Reviewerly/exHarmony huggingface dataset:
Application of Front-Projected Holographic Display + Artificial Intelligence in Theory Teaching

With the advancement of science, teaching methods and teaching tools have also experienced tremendous development. However, compared with practical teaching, the innovation and development of theoretical teaching methods and teaching tools have been slower. The development of modern science and technology provides technology and possibilities for innovative research and exploration of theoretical teaching methods and teaching tools. Based on the development results of front-projected holographic display and artificial intelligence technology, this paper proposes a new theoretical teaching application model that combines the two technologies, and discusses the future challenges that this model may face and the technical problems that need to be solved.
Detection of safety wearable’s of the industry workers using deep neural network

Accidents can occur in work place or in place where people are gathering. Many such accidents are preventable. These types of accidents may occur due to carelessness or improper safety wears. These disasters could clearly be maintained a strategic distance from, or its viable force could be decreased with appropriate well-being wear. The fundamental target of this undertaking is to build up a fair-minded computerized framework that checks and investigates the well-being wear of representatives before allowing them to the work region or shop-floor. Along these lines, this framework will mean to forestall mishaps because of inappropriate security wear and the information gathered is utilized for further investigation. Consequently, this system will expect to thwart disasters on account of less than ideal security wear and the data accumulated is used for further investigation. These incidents could clearly be stayed away from, or its viable force could be decreased with appropriate well-being wear.
Impact of Domain Knowledge Quality on Inverse Reinforcement Learning

Incorporating domain knowledge into conventional reinforcement learning has proven to be difficult due to its inability to fully extract the features of the demonstrator. We thus propose the use two algorithms for inverse reinforcement learning, Bayesian Neural Network and Maximum Entropy to deal with this issue. The primary objective of this work is to determine if varying qualities of domain knowledge, in the form of a demonstrator, would have any significant impact on the rewards obtained from the two algorithms by applying it to the mountain car environment.
Distributed Computing Solution for Steganography Using Visual Cryptography and Genetic Algorithm

Photo steganography is a rising research field for improvising secure data hiding and transmission over the network. The base concept of the proposed system which is Least Significant Bit (LSB) based Steganography along with Visual Cryptography using Genetic Algorithm is implemented over a network of distributed systems connected to each other via LAN. The original message is converted into cipher text and then hidden into the Least Significant Bit – LSB of the original image. Visual Cryptography and Genetic Algorithm are implemented for enhancing the security. Genetic Algorithm is basically used to modify the pixel location of steganography image and thus making the detection of the message complex. To encrypt the visual information, Visual Cryptography is used. To achieve it, we break the image into two shares based on a given threshold. The complete workload of the above proposed systems is divided among Distributed Systems with the help MPI, NFS and SSH and the work is done in parallel among the machines in the distributed network. The purpose of the proposed method is to improve the speed of execution of the enhanced secure algorithm to make the proposed system viable to larger data and a wider spectrum of applications.
Visual Explanations for DNNs with Contextual Importance

Autonomous agents and robots with vision capabilities powered by machine learning algorithms such as Deep Neural Networks (DNNs) are taking place in many industrial environments. While DNNs have improved the accuracy in many prediction tasks, it is shown that even modest disturbances in their input produce erroneous results. Such errors have to be detected and dealt with for making the deployment of DNNs secure in real-world applications. Several explanation methods have been proposed to understand the inner workings of these models. In this paper, we present how Contextual Importance (CI) can make DNN results more explainable in an image classification task without peeking inside the network. We produce explanations for individual classifications by perturbing an input image through over-segmentation and evaluating the effect on a prediction score. Then the output highlights the most contributing segments for a prediction. Results are compared with two explanation methods, namely mask perturbation and LIME. The results for the MNIST hand-written digit dataset produced by the three methods show that CI provides better visual explainability.
Usability Assessment of a Jurisprudence System

The development of a software application involves several challenges related to usability and these challenges need to be observed by the teams throughout the development process. The different forms of interaction, between users and the system, increasingly demand adaptation of human behavior. Thus, usability is an important issue, which depends on factors such as: the user, their characteristics and skills, the task that the user intends to perform, and also the context of use in which the user and the software are inserted. This paper presents a heuristic evaluation of the usability of a Jurisprudence system of the Brazilian Administrative Council for Economic Defense, using a set of 13 usability heuristics and 196 sub-heuristics. The evaluation allowed the experts to find some usability problems, mostly of small and medium severity. The evaluation results were passed on to the development team responsible for the software so that the improvements suggested by the evaluators were all implemented before the software was made available to the end-users.
Joint Sparsity with Mixed Granularity for Efficient GPU Implementation

Given the over-parameterization property in recent deep neural networks, sparsification is widely used to compress networks and save memory footprint. Unstructured sparsity, i.e., fine-grained pruning, can help preserve model accuracy, while structured sparsity, i.e., coarse-grained pruning, is preferred for general-purpose hardwares, e.g., GPUs. This paper proposes a novel joint sparsity pattern using mixed granularity to take advantage of both unstructured and structured sparsity. We utilize a heuristic strategy to infer the joint sparsity pattern by mixing vector-wise fine-grained and block-wise coarse-grained pruning masks. Experimental results show that the joint sparsity can achieve higher model accuracy and sparsity ratio while consistently maintaining moderate inference speed for VGG-16 on CIFAR-100 in comparison to the commonly used block sparsity and balanced sparsity strategies.
Multi-modality Based Affective Video Summarization for Game Players

Games has been considered as a benchmark for practicing computational models to analyze players interest as well as its involvement in the game. Though several aspects of game related research are carried out in different fields of research including development of game contents, avatar’s control in games, artificial intelligent competitions, analysis of games using professional gamer’s feedback, and advancements in different traditional and deep learning based computational models. However, affective video summarization of gamer’s behavior and experience are also important to develop innovative features, in-game attractions, synthesizing experience and player’s engagement in the game. Since it is difficult to review huge number of videos of experienced players for the affective analysis, this study is designed to generate video summarization for game players using multi-modal data analysis. Bedside’s physiological and peripheral data analysis, summary of recorded videos of gamers is also generated using attention model-based framework. The analysis of the results has shown effective performance of proposed method.
Learner Model for Adaptive Scaffolding in Intelligent Tutoring Systems for Organizing Programming Knowledge

Learner models are constructed from learner understanding states regarding learning materials, obtained by observing learner behaviors. A learner model collects information for adaptive tutoring and uses this information to determine system actions such as hints, problem presentations, and explanations appropriate to learner understanding. A learner model thus plays an essential role in intelligent tutoring systems (ITSs). Our previously developed ITSs have adopted a simple learner model that considers once-solved problems as learner-acquired solutions for problem solving. However, such models cannot grasp learner proficiency for solutions, which is an important aspect of constructing learner models because learners often make problem-solving mistakes despite having already learned the solution. In such situations, it is effective for an ITS to apply scaffolding based on learner proficiency regarding the solution. However, excess scaffolding decreases comprehension, a phenomenon known as the "assistance dilemma." A learner model that adaptively scaffolds based on analysis of learner proficiency in solutions can effectively mitigate the assistance dilemma. In this study, we develop and evaluate an ITS with a learner model that considers learner proficiency in solutions. Experimental results indicate that the developed learner model can reduce total feedback without changing learner comprehension.
Integration of Science and Education: Digitalization as the Basis for Integrated Interdisciplinary Learning

The article summarizes the experience of integrating science and education in management and economics at the Russian State University for the Humanities on the basis of research conducted at the Institute of Control Sciences of the Russian Academy of Sciences. The main methodological principles and methods of trans - and interdisciplinary training of bachelors and undergraduates using digital and remote technologies are considered. The goals, tasks, and objects of study are formulated. The methodological principles of teaching based on the object-functional approach in the theory of management are proposed. The experience of teaching, including interactive teaching methods, is considered.
The Effectiveness of Scene-Based Icons Inspired by the Oracle Bone Script in Cross-Cultural Communication

Oracle bone script is an ancient form of writing character used by ancient Chinese. It takes advantage of static pictographic elements to shape scenes, thus conveying dynamic and prosperous messages. The purpose of this study is to demonstrate that scene-based icons inspired by the oracle bone script can be effectively recognized and understood by people from different cultures and thus used to help in cross-cultural communication scenarios. An experiment was conducted with a sample of 16 people from different cultural backgrounds to determine the icons' recognizability. The result indicates that these icons have relatively high recognizability in a cross-cultural context.
An Efficient Approach for Secure Data Outsourcing using Hybrid Data Partitioning

This paper presents an implementation of a novel approach, utilizing hybrid data partitioning, to secure sensitive data and improve query performance. In this novel approach, vertical and horizontal data partitioning are combined together in an approach that called hybrid partitioning and the new approach is implemented using Microsoft SQL server to generate divided/partitioned relations. A group of proposed rules is applied to the query request process using query binning (QB) and Metadata of partitioning. The proposed approach is validated using experiments involving a collection of data evaluated by outcomes of advanced stored procedures. The suggested approach results are satisfactory in achieving the properties of defining the data security: non-linkability and indistinguishability. The results of the proposed approach were satisfactory. The proposed novel approach outperforms a well-known approach called PANDA.
Exploiting Vulnerabilities of IP Cameras: Lebanon Case Study

As applications and devices in the Internet of Things (IoT) increasingly grow, the protection and privacy of IoT devices has been a significant issue. In addition, in many cases there are no security protocols for the defense of these devices that can be a best place for the attacker to take advance of these vulnerabilities which can cause many damage in the level of availability, integrity and privacy of the users in many types of IoT devices. For control and connectivity to their computers, users of IoT devices use the internet. The usage of IoT technology has massively grown over time. Some statistic show that in the end of 2021 the number of these devices can reached 50 billions, we are constantly and unwittingly collecting our sensitive data via IoT devices. In this paper, we present a case study about the IP cameras used in Lebanon and the most vulnerabilities that affect such devices.
Virtual Mouse Control Using Finger Action

In latest era, human–computer interaction has gained more popularity. The mouse is one of the best inventions in human–computer interaction technology. It is used to point the digital world content by using the physical module. To give this existing traditional way of technology an intelligence, we proposed a computer vision-based hand gesture recognition system to interact with the digital world. Our system controls mouse pointer using finger count and finger strip. Our approach mainly focuses on color segmentation, color tracking, extraction of finger contour, and finger counting algorithm. To add-upon this, a speech recognition-based method is used to perform some basic operations on our system. Therefore, the proposed system eliminates the device dependency to interact with the computer, and it would be considered as a good approach to a gesture-based interface for HCI in future.
Verification of denoising performance of edge-preserving noise reduction filter using fast M-estimation method

Random noise injures both the basic image quality and also the following image processing procedures. The low-pass filter is commonly used as the image denoising. Low-pass filter can reduce noise; however, the edge becomes always blur as the side effect. In order to suppress this side effect, we proposed edge preserving noise reduction filter using Fast Mestimation method. As the Proposed method is applied experimentally to the noisy image, it was clarified that the noise was clearly reduced and the performance of edge preserving was realized at the same time. In this study, a quantitative evaluation of the denoising performance of the proposed method is obtained by varying the amount of noise applied and obtaining the denoising ratio.
Attribution of Decorative Marble Papers in the Study of Russian Binding of the 18th — early 20th centuries: Problems and Solutions

An important stage in the study and attribution of the Russian binding of the 18th — early 20th century is the description of the flyleaf and other elements of decorative paper. First of all, this applies to paper with marble drawings (marble paper), found in the Russian book since the 18th century. Modern researchers of Russian binding of the 18th — early 20th centuries face a number of problems related to the lack of literature on the topic, including methodological and reference, the lack of specialized collections and exhibitions of decorative paper in Russia and the lack of development in the domestic book science of the terminology for describing the binding materials. This article substantiates the need to create the nomenclature of drawings and link them to a certain chronological period. The author analyses three main groups of problems: terminological, systematization of marble drawings and their chronological correlation, problems of describing paper as a material. The first group includes different interpretations of term and unclear definition of many terms; phenomena of synonymy and polysemy when using particular names of drawings (patterns). Not only historians of the book, but also librarians, restorers, masters of individual binding, second-hand booksellers and bibliophiles have their own independently formed professional dictionary, which gives place to decorative papers. This inconsistency is reinforced by borrowing French, German and English terms, which, in turn, can also be duplicated. The author notes that systematization of decorative papers with marble drawings can be based on the methods of its colouring, but such a technological approach is not sufficient to describe a specific sample of marble paper. The article shows that various patterns periodically gained and lost popularity, then returned to bookbinding practice, but with a number of characteristic changes and additions. Correct description of the paper in binding is impossible without determining its origin (Russian/foreign), the method of production and colouring (manual/machine) and the specific properties of the material itself. At the same time, there are no methods and schemes for describing decorative paper grades. The article highlights that the development of such method will help to significantly narrow the chronological framework when attributing the binding.
An Assessment of Extreme Learning Machine Model for Estimation of Flow Variables in Curved Irrigation Channels

The bend existence in river and artificial channels irrigation channel is an imminent act. So, the hydraulic flow variables investigation using recording laboratory instruments and computational models has considerable importance. In the present paper, the velocity and flow depth variables in 60° open channel bend are measured in laboratory flume by sensor instrumentation. Furthermore, the main objective of the current study is to evaluate the performance of the ELM model because of its significant advantages such as high learning speed. Accordingly, a robust Extreme Learning Machine (ELM) model is designed and trained based on available experimental data. The present paper is the first application of the ELM model in estimating flow variables in curved channels. The results show that the variables measurement instrumentations act more accurately and the laboratory model can predict bend flow patterns well. Furthermore, the ELM model can predict the flow velocity and depth with low error indices and has an acceptable agreement level with experimental values (Mean Absolute Relative Error (MARE) = 0.020 and 0.034 in depth and velocity prediction model, respectively). Both the laboratory and computational ELM models have good efficiency in different passing flow discharges. The proposed ELM model can be used in the implementation and design of a curved channel in practical cases. Also, the high accuracy measurement instruments can be applied in measuring and controlling flow variables in various laboratory fields.
A Study on the Classification of Chinese Interrogatives Based on Pragmatic Functions

This paper emphasizes the importance of regular questions in question classification, analyzes and summarizes four types of interrogative sentences and their corresponding pragmatic functions. On this basis, we have manually annotated interrogative sentences in selected novels and BaiduQA and have created an interrogative sentences corpus. A formal feature set for question classification is constructed based on corpus, and classification experiments based on rules and statistics are conducted respectively to check the classification effect of each feature. The set of feature rules is formed by optimizing feature combinations iteratively through multiple rounds of experiments, which provides a formal classification basis for the existing Question-answering systems. Through the comparison of various classification models, it is found that the method of finite state automaton can achieve a macro average F1-score of 0.94, whereas the random forest model achieving a macro average F1-score of 0.98, has better performance in classification.
El desarrollo de la comprensión y aplicación de tecnologías en el área de educación para el trabajo de la secundaria

espanolEl articulo presenta resultados de una investigacion aplicada proyectiva con enfoque cualitativo que estudio el empleo de presentaciones multimedia en el proceso de ensenanza-aprendizaje para el desarrollo de la comprension y aplicacion de tecnologias en el area de Educacion para el Trabajo de la Secundaria. Se trabajo con una muestra intencionada de siete docentes del area y ciento veinticinco estudiantes. Se utilizaron las encuestas y fichas de analisis documental. Los resultados evidencian que los docentes de Educacion para el Trabajo emplean estrategias y medios tradicionales, poco efectivos para el desarrollo de la comprension y aplicacion de tecnologias sin embargo los estudiantes se muestran alegres y motivados cuando se utilizan presentaciones. EnglishThe article presents results of an investigation applied projectivewith qualitative approachthat she studied the employment of multimedia presentations in the teaching-learning process for the development of the understanding and application of technologies in the area of Education for the Work of the Secondary. In the investigation one worked with an educational deliberate sample of seven of the area and hundred twenty-five students. They were used as instruments the surveys and records of documental analysis. The results evidence that the educational of Education for the Work use strategies and traditional means, not very effective for the development of the understanding and application of technologies the students are shown however cheerful and motivated when presentations are used in the development of the learning sessions and in their majority, they would like use of these didactic materials to be made.
Firefly Algorithm for Functional Link Neural Network Learning

Functional Link Neural Network (FLNN) is a type of single layer feedforward neural network that is less complex than other multilayer feedforward networks. The network uses less weight parameters which make the network training using Backpropagation algorithm less complicated. However, due to the less numbers of weight parameters, FLNN network is prone to yield inconsistent results. This work proposed Firefly algorithm (FA) as learning algorithm for FLNN. The results from the present investigation suggest that the FA algorithm is able to train the FLNN network and enhance its performance with better accuracy rate.
Web Components Template Generation from Web Screenshot

AI-driven automation is the game-changer in this decade. The one concept that belongs to this domain is to simulate human working processes by using machine learning. An adaptation of this knowledge in web development is popularized topic in the web developer society. Moreover, Web Components, the new paradigm in software engineering practices in web development, becomes the new standard defined by World Wide Web Consortium (W3C). It is an essential building block for modularizing large and complex web applications into smaller pieces and then presenting them via the web browser on the user's computer or mobile. We combine knowledge between Computer Vision (CV) with deep learning and Web Components developer framework together to train the machine to recognize bounding boxes and category labels for each object of interest in an image. This paper introduces the methodology to automatically generate a website by neuron network model composite with many small web components. Our work's best result has a validation loss of 1.873, which can recognize the web object and transform it into the Web Components Template by React web framework.