Reviewerly/exHarmony huggingface dataset:
Semiotic Assessment of Visualization Tools

Semiotic Assessment of Visualization Tools Alena Zakharova 1, Aleksey Shklyar 2 and Evgeniya Vekhter 2 1 Institute of Control Sciences of Russian Academy of Sciences, 65, Profsoyuznaya st., Moscow, 117997, Russian Federation 2 Tomsk Polytechnic University, 30, Lenin Ave., Tomsk, 634050, Russian Federation Abstract A visual analytics means is a set of tools used to obtain a visual representation of the data explored by the user, its interpretation and complete analysis. It is quite possible that such a set will be a developed specialized environment for continuous user interaction with a data stream, which has the capacity to control and replace the way of visual data representation. A wide range of existing visualization tools, as well as unlimited borrowing of technological solutions originally intended for a narrow application area, and the transfer of the capabilities they create to other directions make it urgent to form a sound approach to the comparison and selection of means of data visual representation of a given complexity [1]. The purpose of this selection is to save the end user’s resources, based on the absence or minimization of resource losses arising at the stage of selection and testing of visual analytics tools. The paper shows the possibility of using a semiotic visualization model for purposeful design or selection of visualization tools.
Stereo Image Super-Resolution Based on Pixel-Wise Knowledge Distillation Strategy

In stereo image super-resolution (SR), it is equally important to utilize intra-view and cross-view information. However, most existing methods only focus on the exploration of cross-view information and neglect the full mining of intra-view information, which limits the reconstruction performance of these methods. Since single image SR (SISR) methods are powerful in intra-view information exploitation, we propose to introduce the knowledge distillation strategy to transfer the knowledge of a SISR network (teacher network) to a stereo image SR network (student network). With the help of the teacher network, the student network can easily learn more intra-view information. Specifically, we propose pixel-wise distillation as the implementation method, which not only improves the intra-view information extraction ability of student network, but also ensures the effective learning of cross-view information. Moreover, we propose a lightweight student network named Adaptive Residual Feature Aggregation network (ARFAnet). Its main unit, the ARFA module, can aggregate informative residual features and produce more representative features for image reconstruction. Experimental results demonstrate that our teacher-student network achieves state-of-the-art performance on all benchmark datasets.
Design of Methodology and a Comparative Analysis of Trigram Technique in Similarity of Textual Data

In this knowledge era information sharing and its searching is upgrading day by day by new innovative technology to do this. The major challenge in the academia and scientific community about the similarity of content and method producing for publication or for research purposes. In the academic and scientific organizations it is matter of fact that the scientific and research publications must be plagiarism free. In the submitted documents for the publication from the academia and research organization can be written with the use of free available text on the internet or from the free copyrighted documents having in the local corpus. In this paper, the comparative study has been performed to analyze the textual similarity between two or more documents. The methodology of continuous trigram and its applications for the finding the matching textual content of the similar text from the local corpus as well as from the internet. In the present paper, the comparative analysis of different techniques and their working applications of the similarity finding and retrieval process have been performed.
An indoor and outdoor errand-behavior system based on the coordination of multiple robots

Due to the increase in the number of remote workers, there is a need to develop a robotic system that can run errands outside. In this study, we use a wheeled robot indoors and a quadruped robot outdoors to realize the errand-running by coordinating the two robots.
HEP: Heuristic Similarity Calculation based on the Minimum Entropy Principle

Automatically calculating the semantic similarity of two short sentences is one of the fundamental tasks in natural language processing. There are several models used to evaluate semantic similarity. However, most methods in unsupervised learning are still traditional models. In this paper, we propose a new approach that is in line with the law of nature. We use a minimum entropy weighted model based on the heuristic algorithm to perform this task. Instead of individual weigh vector for each sentence, searched a global weight that applies to all sentences. We choose accuracy and area under the curve (AUC) as evaluation metrics. By calculating the similarity of sentence pairs on different datasets, our minimum entropy weighted model outperforms previous traditional unsupervised models in accuracy and the area under the curve (AUC). This paper optimize the threshold selection in calculation and obtain an excellent experimental results in more general cases. All in all, this paper provides an excellent weighting method for word vectors to form sentence vectors.
Security and Privacy Issues in Edge Learning Systems

Conventional distributed machine learning manages the training data in a centralized mode without considering the privacy and security problems during training or inference. With the rapid development and wide deployment of artificial intelligence technology these days, privacy protection has gained more and more attention. Moreover, EL participants usually are small devices (e.g., smartphones, sensors) that have weak defense ability and can be easily compromised under possible attacks. In this chapter, we first introduce a security guarantee mechanism in Edge Learning including the defense methods for data-oriented attacks and model-oriented attacks. Then, we summarize the mainstream methods of privacy protection including differential privacy, secure multi-party computation, and homomorphic encryption. Finally, we discuss future directions in this field.
Named Entity Recognition Based on Dependency Parsing and BiLSTM-CRF

Dependent parsing can effectively reveal sentence structure information and analyze the relationship between the components in the sentences. In order to improve the recognition effect of named entity recognition in complex situations such as long sentences, this paper proposes a named entity recognition method based on dependency parsing and BiLSTM-CRF. Firstly, the word vector and the character vector are concatenated and sent to the bidirectional long and short-term memory network (BiLSTM) for training. Secondly, using dependency parsing to learn the long-distance dependency between words and interact with each other. Finally, the final feature vector is sent to the conditional random field (CRF) to learn the constraints of the label sequence. The experiments were conducted on the Chinese and English datasets of OntoNotes 5.0, and the F1 values are 75.59 and 86.49 respectively. The results show that the dependency parsing can enhance the relationship between distant words and further improve the accuracy of named entity recognition.
The Piagetian Modeler

A multi-agent cognitive system is explained wherein each agent functions as an independent knowledge manipulation mechanism. Collectively the mechanisms observe the environment via connected devices, coordinate inferences, reflect upon and modify the system’s behavior, and consolidate the memory network comprised of neuro-symbolic elements. The theories underlying the modeler are discussed, along with the modeler’s actual and expected developmental trajectories.
Study of the Medical Image Sharing System Based on a RBAC Expansion Model

Currently, the PACS systems are distributed in various hospitals, and the interconnection of data cannot be achieved. This paper proposes a RBAC extension model which supports the request authorization. In this extended model, the concept of authorization role and other related concepts are creatively presented, and a new access control system scheme is proposed and was initially applied to the image sharing system. Moreover, according to the demand of data sharing system, this paper designs the feature extraction module of image data.
Knowledge Graph Embedding Based on Semantic Hierarchical Spatial Rotation

The knowledge graph is a structured representation of real-world triples. Knowledge graph embedding is an effective method to predict the missing part of the knowledge graph. To better improve the knowledge graph, this paper uses a rotation matrix to embed entities and relationships into three-dimensional space and proposes a new knowledge graph embedding model-semantics Layered spatial rotation model (SROTATE). SROTATE includes an entity-based semantic layering strategy and a three-dimensional space rotation strategy. Specifically, the entity-based semantic layering strategy can layer different entities according to semantics, so that entities with different semantics are distributed on different levels. In addition, the spatial rotation strategy associates the head entity and the tail entity with the rotation matrix, thereby distinguishing entities of the same level. According to the experimental results, SROTATE has a certain performance improvement compared with mainstream algorithms in large-scale data sets and complex relationships.
Priority-Based Scheduling Approach to Minimize the SLA Violations in Cloud Environment

In a virtualized datacenter, the Service Level Agreement for an application restricts the Virtual Machines (VMs) placement. The scheduling algorithm is responsible of route the user request to a specific VM, this scheduling process impact directly the SLA. An optimal scheduling approach aim to minimize the SLA violations and maximize the provider resources utilization. This paper presents a two-phases scheduling approach that can apply in geo-distributed data center environment, the first phase is to select the suitable data center to process the user request, while the second phase is to choose the suitable VM. The purpose is to minimize the SLA violations in term of Response time, availability and reliability. Our solution is also distinguishing standard request from high priority requests during the scheduling decision.
Detection of spam in search engines using a decision treebased on a hybrid genetic algorithm

Social media has the potential to be a double-edged sword. Adolescence is a period of personal and social identity building, and social media plays an important role in this process. It is extremely common not only among the young, but also among people of all ages who seem to be rapidly catching up with this new technological development. Businesses make extensive use of framing their marketing campaigns, they make use of social media. In Reliability Analysis, the cronboach alpha value is 0.820. From Principal component Analysis all the 21 items are rotated and converged with 4 iterisations. There are no validity concerns in Master Validity and the model has get a good fit for structural equation modelling. Studies suggest that allowing people to share their thoughts and feelings while still receiving social support is beneficial. Social support is another important factor in the relationship between social media use and mental health. On professional front, it's been widely used for acquiring markets by new business ventures. Many established organizations are undergoing operational change in their traditional practices so as to adapt to the present online environment for promoting their products and services globally.
Determine the Architecture of ANNs by Using the Peak Search Algorithm and Delta Values

The solution obtained by an Artificial Neural Network does not guarantee that it always yields with the simplest neural network architecture for particular problem. This causes computational complexity of training, deployment, and usage of the trained of an artificial neural network. It has observed that the hidden layer architecture of an artificial neural network significantly influences on its solution. However, still modeling of the hidden layer architecture of an artificial neural network remains as a research challenge. This paper presents a theoretically-based approach to prune hidden layers of trained artificial neural networks, ensuring better or the same performance of a simpler network as compared with the original network and then discusses how to extend the proposed method to deep learning nets. The method was inspired by the facts of neuroplasticity. It achieves the solution by two phases. First, the number of hidden layers is determined by using a peak search algorithm and then newly discovered simpler network with lesser number of hidden layers and highest generalization power considered for pruning of its hidden neurons. Experiments have shown that the resultant architecture generated by this approach exhibits same or better performance as compared with the original network architecture.
Cloud Enhances Agile Software Development

The software industries follow some patterns (i.e., process model to develop any software product). Agile methodology is the most famous and used process model. It is a trend to develop efficient software products with high client satisfaction. In this chapter, the authors discuss agile methodology and its components, benefits, and drawbacks while using the cloud computing in agile software development, existing frameworks for agile-cloud combination, and some security measures.
Development of algorithms that simulate the solution of a problem by a person

Some algorithms, which are often based on the use of elements of higher mathematics, possessing high speed and compact coding in algorithmic languages, are poorly mastered by most students. It can be assumed that this is due to the difficulty of presenting the principles of their work in the form of human actions in ordinary situations. Thus, a certain contradiction arises between the way of solving the problem that a person resorts to without using a computer and the way we force our computer to solve this problem. Comparison of the process of explaining algorithms speaks in favor of algorithms imitating human thinking. The discussion of the advantages of the algorithms themselves is beyond the scope of this article and undoubtedly deserves a separate study. If artificial intelligence is created, then its creator or creators will certainly be ranked among the outstanding geniuses in the history of civilization, no matter what algorithms it uses. However, so far there is no one to solve problems for us and create algorithms, so we will use all available means and try to teach this to children.
PSYCHOLOGICAL DEPENDENCIES BETWEEN JOB SATISFACTION AND GENDER-ROLE DIFFERENTIATION

In the conditions of the rapidly developing economy, the fine and the reprimand are not enough for the successful management of the employees in the organization. High qualification, active participation of the manager, as well as the creation of a favorable working environment in the organization are needed. Therefore, the attitude of employees to work as a whole and to its individual aspects is essential. This attitude affects the motivation for work, work activity and achieving higher results in work.
A Web-Based Indonesian MBTI Prediction : Design, Implementation, and Testing

The person who posts a tweet contains linguistic cues for personality on social media. Myers-Briggs Type Indicator (MBTI) is more widely used and discussed online, so MBTI data is available on social media. This paper aims to design, implement, and test a web-based application to predict an Indonesian user from tweets on Twitter. The framework used in the development of MBTI Prediction is Software Development Life Cycle (SDLC). The architecture design follows the built-in development provided by the Flask framework using API Twitter. MBTI prediction method using combination of machine learning approach and feature extraction approach. Time MBTI prediction is 8.74 seconds per user. The development web-based used Python programming language, and Flask framework with SDLC process has been implemented to design, develop and test good quality application.
Image Processing Methods for Gesture-Based Robot Control

Abstract In this paper we discuss some image processing methods that can be used for motion recognition of human body parts such as hands or arms in order to interact with robots. This interaction is usually associated to gesture-based control. The considered image processing methods have been experienced for feature recognition in applications involving human robot interaction. They are namely: Sequential Similarity Detection Algorithm (SSDA), an appearance-based approach that uses image databases to model objects, and Kanade-Lucas-Tomasi (KLT) algorithm which is usually used for feature tracking. We illustrate the gesture-based interaction by using KLT algorithm. We discuss the adaptation of each of these methods to the context of gesture-based robot interaction and some of their related issues.
Bayesian Network Structure Learning Based on Small Sample Data

Aiming at the problem that it is difficult to learn the optimal solution of Bayesian structure under the condition of small sample data, this paper proposes a Bayesian structure learning algorithm under small data set. Firstly, an improved Bootstrap sampling is proposed to expand the small data, and the extended sample is modified through the maximum weight spanning tree. Secondly, the standard particle swarm optimization (PSO) algorithm is improved, and the calculation method in the update formula is redefined to adapt to Bayesian network structure learning. Finally, the simulation verification of a calculation example proves the effectiveness of the improved algorithm for Bayesian network structure learning.
Prototyping and Testing a Virtual Reality Counterterrorism Serious Game for Active Shooting

An active shooting emergency requires an effective strategy to increase the chances of survival of the attacked population. Educational environments are one of the most common locations of such events. To reduce the impact of these disasters, several emergency plans have been developed and put in place. Traditionally, these emergency plans are taught to occupants using non-interactive videos, drills, etc. However, these conventional methods present many limitations regarding trainees’ knowledge acquisition, engagement and self-efficacy. To overcome them, this paper presents the prototype of an immersive Virtual Reality (VR) Serious Game (SG) for counterterrorism training. A user-centred evaluation of the proposed training SG was conducted with 32 participants. The experiment aimed to investigate the impact of the proposed tool on participants’ knowledge regarding the safest actions to take in case of an active shooter attack. Participants’ intrinsic motivation and self-efficacy levels were also assessed before and after the VR-based training. Findings indicate that participants’ knowledge, intrinsic motivation, and self-efficacy significantly increased immediately after the training.
Content and Structure of Psychological Readiness of Secondary School Students to Solve Creative Mathematical Tasks

Informational technologies, the latest challenges in the field of technical development of society condition irreversible environmental and human changes. In the context of these changes a special place is occupied by those activities that are directly or indirectly related to technical, cybernetic, mathematical activities. This actualizes the manifestation of mathematical abilities, giftedness for math activity in youth, features of creative potential, etc. The purpose of the study is to clarify the content and structure of psychological readiness of high school students for creative mathematical performance. The main content characteristics of the creative mathematical problem as a model for the study of creative mathematical activities are considered in the article. Based on the theory of learning problems, the psychological structure of mathematical task is determined. The structure contains the following components: a) the subject of the task (in its initial state or the ascending subject of the task); b) the model of desired state, ie the requirements (set and required) of the task. The contradiction of the creative mathematical situation first of all characterized by the object and subject of the creative mathematical task. The psychological readiness of high school students for creative mathematical performance is an integrated personality trait aimed at realizing of the cognitive abilities of young people in the process of solving creative mathematical problems. Since the process of creative mathematical thinking (creative mathematical activity) is determined by objective (subject-functional) and subjective-personal (motivation, emotional-volitional, value attitude) components, it is worth saying about subject-functional, subjective-personal types of psychological readiness of high school students to perform mathematical creative activities.
A Low Complexity and Long Period Digital Random Sequence Generator Based on Residue Number System and Permutation Polynomial

Long period digital random sequence plays an important role in reliable communications and high security scenarios. This paper improved the method of generating long period digital random sequences based on the Residue Number System (RNS) and the Chinese Remainder Theorem (CRT), and a sequence mapping method after CRT extension. This paper proves that the period of sequence after mapping will not degenerate if the modulus used in the mapping stage is coprime with the period of the original sequence. By using the parallelism of RNS, the proposed method can generate sequences at high speed with fewer hardware resources. The NIST test results show that the pass rate of each test item is above 98.40%, which meets the NIST test confidence requirements, confirming the randomness of the generated sequences. An image encryption test is given as one of the example applications of the generated sequences. On the theoretical basis, by jointly optimizing the sequence mapping and iteration procedure, a hardware implementation architecture is also presented in this paper. The implementation is based on Xilinx XC7Z020CLG484-3 FPGA and compared with the implementations of classical chaotic maps. The results show that the proposed architecture has longer sequence period with less hardware resource consumption and higher generation speed and is more general. Meanwhile, the proposed architecture has fast phase switching ability, which is about 10 clock periods. This is one of the key attributes when the sequence is used in communication systems.
Multidimensional Neuro-fuzzy System and Fuzzy Coding for a Constant Length Facial Landmark Set Formation

Facial expression recognition is performed using deep and machine learning. Deep networks guarantee recognition accuracy when large, pre-labeled image sets are available for learning. This condition is not always met. Machine learning approaches will be more useful, especially in real-time tasks, under changing shooting conditions. The main problem in this case is formation of a facial landmarks coordinates vector of constant dimension. Input vector fixed size is one of machine learning algorithms main requirements. However, standard computer vision detectors detect a different number of feature points in each video frame. This paper considers problem of constructing a facial landmarks coordinates vector, which will have a constant length. For this, a fuzzy encoder based on flexible two-dimensional membership functions is proposed, as well as a multidimensional neuro-fuzzy system for encoded data classification.