Reviewerly/exHarmony huggingface dataset:
CREATING MOBILE APPS IN MIT APP INVENTOR USING GAMIFICATION ELEMENTS WHEN LEARNING PROGRAMMING IN BASIC SCHOOL

The article describes the possibilities of teaching basic school students the beginnings of programming based on visual environments, in particular, the MIT App Inventor mobile application development environment, using gamification elements. The practical significance of using the MIT App Inventor environment in teaching the basics of algorithms and programming is revealed. The importance of including elements of gamification in the learning process is substantiated in order to increase the motivation of students, develop their teamwork skills and the possibility of independently obtaining a finished product in the form of a popular mobile application. Schoolchildren get the opportunity to use the created software product, modify it for different educational tasks. The article provides specific examples of using the MIT App Inventor mobile application development environment in organizing and conducting an informatics lesson.
Real-Time Shadow Detection From Live Outdoor Videos for Augmented Reality

Simulating shadow interactions between real and virtual objects is important for augmented reality (AR), in which accurately and efficiently detecting real shadows from live videos is a crucial step. Most of the existing methods are capable of processing only scenes captured under a fixed viewpoint. In contrast, this article proposes a new framework for shadow detection in live outdoor videos captured under moving viewpoints. The framework splits each frame into a tracked region, which is the region tracked from the previous video frame through optical flow analysis, and an emerging region, which is newly introduced into the scene due to the moving viewpoint. The framework subsequently extracts features based on the intensity profiles surrounding the boundaries of candidate shadow regions. These features are then utilized to both correct erroneous shadow boundaries for the tracked region and to detect shadow boundaries for the emerging region by a Bayesian learning module. To remove spurious shadows, spatial layout constraints are further considered for emerging regions. The experimental results demonstrate that the proposed framework outperforms the state-of-the-art shadow tracking and detection algorithms on a variety of challenging cases in real time, including shadows on backgrounds with complex textures, nonplanar shadows, fast-moving shadows with changing typologies, and shadows cast by nonrigid objects. The quantitative experiments show that our method outperforms the best existing method, achieving a 33.3% increase in the average Fmeasure on a self-collected database. Coupled with an image-based shadow-casting method, the proposed framework generates realistic shadow interaction results. This capability will be particularly beneficial for supporting AR applications.
An Efficient AES with Custom Configurable Encryption Algorithm Using Dynamic Keys for Secure Data Communication in Networks

In this digital era, people are moving fast towards cloud and big data; hence, it becomes very important for the organization to encrypt the data. To enable secure data communication, the user needs different encryption algorithms. The encryption algorithms are RC4, RC5, SHA, DES,3DES, AES, etc. The mostly used algorithm is the Advanced Encryption Standard [AES]. This utilizes symmetrical keys, where the keys are shared between transmitter and receiver, i.e., the key will be easily hijacked by attackers. In this paper, we proposed the system working on three phases. Firstly, the custom replacement for input values like one character replaced by another character. Secondly, encrypt the data by using AES dynamic keys. To generate a key using 16 bytes, data frame is sending sequentially. Based on the order of this sequence, the key is changed dynamically with encrypted data and finally add padding bits to the ciphertext to provide more security to data or information.
EAC: Encryption Access Control Scheme for Policy Revocation in Cloud Data

Since a lot of information is outsourcing into cloud servers, data confidentiality becomes a higher risk to service providers. To assure data security, Ciphertext Policy Attributes-Based Encryption (CP-ABE) is observed for the cloud environment. Because ciphertexts and secret keys are relying on attributes, the revocation issue becomes a challenge for CP-ABE. This paper proposes an encryption access control (EAC) scheme to fulfill policy revocation which covers both attribute and user revocation. When one of the attributes in an access policy is changed by the data owner, the authorized users should be updated immediately because the revoked users who have gained previous access policy can observe the ciphertext. Especially for data owners, four types of updating policy levels are predefined. By classifying those levels, each secret token key is distinctly generated for each level. Consequently, a new secret key is produced by hashing the secret token key. This paper analyzes the execution times of key generation, encryption, and decryption times between non-revocation and policy revocation cases. Performance analysis for policy revocation is also presented in this paper.
A Personalized Recommendation System based on Knowledge Graph Embedding and Neural Network

The application of Neural Network to recommendation task has gradually drawn attention over the last few years, and a recommendation algorithm combining neural network with collaborative filtering has emerged. Meanwhile, knowledge Graph and Graph Embedding have also developed considerably. In this paper, a new algorithm level solution is presented to realize personalized recommendation that is based on Knowledge Graph Embedding and Neural Network. Knowledge Graph Embedding is used to embed each entity into a low-dimensional vector. The learned vectors are as the input of the neural network to predict the score of an item. Through a series of systematic tests involving the MovieLens-1M dataset, we demonstrate that it can effectively improve the accuracy of rating prediction comparing with the original neural collaborative filtering algorithm.
METHOD FOR COHERECE EVALUATION OF UKRAINIAN TEXTS USING CONVO-LUTIONAL NEURAL NETWORK

The estimation of text coherence is one of the most actual tasks of computer linguistics. Analysis of text coherence is widely used for writing and selection of documents. It allows clearly conveying the idea of an author to a reader. The importance of this task can be confirmed by the availability of actual works that are dedicated to solving it. Different automated methods for the estimation of text coherence are based on the methodology of machine learning. Corresponding methods are based on of formal text representation and following detection of regularities for the generation of an output result. The purpose of this work is to perform the analytic review of different automated methods for the estimation of text coherence; to justify method selection and adapt it due to the features of the Ukrainian language; to perform the experimental verification of the effectiveness of the suggested method for a Ukrainian corpus. In this paper, the comparative analysis of the methods for the estimation of coherence of English texts basing on a machine learning methodology has been performed. The expediency of application of methods that are based on trained universal models for the formalized representation of text components has been justified. The following models using neural networks with different architecture can be considered: recurrent and convolutional networks. These types of networks are widely used for text processing because they allow processing input data with an unfixed structure like sentences or words. Despite the ability of recurrent neural networks to take into account previous data (this behavior is similar to text perception by the reader), the convolutional neural network for conducting experimental research has been chosen. Such choice has been made due to the ability of convolutional neural networks to detect relations between entities regardless of the distance between them. In this paper, the principle of the method basing on the convolutional neural network and the corresponding architecture has been described. Program application for the verification of the suggested method effectiveness has been created. Formalized representation of text elements has been performed using a previously trained model for the semantic representation of words; the training process of this model has been implemented on the corpus of Ukrainian scientific abstracts. The training of the formed networks using pre-trained model has been performed. Experimental verification of method effectiveness for solving of document discrimination task and insert task has been made on the set of scientific articles. The results obtained may indicate that the method using convolutional neural networks can be used for further estimation of coherence of Ukrainian texts.
Improving cyber-physical system performance through actuator-sensor interactions

Cyber-physical systems are used both for sensing the environment around them as well as making changes to that environment (actuation). Interactions between actuators and sensors can create interference or uncertainty, but can also be leveraged to improve the sensing coverage, sensing resolution or capability of a cyber-physical system. The challenge in modifying actuator behavior in a system is to avoid degrading the actuation range or responsiveness. The acceptable range of actuator adaptation can be modelled and tested using physics-based, data-driven, or combination approaches.
ICrA Over Ordered Pairs Applied to ABC Optimization Results

In this paper eight differently tuned Artificial Bee Colony (ABC) algorithms are considered. The influence of the population size, the maximum number of iterations and the probability for choosing a food source \(({p_i})\) on the algorithm performance is investigated. Parameter identification of an E. coli fed-batch fermentation process model is used as a case study. The ABC algorithms with the best performance are defined based on the obtained numerical results. The considered ABC algorithms are compared applying InterCriteria Analysis. An additional knowledge about the ABC algorithms correlations is obtained as a result.
Towards An Empirical and Theoretical Evaluation of Gradient Based Approaches for finding Kernel Minimum Enclosing Balls

In this paper we introduce a projected gradient descent algorithm to find kernel minimum enclosing balls and compare it to a gradient based Frank-Wolfe algorithm. We base our comparison on empirical as well as theoretical observations of the two methods by comparing different aspects of their behaviors that involve runtime, stability, abilities to find novel datapoints as well as convergence rates.
Secured Surveillance Storage Model Using Blockchain

Nowadays, huge amount of surveillance data is being generated continuously by various resources. Storing and processing of surveillance data are a tedious task for any organization. Distributed environment plays a vital role for analyzing the collected data. However, sharing the data among various distributed devices in different layers raises security concerns where an adversary may capture or tamper with features to mislead the surveillance system. In this paper, we propose a blockchain-based model which makes the data stored in it immutable. Here, the files are saved in a distributed ledger technology called the Inter Planetary File System (IPFS), which works on the principle of cryptography. In IPFS, the data is stored as a Merkle tree by using one-way hash function. Our proposed model includes various policy and maintenance mechanisms, certificate authorities, authentication, and revocation of certificates to the data in IPFS with the help of smart contracts.
Comparison of Behavior Rate Models Based on Bayesian Belief Network

We can use knowledge about human behavior frequency in many fields. There are many problems in sociology, psychology and other sciences studying human behavior where it becomes necessary to predict the behavior frequency. Direct evaluation of frequency is not always executable therefore developing of methods of indirect frequency evaluation is actual problem. The term behavior rate or frequency is determined as the mean number of behavior episodes happened during the particular period. A behavior episode is an activity happened in a certain time. Previously an approach based on Bayesian belief networks and data about last behavior episodes was presented. We suggest an improvement by including in the model a hidden variable representing an interval between the last episode in study period and the next episode, i.e. the first episode occurred after the study period. We compare this and the previous models. For learning and testing the models data are synthesized and collected from Vkontakte, the most popular social Network in Russia. The results can be used in various scientific fields, the subject of which is a human behavior: sociology, psychology, marketing etc.
Dual Flow Framework on Bimodality Emotion Recognition Based on Facial Expression and Eye Movement

Emotion affects many aspects of our daily life. Lack of emotional communication in online learning results in poor learning effect and learning experience. This paper discusses the feasibility of recognizing emotions by using eye movements and facial expressions data together. A framework for identifying the learner's emotional state based on expression and eye movement bimodality recognition is proposed based on the complementarity of eye movement and facial expression data. The video stream sequence is used to obtain the learner's facial expression and eye movement information as dual-channel data, which is input into the data stream framework, and the machine learning method is used to predict the learner's emotional state. Dual-channel data flow framework proposed in this paper can be used not only to discover learners' emotional states in learning environments, but also can be applied to the area of identifying mental disorder and human-computer interaction.
A Metamodel for Bridging Heterogeneous Ontologies

Soft ontologies aim to provide more flexible knowledge representations as compared with hierarchy-based ontology models. Soft ontologies can restructure itself according to the necessities of an interaction context. This is especially desirable in systems that need to deal with dynamic domains and situations with a high degree of uncertainty. However, the definition of soft ontologies is a challenging task due to the lack of generic and well defined components. In addition, such definition may suffer from problems with interoperability and integration with other models. We argue that soft ontologies should coexist with other models in an interoperable framework. In this paper, we propose and formalize a metamodel to provide adequate conciliation among various ontology representation approaches. We develop soft ontologies based on matrices of probabilities and apply a triplification process in which concepts are represented as fuzzy RDF statements. Our proposal was evaluated in a case study in an educational context, which uses a soft ontology to express a repertoire of actions in an mBot robot. The results indicate the feasibility of our fuzzy RDF approach to represent soft ontologies and the use of the metamodel to support interoperability in heterogeneous ontology networks.
Privacy-Preserving Delegation of Decision Tree Classification

In the era of cloud computing with security, how to outsource a trained model to the server but preserve the model privacy is a significant problem for decision tree classification (DTC). In this paper, we aim for constructing an interactive system to realize privacy-preserving delegation for DTC. We focus on a basic structure of DTC, and believe that it can be generalization. The main technique to preserve privacy is to build secret sharing-based protocols for the model owner, user, and server. However, to overcome communication issues between the model owner and user, we achieve compression by using pseudorandom generators.
Research and Practice of Library Public Cultural Services Based on SoLoMo

From the basic concept of SoLoMo, combined with the National Library of China in the social, localization and mobile aspects of the service work and marketing case, this paper puts forward a library service marketing framework, the data show that can effectively improve the library service efficiency.
Query Auto-Completion

Search assist plays an important role in modern search engines to reduce users’ search efforts and satisfy their information needs. Query auto-completion (QAC) is among one of the key search assist services, which help users type less while submitting a query. The QAC engine generally offers a list of suggested queries that start with a user’s input as a prefix, and the list of suggestions is changed to match the updated input after the user types each keystroke. In this chapter, we formally introduce the definition of the QAC problem and present state-of-the-art QAC methods. More specifically, how the user’s search intent can be predicted by exploring rich information, including temporal, contextual, personal, and underlying various search behaviors. We also describe the popular datasets and metrics that are utilized in evaluating the performance of QAC methods.
Dual-Branch Feature Fusion Network for Single Image Super-Resolution

Abstract Rcently, the research of single image super-resolution (SISR) based on deep learning has made great progress. However, most of the methods of super-resolution (SR) study use a simple chain structure to obtain higher super-resolution performance. Additionally, most methods do not fully utilize the hierarchical features generated in the middle of the network, thereby cannot achieve relatively high performance. In this paper, we propose a novel dual-branch feature fusion network (DBFFN) to address this two problems in image SR. The backbone of DBFFN is composed of multiple dual-branch feature fusion block (DBFFB) cascaded. The DBFFB has a dual-branch structure which mainly contains two parallel sub-networks, one extracts image fine features via densely connected convolution layers, the other one sub-network extracts image more contextual features by stacking dilated convolution layers. The features extracted from each hierarchical of the DBFFB are cascaded, and then feature fusion is performed. A skip connection is introduced to learn residual between input and output of the DBFFB. Especially, we use the deconvolution layer at the end of the network to enlarge the image. The results of the experiment are exciting, when the scale factor is 3, the performance of our network has 0.56dB, 0.6dB, and 0.79dB improvement on the set5, set14, and urban100 benchmark datasets compared with RDN.
Piano Playing Simulation System Based on Human Posture Recognition

Human posture recognition technology is a research hotspot in the perspective of computer vision and can be effectively applied to many fields. This paper designs and implements a piano playing system based on human posture recognition which recognizes the human posture in the images and videos and then detects and locates the virtual key position. Combining the human body recognition and the results of key positioning, intelligent simulation performance does work, converting videos to sheet music. The experimental results show the effectiveness of the system.
IMPROVING CRITICAL THINKING SKILLS IN A VIRTUAL CHEMISTRY LAB COURSE

In the context of science education, the relationship between the skills that constitute the critical thinking construct and the processes involved in scientific practice is clear. Conscious and intentional use of thinking skills is essential to elaborate hypotheses that will later be confirmed or refuted based on the analysis of evidence and the application of knowledge; as well as to draw conclusions from the results obtained and base them with good arguments. In this sense, laboratory courses are the propitious space for the development of critical thinking skills, however, the design of lab work sessions often follows a standard structure in which the opportunities for the student to apply his thinking skills are few. Higher order thinking skills are essential in any educational level but teaching them is not an easy task. It is necessary that an adequate planning of these skills learning be incorporated as an important and explicit component in the training process of the students. On the other hand, the current health emergency has meant that educational institutions have had to adapt to the virtual or distance modality, including laboratory courses. In the case of chemistry laboratories, there has been discussion for some time about the advantages and disadvantages of the use of resources for the virtual laboratory modality, and there is a relatively large tendency to consider that there are more disadvantages in the case of virtual mode. This paper reports the results of the implementation of a reasoning skills development plan in a General Chemistry Laboratory course for Basic Sciences students in a Peruvian university. In planning the course, developed under the virtual modality, the Four-component instructional design system (4C / ID) model was taken as a reference. Critical thinking skills were monitored by applying Facione's Holistic Critical Thinking Scoring Rubric. The results were compared with those obtained by students from a previous cohort who had the same course under the standard modality. Chi square statistic was used, and it was verified that there were significant differences in the achievements obtained by both groups, being these favourable for the first group of students that followed the implemented model.
Geolocating Traffic Signs using Crowd-Sourced Imagery

Action cameras and smartphones have made it simple and cheap to collect large imagery datasets from the road network while driving. At the same time, several frameworks, e.g., Detectron2 and the TensorFlow Object Detection API, have made it fairly easy to build object-detection models for your imagery datasets. In this paper, we use the Detectron2 framework to detect 18 different common traffic signs from 351.469 images. The purpose is to automate the asset management of traffic signs in large road networks. A task that today often is done in a manual and labor-intensive manner. To improve the accuracy of determining the locations of traffic signs, we develop a new, general method that uses the size of the object detected (in pixels) and the camera's GPS position and heading. To further enhance the accuracy, multiple detections of the same physical traffic sign are clustered. The traffic-sign type and computed location are stored in a spatial data warehouse. The clustered locations are presented on a digital road network in a web app. This app allows visual inspection of the overall approach. We demonstrate that the accuracy of the computed locations is good, e.g., signs are placed on the correct side of the road or in/out of a roundabout.
The Modified Algorithm of Quantum Key Distribution System Synchronization

This paper describes the trends in the development of quantum communication systems. As part of the study, we have analyzed the operation of fiber-optic autocompensation system of quantum key distribution with phase coding of photon states. We have shown that the algorithm of autocompensation system synchronization may be vulnerable to a certain type of attacks. We have also provided you with a description of the developed modified synchronization algorithm. The developed algorithm has enhanced security against unauthorized data retrieval from the quantum communication channel. This paper also outlines the quantitative characteristics of the developed algorithm.