Reviewerly/exHarmony huggingface dataset:
Crypto Warfare Techniques- Status, Challenges, and Future Trends

With the increasing demand for fast internet and electronic commerce, great concern has emerged in the problems related to the safety and security of information related to computers and communications. Cryptography is the science concerned with studying and confirming the confidentiality, safety, security and privacy of the data that can be provided to the user, as well as the confidentiality and reliability of the data user. In this paper, Numerous encryption algorithms have been studied to describe a state-of-the-art overview of Crypto Warfare techniques status, challenges, and future trends. The study supports the importance of these topics by highlighting the urgent needs of this topic. We present various challenges facing Crypto Warfare Techniques and summarize the status and future trends
Fast Label Enhancement for Label Distribution Learning

Label Distribution Learning (LDL) has attracted increasing research attentions due to its potential to address the label ambiguity problem in machine learning and success in many real-world applications. In LDL, it is usually expensive to obtain the ground-truth label distributions of data, but it is relatively easy to obtain the logical labels of data. How to use training instances only with logical labels to learn an effective LDL model is a challenging problem. In this paper, we propose a two-step framework to address this problem. Specifically, we firstly design an efficient recovery model to recover the latent label distributions of training instances, named Fast Label Enhancement (FLE). Our idea is to use non-negative matrix factorization (NMF) to mine the label distribution information from the feature space. Moreover, we take the instance-class similarities into consideration to discover the importance of each label to training instances, which is useful for learning precise label distributions. Then, we train a predictive model for testing instances based on generated label distributions of training instances and an existing LDL method (e.g., SA-BFGS). Experimental results on fifteen benchmark datasets show the effectiveness of the proposed two-step framework and verify the superiority of FLE over several state-of-the-art approaches.
Cognitive Developmental Theories

Theories of cognitive development are reviewed, beginning with pioneering theories by Piaget and Vygotsky. Neo-Piagetian theories which integrated Piagetian theory with other conceptions of cognition were developed by McLaughlin, Pascual-Leone, Case, Fischer, and Chapman. Complexity theories propose that children become capable of dealing with more complex relations as they develop. Information processing theories, neural net theories, dynamic systems theories, and theories of reasoning processes all provide models of the reasoning processes employed by children at different ages. Microgenetic analysis methods are used to study the processes of transition from one level of thinking to the next. Conceptual coherence is achieved by categorizing cognitive processes according to their core properties.
Multi-label Positive and Unlabeled Learning and its Application to Common Vulnerabilities and Exposure Categorization

The widely adopted Common Weakness Enumeration (CWE), which stores and manages software and hardware vulnerability reports known as Common Vulnerabilities and Exposures (CVE) in a hierarchical structure, provides common baseline standard for weakness identification, mitigation, and prevention efforts. In this paper, we propose a machine-learning based method to assign pertinent CWE identifiers to new CVE entries. The proposed method formulates the task as a multi-label classification problem and exploits positive and unlabeled learning to address the lack of multi-labelled samples in learning. In evaluations, the proposed method demonstrated preferable performance compared to traditional multi-label classifiers. In particular, case studies demonstrated that multiple CWE iden-tifiers assigned to CVE entries carry essential information that can benefit security practices.
Improved Auto-regulation VFH Algorithm for Obstacle Avoidance of Unmanned Vehicles

Aiming at the problem of autonomous vehicle obstacle avoidance, the self-regulating VFH (vector field histogram) algorithm based an improved strategy is proposed for the obstacle avoidance problem o unmanned vehicle. On the one hand, this algorithm considers the dynamical constraints and mechanical characteristics of the unmanned vehicle, delimits the active area under the constrain of its the minimum turning radius, and selects candidate directions based on the vehicle width constraint to ensure that the vehicle can safely and smoothly bypass obstacles. On the one hand, this algorithm further improves the smoothness of unmanned vehicle movement by improving a cost function. Simulation results demonstrate effectiveness of the proposed improved strategy.
Accelerating Reinforcement Learning with Local Data Enhancement for Process Control

Reinforcement learning (RL) directly achieves the control goal by interacting with the environment, and is of great significance under uncertainties. However, the extremely low sample efficiency of the RL training process has become a practical problem, therefore it is difficult to directly apply the existing RL schemes to industrial control. In this paper, a novel RL control algorithm based on local data enhancement (CBR-MA-DDPG) is proposed to accelerate its training speed, combining the concept of case-based reasoning and model-assisted imagination. When the operating mode changes, the proposed method can quickly adapt to the new mode and achieve the satisfactory control performance in fewer training episodes. A continuous stirred tank reactor (CSTR) process is used to illustrate the effectiveness of our method. The results show that the control performance of the proposed control scheme is much better than the traditional PID control, and that it has higher training efficiency over the state-of-the-art DDPG algorithm.
Information and Communication Technology (ICT) and Its Mixed Reality in the Learning Sphere

The purpose of this article is to discuss the literature review and observable experiences on mixed reality posed by the use of information and communication technology (ICT) systems in the education system in the South African landscape. Human progress in the 21st century has been characterised by the rise of computer technology, that has become a defining feature in almost all social spheres. However, the reality shows that though ICT is celebrated for its undisputed, “uncertainty” and efficient services, especially when it comes to communication, information and dissemination. It also tends to encourage the abandonment of other modes of communication, specifically in relation to teaching and learning. In discovering the challenges and possibilities posed by the information technology, the notion of a didactical triangle will be explored. Literature reveals that the advent of ICT brought about various developmental opportunities and threats to human life. This article argues that consideration of institution and context is fundamental when it comes to a better understanding of the practicalities of information technology. This article will add value by shedding light on the realities and challenges in relation to ICT on the human life. The conclusion is that interventions that relates to information and communication technology have to seriously consider the structural constraints of access and social inequity as well as the effects of coloniality.
Analysis on the Deep Learning Method is Used for Text Classification

With the rapid advent of modem science and technology, information retrieval and data field focusing on automatic long text classification becomes a hot topic. However, text classification learning based on machine learning needs to be studied and evaluated comprehensively from algorithm, model, evaluation, and other aspects. In this paper, the deep learning method is used for text classification. Firstly, the conventional process of text classification is described, including data pre-processing, text segmentation, feature construction, classification algorithm overview and so on. Secondly, CNN with multiple convolution kernels is applied to text classification. The traditional bag of words model is replaced by the word vector model, and multi-dimensional convolution kernel is used to extract text features. The experiment in this paper demonstrated the promising performance of CNN models in classifying text document, yet posed the following directions that pending more intensive research: 1) How to better compare the proposed models and their performance. What is the most suitable classification tasks for each model in the form of text? 2) How to understand the data better. Particularly, what did the deep learning model learn? 3) Hyper-parameter tuning. The hyper-parameter tuning is a task that builds on experience. 4) Label imbalance: when the data samples are unbalanced in terms of label, how to adjust the sample weight in loss similar to the bootstrap method. 5) What is the smallest neural network structure that can achieve a certain accuracy on a given data set.
Across Borders

Abstract This chapter addresses the rise of international digital media and other forms of globalization. It considers in particular how the advent of digital media have affected the “reading class,” the educated, socially advantaged group of people who read in their leisure time. It uses Nigeria as an empirical case for examining the relationship between reading, on the one hand, and globalization and digitization, on the other.
Trading Accuracy for Privacy in Machine Learning Tasks: An Empirical Analysis

Different kinds of user-generated data are increasingly used to tailor and optimize, through Machine Learning, the operation of online services and infrastructures. This typically requires sharing data among different partners, often including private data of individuals or business confidential data. While this poses privacy issues, the current state-of-the-art solutions either impose strong assumptions on the usage scenario or drastically reduce the data quality. In this paper, we evaluate through a generic framework the trade-offs between the accuracy of Machine Learning tasks and the achieved privacy (measured as similarity) on the input data, discussing trends and ways forward.
An intelligent hand-washing monitoring platform based on gesture recognition technology

This paper uses gesture recognition technology based on YOLO v3 to successfully identify and detect the seven steps of the seven-step hand-washing method prescribed by the hospital. The paper also uses TensorRT to improve the trained seven-step hand-washing gestures recognition model and increase its detection frame rate on video files. Finally, the model runs on NVIDIA’s Jetson Nano, a smart mobile terminal, which is able to detect the hand-washing gestures in real time with a camera and other equipment for convenience.
Improved A* algorithm for automatic guided transport vehicle path planning

For the automatic guided vehicle (AGV) path planning problem, an improved A* algorithm is proposed for solving it. The algorithm combines the warehouse environment to generate rasterized information. Firstly, an AGV model with a safe driving area is established to avoid the AGV from crossing the obstacle vertices diagonally; secondly, heuristic function is improved in the traditional A* algorithm and an adaptive heuristic function is designed to obtain the globally optimal solution more efficiently; thirdly, a geometric method is used to shave off the redundant linear points and unnecessary corners in the path search; finally, A new feasible point is generated at the corner of the obstacle and a path is obtained that contains a start point, feasible points and an end point. The data output display that the improved A* algorithm can effectively plan a path with shorter distance and safer transportation, reduce the path search time, lower the corner angle of the AGV, and plan an optimal path with higher quality.
Knowledge Graph-Based Sequence Recommendation

In existing recommendation, most entities have strong relationship constraints. Therefore, designing a personalized recommendation for users has become a key research direction in the field of recommendation. Based on the diversified scheme of skincare products as the research background, we focused on the relative order of recommended items, and demonstrate the general application scheme of knowledge graph in the field of sequence recommendation. In view of the reasoning ability and interpretability of the knowledge graph, combined with the constraint relationship which is existing in the graph, we designed a sequence recommendation algorithm in the field of skincare products. We also verified the results and conducted feasibility analysis.
Efficiently Processing Big Data in Real-Time Employing Deep Learning Algorithms

Big Data and deep computation are among the buzzwords in the present sophisticated digital world. Big Data has emerged with the expeditious growth of digital data. This chapter addresses the problem of employing deep learning algorithms in Big Data analytics. Unlike the traditional algorithms, this chapter comes up with various solutions to employ advanced deep learning mechanisms with less complexity and finally present a generic solution. The deep learning algorithms require less time to process the big amount of data based on different contexts. However, collecting the accurate feature and classifying the context into patterns using neural networks algorithms require high time and complexity. Therefore, using deep learning algorithms in integration with neural networks can bring optimize solutions. Consequently, the aim of this chapter is to provide an overview of how the advance deep learning algorithms can be used to solve various existing challenges in Big Data analytics.
A Novel Approach for Sentiment Analysis and Optimization Using PSO

Opinion Analysis is the premise of all the business for offering better quality types of assistance and furthermore to improve the nature of administrations with the assessment of the input. The proposed work aims of the sentiment analysis with the optimization for the examination process of feature selection with the senti-lexicon approach. The approach which we followed for the sentiment examination is based on the Senti-Lexicon approach, in which we have created with individual files for the examination of the positive, negative words, emotions and more. Together with that, the emphasis is over the examination of the intensifiers, negations etc. The key feature of the approach is that, the files are flexible so the dictionary or the datasets which are used for the polarity examination can be extended to any extent. The PSO based optimization will helps us to quickly identify the sub-feature to which category the review belongs and overall optimize the approach than other available methods.
Improvement of Feature Extraction Based on HOG

In recent decades, with the rapid development of science and technology, pedestrian detection has gradually begun to mature from the beginning. Pedestrian detection involves a number of disciplines and fields to achieve joint cooperation. As the basis of pedestrian detection, image processing needs to ensure the quality and speed of detection at the same time. Face recognition based on directional gradient histogram (HOG) has good accuracy in pedestrian detection. But at the same time, compared with other pedestrian detection feature extraction methods, the disadvantage of hog is that it takes too much time and can not guarantee the detection speed while improving the accuracy. On this premise, based on the idea of clustering, the hog features are clustered according to their gradient directions, and then the appropriate features are found out by statistical calculation to form the combined features, and the subsequent steps are carried out by the combined features. Through the experiment, without sacrificing the detection accuracy, the detection efficiency can be effectively improved by reducing the data dimension.
Compensation system and job satisfaction: the application of the job descriptive index in the public-sector of Jordan

The current study examined the effect of compensation system in the prediction of employee job satisfaction within the public-sector of Jordan through using the job-descriptive-index (JDI). Data was collected through questionnaire across (314) respondents. Structural-equation-modelling applied and following the completion of the analysis, direct-monetary compensation was found to be partially related to JDI dimensions, whilst, in contrary to expectations indirect-monetary compensation were seen to have no role in achieving job satisfaction, further, non-monetary compensation significantly influenced JDI dimensions of satisfaction, in addition to that, demographic characteristics were found to be significant in affecting job satisfaction. Such results indicate an issue with the provided compensation system that need to be addressed by the public-sector administrative. Further works need to be carried out in order to explore how provided compensation could be effective booster for employee satisfaction especially in the context of Jordan.
PHILOSOPHICAL DAVOS AS A REASON FOR DIVORCE. M. FRIDMAN’S VERSION. PART 2

This article is a continuation of the previous publication published under the same title (part 1). Both parts are devoted to the problem of understanding the split that once occurred in world philosophy and was embodied in the division of philosophy into two directions, continental and analytical philosophy. In the second part, the author continues to discuss the reasons for the split and the argumentation cited by various authors, proving the conceptual difference of directions. The article analyzes the main ideas of the authors of the Vienna Circle Manifesto and the argumentation of R. Carnap in his dispute with M. Heidegger, the subject of which is devoted to the so-called overcoming of metaphysics. The reason for writing both parts was the work of M. Fridman “Parting of the Ways: Carnap, Cassirer and Heidegger”, translated into Russian. The result of the second part is the conclusions formulated by the author of the article regarding the difference between the named directions. The author believes that it makes sense to discuss their differences not in the categories of truth and falsity of teachings and concepts, but in the categories of different orientations and attitudes, which do not deny but complement each other. On the one hand, analytical philosophy claims to be scientific orientation, clear and precise language, logic and mathematics. In contrast to it, continental philosophy does not pretend to be scientific, to a clear and precise language, but prefers innuendo, rejection of doctrinalism, and the exploratory nature of philosophizing.
Cubic Curves Over the Finite Field of Order Twenty-Five

Abstract An arc of degree three is a set of points in projective plane no four of which are collinear but some three are collinear, and a cubic curve is a non-singular projective plane cubic curve. There are cubic curves formed an arc of degree three over a finite field. The aims of this paper are to give the inequivalent cubic curves forms over the finite field of order twenty-five according to its inflexion points, and the incomplete curves have been extended to complete arcs of degree three. As a conclusion over F 25 , the largest arc size of degree three constructed from the points of cubic curves is 36; that is, 36≤ m r (2,25) ≤51.
KERNEL-BASED IMAGE FILTERING: FAST ALGORITHMS AND APPLICATIONS

Image filtering is a fundamental preprocessing task in computer vision and image processing. While the dominant applications of kernel filtering are enhancement and denoising, it can also be used as a powerful regularizer for image reconstruction. In general, the brute-force implementations of kernel filtering is prohibitively expensive. They are often too slow for real-time applications. In the first half of the thesis, we propose fast algorithms for bilateral filtering (BLF) and nonlocal means (NLM). In particular, we demonstrate that by using the Fourier approximation of the underlying kernel, we can obtain state-of-the-art fast algorithms for BLF of grayscale images. We next extend the idea for fast filtering of color images, which involves the approximation of a three-dimensional kernel. We next propose a fast separable formulation for NLM of grayscale images. In the second half of the dissertation, we turn to some applications of kernel filtering. We introduce a scale-adaptive variant of BLF that is used for suppressing fine textures in images. We develop a fast implementation of a symmetrized variant of NLM that is used for regularization (i.e., as a prior) within the plug-and-play framework for image restoration. The core idea can be extended to other forms of kernel filtering.
Selected Security Threats of the 21st Century

Although the security concerns we have covered are not present in every business, they are of significant interest to many businesses and private individuals where the impact of the crime is often felt in increased costs for merchandise and services and diminished profits. With the growing use of the Internet and associated technology, many of these crimes are changing direction to make use of the reach of the Internet or exploit computer technology, in some cases, making them more difficult to defend against and increasing the magnitude of potential losses. In order to combat these crimes, security and law enforcement will need to enhance their tools by becoming proficient at monitoring and investigating crimes using computers and related information technology.
Translation of Medical Texts using Neural Networks

The quality of machine translation is rapidly evolving. Today one can find several machine translation systems on the web that provide reasonable translations, although the systems are not perfect. In some specific domains, the quality may decrease. A recently proposed approach to this domain is neural machine translation. It aims at building a jointly-tuned single neural network that maximizes translation performance, a very different approach from traditional statistical machine translation. Recently proposed neural machine translation models often belong to the encoder-decoder family in which a source sentence is encoded into a fixed length vector that is, in turn, decoded to generate a translation. The present research examines the effects of different training methods on a Polish-English Machine Translation system used for medical data. The European Medicines Agency parallel text corpus was used as the basis for training of neural and statistical network-based translation systems. A comparison and implementation of a medical translator is the main focus of our experiments.
Research on Improvement of PSO and CNN in Intelligent Classification Technology of Coal Mine Safety Risk

This paper mainly studies the coal mine safety risk, including the classification, management strategy of coal mine safety risk and the development of risk management information technology. For the management of coal mine safety risks, it is very effective to formulate a safety risk classification management system, which can find and manage various safety hazards and risks as soon as possible, and promote efficient and safe operation. Nowadays, people increasingly take safety issues seriously in the coal industry, so the development of safety risk classification and control system has become an important safety management tool. To solve the slow convergence rate of CNN algorithm, this paper uses PSO to improve the error back propagation of CNN by using the training parameters and error functions of CNN as PSO particles and fitness functions respectively. The experimental simulation of AR face database in gender identification verifies that the improved algorithm has fast convergence rate and high recognition accuracy.