Reviewerly/exHarmony huggingface dataset:
The Comprehensibility Assessment of Visualization of Semantic Data Representation (VSDR) Reflecting User Capability of Knowledge Exploration and Discovery

The visual representations of search result querying from encyclopedia by semantic searching tools are rarely found on the internet. Most of the available tools require SPARQL to make some specific queries which are difficult to use, and the visual representation of results on a screen is too complicated to comprehend. The necessity of reducing the complexity of visual structure, supporting user interaction, and increasing the perception of knowledge discovery is considerable. The framework of Visualization of Semantic Data Representation (VSDR) is designed by using the 3-layer approach based on the hyperbolic tree model for visual representation of results from the encyclopedia. The architecture of the searching tool is also designed for novice users. This study presents the result of VSDR usage to the extent of the VSDR's capabilities in terms of comprehensibility, and how the user can perceive a VSDR structure with discoverable knowledge. The result shows that VSDR is an effective searching tool for knowledge exploration and discovery. It has a recognition support reflected in users' perception. It also helped users find answers, learn and gain new knowledge through the representation.
Re-ranking with ranking-reflected similarity for person re-identification

A recent concern for person re-identification (Re-ID) is re-ranking after initial results to improve Re-ID accuracy. In this paper, we propose a novel re-ranking method using a ranking-reflected metric to measure the similarity between the ordered set of K-nearest neighbors (OKNN) of a probe and that of a gallery. The proposed metric for ranking-reflected similarity (RSS) reflects the ranking of the shared elements between the two OKNNs. Using RSS, a re-ranking procedure is proposed that prioritizes galleries having neighbors similar to a probe’s neighbor in the perspective of ranking order. In the experiment, we show that the proposed method improves the Re-ID accuracy by add-on to the state-of-the-art methods.
Wavelet Analysis—An Overview

Wavelet analysis is a new branch of mathematics widely applied in signal analysis, image processing, numerical analysis, etc. The name wavelets means small waves (the sinusoids used in Fourier analysis are ‘big waves’), and in short, a wavelet is an oscillation that decays quickly.
Evolution of the Indian subcontinent: Introduction

Geological JournalVolume 54, Issue 5 p. 2755-2758 SPECIAL ISSUE ARTICLE Evolution of the Indian subcontinent: Introduction K.R. Hari, K.R. Hari School of Studies in Geology and Water Resource Management, Pt. Ravishankar Shukla University, Raipur, IndiaSearch for more papers by this authorE. Shaji, E. Shaji Department of Geology, University of Kerala, Trivandrum, IndiaSearch for more papers by this authorS. Ganguly, S. Ganguly Department of Earth Science, Goa University, Taleigão, IndiaSearch for more papers by this authorV.J. Rajesh, V.J. Rajesh Department of Earth and Space Science, Indian Institute of Space Science and Technology, Trivandrum, IndiaSearch for more papers by this authorM. Santosh, Corresponding Author M. Santosh santosh@cugb.edu.cn orcid.org/0000-0002-1073-8477 School of Earth Sciences and Resources, China University of Geosciences, Beijing, China Correspondence M. Santosh, School of Earth Sciences and Resources,China University of Geosciences, Beijing, China. Email: santosh@cugb.edu.cnSearch for more papers by this author K.R. Hari, K.R. Hari School of Studies in Geology and Water Resource Management, Pt. Ravishankar Shukla University, Raipur, IndiaSearch for more papers by this authorE. Shaji, E. Shaji Department of Geology, University of Kerala, Trivandrum, IndiaSearch for more papers by this authorS. Ganguly, S. Ganguly Department of Earth Science, Goa University, Taleigão, IndiaSearch for more papers by this authorV.J. Rajesh, V.J. Rajesh Department of Earth and Space Science, Indian Institute of Space Science and Technology, Trivandrum, IndiaSearch for more papers by this authorM. Santosh, Corresponding Author M. Santosh santosh@cugb.edu.cn orcid.org/0000-0002-1073-8477 School of Earth Sciences and Resources, China University of Geosciences, Beijing, China Correspondence M. Santosh, School of Earth Sciences and Resources,China University of Geosciences, Beijing, China. Email: santosh@cugb.edu.cnSearch for more papers by this author First published: 18 September 2019 https://doi.org/10.1002/gj.3592Citations: 2Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume54, Issue5Special Issue: Evolution of the India subcontinentSeptember/October 2019Pages 2755-2758 RelatedInformation
Learning Deterministic Variable Automata over Infinite Alphabets

Automated reasoning about systems with infinite domains requires an extension of automata, and in particular, finite automata, to infinite alphabets. One such model is Variable Finite Automata (VFA). VFAs are finite automata whose alphabet is interpreted as variables that range over an infinite domain. On top of their simple and intuitive structure, VFAs have many appealing properties. One such property is a deterministic fragment (DVFA), which is closed under the Boolean operations, and whose containment and emptiness problems are decidable. These properties are rare amongst the many different models for automata over infinite alphabets. In this paper, we continue to explore the advantages of DVFAs, and show that they have a canonical form, which proves them to be a particularly robust model that is easy to reason about and use in practice. Building on these results, we construct an efficient learning algorithm for DVFAs, based on the $$\textsc {L}^*$$ algorithm for regular languages.
Quantum Cryptography Protocols for IOE Security: A Perspective

Information Security is a major problem nowadays because the information contains personal info, company’s transactions etc., and with that information anyone can harm anyone’s life. Every System in the world tries to peer into the network and want to manipulate or want to access the data. In addition to this nowadays IoT, IoE came into picture where people’s personal data can be gathered. Mainly in IoE, people’s data is given high importance. So, there is great need of providing security for the data. In this kind of situations, Cryptography plays a key role to protect the data. Classic Cryptography algorithms like RSA, ECC are not more efficient in protecting the data as they are based on mathematical calculations. These mathematically based algorithms may be decrypted in one or other way. So, to enhance the security for data, Quantum came into the picture. This Quantum Cryptography is more efficient when compared to other algorithms as it is based on the concept Quantum Entanglement and Heisenberg uncertainty principle. This paper deals with all aspects of quantum key distribution which are major primitive for Quantum Cryptography along with Quantum Key Distribution Protocols used for developing IoE security.
Bitcoins, Cryptocurrencies and BlockChains

The U.S. has approximately 1,600 cryptocurrencies. No cryptocurrency is qualified to be called money because none has been designated by the U.S. government as being legal tender. Cryptocurrencies are called virtual currencies because they possess a few of the qualities of money. In this article, three issues related to cryptocurrencies are analyzed. First, bitcoins are considered, because they are the principal cryptocurrency. Second, an assessment of the processes the Federal Reserve and the central bank of Sweden are going through to evaluate the possibility of issuing some not-yet-fully-defined new form of electronic currency. Third, an examination of the viability of blockchain, which was introduced as an internal component of bitcoin, as a successful stand-alone technology.
ID3 algorithm approach for giving scholarships

Universitas Maarif Hasyim Latif (UMAHA) is a College that organizes scholarship programs for its students. However, in the implementation of the program, some problems occur, namely the University has difficulty in making decisions, and a large number of student data applying for scholarships causes a long time to select the process. So, we need a tool/system to help solve the problem. The purpose of this study is to apply the ID3 decision tree algorithm in a decision support system that can help facilitate the decision making of scholarships by the UMAHA. Referring to Pressman, this study uses the Waterfall Method. Waterfall method is a systematic and sequential software approach starting from analysis, design, coding, and testing. The test results by applying the ID3 decision tree algorithm indicate that the decision support system built can help the effectiveness and efficiency of the scholarship selection decision making the process by UMAHA, and the decision support system built has a high level of decision-making accuracy. For the development of further research, decision making can be based on more criteria such as student activity. Then in addition to academic achievement in this case using the GPA of the previous semester using non-academic achievements such as achievements in the fields of sports, arts, music.
A novel approach to measuring enterprise procurement decision process: an information distance perspective

Investigating from the production process, enterprise procurement is one most front-end activity, which is the precondition of other activities for an enterprise. Therefore, how to judge and compare scientifically different enterprise procurement decision processes is one of essential works to enhance management on procurement links and increase procurement efficiency. In this paper, enterprise procurement decision process is viewed as one information operation process that is composed of the links of information acquisition, transferring, processing, re-transferring and effecting, also the process of a series of transition of procurement decision information states. Thus, from an information distance perspective, we introduce the distance of information-state transition (DIT) theory and present a novel DIT-based measurement approach for enterprise procurement decision process. By classifying the Series and Parallel forms as well as 4 types of common structures of this process, we propose a useful systematic procedure to describe the operation process of procurement decision information qualitatively and measure enterprise procurement decision process quantitatively, through constructing DIT-based measurement models. Finally, a case study is implemented, thus verifying feasibility and effectiveness of the proposed approach.
Analysis of Back-Translation Methods for Low-Resource Neural Machine Translation

Back translation refers to the method of using machine translation to automatically translate target language monolingual data into source language data, which is a commonly used data augmentation method in machine translation tasks. Previous researchers’ works on back translation only focus on rich resource languages, while ignoring the low resource language with different quality. In this paper, we compare various monolingual selection methods, different model performance, pseudo-data and parallel corpus ratios, and different data generation methods for the validity of pseudo-data in machine translation tasks. Experiments on Lithuanian and Gujarati, two low-resource languages have shown that increasing the distribution of low-frequency words and increasing data diversity are more effective for models with sufficient training, while the results of insufficient models are opposite. In this paper, different back-translation strategies are used for different languages, and compared with common back-translation methods in WMT news tasks of two languages, and the effectiveness of the strategies is verified by experiments. At the same time, we find that combined back-translation strategies are more effective than simply increasing the amount of pseudo-data.
A method for automatic estimation of meaning ambiguity of emoticons based on their linguistic expressibility

The ways of how people communicate on the Internet have adapted to the limited communication channel. Since the most meaning-rich while also concise and compressible mean of communication is still represented by textual information, Internet communication has adapted to support it with other information required for natural communication, while retaining the consistency of the medium. One of the means widely used by many generations of Internet users are the emoticons, used to express information that cannot be fully transmitted only by text, such as emotions or feelings. However, not all emoticons developed and widely used everyday are easily understandable to all users. Emoticon usage proficiency differs among specific persons, as well as between more general user groups. Moreover, the meaning of emoticons themselves can also be ambiguous, especially, when used without other information. In this research, we aimed to determine a correspondence between emoticons and linguistic expressions corresponding to them, such as onomatopoeia, etc., to estimate the general level of meaning ambiguity of emoticons. In the research, we first performed a survey in which we asked users about which emoticon conveys which meaning. Then we applied the results of the survey to quantify the understandability, or meaning ambiguity, of the emoticons. Based on the quantification, we propsed a method for specifying the emoticon’s meaning ambiguity, in which we applied the aility of the emotion to be expressed in everyday vocabulary, or its linguistic expressibility. Three versions of the proposed method showed that it is possible to estimate emoticon meaning ambiguity, although future experiments and additional data collection is required to maximize the performance. The method could help users apply more meaningful and understandable emoticons in their everyday communication.
ADAMiSS: Advanced Data Analysis, Mining and Search, System

The complexity of contemporary data warrants a need for better analysing tools in investigative areas. Human processing of data is no longer a viable option. We present an architecture of a novel universal system for analysis of graph-structured data, where data-mining and similarity-search operators can be used to discover or search for unknown information. We also present results that were obtained by our prototype implementation on two real-world data collections: the Twitter Higg's boson dataset and the Kosarak dataset.
Modelling Early User-Game Interactions for Joint Estimation of Survival Time and Churn Probability

Data-driven approaches which aim to identify and predict player engagement are becoming increasingly popular in games industry contexts. This is due to the growing practice of tracking and storing large volumes of in-game telemetries coupled with a desire to tailor the gaming experience to the end-user's needs. These approaches are particularly useful not just for companies adopting Game-as-a-Service (GaaS) models (e.g. for re-engagement strategies) but also for those working under persistent content-delivery regimes (e.g. for better audience targeting). A major challenge for the latter is to build engagement models of the user which are data-efficient, holistic and can generalize across multiple game titles and genres with minimal adjustments.This work leverages a theoretical framework rooted in engagement and behavioural science research for building a model able to estimate engagement-related behaviours employing only a minimal set of game-agnostic metrics. Through a series of experiments we show how, by modelling early user-game interactions, this approach can make joint estimates of long-term survival time and churn probability across several single-player games in a range of genres. The model proposed is very suitable for industry applications since it relies on a minimal set of metrics and observations, scales well with the number of users and is explicitly designed to work across a diverse range of titles.
CHALLENGES FOR INTERACTION DESIGN EDUCATION IN THE SOUTH: A CASE STUDY OF BOTSWANA

Abstract In a long‐term partnership between a university in the UK and one in Botswana teaching interaction design, the Botswana cohort of students consistently performed less well than their UK counterparts. To investigate this, we conducted a pair of design protocol studies: one in the UK and one in Botswana. Our findings show that designer behaviour differs in the two contexts in terms of process, use of ideation techniques and solution and problem focus, when approaching the task. More specifically, UK students follow the approach taught in the module, while this process conflicts with Botswana students' behaviours. However, we also find that Botswana students prioritise the external context of the designed product rather than its interaction characteristics, which is problematic as interaction is the key feature of interactive products. These results are significant because without leveraging indigenous knowledge to design interaction that supports local user characteristics and context, sustainable and equitable development through technological innovation will be suppressed. In this paper, we highlight challenges for interaction design education in Botswana, which include how to recognise and support a diversity of design approaches while providing suitable pedagogic scaffolding. © 2020 John Wiley &amp; Sons, Ltd.
Construction of cospectral graphs

Construction of non-isomorphic cospectral graphs is a nontrivial problem in spectral graph theory especially for large graphs. In this paper, we establish that graph theoretical partial transpose of a graph is a potential tool to create non-isomorphic cospectral graphs by considering a graph as a partitioned graph.
A Generic Summary Structure for Arbitrarily Oriented Subspace Clustering in Data Streams

Nowadays, as lots of data is gathered in large volumes and with high velocity, the development of algorithms capable of handling complex data streams in (near) real-time is a major challenge. In this work, we present the algorithm CorrStream which tackles the problem of detecting arbitrarily oriented subspace clusters in high-dimensional data streams. The proposed method follows a two phase approach, where the continuous online phase aggregates data points within a proper microcluster structure that stores all necessary information to define a microcluster's subspace and is generic enough to cope with a variety of offline procedures. Given several such microclusters, the offline phase is able to build a final clustering model which reveals arbitrarily oriented subspaces in which the data tend to cluster. In our experimental evaluation, we show that CorrStream not only has an acceptable throughput but also outperforms static counterpart algorithms by orders of magnitude when considering the runtime. At the same time, the loss of accuracy is quite small.
Machine Learning Cyberattack Strategies with Petri Nets with Players, Strategies, and Costs

A model of a structured query language injection attack was designed using the Petri nets with players, strategies, and costs formalism. The formalism models the attacker and defender as competing players that can observe a specific subset of the net and act by changing the transition firing rates that the respective player can control. This model of the attack was based on the Common Attack Pattern and Enumeration Classification database and was validated by a panel of subject matter experts to be representative of a structured query language injection attack. The model was simulated with a reinforcement learning algorithm using an $$\upvarepsilon $$ -greedy selection method. The algorithm learned within each iteration an optimal solution by varying player-controlled transitions rates. This paper describes the validation of the model, the design of the algorithm, and the results from 4 different $$\upvarepsilon $$ -values.
Automated Machine Learning and Bayesian Optimization

Automated machine learning (AutoML) is introduced in this chapter to illustrate model selection and hyperparameter tuning and the specific features of the resulting optimization problems.
Password Expiration Strategy: A Perspective of Ecological Memory

Based on the ecological user memory, this paper establishes a security strategy model (SSM) to quantify the memory cost of passwords and the value of accounts to users in assword expiration strategy. This paper introduces the theory of human ecological memory to explain the memory cost of account password to users. We experiment on 304 users to prove that when users modify a single account, the security benefits of modifying a high-value account are greater for users. In the case of modifying multiple accounts, modifying accounts with password reuse will gain greater security for users. Experimental results show that when users modify password accounts, adding personal information can increase password strength to some extent while minimizing memory costs. In this paper, we for the first time use user ecological memory as one of the criteria to evaluate user security benefits, which might be the trend of future research on user behavior creating and password modification policy.
Subunits Inference and Lexicon Development Based on Pairwise Comparison of Utterances and Signs

Communication languages convey information through the use of a set of symbols or units. Typically, this unit is word. When developing language technologies, as words in a language do not have the same prior probability, there may not be sufficient training data for each word to model. Furthermore, the training data may not cover all possible words in the language. Due to these data sparsity and word unit coverage issues, language technologies employ modeling of subword units or subunits, which are based on prior linguistic knowledge. For instance, development of speech technologies such as automatic speech recognition system presume that there exists a phonetic dictionary or at least a writing system for the target language. Such knowledge is not available for all languages in the world. In that direction, this article develops a hidden Markov model-based abstract methodology to extract subword units given only pairwise comparison between utterances (or realizations of words in the mode of communication), i.e., whether two utterances correspond to the same word or not. We validate the proposed methodology through investigations on spoken language and sign language. In the case of spoken language, we demonstrate that the proposed methodology can lead up to discovery of phone set and development of phonetic dictionary. In the case of sign language, we demonstrate how hand movement information can be effectively modeled for sign language processing and synthesized back to gain insight about the derived subunits.