Reviewerly/exHarmony huggingface dataset:
Blockchain

This chapter provides a conceptual understanding of blockchain, its purpose, and fundamental concepts before discussing two prominent blockchains, Bitcoin and Ethereum, which are, respectively, representative of the first and second generations of this technology. Some applications that are characteristic of the third generation are presented along with their potential and limitations. They also serve to substantiate some of the problems that are discussed later. Thus, the current developments that are addressing the most pressing current issues and the approaches to overcome them are discussed in the vision of the future of blockchain. What will be the fourth generation, with obligatory resolution or mitigation of many of these problems, which could lead to a widespread adoption of the technology in several other sectors, is described.
The Research Field of Reality Environments in Education

Reality technologies are being introduced in schools due to high anticipated or claimed educational benefits. This article explores the reliability of foundations of such claims for use of three kinds of technology-based ‘environments': virtual, augmented and mixed reality. It is a follow-up of a previous stage in a research review and includes 35 articles. A variety of problematic issues appear, among which can be found ‘false dichotomies,' ‘lack of precision' and ‘neuromyths.' Examples are also given of fruitful approaches.
Technology Enhanced Learning: The Role of Ontologies for Feedback in Music Performance

In this paper, we present an analysis of feedback as it occurs in classroom-based and technology supported music instrument learning. Feedback is key to learning in music education, and we have developed technology based on ideas from social media and audio annotation which aims to make feedback more effective. The analysis here aims to enhance our understanding of technology-mediated feedback. The result of this analysis is three ontologies describing feedback and feedback systems. First, we developed the \emph{teacher's ontology} using a qualitative, observational approach to describe the types of feedback that music instrument tutors give to their students. We used this ontology to inform the design of an online music annotation platform for music students. Second, we developed the \emph{grounded ontology} using a grounded theory approach, based on 2,000 annotations made by students and tutors using the annotation platform. We compare the grounded and teacher's ontologies by examining structural, semantic and expressive features. Through this comparison, we find that the grounded ontology includes elements of the teacher's ontology as well as elements relating to practical and social aspects of the annotation platform, while the teacher's ontology contains more domain knowledge. The third ontology is a formalisation of the transactional capabilities of the platform, and we refer to it as the \emph{platform ontology}. We present it using the OWL language, and we show how this allows us to develop several practical use cases, including the use of semantic web capabilities in music education contexts.
A Weighted Center Graph Fusion Method for Person Re-Identification

Feature fusion is widely used in person re-identification (re-ID) and has been proven effective. However, it is difficult to know which features are effective to identify a specific person and how to fuse features to explore complementary information and apply the advantages of each feature. Motivated by these problems, this paper proposes a new method of person re-ID to fuse the recognition results of multiple features at the rank level. Three innovations are included in this method: first, multiple metric spaces are constructed based on the correlation of different features to generate multiple rank results; second, the most similar candidates in each corresponding rank list is converted into a graph structure by our proposed weighted center graph (WCG), and we use an adaptive value K to automatically seek the most similar images of each query, thus improving the accuracy of candidate targets. Finally, to evaluate the effect of each WCG, a discriminative power coefficient is designed and used to assign a proper coefficient for each WCG according to the discriminative power of corresponding features. The result can be obtained by re-ranking the fused WCG. The extensive experiments on five datasets demonstrate the matching rate of our proposed method by comparing with several state-of-the-art methods. Our code is available at https://github.com/gengshuze/WCG.git.
System-Based Coaching Technology Mechanism for the Development of Educational Systems

In order to further explore the performance of coaching technology in promoting the overall development of the education system, this paper applies the idea of system theory to deconstruct the five key elements and their interaction relations in the education system which affects the effect of education and training objectives. Meanwhile, the present situation and existing problems of each element are discussed including their influences for the system goal. The key analysis and exposition are made with the input and output of the core elements of the system to promote the overall coordinated development of the education system and improve the effectiveness. Based on the system theory we could conclude that this paper is of practical guiding value to promote the coordinated development of the key elements of the education system by using coaching technology, further to promote the overall evolution of the educational system and to promote the effective promotion of the overall goal of education and educating people.
FSCRank: A Failure-Sensitive Structure-Based Component Ranking Approach for Cloud Applications

Cloud computing has attracted a growing number of enterprises to move their business to the cloud because of the associated operational and cost benefits.Improving availability is one of the major concerns of cloud application owners because modern applications generally comprise a large number of components and failures are common at scale.Fault tolerance enables an application to continue operating properly when failure occurs, but fault tolerance strategy is typically employed for the most important components because of financial concerns.Therefore, identifying important components has become a critical research issue.To address this problem, we propose a failure-sensitive structure-based component ranking approach (FSCRank), which integrates component failure impact and application structure information into component importance evaluation.An iterative ranking algorithm is developed according to the structural characteristics of cloud applications.The experimental results show that FSCRank outperforms the other two structure-based ranking algorithms for cloud applications.In addition, factors that affect application availability optimization are analyzed and summarized.The experimental results suggest that the availability of cloud applications can be greatly improved by implementing fault tolerance strategy for the important components identified by FSCRank.
Balanced-kNN: A New Lazy Learning Algorithm and its Evaluation

This paper proposes a new lazy learning algorithm, named balanced-kNN, for high performance robust classification of noisy patterns. K-nearest neighbor (k-NN) is a simple and powerful method with a high accuracy for various real world applications using unbiased datasets. However, noisy datasets are often gathered in real world applications. This paper presents a new robust algorithm, balanced-kNN, and compares the prediction accuracy with some conventional methods by using UCI datasets. The experimental results show that the balanced-kNN algorithm can perform more efficient classification of noisy data than the normal-kNN and weighted-kNN algorithms.
Factors Related to EFL/ESL Readers' Reading Strategy Use

This Article systematically reviews the use of reading strategies among college-level English as a foreign/second language (EFL/ESL) learners and its relationship with two non-cognitive factors: gender and motivation. The author reviews empirical studies published from 2000 to 2017 in order to answer two research questions: (a) What gender disparities exist in college-level EFL/ESL learners' use of reading strategies? (b) How do motivation factors relate to college-level EFL/ESL learners' use of reading strategies? Findings indicate that: (1) motivation factors, including achievement goals, interest in reading, and self-efficacy, positively relate to reading strategy use. (2) gender has an influence on strategy use and female readers show higher use of reading strategies. (3) Interaction effects among factors exist. EFL/ESL learners' strategy use is shaped by multiple factors jointly.
Computer Programming for Librarians: A Study of Job Postings for Library Technologists

The rapid changes in technology used in the library and cultural heritage sectors can make it difficult for early career librarians and LIS students interested in library technology positions to know what skills will be most beneficial to their job search. In this study, 492 librarian and archivist job postings from the Code4Libs Jobs website from between 2008 and 2018 were collected and analyzed to find which programming technologies are most in demand overall and which are most requested for specific types of jobs and specific types of employers, including changes over time. The results provide a glimpse of programming languages and technologies that are widely useful in the field of library technology today, as well as those that are growing in use.
A zigzag survey design for continuous transect sampling with guaranteed equal coverage probability

Marine resource surveys in large areas have high cost, and to find an optimal survey design with regard to efficiency and scientific outcome is an important issue. A randomized zigzag design for straight line and curved transects is developed that guarantees equal coverage probability, i.e., each point in the study area has the same probability of being covered by the transect. The basic idea is to fit automatically either the smallest rectangle, or the smallest circular sector enclosing the actual area. Then a recipe for the location of zigzag legs that provide equal coverage probability everywhere in the rectangle or circular sector is outlined, and thereby also at any location within the study area, which simplifies unbiased abundance estimation. The cost of this approach is the unwanted distance to be traveled from the point where a transect leg leaves the study area to the point where the next leg enters. A comparison of a randomized parallel, straight line zigzag, and curved zigzag approach applied to 7 sandeel areas with great variety revealed an average off-effort traveling distance of 28%, 9% and 6%, respectively. Thus, it appears that the developed zigzag design is far more efficient than the parallel design.
Exploratory Analysis of Block Chain Security Vulnerabilities

Distributed ledger technology or block chain, is a progressing future domain, and accepting its constraint and boundaries will be serious to employing it into the Internet of Things or in any other technologies. Obviously, the security and trust and privacy issues will pitch in as well as other constrains. The Distributed ledger technology has drawn major attraction of the next-generation financial technology due to its security, and also it’s extensively used in the design of smart cities and other areas of business or industry throughout the world. In this paper, we will first discuss on block chain domain and its process and then focuses on possibilities of block chain security analysis threat occurrence and in the area of public domain where it’s attracting more and more hackers’ threats. This paper provides helpful guidance and reference for future research works.
A RFP System for Generating Response to a Request for Proposal

Responding to Request for Proposal (RFP) with comprehensive solutions is central to IT Services business. Typically, an RFP comprises a set of questions spanning across various domain areas. Current industry practices largely rely on Subject Matter Experts to analyze questions and search multiple information sources to come up with the best possible response for a question keeping in view the customer context. With expertise typically in short supply, it becomes increasingly difficult to manage growing RFP volumes. To address this problem we propose a generic solution meta-model and a system that can generate a draft response to an RFP to be augmented manually later. The generation step uses NLP, modelling and search techniques augmented with a knowledge base to generate knowledge search queries to retrieve suitable answers to a set of RFP questions and compose RFP response. In this paper, we share a RFP response generation approach, its implementation, results and lessons learnt from deployment with one of the business units having high volumes of proposal turnover. Initial results show that RFP system is effective in enabling response generation with ~76% mean query precision and 86% mean query recall.
A Hierarchical Secret Sharing Scheme Based on Information Dispersal Techniques

Hierarchical secret sharing schemes are known for how they share a secret among a group of participants partitioned into levels. In this study, we consider using a systematic information dispersal algorithm (IDA). We then apply the general concept of hierarchy to the generator matrix used in a systematic IDA and propose an ideal hierarchical secret sharing scheme applicable at any level. For perfect privacy, secret sharing schemes depend on the fact that an adversary can only pool at most $$k-1$$ shares. However, in our hierarchical scheme, we need to consider an adversary can also pool k or more shares of lower-level participants. Moreover, considering practical use, we present our evaluation of our software implementation.
A Smart Algorithm for Quantization Table Optimization: A Case Study in JPEG Compression

Image compression is a significant dilemma in digital image processing. JPEG is the ultimate standard for compressing still images in the last few decades. Quantization table in JPEG determines the quality of the image and the performance of the JPEG algorithm. Smartness is capacity to derive or extract knowledge from previous experience and uses the same for current. With this notion in mind, this chapter presents a smart algorithm for quantization table optimization in the JPEG baseline algorithm. Section 13.1 portrays JPEG Standard as a case study where the need for quantization table optimization is described and also we discuss the overview of differential evolution (DE) as a solution for this optimization problem with its strength and weakness. Section 13.2 deals with the need for augmenting knowledge in DE algorithm and explains the design of smart optimization algorithms. Further, we enumerate the methodology for achieving the same. Sections 13.3 and 13.4 compare the applicability of smart optimization algorithms for various test images along with the simulation results, verified using statistical hypothesis testing.
Equatorial part segmentation model for 360-deg video projection

The 360-deg or virtual reality video can capture all sides around the observer at the same time and provides the freedom to select any part of the surroundings for display using a head mounted display in the case of a single user or smart TV, curved screen, and even with video projectors placed in a 360-deg projection room in the case of multiusers. In order to encode these spherical videos with standard codecs, a projection step is necessary to transform the original 3-D space scene into a regular 2-D video sequence. Thus different geometric shapes can be used as equirectangular projection (ERP), cubemap projection (CMP), segmented sphere projection (SSP), etc. The proposed model presents the 360-deg video on eight square faces, six faces for the equatorial part, and two faces for the top and bottom views. This shape can ensure a better projection of the video thanks to the size ratio of the lateral faces, which is very close to the size ratio of televisions and video projectors used in 360-deg rooms. Quality metrics comparison shows average gains when compared with ERP and SSP models and the encoding time is hugely reduced when compared with CMP model.
Computational Music Archiving as Physical Culture Theory

The framework of the Computational Music and Sound Archive (COMSAR) is discussed. The aim is to analyze and sort musical pieces of music from all over the world with computational tools. Its analysis is based on Music Information Retrieval (MIR) tools, the sorting algorithms used are Hidden-Markov models and self-organazing Kohonen maps (SOM). Different kinds of systematizations like taxonomies, self-organazing systems as well as bottom-up methods with physiological motivation are discussed, next to the basic signal-processing algorithms. Further implementations include musical instrument geometries with their radiation characteristics as measured by microphone arrays, as well as the vibrational reconstruction of these instruments using physical modeling. Practically the aim is a search engine for music which is based on musical parameters like pitch, rhythm, tonality, form or timbre using methods close to neuronal and physiological mechanisms. Still the concept also suggests a culture theory based on physical mechanisms and parameters, and therefore omits speculation and theoretical overload.
A Study on the Preview Effectiveness of Learning Contents in ePUB3 eBook-Based Flip Blended Learning Models

In e-learning, the flip blended learning model is used to provide students with learning contents inside/outside classrooms. It encourages students to preview these contents outside classrooms before the lesson. Then, various learning activities are taken inside classrooms in the lesson. An important issue about its success is the effectiveness of the preview because it affects the subsequent learning activities in the lesson. Further, for an ePUB3 ebook-based learning model, ePUB3 ebooks are used and hence embedded ePUB3 track and test functions can be used for tracking the preview accesses on these eBooks and examining the preview outcomes. As such, the preview effectiveness can be captured by checking these accesses and their relationships with the test outcomes for making adequate actions on subsequent lesson activities. In this paper, the authors explore such an effectiveness study on the preview of learning contents via ePUB3 track and test functions. For illustration, this study is applied to a script writing course and respective discussions are presented for showing its usefulness.
Prototype Selection Method for Vehicle Condition Monitoring using Machine Learning

The authors are developing a condition monitoring system using vibration analysis and machine learning for the purpose of monitoring the condition of railway vehicle equipment. In railway vehicles, vibrations change due to long-term state change, so long-term data should be used for learning. In this case, it is not practical to use all data, so it is necessary to use only some part of the data, which is called prototype data. Therefore, a prototype selection method based on the neighborhood method is proposed in this paper. As a result of applying the proposed method to the vibration data during the abnormal simulation test, the expected effect was confirmed.
Effect of long-term orientation on voluntary security actions

Purpose This paper aims to examine the impact an individual’s long-term orientation (a cultural dimension) has on their attitude, behavioral intention and actual voluntary security actions taken in the context of the dangers related to poor account access management. Design/methodology/approach The paper relied upon survey data and actual usage information from a culturally diverse sample of 227 individuals who were introduced to the specific security problem and the accepted solution of using a password manager application. Findings The paper provides empirical evidence that the effect of positive attitudes increased when individuals were more long-term oriented, but the effect was reversed for average/negative attitudes toward the voluntary security behavior. Furthermore, participants with high long-term orientation and strong positive attitudes toward the security action actually adopted password manager applications 57 per cent more than the average adoption rate across the sample. Research limitations/implications Due to the research approach (survey data), security context and sample population, the research results may lack generalizability. Practical implications The findings suggest that security awareness messaging and training should account for differences in long-term orientation of the target audience and integrate the distinctly different types of messages that have been shown to improve an individual’s participation in voluntary security actions. Originality/value The paper addresses previous research calls for examining possible cultural differences that impact security behaviors and is the only study that has focused on the impact of long-term orientation, specifically on voluntary security actions.
New Assumptions on Isogenous Pairing Groups with Applications to Attribute-Based Encryption

We introduce new isogeny-related assumptions called Isog-DDH and Isog-DBDH assumptions. By using the assumptions, we reinforce security of several existing (hierarchical) identity-/attribute-based encryption (HIBE/ABE) schemes. While the existing schemes are proven from the standard DBDH assumption, our reinforced secure ones have two incomparable security proofs: one is proven from the DBDH as well and another is proven from the Isog-DDH assumption which is incomparable with DBDH. As a result, if either DBDH or Isog-DDH assumption holds, the proposed HIBE/ABE schemes are secure. For obtaining our (H)IBE secure in the standard model, we assign a unique (product) group called ID-group to each (H)ID, and introduce a new proof technique, i.e., ID-group partitioning by using isogenies as trapdoors.