Reviewerly/exHarmony huggingface dataset:
Beyond the Physical Library Space

This chapter examines the transformation of the physical library into a digital-oriented library environment. Space is regarded as an important feature of the physical library because the quantity of materials that can exist in the physical library is usually determined with regards to space. However, the emergence of digital technologies in the 21st century brought about the notion of digital-oriented library environment, in which a number of materials such as books, articles, and other print-based material can be converted to digital formats such as e-books, PDFs, and multimedia files which could be further stored on digital devices or uploaded online for users to access conveniently. The creation of the digital-oriented library environment has the capability of enabling librarians with innovative skills in order to meet the immediate information needs of library users. Therefore, librarians are required to understand their new roles in the 21st century and work towards creating a digital-oriented library environment.
Developing solution for implementing IRBIS LAS on the PHP platform

Designing a cross-platform software for implementing IRBIS LAS on the PHP platform is discussed. The new print format language interpreter for IRBIS LAS based on J-ISIS and CISIS formatting language features and capabilities, is also developed.
Goal Model Evaluation Based on State-Space Representation

Goal models have been used for the last two decades in various disciplines to represent business, organizations, and individuals' objectives. Several methodologies and standards have emerged, and various goal analysis and evaluation algorithms have been introduced serving different sectors, including decision support. In contrast to most researches which are based mainly on simulation to predict the satisfaction levels of final goals, this research proposes a new framework for evaluating goal models based on the state-space representation that is used extensively in control systems. This new approach brings the theories and literature of state-space representation of systems to goal models, opening a new direction for using its available mature techniques and tools, for goal model analysis and evaluation. A hypothetical goal model, which can be used for policymaking after the COVID-19 pandemic, is presented as an example of how the proposed framework can be used, and the results that can be obtained.
Study of online learning resource recommendation based on improved BP neural network

Personalised recommendation has gradually become an effective way to solve the problem of information overload in the era of big data. Therefore, in order to improve the efficiency of online learning, this paper discusses the design of online learning resource recommendation algorithm based on improved BP neural network, and the results show that it has high value for popularisation and application. Based on the transmission network, the improved BP neural network of momentum factor can achieve more efficient data mining. After training learning resources and user data, it can match the real score and the predicted score, so as to ensure the accuracy of personalised recommendation. The main contribution of this paper is to propose a recommendation algorithm to online learning resources through improved BP neural network algorithm, and the feasibility of the algorithm is verified. The research method of this paper provides a reference for the research of personalised recommendation algorithm of online resources.
Diagnosis of Breast Cancer by Image Processing and Acquisition

Abstract — Cancer is one of the deadliest disease in the current world and among breast cancer is found about 24%-28% among all the reported cases. In this paper development of CAD systems is discussed which reduces human interference and increases preciseness in the diagnosis of breast cancer. Mammography is a technique widely followed by radiologists for the detection and diagnosis of tumors. Introduction of digital imaging helped in acquiring high quality 3D images (by tomosynthesis). This paper discussed the research performed on the two approaches for final detection of masses i.e., image processing approach and image acquisition (using image sensors) approach including system design and optimizations. The paper contains detailed discussions about masses, enhancement techniques, classifiers and overall process flow with calculations of some required parameters at different steps. Keywords — CAD systems, Breast Cancer, Tomosynthesis, Enhancement, Classification, Image Acquisition. Cite this Article Aman Gautam, Brijesh Kumar. Diagnosis of Breast Cancer by Image Processing and Acquisition. Journal of Image Processing & Pattern Recognition Progress . 2019; 6(2): 30–36p.
Track Related Bursty Topics in Weibo

Weibo has become an important means for people to share, disseminate and obtain information in real time. BTM can effectively discover bursty topics in Weibo, but cannot track the related bursty topics. Based on the time series of Weibo, the binary word pair is used for topic modeling and the bursty topics in the Weibo are extracted to form a new topic set. And then, the similarity of the topics in adjacent time periods can be calculated by KL metric and the related bursty topics can be tracked. The experimental results show that the method can effectively segment the time series of Weibo topics, and realize the discovery and tracking of related topics in Weibo.
Algebraic models of simple type theories

We develop algebraic models of simple type theories, laying out a framework that extends universal algebra to incorporate both algebraic sorting and variable binding. Examples of simple type theories include the unityped and simply-typed λ-calculi, the computational λ-calculus, and predicate logic. Simple type theories are given models in presheaf categories, with structure specified by algebras of polynomial endofunctors that correspond to natural deduction rules. Initial models, which we construct, abstractly describe the syntax of simple type theories. Taking substitution structure into consideration, we further provide sound and complete semantics in structured cartesian multicategories. This development generalises Lambek's correspondence between the simply-typed λ-calculus and cartesian-closed categories, to arbitrary simple type theories.
Privacy-preserving frequent itemset mining in vertically partitioned database using symmetric homomorphic encryption scheme

Frequent itemset mining (FIM) and association rule mining (ARM) are two popular and widely used mining techniques for transactional databases with wide range of applications such as medical diagnosis, market basket analysis, protein sequences, census data, etc. In privacy-preserving frequent itemset mining (PPFIM) over vertically partitioned database, data owners wish to learn frequent itemsets from their concatenated dataset without disclosing their sensitive information. A collusion resistant scheme has been proposed using Du-Atallah's SBDP protocol for multiparty multi-vector scenario which works under two party two-vector case. Also, a scheme is designed by generalising the Du-Atallah's scheme for more than two parties. However it fails in privacy requirements when more than two parties/vectors are considered. In this paper, we give a critique on this approach and focus on efficient and more secure methods for privacy preserving FIM in vertically partitioned databases. To ensure privacy of raw data of different data owners having heterogeneous attributes, we proposed a secure-sum algorithm which uses symmetric homomorphic encryption scheme as a sub-part and a semihonest trusted third party (STTP) environment for calculating the support count of itemsets in privacy preserving manner.
Research of Radar Emitter Signal Model and Clustering Sorting Simulation

At present, radar signal sorting has become a necessary radar signal processing step for early warning investigation and electronic countermeasures. The paper analyzes and studies the typical radar signals used in signal sorting. Based on this, the signal simulation and sorting experiments are carried out. The results show that the typical radar signals obtained by the simulation are effective and only used. The effect of sorting between inter-pulse parameters is not very satisfactory.
DETERMINANTS OF KNOWLEDGE MANAGEMENT STRATEGIES ON ORGANIZATIONAL PERFORMANCE IN LIBYAN TRANSPORTATION INDUSTRY

The aim of this study is to identify the determinants of knowledge management strategies in the Libyan transportation industry for organizational performance. This study began to propose the research method through an Exploratory Factor Analysis (EFA), after proposing a testable conceptual framework, where the items of further research can have some light to move forward. For this analysis, the sample size was 529 and simple random sampling was employed. The results of this research indicate that the main aspects of organizational performance in the Libyan Transportation Industry are the knowledge management system, knowledge management process, knowledge management strategy, organizational performance. Through proposing an empirically validated research model that could be an important implementation tool for the success of the performance of the Transportation Industry, this research has added to established knowledge.&#x0D; &#x0D; JEL: L10; L25; L91&#x0D; &#x0D; &lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0726/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Embedding Structured Dictionary Entries

Previous work has shown how to effectively use external resources such as dictionaries to improve English-language word embeddings, either by manipulating the training process or by applying post-hoc adjustments to the embedding space.We experiment with a multitask learning approach for explicitly incorporating the structured elements of dictionary entries, such as user-assigned tags and usage examples, when learning embeddings for dictionary headwords.Our work generalizes several existing models for learning word embeddings from dictionaries.However, we find that the most effective representations overall are learned by simply training with a skip-gram objective over the concatenated text of all entries in the dictionary, giving no particular focus to the structure of the entries.
Compress Polyphone Pronunciation Prediction Model with Shared Labels

It is well known that deep learning model has huge parameters and is computationally expensive, especially for embedded and mobile devices. Polyphone pronunciations selection is a basic function for Chinese Text-to-Speech (TTS) application. Recurrent neural network (RNN) is a good sequence labeling solution for polyphone pronunciation selection. However, huge parameters and computation make compression needed to alleviate its disadvantages. Meanwhile, Large-scale-labels classification leads to more complicated network and heavy computation cost. In contrast to existing quantization with low precision data format and projection layer, we propose a novel method based on shared labels, which focuses on compressing the fully-connected layer before Softmax for models with a huge number of labels in TTS polyphone selection. The basic idea is to compress large number of target labels into a few label clusters, which will share the parameters of fully-connected layer. Furthermore, we combine it with other methods to further compress the polyphone pronunciation selection model. The experimental result shows that for Bi-LSTM (Bidirectional Long Short Term Memory) based polyphone selection, shared labels model decreases about 52% of original model size and accelerates prediction by 44% almost without performance loss. It is worth mentioning that the proposed method can be applied for other tasks to compress model and accelerate calculation.
Knowledge Graph Embedding Based on Relevance and Inner Sequence of Relations

Knowledge graph Embedding can obtain the low-dimensional dense vectors, which helps to reduce the high dimension and heterogeneity of Knowledge graph (KG), and enhance the application of KG. Many existing methods focus on building complex models, elaborate feature engineering or increasing learning parameters, to improve the performance of embedding. However, these methods rarely capture the influence of intrinsic relevance and inner sequence of the relations in KG simultaneously, while balancing the number of parameters and the complexity of the algorithm. In this paper, we propose a concatenate knowledge graph embedding method based on relevance and inner sequence of relations (KGERSR). In this model, for each $$<head, relation, tail>$$ triple, we use two partially shared gates for head and tail entities. Then we concatenate these two gates to capture the inner sequence information of the triples. We demonstrate the effectiveness of the proposed KGERSR on standard FB15k-237 and WN18RR datasets, and it gives about 2% relative improvement over the state-of-the-art method in terms of Hits@1, and Hits@10. Furthermore, KGERSR has fewer parameters than ConmplEX and TransGate. These results indicate that our method could be able to find a better trade-off between complexity and performance.
From traveller to prosumer: A diachronic linguistic analysis of past travel diaries and today’s online commentaries

Considering the idea that online media and user-generated content (Chatterjee 2001; Prati 2007) have radically changed the content and aspect of odeporic literature (Pifferi 2013) and that travel diaries and commentaries are a product of the education, tendencies and trends linked to specific time periods, the present paper proposes a diachronic linguistic analysis of two corpora consisting of past and present odeporic literature. Corpus A is a collection of 25 travel books and diaries written by British travellers, describing the city of Bergamo, in particular Citta Alta, between the early 17th century and the beginning of the 20th century. Corpus B is instead a collection of 120 travel reviews published online on TripAdvisor.com in 2017 and 2018, once again by British tourists, evaluating the same city through the eyes of today’s travellers. A linguistic analysis of the two corpora was carried out, proposing a selection and categorization by frequency of the most popular places visited in previous centuries as compared to today, and the related evaluative elements in the two corpora. The digital and paper material collected was analysed with the intention of revealing how a traveller’s perception and evaluation of a specific location and of the voyage itself has changed over the centuries and, ultimately, how the prosumer role of today’s traveller (Milano 2011; Vasquez 2012, 2014) has evolved thanks to computer-mediated genres and social media (Zeng & Gerritsen 2014).
A Bell-state analyzer for photonic frequency

We demonstrate the first Bell-state analyzer for frequency-bin entangled photons and unambiguously distinguish two of four Bell states with fidelities over 92%. This is an important step toward the vision of a quantum internet that is compatible with both heterogeneous nodes and dense spectral multiplexing.
The Petrograd Philosophical Society and the “Academia” Publishing House: Materials from A.A. Krolenko’s Archival Fond in the Manuscript Department of the National Library of Russia

Drawing on documentary sources from the personal provenance archival fond of the famous publisher and bibliologist A.A. Krolenko (Manuscripts Department of the National Library of Russia) the article clarifies the role of the Petrograd Philosophical Society in the creation and initial activities of the famous publishing house “Academia.” It thus enables to redefine some processes of the Russian scientific and cultural life in the first years of Soviet power. The article draws on the documentary complex of the personal fond of A.A. Kolenko in order to show some important forms, methods, and trends of the publishing activities of the Petrograd Philosophical Society in the early 1920s. Interesting information on the topic is being introduced into the scientific circulation for the first time: concerning the creation of the publishing house, organizing of the bookstore, preparing translations of Plato's works, founding the “Mysl” magazine, etc. The chronological scope of the article is limited to the period of 1921–1923, since it was during these years that the publishing house “Academia” operated within the frameworks of the Petrograd Philosophical Society and under its auspices. The article draws on diverse in their composition and content documents from the A.A. Kolenko’s fond: records management documents, reports, diary entries, photos, memories, letters, etc. Taken together, these archival sources testify to the most serious work done by the Petrograd Philosophical Society to organize the publishing house and ensure its work under the difficult conditions of the initial period of the New Economic Policy. The identified archival documents form a single complex on the history of the remarkable publishing house “Academia,” at the origins of which stood the Petrograd Philosophical Society.
«Ecology, Books and Us». The program of ecological education in Library of Family Reading of Lomonosov town

The program Family Reading Library in the town of Lomonosov: «Ecology, Books and Us» is discussed. The program is targeted at building ecological literacy and culture in the children, from preschoolers to senior schoolchildren of St. Petersburg Petrodvorets District.
A Novel On-chip Photonic Synapse Based on Slot-ridge Waveguides with PCMs

A novel on-chip photonic synapse based on slot-ridge waveguides with non-volatile phase-change materials (PCMs) has been proposed and demonstrated on SOI platform, which achieved larger weight dynamic range and lower power loss.
A Noise-Free Homomorphic Encryption based on Chaotic System

Fully homomorphic encryption (FHE) was one of the most prominent research topics of the last ten years. And it is considered as a major cryptographic tool in a secure and reliable cloud computing environment. The reason behind that because it allows computations over encrypted data, without decrypting the original message. This paper developed a new symmetric (FHE) algorithm based on Enhanced Matrix Operation for Randomization and Encryption (EMORE) algorithm using a chaotic system. The proposed algorithm was considered a noise-free algorithm. It generates the ciphertext in a floating-point number's format, overcomes the problem of plaintext ring and modular arithmetic operation in EMORE by the hardness of a chaotic system, and provides another level of security in terms of randomness properties, sensitivity to the initial condition, and large key size (>2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">100</sup> ) of a chaotic system. Besides that, the proposed algorithm provides the confidentiality and privacy of outsourced data computing through homomorphism property of it. By using both numerical and statistical tests, these tests proved that the proposed algorithm has positive randomness properties and provide secure and reliable encryption (through encryption-decryption time, key sensitivity, keyspace, and correlation). Finally, the simulation results show that the execution time of the proposed algorithm is faster about 7.85 times than the EMORE algorithm.
Comprehensive Study of Blockchain Technology - Architecture, Consensus and Future Trends

Blockchain is considered to be a disruptive core technology. Although many researchers have realized the importance of blockchain, but the research of it is still emerging. It is the record-keeping technology behind bitcoin and is one of the hottest and fastest growing skills in the IT sector today. It serves as an immutable ledger which allows transactions to take place in a decentralized man Blockchain-based applications are rising up, covering numerous fields including finance, healthcare, product management, Internet of Things (IoT), and many more. However, there are still some challenges of blockchain technology such as scalability and security problems which need to be overcome. This paper comprises of a comprehensive study of Blockchain technology. We have included here a deep dive into how blockchains work, its architecture, consensus and various applications. Furthermore, technical challenges are briefly listed.
A data model for enhanced data comparability across multiple organizations

Abstract Organizations may be related in terms of similar operational procedures, management, and supervisory agencies coordinating their operations. Supervisory agencies may be governmental or non-governmental but, in all cases, they perform oversight functions over the activities of the organizations under their control. Multiple organizations that are related in terms of oversight functions by their supervisory agencies, may differ significantly in terms of their geographical locations, aims, and objectives. To harmonize these differences such that comparative analysis will be meaningful, data about the operations of multiple organizations under one control or management can be cultivated, using a uniform format. In this format, data is easily harvested and the ease with which it is used for cross-population analysis, referred to as data comparability is enhanced. The current practice, whereby organizations under one control maintain their data in independent databases, specific to an enterprise application, greatly reduces data comparability and makes cross-population analysis a herculean task. In this paper, the collocation data model is formulated as consisting of big data technologies beyond data mining techniques and used to reduce the heterogeneity inherent in databases maintained independently across multiple organizations. The collocation data model is thus presented as capable of enhancing data comparability across multiple organizations. The model was used to cultivate the assessment scores of students in some schools for some period and used to rank the schools. The model permits data comparability across several geographical scales among which are: national, regional and global scales, where harvested data form the basis for generating analytics for insights, hindsight, and foresight about organizational problems and strategies.
Enlisting human resources to mitigate information security risks

Purpose The purpose of this paper is to suggest ways to integrate human resources (HR) and information security management (ISM) within a firm to help reduce expensive and embarrassing failures in information security breaches. Design/methodology/approach This paper is written for the practitioners. It includes a general review of literature in information technology and HR to help explain ways to decrease the chance of ISM failures. Findings Employees often become careless about information security in the workplace, and the threat to corporate information systems is serious. Although security training is essential, a more comprehensive approach to addressing the security issue is needed. As human factors account for most security breaches, including HR personnel as a partner with IT may help address some of the weaknesses that training alone cannot resolve. Originality/value This paper discusses the human factors that cause information technology breaches and how combining HR practices and ISM may generate a competitive advantage for the organization. This paper then offers practical suggestions that HR may use to help with ISM issues.