Reviewerly/exHarmony huggingface dataset:
Encapsulation Video Classification and Retrieval Based on Arabic Text

Since Arabic video classification is not a popular field and there isn’t a lot of researches in this area especially in the educational field. A system was proposed to solve this problem and to make the educational Arabic videos more available to the students. A survey was fulfilled to study several papers in order to design and implement a system that classifies videos operative in the Arabic language by extracting its audio features using azure cognitive services which produce text transcripts. Several preprocessing operations are then applied to process the text transcript. A stochastic gradient descent SGD algorithm was used to classify transcripts and give a suitable label for each video. In addition, a search technique was applied to enable students to retrieve the videos they need. The results showed that SGD algorithm recorded the highest classification accuracy with 89.3 % when compared to other learning models. In the section below, a survey was introduced consisting of the most relevant and recent papers to this work.
Deep Learning Approach for Automatic Romanian Lemmatization

This paper proposes a deep learning sequence-to-sequence approach to improve the task of automatic Romanian lemmatization. The study compares 24 systems using different combinations of recurrent, convolutional and attention layers, while the text input consists of word-lemma pairs, both at word and trigram level. As Romanian is a low resourced language in the field of text processing, the aim of this study is to use as little input information as possible. Thus, to increase the lemmatization accuracy, additional lexical features (such as context and POS tags) have been provided only gradually. For the trigrams case, two scenarios were proposed: to predict the lemma for every word from the sequence or to predict the lemma only for the word in the middle. The lemmatizers have been analyzed on two Romanian datasets: the Romanian Explicative Dictionary (DEX) and the belletristic subset of the CoRoLa corpus. For the DEX dataset, the best results were obtained with the LSTM-based systems at both word (99.32%) and character level (99.43%). For the CoRoLa subset, the CNN-based architecture outperforms at trigram (95.86%) and word level (99.09%) while the LSTM-stacked system obtained the highest accuracy at character level (98.78%).
THE INFLUENCE OF LEARNING INTEREST ON THE WRITING SKILLS OF PROCEDURE TEXTS FOR GRADE XI

Abstract This study aims to prove whether there is an influence of learning interest on the ability to write procedural texts. The hypothesis tested is that there is an influence of interest in learning Bahasa Indonesia on the ability to write procedural texts. This study uses a quantitative description method because the research data is in the form of numbers, analyzed using statistics and describes the relationship between variables. The number of samples was 62 students of class XI SMK PP Negeri Sumedang. The instruments used in this research are test and non-test. The test instrument is in the form of a procedure text writing skill test result. Meanwhile, the non-test instrument was in the form of a questionnaire containing 20 indicators of interest including attention, enthusiasm, great desire, and interest in learning bahasa Indonesia. The results of the correlation analysis is 0.689, it indicates that there is a strong relationship between the interest in learning Bahasa Indonesia and the ability to write procedural texts. The regression value (r) square is 0.839, it indicates the influence of the independent variable on the dependent variable. Test the significance or significance of the regression coefficient obtained sig. 0.000 < 0.05 and tcount = 17.665. There is a significant effect of interest in learning bahasa Indonesia on the ability to write procedural texts.
ON THE ISSUE OF DISSEMINATION AND UTILIZATION OF INTELLIGENCE FOR BORDER SECURITY BY THE NIGERIA CUSTOMS SERVICE

The task of regulating the activities along the nation’s border and ensuring national security calls for designing and redesigning security architect particularly in dissemination and utilization of intelligence among the security agencies and in the Nigeria Customs Service (NCR). The prevalence of violence in Nigeria and illegal inflow of arms, drugs and persons informed the need to examine the dissemination and utilization of intelligence, its implication on national security and the challenges associated with it. The paper was descriptive and combined primary and secondary sources of data collection. The primary source adopted questionnaire while the secondary combined interview and available materials. The study found that NCS relied largely on human intelligence, while there was weak cooperation between the NCR and other security agencies which in turn negatively affected dissemination and utilization of intelligence with grave implications of national security. In dissemination and utilization of intelligence, the NCR was largely guided by the relevance, timeliness and trustworthiness of the intelligence. Challenges associated with dissemination and utilization of intelligence included poor technological development, jealousy and lack of trust. The study recommended that the NSC should endeavour to improve the relationship between it and other security agencies while the poor network of communication equipment should be attended to and for the borders to be properly manned to checkmate illegal inflow of banned and dangerous items to ensure national security.
Methodological Map as a New Element of the Diagnostic System of Personnel Training for Modern Economy

The article is devoted to the analysis of the possibilities and the description of the methodological map, which is the Russian analogue of the “individual cell” of the student and has proven itself well in the United States. The methodological map reflects the static profile of the trainee’s abilities and, when periodically filled in, represents a dynamically developing trajectory of a professional orientation, regardless of the profile.At present, the general need of the economy for high-quality specialists based on the selection of students capable of mastering complex, high-tech technologies that have a clear potential for professional growth and personal development. At the same time, we can state the absence of a developed relationship between the education system and the vocational guidance system, which strategically can lead to problems in providing the most technologically advanced segments of the economy with professional staff. At the same time, thereis a positive international experience in solving this problem, the elements of which can be adapted to the existing Russian system.The article presents the content and describes the capabilities of the methodological map developed by the staff of the M.V. Lomonosov, and is an analogue of the “individual cell” developed in the United States. The methodological map is information about the level of several types of competence of any student. To work with such a card, the teacher needs to expand his competence and become an expert testologist with a sufficient level of test competence. An obvious advantage of the methodological map is the ease of its digital processing, the accumulation of information about the student in centralized databases. The methodological map, after appropriate modernization, can be the basic option for training highly qualified specialists who have chosen the path of a scientist. The development of such a diagnostic system has significant positive features, the most obvious of which is the ability to ensure the connection between the content of education and the personnel needs of society and the state, and, as a result, to ensure their sustainable socio-economic development.
Sparse Progressive Neural Networks for Continual Learning

Human brain effectively integrates prior knowledge to new skills by transferring experience across tasks without suffering from catastrophic forgetting. In this study, to continuously learn a visual classification task sequence, we employed a neural network model with lateral connections called Progressive Neural Networks (PNN). We sparsified PNNs with sparse group Least Absolute Shrinkage and Selection Operator (LASSO) and trained conventional PNNs with recursive connections. Later, the effect of the task prior on current performance is investigated with various task orders. The proposed approach is evaluated on permutedMNIST and selected subtasks from CIFAR-100 dataset. Results show that sparse Group LASSO regularization effectively sparsifies the progressive neural networks and the task sequence order affects the performance.
MULTIMODAL APPROACH FOR FACE DETECTION AND VERIFICATION

The availability of large annotated datasets and affordable computational power have led to impressive improvements in the performance of Convolutional Neural Networks (CNNs) on various face analysis tasks. In this paper, we describe a deep learning pipeline for unconstrained face identification and verification which achieves state-of-the-art performance on several benchmark datasets. We provide the design details of the various modules involved in automatic face recognition: face detection, landmark localization and alignment, and face identification/verification. We propose a novel face detector, Deep Pyramid Single Shot Face Detector (DPSSD), which is fast and detects faces with large scale variations (especially tiny faces). Additionally, we propose a new loss function, called the Crystal Loss, for the tasks of face verification and identification. Crystal Loss restricts the feature descriptors to lie on a hypersphere of a fixed radius, thus minimizing the angular distance between positive subject pairs and maximizing the angular distance between negative subject pairs. We provide evaluation results of the proposed face detector on challenging unconstrained face detection datasets. Then, we present experimental results for end-to-end face verification and identification on IARPA Janus Benchmarks A, B and C (IJB-A, IJB-B, IJB-C), and the Janus Challenge Set 5 (CS5).
FPGA Hardware Co Simulation of Image Encryption using Hybrid Chaotic Maps Based Stream Cipher

In This paper, new model of image encryption is designed. This model using stream cipher based on finite precision chaotic maps. The model designed in efficient way by using Xilinx System Generator (XSG). Pseudo Random Bit Generator (PRBG) depends on chaotic maps is proposed to design Fixed Point Hybrid Chaotic Map-PRBG (FPHYBCM-PRBG). National Institute of Standards and Technology (NIST) randomness measures tested the randomness of the proposed FPHYBCM-PRBG system. The security analysis, such as histogram, correlation coefficient, information entropy, differential attack (NPCR and UACI) are used to analyze the proposed system. Also, FPGA Hardware Co-Simulation over Xilinx SP605 XC6SLX45T provided to test the reality of image encryption system. The results show that FPHYBCM-PRBG is suitable for image encryption based on stream cipher and outperform some encryption algorithms in sufficient way to enhance the security and robust against brute force attack with low maximum frequency and throughput.
THE EFFECT OF MEDIA AND CRITICAL THINKING ON THE ABILITY TO READ COMPREHENSION IN INDONESIAN LANGUAGE LEARNING AT SMK NEGERI 4 CILEGON

The research was to determine how the effect of instructional media in the form of print media in the form of short stories and think critically about the reading comprehension ability. The research method used was quasi-experiment. The population in this study was all of the students grade X SMK Negeri 4 Cilegon. Data analysis used was a descriptive statistic and inferential statistic method performed by ANAVA 2 lines. The results showed that there are significant between instructional media whit reading comprehension ability. Expressed with the test results Microsoft Office Excel 2007.It showed that sig point for instructional media line 10,696 > 4,11. Then there is the influence of reading comprehension ability with sig for critical thinking line is 7,992 > 4,11. And there is a significant interaction effect of instructional media and think critically about the reading comprehension ability with sig is 70,061 > 4,11.  Following was Tukey test, and there is a different reading comprehension ability with print media and high critical thinking with audio media and high critical thinking showed by significant 11,640 > 4,33. There is a different reading comprehension ability with audio media and low critical thinking with print media and low critical thinking showed by significant 5,099  > 4,33. There is a different reading comprehension ability with print media and high critical thinking with print media and low critical thinking showed by significant   11,197  > 4,33. There is a different reading comprehension ability with audio media and high critical thinking with audio media and low critical thinking showed by significant 5,543 > 4,33.
Provably Safe Artificial General Intelligence via Interactive Proofs

Methods are currently lacking to prove artificial general intelligence (AGI) safety. An AGI ‘hard takeoff’ is possible, in which first generation AGI1 rapidly triggers a succession of more powerful AGIn that differ dramatically in their computational capabilities (AGIn &lt;&lt; AGIn+1). No proof exists that AGI will benefit humans or of a sound value-alignment method. Numerous paths toward human extinction or subjugation have been identified. We suggest that probabilistic proof methods are the fundamental paradigm for proving safety and value-alignment between disparately powerful autonomous agents. Interactive proof systems (IPS) describe mathematical communication protocols wherein a Verifier queries a computationally more powerful Prover and reduces the probability of the Prover deceiving the Verifier to any specified low probability (e.g., 2−100). IPS procedures can test AGI behavior control systems that incorporate hard-coded ethics or value-learning methods. Mapping the axioms and transformation rules of a behavior control system to a finite set of prime numbers allows validation of ‘safe’ behavior via IPS number-theoretic methods. Many other representations are needed for proving various AGI properties. Multi-prover IPS, program-checking IPS, and probabilistically checkable proofs further extend the paradigm. In toto, IPS provides a way to reduce AGIn ↔ AGIn+1 interaction hazards to an acceptably low level.
Simulation of Regional Economic Development Potential Forecast Model Based on Remote Sensing Information Mining

This research paper conducts the research on the simulation of regional economic development potential forecast model based on remote sensing information mining. With the help of relevant advanced technology, the logistics system can improve its operational efficiency and enhance its logistics supply capacity. At the same time, to a certain extent, the potential logistics demand in the region will be further stimulated by growth. For the efficient analysis, the remote sensing models and the image processing frameworks are combined for the optimal selection. The prediction model is verified through the test. Compared with the other approaches, the proposed is efficient.
TEMPORAL CONDENSATION OF TAMIL NEWS

Since the dawn of the Internet, we have been inundated with an excess of information. The volume of information available on the Internet is expected to grow exponentially. This brings a need for summarization of information. Thus, making summarization one of the most sought-after topics in the domain of natural language processing. It is essential to be informed about the vital happenings, and newspapers have been serving this purpose for a very long time. Sadly, there is a perception among the general public that no news agency today can be unequivocally trusted, the credibility of news articles is uncertain. Therefore, one has to read news articles from various sources to get an unbiased view on topic. When a query related to an event is entered in SEs like google, the search renders an overwhelming number of responses, it is humanly impossible to read all of them. In an effort to address the aforementioned problems, a condensation of news articles covering the Tamilnadu Legislative Assembly election is performed. The news articles were collected from various news sources over a period of two months. The collected articles were translated from Tamil to English. These articles included news about various events, in order to segregate Tamilnadu related news from them k-means clustering was performed on the dataset. The relvant news articles acquired was pre-processed to remove ambiguity and mistakes from translation. These articles were summarized individually using a linear regression model that gave importance to features such as named entities, number of words that were similar to title etc. The acquired individual summaries were summarized using BERT extractive summarizer as it would reduce redundancy. When generated summary was compared with introduction and title of the article in the absence of an introduction a precision of 0.512, recall of 0.25 and f-measure of 0.31 were obtained.
A hybrid approach for categorizing images based on complex networks and neural networks

There are several methods for categorizing images, the most of which are statistical, geometric, model-based and structural methods. In this paper, a new method for describing images based on complex network models is presented. Each image contains a number of key points that can be identified through standard edge detection algorithms. To understand each image better, we can use these points to create a graph of the image. In order to facilitate the use of graphs, generated graphs are created in the form of a complex network of small-worlds. Complex grid features such as topological and dynamic features can be used to display image-related features. After generating this information, it normalizes them and uses them as suitable features for categorizing images. For this purpose, the generated information is given to the neural network. Based on these features and the use of neural networks, comparisons between new images are performed. The results of the article show that this method has a good performance in identifying similarities and finally categorizing them.
Conception of Culture in the Mediative Process

The purpose of this paper is to highlight the integrated features of the “mediation” concept in the mediation and cultural context.&#x0D; The methodology. The study investigates this issue by using the basic methods and principles of cultural studies as well as a number of theoretical research methods (analysis and synthesis, comparative (compared the views of scientists, traditions, values of different cultures), and terminological analysis.&#x0D; The results. The paper highlights the understanding of the notion “culture” in the mediation process that is analyzed as a means of successful communication of equal partners in a cross­cultural environment in the age of globalization. It investigates the issues of culturological empathy as a leading component of the process of interaction of individuals in a multicultural society, which determines the effective conflict­free human activity without losing their individuality. It is proved that the understanding of culturological features reveals the potential of new opportunities that ensure the coherence of actions of social actors based on the relevant cultural values.&#x0D; The topicality of this paper is in the generalization of the theoretical concepts in the modern science as for interpretation of interrelations of the phenomenon of culture and mediation.&#x0D; The practical significance presupposes the ways of implementation of the theoretical cultural principles in the mediation process that enhances the performance.
THE LEVEL OF STUDENTS SATISFACTION ON LEARNING ENGLISH FROM HOME AT MTI PASIA BOARDING SCHOOL DURING COVID-19 PANDEMIC

This research was motivated by the disruption of the student learning process during the Covid-19 pandemic at MTI PASIA. This research was conducted to know how the level of student satisfaction in class X, XI, and XII at MTI PASIA on learning from home using E-learning during the Covid-19 pandemic.The design of this research was survey and the instrument used is a questionnaire. The subjects of this research were students of class X, XI and XII with a total of 167 students and students who will be used as research samples as many as 118 students. The instrument validity test was carried out by distributing instruments to 3 experts as instrument validators and to analyze the data using quantitative descriptive techniques with percentages.Based on the results of research conducted by researchers, the title of this research is the level of satisfaction of students learning English from home at MTI PASIA during the COVID-19 pandemic. Researchers conducted research to know how the level of student satisfaction in learning English towards the objectives and learning components. The results obtained that the level of satisfaction of student towards learning objectives and learning component are 45.92%, teacher 48.30%, student 48.68%, material 50.78%, method 49.83%, strategy 48.89%, media 49.65% and evaluation 49, 48%. Theaverage answer was 48.94% or ≥40% -≤70% and that means they are quite satisfied, according to data interpretation that has been determined.
Techniques for Recognizing and Extracting Facial Emotions

Outward appearance assumes an imperative part in face acknowledgment frameworks and picture handling methods of human machine interface, A human outward appearance is at least one movement or place of the muscles underneath the skin of the face. Facial demeanours are a type of nonverbal communication, they are essential methods for passing on friendly data between humans, but they additionally happen in most different vertebrates and some other creature species. Humans can receive an outward appearance deliberately or automatically and the neural components are liable for controlling the demeanour contrast in each case. Facial feelings assume the most significant part to perceive the goals of others. In this paper detailed study of steps involved in facial emotion recognition and extraction from different medium is discussed. Finally, a system is proposed here to extract facial information from both grey scale & RGB scale images using Robert’s & Sobel’s Techniques. It is trained by a dataset using a Convolutional Neural Network (CNN) model to predict for the test images which can be verified from the result.