Reviewerly/exHarmony huggingface dataset:
Empirical Study on the Correlation and Volatility between Bitcoin and the Blockchain Index

The encrypted money market has attracted the attention of investors all over the world. Among the encrypted currency, Bitcoin is undoubtedly the most popular. Because blockchain technology is the crucial support of bitcoin, exploring the relationship between bitcoin and the Blockchain Index is necessary. In addition, the volatility of bitcoin and the Blockchain Index is crucial for investors. Therefore, this paper uses the Granger causality test to explore the correlation between bitcoin and the Blockchain Index. Furthermore, their volatility is analyzed by a GARCH-class model.
Extending CoNavigator into a Collaborative Digital Space

We present an extension of the existing CoNavigator collaboration system that allows for persistence of in-person collaboration sessions through a digitally projected overlay and camera system. This augmented reality environment allows participants in a CoNavigator session to resume from a previous session without having to laboriously recreate the physical state of the space manually. By combining features traditionally found only in digital space with the intuitive nature of a tactile physical environment, we hope to produce a tool that builds on the work of CoNavigator and lowers barriers to adoption while increasing efficacy.
Purity Filtering: An Instance Selection Method for Support Vector Machines

Support Vector Machines can achieve levels of accuracy comparable to those achieved by Artificial Neural Networks, but they are also slower to train. In this paper a new algorithm, called Purity Filtering, is presented, designed to filter training data for binary classification SVMs, in order to choose an approximation of the data subset that is more relevant to the training process. The proposed algorithm is parametrized so to allow a regulation of both spatial and temporal complexity, adapting to the needs and possibilities of each execution environment. A user-specified parameter, the purity, is used to indirectly regulate the number of filtered data, even though the algorithm has also been adapted to let the user directly specify the number of filtered data. Using this algorithm with real datasets, reductions up to 75% of training data (using only 25% of the data samples to train) were achieved with no major loss on the quality of classification.
System Approach to the Formation of the Information Culture Levels in Cyberspace

The article deals with the problem of formation of information culture of cyberspace by methods of system analysis. An analysis of modern approaches to the formation of the concept of “information culture” taking into account trends in the development of information and communication technologies is conducted. A methodology for forming information culture levels on the basis of the systematic approach is presented. The peculiarities of informational culture formation in conditions of uncertainty are specified.
Enhancing Dynamic-Viewport Mobile Applications with Screen Scrolling

The pervasive penetration of mobile smart devices has significantly enriched Internet applications and undoubtedly reshaped the way that users access Internet services. Different from traditional desktop applications, mobile Internet applications require users to input via touch screens and view outputs on the displays with considerably limited size. The significant conflict between the limited-size of touch screens and the richness of online media contents widely exists in dynamic-viewport mobile applications, a class of mobile Internet applications that download contents beyond the user's viewing region (referred to as viewport). As dynamic-viewport mobile applications usually use HTTP for content downloading, to improve their quality of experience (QoE) and cost efficiency, in this paper, we present a Mobile-Friendly HTTP middleware (MF-HTTP), which can interpret user touch screen inputs and optimize the HTTP downloading of media objects for such applications. We first demystify screen scrolling in mobile operating systems and precisely break down the viewport moving process. We identify the key influential factors for media object downloading and develop an optimal download scheme. Towards building a practical middleware, we further discuss and address the implementation issues in detail. We implement a MF-HTTP prototype based on Android platforms and evaluate the performance of MF-HTTP by conducting concrete case studies on two representative dynamic-viewport mobile applications, namely, web browsing and 360-degree video streaming.
An Approach for Semantic Web Discovery Using Unsupervised Learning Algorithms

With the huge amount of available web services, it becomes increasingly difficult to find target web services for users accurately and effectively. For this reason, research in web service clustering has recently gained much attention. Most existing clustering methods perform well when dealing with long text documents. However, the textdescriptions of web services are in the form of short text. Meanwhile, it is meaningful to consider word order information in the textdescriptions of web services. Hence, we presented a service discovery approach based on web service clustering considering this issue. In our approach, web service discovery was divided into two parts: web service clustering and web service selection. In the process of web service clustering, the textdescriptions of web services were represented as vectors. In order to make vector representations reflect as much as possible the semantic information contained in the web service textdescriptions, we tried four different unsupervised sentence representations. In another part, LDA was used to mine topic semantic information of web services after user’s web request was placed into a specific cluster according to its web service textdescription vector. The final efficiency of web service discovery was used to measure the effectiveness of our approach.
Analysis of Detection of Drow and Entire Co-Occurrence Matrix GLCM Method on The Classification of Image

Abstract Grey level co-occurrence matrix or GLCM is a method for obtaining features of a textural image. On the previous study of GLCM, using only 4-degree degrees 0, 45, 90 and 135 in calculating the result of features. Periodicity, directionalityand randomness are the three most important factors in characterizing textures. Therefore, textural directionality is a basic feature of the image and plays an important role in image descriptions, which can be used to describe image textures. So this research will analyse the effect of modification of directionality or direction of adegree of texture distribution on GLCM by using 8 degree direction, which is degree 0, 45, 90, 135, 180, 225, 270, and 315 to know theeffect that impact on theimage by using support vector machine as aclassifier. The results showed that the SVM classifier was not able to work well on GLCM feature extraction, but the performance of the modification resulted in a consistent increase in accuracy by performing 10 tests.
A Novel Framework to Automatically Generate IFML Models From Plain Text Requirements

User Interfaces (UI's) are highly important in this era of web and mobile applications. Therefore, an efficient and accurate development of UI's is desirable in early Software Development Life Cycle (SDLC) phases. To achieve this, Object Management Group (OMG) introduced Interaction Flow Modeling Language (IFML) standard in 2013. IFML provides the modeling of manifold UI's for different applications like mobile, web and desktop. Although IFML is based on Model Driven Engineering (MDE) principle, the development of user interface models from initial requirements is still complex and time consuming task. Particularly, it requires domain expertise to understand several IFML concepts like domain model, view container etc. for the proper modeling of user interfaces. Consequently, there is a strong need of an approach to automate the development of IFML models from initial plain text requirements. This article presents a novel framework to automatically generate IFML models from textual requirements by utilizing the features of Natural Language Processing (NLP). Particularly, a set of NLP rules are developed to extract important IFML elements like View Components, Events etc. from textual requirements. Furthermore, a comprehensive algorithm is developed for the systematic execution of NLP rules in order to generate both IFML Domain and Core models. As a part of research, a sophisticated $T$ ext to IF ML (T2IF) tool is developed. The feasibility of proposed framework is demonstrated through movie manager and online bookstore case studies. The evaluation results prove that the proposed framework is capable of generating IFML models from textual requirements with high accuracy.
Modeling the Induced Action Alternation and the Caused-Motion Construction with Tree Adjoining Grammar (TAG) and Semantic Frames

The induced action alternation and the caused-motion construction are two phenomena that allow English verbs to be interpreted as motion-causing events.This is possible when a verb is used with a direct object and a directional phrase, even when the verb does not lexically signify causativity or motion, as in "Sylvia laughed Mary off the stage".While participation in the induced action alternation is a lexical property of certain verbs, the caused-motion construction is not anchored in the lexicon.We model both phenomena with XMG-2 and use the TuLiPA parser to create compositional semantic frames for example sentences.We show how such frames represent the key differences between these two phenomena at the syntax-semantics interface, and how TAG can be used to derive distinct analyses for them.
System for Analysing Big Weblog Data

The behavior and purposes of Internet usage of the users need to be understood based on the Web usage history within an organization. The data are stored as huge log files. Often, data are stored separately and exist at various places; therefore, it is difficult to manage or utilize the data. This research aims at examining and developing an analysis tool for log files applying Hadoop and Hive. The development was divided into two parts. First, data from the Web History were gathered by using PHP via SQLite in order to classify the data into website categories, especially Google, YouTube and Facebook. The obtained data were then used to analyze the categories of accessed websites. The findings were recorded on Hive by an enhanced algorithm to be able to analyze the categories. The algorithm was also designed to analyze words and phrases used in Google search. Second, behavior and purposes of accessing websites during class was analyzed. The results can be displayed in real time in a percent format and the frequency of Website accesses.
Touch Presence, Absence and Connection

Technologies are intrinsically linked to the ways in which physical, temporal and emotional distances are thought of and managed. Likewise, social relations and communication technologies mutually shape each other as they are developed and maintained. This chapter explores the social connections that digital touch technologies are beginning to shape, with a focus on the related experiences of presence and absence through mediated touch and the questions this raises for the design space of interpersonal relationships, that is, the mediation of touch between people. We first consider how these concepts have been defined and addressed in the literature on communication technologies in general, and touch technologies in particular. We then use three extended examples from InTouch case studies to explore and reflect on these concepts. We consider how touch technologies might challenge us to think about the interaction between human and machine. We close with a consideration of design implications and possibilities for future research.
Foveated Nonlocal Means Despeckle Filtering for Ultrasound Imaging: Imaging Perspective

Ultrasound speckle noise degrades imaging contrast and hides anatomical details; thus causing inaccuracy in clinical diagnosis. Although speckle reduction methods such as classical nonlocal means (NLM), optimized Bayesian nonlocal means (OBNLM), and speckle reducing anisotropic diffusion (SRAD) filters have been proposed for years, they still suffer two major problems - insufficient preservation of characteristic details such as calcifications and inordinate blurring making image appearance artificial. To solve the two problems, we propose a novel foveated nonlocal means despeckle filtering technique, inspired by the human visual system. Conventional NLM filters despeckle via searching for analogous patches at different areas within the image and then estimating the impulse response by the degrees of similarity appraised by a windowed Euler distance between the target and searching patches. In our technique, foveated self-similarity is used instead of conventional self-similarity. The foveated self-similarity is based on a new patch operator mimicking human retina properties, sharpening patch pixels in the center and blurring them near the periphery. Moreover, throughout the literature, the tuning of the search window and patch sizes and other parameters are not consistent; nonetheless, in this study, they are tuned universally from imaging perspective, i.e., according to the size of point spread function which allows the adaption to different imaging systems and settings. Simulations and clinical data (not shown here) were used to verify our proposed method. The performance of our proposed method is also compared with the classical despeckle filters. The results demonstrate that the proposed technique can remove speckles forcefully while more effectively retaining structural edge details, textures, and point-like structures. Quantitative measures such as contrast-to-noise ratio, edge preservation index and contrast measure are also presented.
Evenly brightening using kurtosis Gaussian pattern to simplify image binarization

Abstract A problem of separating object from background using thresholding in image segmentation is uneven image brightness. Some binary image objects vanish because the pixel intensity value on the edge side is greater than the pixel’s background intensity. The others at the edge of image are covered by background pixel. The background pixel at the edge is bigger than the pixel value of the object in the inside of image. A light source type affects the spread of brightness adding thresholding difficulty. The object’s light distribution from the exposed light source follows the Gaussian distribution. A kurtosis value depends on the light source type. Following the kurtosis of standard deviation, the method for creating brightness evenly is designed. The Gaussian pattern is formed from the distribution of Gaussian values in the cells of the Gaussian matrix. The pixel values in the corresponding images will be added to the matrix cells. The pixel value will increase according to the Gaussian matrix pattern. This process results in the highest pixel value not getting added value, while the lowest pixel intensity value will get the greatest value addition.
Correct Number of Clusters (CNC) Description Length in Arbitrary Shape Clustering

One of the main challenges in clustering unlabeled data sets is determining the unknown correct number of clusters (CNC). K-means is a well known and widely used clustering algorithm in this context which requires the correct number of the clusters for proper performance. To address this problem, various validity indices approaches aim to optimize a desired criteria based on measuring the compactness of cluster and the separation between cluster. K-MACE algorithm is a validity index clustering method based on estimating the average error between the correct cluster center and the estimated cluster center for each data point. We propose a modified version of K-MACE that is based on minimizing the CNC codelength. The proposed theory handles clusters that are arbitrary shaped and/or nonlinearly separable. Simulation results confirms superiority of the proposed method over well known validity index methods in the sense of accurate CNC estimation as well as optimizing Adjusted Random Index (ARI) and the Normalized Variation Information (NVI) measures.
On the Role of the Human Resource Management System

The HRM (Human Resource Management) system is a subsystem of any action system (AS). There is a cognitive and scientific “problem of the role of the human resource management system” within AS. The objective of this paper is determining whether the unique system characteristics and role of HRM are reflected in shaping the HRM system, and particularly in its structure. The relationships of the system characteristics and role of HRM to its shaping and structure has not been fully recognized scientifically. It is claimed here that the HRM subsystem is the most important and well-adjusted subsystem of any AS, and that the HR system should be managed according to the principles of unique dialectic management, paradox and chaos management. The paper deals with the following domains: material – AS category; 2) objective – “HRM system” category; 3) universality of time and space. The paper employs a praxeological and systematic approach, prognostic methodology and hypothetical-deductive reasoning.
Energy Consumption of Batch and Online Data Stream Learning Models for Smartphone-based Human Activity Recognition

Online Smartphone-based Human Activity Recognition (SHAR) systems are increasingly used in different kinds of applications such as in smart homes and health monitoring. In order to allow users to train their own personalized models quickly and without privacy concerns, a SHAR model has to be lightweight due to the limited resources of mobile devices. Therefore, time, memory and energy costs have to be analyzed before integrating the learning process in on-device scenarios. In a previous paper, we presented a comparative study for different machine learning (ML) algorithms, regarding training time, accuracy and memory usage, exploiting two different learning approaches: i) CPU-based vs GPU-based batch learning, and ii) Online data stream learning. In this paper, we complete our study by analyzing power consumption for both approaches. Results show that the same previous best models show also the best power values. The impact that has the CPU on the energy cost is also highlighted.
Disassembly Discussed: Creative Textile Sampling as a Driver for Innovation in the Circular Economy

This article argues that in order to face current and future sustainability challenges in the textile industry, the hands-on approach of creative textile designers can be harnessed not only to develop new solutions to material recyclability issues, but also to help in developing new design mind-sets in the circular economy.The article will focus on the importance of making in the textile design process. This research addresses the challenges posed by blended materials to efficient recycling. While technological progress is enabling us to recycle more types of materials (Östlund, 2015 Östlund, Å., Wedin, H., Bolin, L., Berlin, J., Jönsson, C., Posner, S., Smuk, L., Eriksson, M. and Sandin, G. 2015. “Textilåtervinning: tekniska möjligheter och utmaningar.” Naturvårdsverket, Stockholm. [Google Scholar]), to achieve fibre-to-fibre regeneration simple and mono-material textiles are still more economically and environmentally sustainable (Maldini et al., 2017 Maldini, I., Duncker, L., Bergman, L., Piltz, G., Dusha, L., Cunningham, G., Vooges, M., Grevinga, T., Tap, R., van Balgooi, F., 2017. Measuring the Dutch Clothing Mountain. Amsterdam University of Applied Sciences (CREATE-IT Applied Research), Amsterdam. [Google Scholar]). Creative textile design is, however, intrinsically linked to the juxtaposition and combination of different materials and techniques (Dormer, 1997 Dormer, P. (Ed.), 1997. The Culture of Craft: Status and Future, Studies in Design. Manchester: Manchester University Press. [Google Scholar]). Moving away from a mono-material approach to recyclability, the project explores the potential of design for disassembly as a solution to replicate the qualities and attraction of blends while allowing the individual components to be recovered for recycling. Thus, the constraints laid down by recyclability criteria can be creative impulses rather than limitations (Brown and Katz 2009 Brown, T. and Katz, B. 2009. Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation. New York: Harper Business. [Google Scholar]).Current approaches to sustainable innovation mainly come from a problem-solving perspective which is removed from the textile designer’s experience (Igoe, 2013 Igoe, E. 2013. In Textasis: Matrixial Narratives of Textile Design (Ph.D.). Royal College of Art. [Google Scholar]). Through playful experimentation in sampling textiles for disassembly, this research aims to explore solutions from a design and making-led perspective. Textile design practice and material experimentation in the studio have been used as drivers for material innovation. The samples not only lead to original recyclable materials which combine resources for optimal performance and aesthetics, but also draft guidelines for the creation of textiles in the circular economy.
RelSeeker: Relationship-based Query Language in a Graph Database for Social Networks

Thanks to the current massive increase in data related to social activity, such as social networks and real-time recommendations, large amounts of related data are generated every second. Traditional relational databases are insufficiently able to handle this situation. A graph database is suitable for representing relationships in social activity because social networks are easily represented by graphs. Graph databases allow users to store data as nodes and relationships so that the data may be queried as a graph. However, no mechanisms exist that can effectively carry out queries regarding relationships, such as queries about properties of a relationship or relationships of relationships. To solve these problems, we introduce the concept of relationship-based query and develop a graph database query language called RelSeeker, which is based on Datalog. In RelSeeker, a user can create query rules using the properties of relationships, nodes, and relationships of relationships. Moreover, Datalog is suitable for describing relationships of relationships in the graph database. These key features of RelSeker enable effective relationship-based query processing on a graph database. The proposed query language RelSeeker is an extension of Datalog, which can deal with data structures and manipulates a graph database through creating, getting, and updating operations.
A Load Balancing Algorithm in Cloud Computing Based on Modified Particle Swarm Optimization and Game Theory

Cloud computing is the internet-based innovative technology, that offers many services and technologies over the internet, it is the fruit of increasing technological development in the grid, parallel, distributed and clustering systems as well as virtualization and data storage. Many load balancing algorithms and scheduling techniques are required for achieving the optimal utilization of resources and decreasing the time of shifting load, from the overload machines to the underload machines. In this paper, we present a new evolutionary algorithm called SASPSOLB to balance the workload in cloud computing based on game theory and a modified particle swarm optimization the load balancing problem has been modeled as a constrained optimization problem, to find the optimal or near-optimal solution, using the Nash equilibrium concept to reduce the expected response time, we consider the game assumed as a noncooperative game among users. The executed algorithm presented in this paper is compared with another one based on genetic optimization. The simulation results show that our algorithm performs better in term of expected response time and the Makespan.