Reviewerly/exHarmony huggingface dataset:
Anytime, anywhere access to information resources: a case study from Kuwait

Purpose The purpose of this paper is to understand the pervasiveness of mobile devices in ubiquitous access of information. Moreover, the study tries to ascertain the growing demands of mobile devices in library settings. Design/methodology/approach A survey was administered to gain in-depth information regarding the use of mobile devices and their use in information access. Further, it investigated the general uses of mobile devices in day to day life. The survey also contained some questions aimed at obtaining more comprehensive information about the impediments faced by the students in library settings and the way out. Findings The study highlights the rapid growth of mobile device usage among students at the College of Basic Education, Public authority for Applied Education and Training, Kuwait, and indicates the urgent need of libraries to the embrace mobile web. Originality/value The use of mobile devices in comfort zones demands libraries to disseminate information anytime, anywhere. This study examines the academic library users’ insistence for such services and the competence of libraries to provide services “on the go”.
Line Segmentation Free Probabilistic Keyword Spotting and Indexing

Probabilistic Keyword Spotting and Indexing (PKWSI) allows effective search through untranscribed large collections of images. However, when text-line detection fails to detect foreground text, the PKWSI techniques also fail dramatically. In this paper, we develop a new line segmentation-free approach using a uniform line-sized image slicing instead of previous text-line detection. As a result, new issues arise due to overlapping slices, leading to several spot hypotheses for the same word. We develop solutions to take advantage of multiple spots and to consolidate them into single hypotheses. We test our approach on a difficult historical handwritten dataset and it yields promising results.
SBOLExplorer: Data Infrastructure and Data Mining for Genetic Design Repositories

This paper describes SBOLExplorer, a system that is used to provide intuitive searching within the SynBioHub genetic design repository. SynBioHub stores genetic constructs encoded in the SBOL data format. These constructs can represent genetic parts, circuits, and sequences. These constructs are often numerous, exist in various states of completeness and documentation, and do not lend themselves to simple searching and discovery. In particular, this paper focuses on improving the search capabilities of SynBioHub. Inspiration is drawn from the techniques used to organize and search over the World Wide Web, a linked data set with many of the same properties of the SBOL data in SynBioHub. SBOLExplorer integrates these methods into SynBioHub’s data representation and search, providing significant improvement over the previous search implementation based on pattern-matching.
Software Architecture as a Thinging Machine: A Case Study of Monthly Salary System

Software architecture is concerned with the study of a software system’s high-level structures and involves the design and production of such structures. According to experts n the field, software teams struggle to communicate software architecture. To communicate the software architecture of a software system, developers use diagrams that tends to be a confused mess of boxes and features, unstable notation, ununiform naming, unlabeled relationships, generic terminology, missing technology decisions, mixed abstractions, etc. This paper proposes a new diagrammatic representation, called a thinging machine (TM), as a methodology in software architecture. We show that a TM can express situations that arise in practice and is therefore an alternative to tools currently in use. The general aim is to provide alternative representations that may be used to develop more refined tools in this field of study.
Graph-Based Format for Modeling Multimodal Annotations in Virtual Reality by Means of VAnnotatoR

Projects in the field of Natural Language Processing (NLP), the Digital Humanities (DH) and related disciplines dealing with machine learning of complex relationships between data objects need annotations to obtain sufficiently rich training and test sets. The visualization of such data sets and their underlying Human Computer Interaction (HCI) are perennial problems of computer science. However, despite some success stories, the clarity of information presentation and the flexibility of the annotation process may decrease with the complexity of the underlying data objects and their relationships. In order to face this problem, the so-called VAnnotatoR was developed, as a flexible annotation tool using 3D glasses and augmented reality devices, which enables annotation and visualization in three-dimensional virtual environments. In addition, multimodal objects are annotated and visualized within a graph-based approach.
The Distributed Ledger-Based Technique of the Neuronet Training Set Forming

The generating of training datasets for machine learning projects is a topical problem. The cost of dataset formation can be considerably high, yet there is no guarantee of an acceptable quality of prepared data. The important issue of dataset generation is the labeling noise. The main causes of this phenomena are: expert errors, information insufficiency, subjective factors and so on. Labeling noise affects the learning stage of a neuronet and so increases the number of errors during the one’s functioning. In the current paper the technique to decrease the labeling noise level is proposed. It is based on the principals of the distributed ledger technology. While there is a possibility to decrease the labeling errors number, the services integration on the basis of distributed ledger allows to improve the efficiency of dataset forming.
EXTENSIONS OF THE SHANNON ENTROPY AND THE CHAOS GAME ALGORITHM TO HYPERBOLIC NUMBERS PLANE

In this paper we provide extensions to hyperbolic numbers plane of the classical Chaos game algorithm and the Shannon entropy. Both notions connected with that of probability with values in hyperbolic number, introduced by D. Alpay et al \cite{eluna}. Within this context, particular attention has been paid to the interpretation of the hyperbolic valued probabilities and the hyperbolic extension of entropy as well.
Penerapan Metode Random Forest untuk Prediksi Win Ratio Pemain Player Unknown Battleground

Online game that is on the rise as one of e-sports is the unknown battleground player. To win the game requires the right strategy. In this paper, a study of effective playing strategies in the player unknown battleground game is explored by extracting data from player statistics taken from the kaggle website. From the statistical data, a model is made to predict the win ratio of each player using the random forest method. From the results of the study, random forest was able to produce an accuracy of 88.19%.
Agile Self-selecting Teams Foster Expertise Coordination

Aim/Purpose: This paper aims to discuss the activities involved in facilitating self-selecting teams for Agile software development projects. This paper also discussed how these activities can influence the successful expertise coordination in Agile teams. Background: Self-selecting teams enable Agile team members to choose teams based on whom they prefer to work with. Good team bonding allows Agile team members to rely on each other in coordinating their expertise resources effectively. This is the focal point where expertise coordination is needed in Agile teams. Methodology: This study employed Grounded Theory by interviewing 48 Agile practitioners from different software organizations mainly based in New Zealand. This study also carried out several sessions of observations and document analysis in conjunction with interviews. Contribution: This study contributes to the body of knowledge by identifying the way self-selecting teams support expertise coordination. Findings: Our findings indicated that the activities involved tend to influence the successful expertise coordination in Agile teams. Self-selecting teams are essential to supporting expertise coordination by increasing inter-dependencies between Agile team members, ensuring a diverse range of knowledge and skills in teams. Recommendations for Practitioners: The self-selecting team activities can be used as a guideline for Agile software organizations in forming self-selecting teams in the fastest and most efficient way. It is vital for management to facilitate the process of self-selecting teams in order to optimize successful expertise coordination. Recommendation for Researchers: There is potential for further Grounded Theory research to explore more activities and strategies involved in self-selecting teams. Impact on Society: Self-selecting teams in Agile software developments projects tend to boost the productivity of software development. Future Research: Several hypotheses can be tested through a deductive approach in future studies.
High capacity data hiding with absolute moment block truncation coding image based on interpolation

Data hiding is a way of hiding secret data on cover-media and it is used for a variety of applications. An important of the data hiding is to conceal the data in a secret way without loss of cover-media. Until now, continuous research on absolute moment block truncation coding based data hiding methods have improved a performance on data concealment and image quality. However, the current absolute moment block truncation coding based data hiding technology has a limitation in deriving a method that significantly surpasses existing performance. In this paper, we propose a new method to overcome this problem. To do this, first the original image is transformed to the cover image using absolute moment block truncation coding and is expanded the image using neighbor average interpolation algorithm. The proposed three data hiding methods are based on the generated cover image. The first method is to directly replace the pixel value, which is a component of each block, with the same secret value. The second method is to replace the pixels to match the secret bits only for the extended pixels in each block of the cover image. The third method is to apply Hamming code to each block to minimize the number of replacement pixels for data hiding. Experimental results show that our method is superior in terms of efficiency compared to traditional absolute moment block truncation coding based data hiding methods.
Implementing the Topological Model Succinctly

We show that any embedding of a planar graph can be encoded succinctly while efficiently answering a number of topological queries near-optimally. More precisely, we build on a succinct representation that encodes an embedding of $m$ edges within $4m$ bits, which is close to the information-theoretic lower bound of about $3.58m$. With $4m+o(m)$ bits of space, we show how to answer a number of topological queries relating nodes, edges, and faces, most of them in any time in $\omega(1)$. Further, we show that with $O(m)$ bits of space we can solve all those operations in $O(1)$ time.
Multi-Writer Multi-Reader Conjunctive Keyword Searchable Encryption

We explore the area of searchable encryption aiming to identify the schemes supporting multiple data owner (writers) and multiple data users (readers). Especially, we observe multi-writer multi-reader (MWMR) searchable encryption schemes focusing on multi-keyword search. However, such MWMR schemes offer a centralised token generation approach whereby an enterprise trusted authority (ETA) issues a search token to each reader in system, and thus introduce two serious issues, viz. leakage of keywords to ETA and O(q · R) communication overhead for R readers and q queries per reader. In this paper, we alleviate these issues by proposing an MWMR scheme with a decentralised token generation approach. With such an approach, a registered data reader constructs a search token without interacting with ETA and thus provides an efficient token generation with keyword privacy from ETA. Additionally, we incorporate a more expressive especially, conjunctive keyword search with the scheme. With formal security analysis, we prove that the scheme effectively stands against chosen keyword attack performed by inside or outside attacker. With theoretical and empirical analysis, we justify the effectiveness of the proposed scheme.
κ-Entropy Based Restricted Boltzmann Machines

Restricted Boltzmann Machines achieved notorious popularity in the scientific community in the last decade due to outstanding results in a wide range of applications and also for providing the required mechanisms to build successful deep learning models, i.e., Deep Belief Networks and Deep Boltzmann Machines. However, their main bottleneck is related to the learning step, which is usually time-consuming. In this paper, we introduce a Sigmoid-like family of functions based on the Kaniadakis entropy formulation in the context of the RBM learning procedure. Experiments concerning binary image reconstruction are conducted in four public datasets to evaluate the robustness of the proposed approach. The results suggest that such a family of functions is suitable to increase the convergence rate when compared to standard functions employed by the research community.
Controlled quantum dialogue scheme based on different unspecific two-particle entangled state

In this paper, a controlled quantum dialogue (CQD) scheme based on different unspecific two-particle entangled state is proposed, in which legitimate users can exchange their secret messages according to unitary operations they chose and measurement results published by Charlie, realizing transmitting unspecific secrets by using unspecific entangled state. We insert decoy particles twice and get rid of useless secret messages carried by F particles to ensure security of the whole communication system, realizing the three-times channel detection. Compared to previous CQD protocols, the proposed scheme is proved to stand attack from third-party controller, and it is very secure.
A Novel End-to-End Multiple Tagging Model for Knowledge Extraction

It is an emerging research topic in NLP to joint extraction of knowledge including entities and relations from unstructured text and representing them as meaningful triplets. Despite significant progresses made by recent deep neural network based solutions, these methods still confront the overlapping issue that different relational triplets may have overlapped entities in a sentence, and it is troublesome to address this issue by current solutions. In this paper, we propose a novel multiple tagging model to address the overlapping issue and extract knowledge from unstructured text. Specifically, we devise a multiple tagging scheme that transforms the problem of joint entity and relation extraction into a multiple sequence tagging problem. By using GRU as the building block for encoding-decoding, the proposed model is capable of handling the triplet overlapping problem because the decoder layer allows one entity to take part in more than one triplet. The whole network is end-to-end trianable and outputs all triplets in a sentence directly. Experimental results on the NYT and KBP benchmarks demonstrate that the proposed model siginificantly improves the recall of triplet, and consequently, achieving the new state-of-the-art in the task of triplet extraction on both datasets.
Uniqueness Results for Grey Scale Digital Images

We address the problem of reconstructing digital images with finitely many grey levels from the knowledge of their X-rays in a given finite set of lattice directions. The main result of the paper provides sets of 2p (p ≥ 3) lattice directions which uniquely determine images with p grey levels, cont ained in a finite lattice grid. This extends previous uniqueness results for binary images.
Types, Tokens, and Hapaxes: A New Heap’s Law

Abstract Heap’s Law https://dl.acm.org/citation.cfm?id=539986 Heaps, H S 1978 Information Retrieval: Computational and Theoretical Aspects (Academic Press). states that in a large enough text corpus, the number of types as a function of tokens grows as N = K{M^\beta } for some free parameters K, \beta . Much has been written http://iopscience.iop.org/article/10.1088/1367-2630/15/9/093033 Font-Clos, Francesc 2013 A scaling law beyond Zipf’s law and its relation to Heaps’ law (New Journal of Physics 15 093033). , http://iopscience.iop.org/article/10.1088/1367-2630/11/12/123015 Bernhardsson S, da Rocha L E C and Minnhagen P 2009 The meta book and size-dependent properties of written language (New Journal of Physics 11 123015). , http://iopscience.iop.org/article/10.1088/1742-5468/2011/07/P07013 Bernhardsson S, Ki Baek and Minnhagen 2011 A paradoxical property of the monkey book (Journal of Statistical Mechanics: Theory and Experiment, Volume 2011). , http://milicka.cz/kestazeni/type-token_relation.pdf Milička, Jiří 2009 Type-token &amp; Hapax-token Relation: A Combinatorial Model (Glottotheory. International Journal of Theoretical Linguistics 2 (1), 99–110). , https://www.nature.com/articles/srep00943 Petersen, Alexander 2012 Languages cool as they expand: Allometric scaling and the decreasing need for new words (Scientific Reports volume 2, Article number: 943). about how this result and various generalizations can be derived from Zipf’s Law. http://dx.doi.org/10.1037/h0052442 Zipf, George 1949 Human behavior and the principle of least effort (Reading: Addison-Wesley). Here we derive from first principles a completely novel expression of the type-token curve and prove its superior accuracy on real text. This expression naturally generalizes to equally accurate estimates for counting hapaxes and higher n -legomena.
Emerging Technology of Man’s Life-Long Partnership with Artificial Intelligence

Computer networks and electronic era dissemination marked end of Gutenberg’s epoch big data of knowledge being recorded in Internet (INet) files. Trans-disciplinary activity impels man to learn and cognate world satiated by wholesome system meanings. Serious problem is revealed: how to strengthen significantly the thinking code of a man for natural life in system-informational culture (SIC). Investigation showed that rational consciousness auto-molding occurs based on fundamental concepts descript in language of categories. Besides, SIC subject incarnation can be achieved only in life-long partnership with artificial intelligence (IA). Corresponding technology emerges which consists of IA adaptation to self-reflecting subject. Their reciprocal universal tutoring results in deep-learned IA and rational man neurophenomenology. Artificial neuro-object will assist man to identify and understand universalities using system axiomatic method on personal cogno-ontological knowledge base grounds.
Comparative Study of Machine Learning Algorithms and Correlation between Input Parameters

The availability of big data and computing power have triggered a big success in Artificial Intelligence (AI) field. Machine Learning (ML) becomes major highlights in AI due to the ability of self-improved as it is fed with more data. Therefore, Machine Learning is suitable to be applied in financial industry especially in detecting financial fraud which is one of the main challenges in financial system. In this paper, 15 different types of supervised machine learning algorithms are studied in order to find the highest accuracy that should be able to detect credit card fraudulent transactions. The best algorithm among these algorithms is then further used and studied to find the correlation between the input variables and the accuracy of the results produced. The results have shown that Multilayer Perceptron (MLP) produced the highest accuracy among the 15 other algorithms with 98% accuracy of detection. Besides that, the input parameters also play an important role in determining the accuracy of the results. Based on the result, when input parameter known as ‘V4’ decreased, the recorded accuracy has increased to 99.17%.
Expert Systems for Management of Natural Resources

Management of natural resources embraces a whole range of subjects pertaining to both the concept of good management as well as to “natural resources.” The management part suggests elements of control, decision-making, execution of actions including both intervention and monitoring, as well as elements such as guidance and protection. Expert systems for management of natural resources date back to the origin of the expert system era itself. Ecological systems have emerged as archetypes of complex knowledge-based systems. Often dealing with difficult data fusion supported by remote sensing, they cover aspects of both local and global value. The Contouring Assistant knowledge base embodies years of gridding and mapping experience. As an expert system, it incorporates many of the standard features. The knowledge base built is a synthesis of expertise from many individuals.
Contour Based Tracking for Driveway Entrance Counting System

Managing vehicle in free-flow entrance is tiring to do manually by a guard control especially due to the increase in transportation demand. Providing an accurate vehicle counting approach is vital for traffic management and it will surely be an essential part in tomorrow's smart cities. Therefore, the main objective of this paper is to propose a more accurate vehicle counter by using the tracking and heuristic rules approaches. EzCam v1.0 is a vehicle surveillance system for a free-flow entrance where a module of vehicle counting based on proposed idea has been applied. The proposed method does not require high computational resources more than any relatively affordable non task specific hardware. It employs single threshold, contour extraction and sequential frame analysis and finally, vehicle counting process subsequently. The tracking-based method employs foreground object detection method and a mechanism for object filtering approach as compared to Chris Dahms approach which does not consider any object rejection and accept all contour information as relevant to be counted as vehicles. As a result, EzCam v1.0 which utilizes the exploited contour-based approach is able to achieve up to 94 percent of accuracy rate and outperforms the classic Chris Dahms method which obtained an accuracy of 88 percent. Therefore, the Exploited Contour based tracking method helps vehicle counting system to perform better accuracy in comparison to Chris Dahms approach.
Knowledge Extraction from a Small Corpus of Unstructured Safeguarding Reports

This paper presents results on the performance of a range of analysis tools for extracting entities and sentiments from a small corpus of unstructured, safeguarding reports. We use sentiment analysis to identify strongly positive and strongly negative segments in an attempt to attribute patterns on the sentiments extracted to specific entities. We use entity extraction for identifying key entities. We evaluate tool performance against non-specialist human annotators. An initial study comparing the inter-human agreement against inter-machine agreement shows higher overall scores from human annotators than software tools. However, the degree of consensus between the human annotators for entity extraction is lower than expected which suggests a need for trained annotators. For sentiment analysis the annotators reached a higher agreement for annotating descriptive sentences compared to reflective sentences, while inter-tool agreement was similarly low for the two sentence types. The poor performance of the entity extraction and sentiment analysis approaches point to the need for domain-specific approaches for knowledge extraction on these kinds of document. However, there is currently a lack of pre-existing ontologies in the safeguarding domain. Thus, in future our focus is the development of such a domain-specific ontology.