Reviewerly/exHarmony huggingface dataset:
Haze removal with automatic recovery of atmospheric light

Most haze removal methods are based on the classical haze imaging model, which can effectively describe the image from the haze, where one of the key factors is the atmospheric light. Existing methods either depend on the user input or keep to the color constancy. In this paper, we present a new and effective method for computing the atmospheric light based on the salient region of the input haze image. The method computes the salient map and the darkness channel map of the input image and adaptively selects the region of the atmospheric light according to the assumption that the atmospheric light does not belong to the salient region of the input image. Then, the atmospheric light is estimated by combining the statistical characteristics between the darkness channel and the region of the atmospheric light. Finally, the transmission map is computed and refined by the weight-guided filter and the clear image without the haze is produced. Compared with some existing methods, a number of experimental results show that the proposed method can not only accurately recover the atmospheric light, but also effectively remove the haze from bad weather condition and improve the image quality such as the contrast, sharpness and color.
Learning House Furniture Manipulation for Daily Assistive Robots from human demonstration

Human imitation is suggested as a useful method for humanoid robots achieving daily life tasks. Furniture operation has a feature that it can be described as a combination of five kinds of actions of turning, pulling, pushing, grasping and releasing. In this paper, we focused on learning from the human demonstration with interaction and proposed learning furniture manipulation system. We conducted a furniture/home appliance manipulation with the life-sized humanoid robot PR2.
Simplifying E-Commerce Analytics by Discovering Hidden Knowledge in Big Data Clickstreams

E-commerce analytics has received increased interest because of its importance in marketing, product evaluation and review, supply chain and logistics optimization, etc. The increased interest requires unique and scalable methods for clickstream analytics since the combination of the number of online users, retail stores and the volume of the available products is massive, due to the expanding internet usage and e-commerce over billions of mobile devices. In this chapter, a sequential frequent itemsets detection methodology (SAFID) is significantly modified and upgraded in order to solve the clickstream analytics problem by analyzing a composite dataset which simulates monthly holiday season traffic of the top hundred U.S. online retail shops. The dataset consists of more than ten billion records. It is shown that the methodology can perform the analysis very efficiently in a simple desktop. It can detect all the frequent and infrequent clickstream patterns which can provide valuable knowledge to marketers of online retail stores.
A Well-Organized Safeguarded Access on Key Propagation by Malleable Optimization in Blend With Double Permutation

With the rapid developments occurring in cloud computing and services, there has been a growing trend of using the cloud for large-scale data storage. This has led to a major security dispute on data handling. Thus, the process can be overcome by utilizing an efficient shielded access on a key propagation (ESAKP) technique along with an adaptive optimization algorithm for password generation and performing double permutation. The password generation is done by adaptive ant lion optimization (AALO) which tackles the problem of ineffiency. This build has stronger security which needs an efficient selection property by eliminating the worst fit in each iteration. The optimized password is utilized by an adaptive vignere cipher for efficient key generation in which adaptiveness is employed to prevent the dilemma of choosing the first letter of alphabet which in turn reduces the computation time and improves the security. Additionally, there is a need to encrypte the symmetric key asymmetrically with a Elliptic Curve-Diffie Hellman algorithm (EC-DH) with a double stage permutation which produces a scrambling form of data adding security to the data.
Developer Payroll Approaches for Startup Environment Based on Agile Project Management

Formulating developer payroll in a software development project is important for organizations to provide an equitable salary formulation that can motivate developers to work professionally. Payroll can also be used as references in planning the cost of software development projects in order to meet startup financial success. In the literature, developer payroll estimations can be obtained by multiplying the number of working hours on a project with the developer’s wage. However, multiplying the number of working hours with the developer’s wage is considered unfair because each developer has different competencies and performance. In addition, not much attention given to understanding how to estimate developer payroll in software development organizations by considering different competencies and performance. For those reasons, it is important to understand how to overcome the different developer’s competencies and performance in developer payroll formulation. Thus, the competency and performance factors can be formulated into the developer payroll. This paper proposes a method in calculating developer payroll for a startup environment based on Agile Project Management by considering the developer’s competency and performance parameters. The parameters consist of how many years developers have been working, what the type of developer role is, how many bugs developers produce and how many days developers are not able to complete the task on time. This study is expected to provide an alternative method to formulate developer payroll at startup environment.
Multivariable Root Loci of Discrete-Time Uniform Systems

In this paper, the properties of root loci of discrete-time uniform control systems, i.e. multivariable control systems with identical transfer functions of separate channels and rigid cross-connections described by a square numerical matrix, are discussed. It is shown that the root loci of a discrete uniform system of an arbitrary dimension N (i.e. with an arbitrary number N of separate channels) can be obtained by superposition of the root loci of N fictitious one-dimensional discrete systems, called characteristic systems. In other words, the construction of root loci of an N-dimensional discrete uniform system is reduced to the construction of root loci of N characteristic systems with one input and one output. A numerical example illustrating the properties of root loci of the discussed class of multivariable control systems is presented.
Applying Clustering and Co-occurrence Methods to Identifying Key Events and Their Relations in Chinese Stock Market

Based on needs to extract effective information from massive financial news, this paper† uses text clustering to discover events and designs a news event correlation network model based on word vectors. We collect financial news of the whole year of 2017 and discover key news events automatically through text clustering. The extracted event keywords were modeled based on word co-occurrence analysis. The cosine distance of the word vector was used to measure the correlation between keywords, so that the model could contain more semantic information. Finally, an association network of financial news events was constructed to analyze the correlation between events. Preliminary experiments show that this method can extract news events objectively and measure the correlation between event pairs effectively.
Good Group Sparsity Prior for Light Field Interpolation

A light field, which is equivalent to a dense set of multi-view images, has various applications such as depth estimation and 3D display. One of the essential problems in light field applications is light field interpolation, i.e., view interpolation. The interpolation accuracy is enhanced by exploiting an inherent property of a light field. One example is that an epipolar plane image (EPI), which is a 2D subset of the 4D light field, consists of many lines, and these lines have almost the same slope in a local region. This structure induces a sparse representation in the frequency domain, where most of the energy resides on a line passing through the origin. On the basis of this observation, we propose a group sparsity prior suitable for light fields to exploit their line structure fully for interpolation. Specifically, we designed the directional groups in the discrete Fourier transform (DFT) domain so that the groups can represent the concentration of the energy, and we thereby formulated an LF interpolation problem as an overlapping group lasso. We also introduce several techniques to improve the interpolation accuracy such as applying a window function, determining group weights, expanding processing blocks, and merging blocks. Our experimental results show that the proposed method can achieve better or comparable quality as compared to state-of-the-art LF interpolation methods such as convolutional neural network (CNN)-based methods.
Intuitive Cognition as a Specific Phenomenon of Philosophy: Grounds and Features

The article raises the problem of intuition as a specific phenomenon of epistemology. The author emphasizes the direct nature of intuitive cognition implying the presence of a minimum of preliminary reasoning, the lack of scientific rigor in interpretations and an independent attitude to conclusions and evidence. Intuitive cognition differs from rational cognition in its spontaneity, self-evidence. The advantage of intuition is to overcome the well-known, familiar in solving problems, the possibility of expanding the boundaries of knowledge. Intuitive knowledge as a representation of an object in its totality can be unconscious. Intuition allows us to "grasp" reality, to see its fluidity and variability. Discovery by intuition is unexpected. There are often times when all hope on solving the problem is lost. As a result of intuitive activity there is a complete and qualitative change of previous ideas, new knowledge appears, a different picture of the world is formed. Active use of intuition is mainly stipulated by the high level of intelligence of the scientist, his professionalism. The possibility of intuitive insight is greatly enhanced if the researcher is highly qualified. As a result of research the author succeeded to reveal that intuition is specific for searches and "insights", and results have an unexpected character. Intuition as a form of cognition is unique in its dynamism. It is the result of the unity of sensuous and rational knowledge. A special condition for the emergence of intuitive "insight" is the presence of a situation of continuous search. It is important not just to state the problem, but also to persevere towards the goal, careful and scrupulous research, the desire not to stop working even in case of failures. In the scientific world there is always a variety of opinions and positions. According to the results, we can conclude that intuition is one of the important epistemological factors contributing to the continuous expansion of the entire space of knowledge about nature, society, man and his thinking. It is the integration of all types and structures of cognitive activity including various forms of intuition, that gives fruitful results, helps the scientist to achieve his goal.
Study on Clutter Suppression Method for Micro-motion Target

In order to solve the problem of the energy loss of echo in the time-domain clutter suppression of narrow-band pulse radar, a novel method based on complex empirical mode decomposition(CEMD) for suppressing clutter, is proposed. Firstly, the model of the rotor micro-motion target in ground clutter, is established. The differences of the clutter and micromotion signal in the spectral width, are analyzed. Then, the time-frequency analysis results of the rotor micro-motion target and clutter are obtained by Gabor transform. Secondly, the clutter and micro-motion signal are separated by CEMD, and by defining the proportion of the total energy occupied by different intrinsic mode functions (IMF), high frequency components are preserved and reconstructed as the rotor micro-motion target in time-frequency domain. Finally, the image of the reconstructed time-frequency result is processed by over-threshold processing. Simulation results verify the effectiveness of the proposed method.
Power Attack and Evaluation on Elliptic Curve Cryptosystem in a Commercial Smart Card

Elliptic Curve Cryptography (ECC) is a widely used cryptographic algorithm in our life. Although its theory has become one of the focuses, it is difficult to ensure its security during the process of implementation. One can break it easily by using side-channel attack such as power attack. In this paper, a key recovery platform is built with computer, filter, signal acquisition module, oscilloscope, and so on. Then, we implement the simple power attack on ECC, and employ self-correlation analysis to make it easy to recover the secret key automatically. Finally, some relevant and effective countermeasures are suggested against power attacks.
Conceptions of Learning and Convergence Education in Liberal Arts Education: Focusing on the Perceptions of Local Private College Students

The purpose of this study is to understand conceptions of learning, learning approach, perceptions of convergence education in liberal arts education. Five hundred thirty six students enrolled in a private university in Chungcheongdo participated in the study. Findings of the study report a significant difference across academic years in the conceptions of learning, particularly perceiving learning as a process to grow as a member of a society. A regression analysis on convergence thinking revealed that students with higher convergence thinking level have a higher tendency to consider learning as remembering, understanding, and using activities, continuous process, and social development. Another regression analysis on convergence value-production found that students with greater production of convergence value are more likely to perceive learning as remembering, understanding, and using activities, and for personal change. Findings of this study add to the current literature by enhancing the understanding of learners in local private college students in Korea. The findings also are noteworthy regarding that it explored the perceptions of learners on current convergence education in liberal arts education. Suggestions for future research and educational program design are followed.
Analysis on Opinion Words Extraction in Electronic Product Reviews

Online user reviews are increasingly becoming important for measuring the quality of different products and services. Sentiment classification or opinion mining involves studying and building a system that collects data from online and examines the opinions. Sentiment classification is also defined as opinion extraction as the computational research area of subjective information towards different products. Opinion mining or sentiment classification has attracted in many research areas because of its usefulness in natural language processing and other area of applications. Extracting opinion words and product features are also important tasks in opinion mining. In this work an unsupervised approach was proposed to extract opinions and product features without training examples. To obtain the dependency relation between the product aspects and opinions, this work used StanfordCoreNLP dependency parser. From these relations, rules are predified to extract product and opinions. The main advantage of this approach is that there is no need for training data and it has domain independence. Acoording to the experimental results, the modified algorithm gets better results than the double propagation algorithm.
Evaluating the Performance of Proposed IODAM Algorithm

Distributed Association Rule Mining (DARM) is a technique used to identify the desired buying patterns of the products bought by the customers from the transaction set that are distributed in various nodes located geographically at different locations. The paper describes the performance of proposed DARM algorithm called IODAM (Incremental Optimized Distributed Association Mining) using the various experiments conducted for calculating execution time on given datasets in a distributed environment. Scalability refers to the evaluating the performance of the algorithm when the nodes are added in the distributed environment. Scalability testing for factors such as Scaleup and Speedup is performed and measured for the IODAM algorithm, and the algorithm withstands all the factors efficiently.
Keyphrase Extraction Based on Multi-Feature

The keyphrase extraction algorithm can be used in many fields such as information retrieval, recommendation system, text categorization, etc. The efficient keyphrase extraction method can quickly summarize the core content of the text. In order to improve the accuracy of extracting keyphrase, we propose a text keyphrase extraction method based on multi-feature. First, construct word graph by using the word co-occurrence relationship; Secondly, count the number of sentences in which the target word appears in the sentence, calculate the sum of the reciprocals of the positions of the words in the document, add the word span factor to filter the high frequency noise data, and extract the subject information by using the LDA model; Finally, integrate these characteristics and conduct a random walk on the word graph until the score becomes stable, rank the nodes according to the score and select appropriate words as keyphrase. We conducted experiments on three data sets SemEval2010, KDD and WWW. By studying the selection of various parameters of our algorithm model and comparing with various algorithms, the results show that the quality of keyphrase extraction in our model is improved.
Improved non-local mean denoising algorithm based reliability of reference pixel

Abstract In this paper, we propose a method for improved the non-local mean denoising algorithm by using the similarity in comparison with reference pixels. The noise in image is eliminated by the calculation to follow values as the patterns are similar in non-local mean denoising alogirthm. In the conventional algorithm, all the comparison results of the pixels are included in the denoising process, and even the low similarity result is included. The method causes the denoised effect to incorrect. In this paper, we propose an algorithm that reduces the influence of low-similarity reference pixels or improves the effect of high-similarity reference pixels. The proposed method improves both the object quality and the subject quality.
METHODOLOGICAL RECOMMENDATIONS FOR VISUALIZING EDUCATIONAL INFORMATION

The article presents recommendations on the visualization of educational information and its use in the educational process at informatics lessons. The recommendations will be useful to both current teachers in preparation for lessons, and students of pedagogical universities. The principles of visualization of educational information, visualization methods, as well as visualization software, including online services, are examined in detail. The tasks that are solved in the course of informatics when using visualization are outlined.
Educational and information environment of postgraduate education institutions as a resource for designing individual educational training of teachers

The new Ukrainian school needs the teachers able to work on their continuous development and decide on their personal paths with regard to their interests, needs, motivation, opportunities, and experience. The educational environment of postgraduate education should fully allow for the choice of types, forms and pace of education as well as variability of programs. In the context of the national educational space, a number of professional development projects has been proposed to teachers in accordance with the declared change. Despite the diversity of their content and forms, postgraduate educational institutions need their own educational and information environment that would play a leading role in the professional development of teachers and enable them to continuously follow their individual educational trajectories. The article analyzes modern approaches to understanding the essence of educational and information environment of postgraduate pedagogical education institutions. Some results of sociological research on teachers’ readiness to design their own professional development are presented. The practice of individualizing advanced training and professional development of teachers through face-to-face and distance learning using the authors’ platform DLET (Distance Learning Environments for Teachers) at Volyn Institute for Postgraduate Pedagogical Education is described. The platform is based on the principles of accessibility, understandability, personal orientation, motivation and interests; and enables organization of guided independent work of participants under regional educational projects. The articles concludes by stressing the importance to update educational and information resources of postgraduate pedagogical education institutions and to use benefits of the information space objects of the industry and Internet platforms.
Online Handwritten Signature Verification: The State of the Art

Handwritten signature is the most common method for biometric verification. The purpose of this research is to analyze the existing approaches to the implementation of the algorithm for verification of handwritten signatures. Existing researches use various technologies, such as neural network, hidden Markov model and SVM algorithm, to solve the task of signature verification, and they are constantly introducing new ideas, concepts and algorithms. Signature verification is a real challenge for researchers due to many difficulties that may arise in the process of creating such system. The most promising algorithm will form the basis of the developed authentication system based on a handwritten signature.
On Possibilities and Methods of Analysis of Thematic Expressions in Spoken Texts

Abstract The treatise focuses on mutual comparison of three methods of detection of prominent text units (prominent in relation to the contents of the text). The methods are: 1) analysis of key words based on comparison of source and referential corpora, 2) thematic concentration and h-point, and 3) the TF*IDF method. We try to thematize their pros and cons and, using the results of the carried out analyses, propose the optimal method for the extraction of thematic words from the spoken texts the frequency structure of which differs distinctly from the frequency structure of written texts.