmony huggingface dataset:
Slow Learner Student Profile (Case Study at MAN 1 Padang Lawas)

This research is motivated by the phenomenon of students who are slow in learning, for example: slow in accepting or capturing learning, slow in managing lessons, slow reading, slow reading comprehension, slow working, slow doing assignments, slow in solving problems and so on.  Children who are slow to learn are called slow learners. The inability of these students will affect themselves and affect others.  The purpose of this study were (1) To describe the case of slow learner students, (2) to determine the form of slow learner student delay, (3) to determine the case study of slow learner students. This type of research is a qualitative descriptive study in the form of a case study.  The subjects in this study were five students at MAN 1 Padang Lawas North Sumatra.  The data collection instruments used in this study were interview, observation, and documentation guidelines.  The data analysis technique used in this research is thematic analysis by conducting transcripts of interviews that have been verbatim. The results of this study reveal the profile of the slow learner students carried out, the five subjects were unable to achieve their learning objectives in the process or their learning activities, the five subjects had difficulty memorizing, counting, and several other obstacles.  Unlike other students in their socialization, the five subjects preferred to be silent and did not play an active role in the class.  Meanwhile, their emotional uncontrollable causes they cannot achieve their goals in learning well.  Based on the research results, it is suggested that guidance and counseling teachers can be used as a guideline in determining the delivery of BK services, especially those related to slow learner students through information services, content mastery services and individual counseling services.
Incrementality and Locality of Language Comprehension: The Pivotal Role of Semantic Interpretation Schemata

We introduce a computational model of language comprehension that combines locality of syntactic and semantic analysis with incrementality of processing. As the model incorporates inheritance-based abstraction mechanisms we are able to specify a parsimonious inventory of abstract, simple and domain-independent semantic interpretation schemata.
Pembuatan Sistem Informasi Top Up Gaming Berbasis Website

Games are software designed to fill spare time for people who like to play games that are only for entertainment. Each game player can buy items that are used to win games, and items can increase the abilities of the actor in the game itself. This game is an online game, and there are rampant cases of fraud such as taking over accounts for giving passwords and emails to random sites. Overcome this problem by creating a more schematic of the system. This e-commerce information system only requires the address of the game. The system that you want to create only requires email, the point is that if there is something unwanted, the customer can submit a complaint and the admin will resolve it by matching the customer's email. Making this website, using Django. Django is a website framework that uses the Python language. Security in transactions is guaranteed by using the Paypal API, a system that can assist in purchase transactions. The Black Box test results show that the system is good and is in line with expectations for the security of an item purchase transaction in the testing process. So that each user feature can run well because there is no fraud between sellers and buyers.
Image Denoising Using Expected Patch Log Likelihood and Hyper-laplacian Regularization

Image denoising is a fundamental problem of the field of computer vision and image processing. To decrease the computational complexity, many patch-based priors have been presented in image denoising methods, which have achieved the state-of-the-art performances. However the formation of global prior from local patch interaction is still challenging for patch-based image denoising methods. This paper proposed a new image denoising method by introducing a hyper-laplacian regularization term into the expected patch log likelihood (EPLL) framework. In EPLL framework, the patch-based prior can be used in global image denoising process by increasing penalty parameter with iteration. Meanwhile gradients in the whole image satisfy heavy tailed distribution, which can be well depicted by hyper-laplacian. The unified framework could benefit from the local patch-based prior and non-local gradient sparsity prior. The proposed method is extensively evaluated on Berkeley Segmentation Dataset (BSD), and has the comparable performances with the state-of-the-art image denoising methods.
KESANTUNAN BAHASA WHATSAPP MAHASISWA TERHADAP DOSEN PRODI PENDIDIKAN BAHASA INDONESIA DI UNIVERSITAS INDRAPRASTA PGRI

This research was conducted in 2018/2019 in Indonesian Language Program, Universitas Indraprasta PGRI, started in October 2018 until January 2019. This Source of data is messages sent by under graduate students through WhatsApp to Indonesian Language program lecturers, Universitas Indraprasta PGRI. Tehnique of recording data were reading and comphrending each chat between under graduate students and their lecturers through screenshoot. Analysis was conducted based on politeness principles and intrepreted their application and of politeness principles violence. The result showed if under graduate students of Universitas Indraprasta PGRI have used six politeness principles explained
The importance of developing moral qualities in high school students in plurilingual education

The present research discusses the development of moral, spiritual and other panhuman values of high school students as the main personality characteristic; describes the content and structure of moral qualities and characterizes the process of their development; analyzes in detail the importance of the of high school students moral qualities development and describes theoretical and practical research aspects. The authors of the present research listed the methodological approaches on which the development of high school students moral qualities is based on. The research presents the results of experimental-pedagogical work on the development of high school students moral qualities and the comprehensive analysis of the experimental work with its results given in the tables.
Penerapan Strategi Manajemen Pengetahuan untuk Meningkatkan Kinerja Institusi Pendidikan dalam Menghadapi Revolusi Industri 4.0

The purpose of this study was to find out the effect of implementing knowledge management strategies on the performance of educational institutions in the face of the industrial revolution 4.0. The approach used in this research is a quantitative approach. The sample used in this study is the academic community in one of the private universities in Serpong. The data analysis technique used in the study was Partial Least Square Structural Equation Modeling (PLS-SEM). The results showed that the application of knowledge management strategies was proven to significantly affect the performance of educational institutions. Knowledge management strategies have also been shown to have a strong influence in improving human relations among students. Relationships among humans can be a mediating variable for the relationship between knowledge management strategies and educational institution performance and have the same influence as the direct influence of knowledge management strategies on the performance of educational institutions.
Implementation of CoAP Algorithm to Estimate the Motion of Video Stream

The IOT technology led to the emergence of CoAP to eliminate the constraints occurred in lossy environment. This protocol aids at smooth hazzle free communication between the IOT devices. But these protocols weren't reliable for video streaming applications. Hence for a reliable video streaming applications in Wireless network analyses and performance evaluation of this protocol is taken for study in this article. This investigation is carried out by multiple studies adjusting the transmission attributes in the grid algorithm corresponding to each frame to reduce time complexity and to augment the frame rates. Hence in this study the video is disintegrated into frames. For this purpose poor quality video is taken considering mechanical vibrations, poor quality of a lens, and the object movement in the video scenario.  So we propose a flow estimation based stabilization method using a novel search algorithm. The proposed method is implemented using a Matlab simulation with a real-time video sequence after it is converted into frames. The image processing toolbox has to be used to perform this compression.
Learning and testing junta distributions with sub cube conditioning

We study the problems of learning and testing junta distributions on $\{-1,1\}^n$ with respect to the uniform distribution, where a distribution $p$ is a $k$-junta if its probability mass function $p(x)$ depends on a subset of at most $k$ variables.The main contribution is an algorithm for finding relevant coordinates in a $k$-junta distribution with subcube conditioning (Bhattacharyya et al 2018., Canonne et al. 2019). We give two applications:\begin{itemize}\item An algorithm for learning $k$-junta distributions with $\tilde{O}(k/\eps^2) \log n + O(2^k/\eps^2)$ subcube conditioning queries, and\item An algorithm for testing $k$-junta distributions with $\tilde{O}((k + \sqrt{n})/\eps^2)$ subcube conditioning queries. \end{itemize}All our algorithms are optimal up to poly-logarithmic factors.Our results show that subcube conditioning, as a natural model for accessing high-dimensional distributions, enables significant savings in learning and testing junta distributions compared to the standard sampling model.This addresses an open question posed by Aliakbarpour et al 2016.
RANCANG BANGUN MEDIA PEMBELAJARAN INTERAKTIF KOMPUTER DAN JARINGAN DASAR DI SMK

This study was discussed about the design of interactive learning media on Computer and Basic Network subject at the first year students. The purpose of this study was developed a valid and practical interactive learning media by using Adobe Director Software on Computer and Basic Network subject at SMKS Mahyal Ulum Al-Aziziyah. This type of study used the IDI model (Instructional Development Institute). The step of this study consists of three steps, namely (1) define, (2) develop, and (3) evaluate. To collect the data, some instruments are used to analyze such as validation questionnaire, and practical questionnaire. The data analysis were used the Aiken's V formula. Based on the results of the study, it can be concluded that the design was produced interactive learning multimedia products by using Adobe Director Software in Computer and Basic Network subjects at the class X TKJ of SMKS Mahyal Ulum Al-Aziziyah. It could be seen based on the Aiken's V coefficient value of 0.78 which is declared valid and practical with an average value of 83.72% after going through the development step which are analyzed based on validity and practicality. Keywords: Interactive Learning Media, Adobe Director, Instructional Development Institute, Aiken’s V.
Visualization multi-instance data sets [Visualización de conjuntos de datos de múltiples instancias]

In pattern recognition, multiple-instance learning algorithms have gained importance since they avoid that the user must delimit, the images individually in order to recognize the objects. This is an advantage over traditional learning algorithms since these considerably reduce the time required to prepare the data set. However, a disadvantage is that the resulting data sets are often complex, making it difficult to visualize them using traditional information visualization techniques. Thus, this work proposes a tool for the visualization and analysis of data sets of the multi-instance learning paradigm. The visualization proposal was evaluated using the expert criteria. In addition, different tests were carried out that show that a correct visualization can help to make decisions about the data set to improve the classification precision. © 2020, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.
The Smart Card – An Authentic &amp;amp; Secure Elearning ICT Toolkit

In this era of information technology, browsing information and the concept of information on the human mind and attitude has bombarded from all sides, which won’t allow any human being to escape from the invisible pitfall of information. In order to streamline this aggressive mode of information sciences at this defining moment, the gravity of the situation must be analyzed accurately. On one side it had shaken the ideological bases of our nation while on the other it provides and fulfills the realization of a fundamental right i.e “education for all”. With the institutionalization of this golden opportunity, the literacy rate can easily be made 100% in a short span of time. Despite the insufficient budget, technology can easily materialize the motto of education for all. Failing which the multinational corporations and agencies will fill the vacuum in an unwanted way. These organizations with their terrible tentacles and enormous financial assets will alter our national identity. They will drain our brains to their own countries and will train them according to their needs. The aim of this study is to spread the use of a simple tool called “smart card” used for personal communication like distance learning and eLearning. A smart card is a small computer with its own Operating System, Random Accesses Memory (RAM), Electrically Erasable Programmable Read-Only Memory (EEPROM) and a small memory for storing high cryptographic functions and is capable of storing, managing, controlling and conveying information in a significant manner.
Instagram Image Filtration with Computer Vision

Instagram is one of the famous and fast-growing media sharing platforms. Instagram allows users to share photos and videos with followers. There are plenty of ways to search for images on Instagram, but one of the most familiar ways is ’hashtag.’ Hashtag search enables the users to find the precise search result on Instagram. However, there are no rules for using the hashtag; that is why it often does not match the uploaded image, and for this reason, Users are unable to find the relevant search results. This research aims to filter any human face images on search results based on hashtags on Instagram. Our study extends the author’s [2] work by implementing image processing techniques that detect human faces and separate the identified images on search results based on hashtags using the face detection technique.
The contribution of lexical variables to the measurement of Italian texts’ readability level

The present study aims to reveal the contribution of type/token and content word/token ratio to the difficulty level calculation of Italian texts. In the text readability analysis, these lexical traits of a text are often used in combination with other linguistic (e.g. syntactic structure of the text) and quantity criteria, (e.g. word number), without providing a clear indication of their weight to the final readability score. For this purpose, we used the readability formulas Gulpease and Read-It, (Dell’Orletta, Montemagni and Venturi 2011) to analyze a corpus of 184 Italian texts, that have been used for the examination of reading comprehension by the National Language Certification System of Greece (KPG), from 2011 to 2017. The results showed a strong correlation of the type/token and content word/token variables with the difficulty level of the analyzed texts and a higher contribution of the lexical group to the final score, in comparison with the word frequency variable.
HiABP: Hierarchical Initialized ABP for Unsupervised Representation Learning

Although Markov chain Monte Carlo (MCMC) is useful for generating samples from the posterior distribution, it often suffers from intractability when dealing with large-scale datasets. To address this issue, we propose Hierarchical Initialized Alternating Back-propagation (HiABP) for efficient Bayesian inference. Especially, we endow Alternating Backpropagation (ABP) method with a well-designed initializer and hierarchical structure, composing the pipeline of Initializing, Improving, and Learning back-propagation. It saves much time for the generative model to initialize the latent variable by constraining a sampler to be close to the true posterior distribution. The initialized latent variable is then improved significantly by an MCMC sampler. Thus the proposed method has the strengths of both methods, i.e., the effectiveness of MCMC and the efficiency of variational inference. Experimental results validate our framework can outperform other popular deep generative models in modeling natural images and learning from incomplete data. We further demonstrate the unsupervised disentanglement of hierarchical latent representation with controllable image synthesis.
Security and Privacy Attacks on Machine Learning Algorithms

Machine Learning has gained a significant increase in popularity in the recent times. ML models are being used in almost every other field including medicine, finance and many more. As machine learning has increasingly been deployed in critical real-world applications, the dangers of manipulation and misuse of these models has become of paramount importance to public safety and user privacy. In applications such as online content recognition to financial analytics to autonomous vehicles all have shown to be vulnerable to adversaries wishing to manipulate the models or mislead models to their malicious ends. Technical community's understanding of the nature and extent of these vulnerabilities remains limited even though there has been a growth in recognition that ML exposes new vulnerabilities in software systems. Identifying various types of privacy and security attacks possible on ML models and demonstrating those attacks is the focus of the project. For security part adversarial attacks on Machine Learning models will be introduced and privacy part model inversion attack and membership inference attack will be performed to show that ML models leak information.
A survey and proposed methodology for the action recognition in sports

Several techniques have demonstrated the need to detect artifacts that are semantically relevant for identifying people in videos. There are no other techniques used to connect the project with the research to be translated in the direction of training for single deepening convolution neural network (CNN) by a broad text company. The proposed method discusses sports action recognition with a 3D convolutional network. Two different experiments are being performed. Two similar activities are distinguished: running and walking. The 3D convolution network has been shown to know space- and time-linked video sequence characteristics. The best thing was that the A1 architecture was at 85%. We compared three different networks of this kind with the latter experiment on the UC F101 dataset. Fifteen events have been picked. Architecture A1 has reached 80.7% precision. The results show that the 3D convolution architecture of the subtitle network can achieve relatively high precision. Computer vision and machine learning are among the most complex problems of automatic video analysis. An important part of this work concerns human activity identification because the majority of video semi-production occurs by humans and their behaviors. HAR is video based, but human activity recognition (HAR) is one of the most important and challenging applications in many fields. For hockey images, we introduce a multi-label deep HAR 3D CNN method; the recent performance of the CNNs for solving different challenges. Test method on two scenarios: a collection of k-binate interconnections vs. an individual k-output interconnection in a dataset that is accessible publicly.
Vehicle Trajectory Outlier Detection for Road Safety

This work is making use of trajectory analytics for vehicular traffic study through developing and testing a system to identify outlier trajectories of vehicles. Real traffic data is analyzed to provide useful insights of traffic and generate traffic alerts to identify violating behavior, with the aim to facilitate traffic safety. Road safety is very critical issue, and this problem is faced by most people on the road. We developed a computer vision system that analyzes the trajectories of vehicles on the road to identify outlier trajectories. Outlier trajectories can be many types (non-typical/irregular behavior) on the road, such as backing up on a highway or changing lanes, turning, and other abnormal behavior. The work involves detection of vehicles and then analyzing its behavior on road. Yolov3, a TensorFlow model, is used for vehicle detection. Learning models are used to distinguish between normal and abnormal behavior. Finally, a system is developed using machine learning techniques, which is further trained using sufficient dataset examples. The performance of the developed system is tested in real-life situation, and it is found to be working quite comparable to human level detection of abnormal vehicle behavior.
Using Latent Fine-Grained Sentiment for Cross-Domain Sentiment Analysis

The authors investigate the problem task of multi-source cross-domain sentiment classification under the constraint of little labeled data. The authors propose a novel model which is capable of capturing both sentiment terms with strong or weak polarity from various source domains which are useful for knowledge transfer to unlabeled target domain. The authors propose a two-step training strategy with different granularities helping the model to identify sentiment terms with different degrees of sentiment polarity. Specifically, the coarse-grained training step captures the strong sentiment terms from the whole review while the fine-grained training step focuses on the latent fine-grained sentence sentiment which are helpful under the constraint of little labeled data. Experiments on a real-world product review dataset show that the proposed model has a good performance even under the little labeled data constraint.
Exploratory Study to Measure Awareness of Cybercrime in Saudi Arabia

Saudi Arabia is one of the fastest-growing nations in respect to using technology and the Internet. In recent years, the usage of these innovations has grown significantly. There’s a relationship between increasing Internet usage and rising cybercrimes. Although the spread of various protection techniques with different levels of security, there is a lack of application and public awareness. Protection techniques alone are not sufficient to confront cybercrimes, so awareness of cybersecurity is considered one of the most important reasons for reducing cybercrimes. Saudi Arabia is strengthening its interest in the field of cybersecurity. However, there is still an urgent need for research and investigation in awareness of cybersecurity and cybercrime. This study has been applied to students of Jouf University. The study has targeted a group of students who are not fully aware of digital crimes related to technology and the Internet. The study has shown a significant increase in awareness of cybersecurity. However, there is still a group of students with little or absence of awareness of understanding of cybersecurity. The survey analysis results have shown that for most of the participants who encountered cybercrimes, the respondents didn’t inform about the incidents to the local authorities and law enforcement.  The majority of the participants emphasized the importance of enriching the digital content for the public with information about cybercrimes and how to deal with it according to local legislation. Literature review and survey results analysis show that there is a need for a framework that may help in developing awareness among Saudi's residents about cybercrimes.