Reviewerly/exHarmony huggingface dataset:
Selection and Acquisition of Electronic Resources in Academic Libraries

With information explosion, there has been a rapid increase in the number of e-resources published across the world. In addition to this, the cost of e-resources has risen steeply. This has resulted in libraries finding it difficult to acquire all the required information resources from the budget available from its parent body. The problem of libraries is compounded by the growing costs of maintaining both print and online subscription and issues related to ‘perpetual' electronic access to back files. The print industry in the world is said to be on the decline. People prefer the electronic versions of the reading materials, because they are more portable, accessible and affordable. But there are many challenges/hurdles to this path, which we have to overcome with time, effort and ingenuity. There are certain challenges relating to their selection, acquisition, maintenance and preservation, etc., which need joint efforts of library professionals and associations. Electronic publishing of scholarly journals, emerging of consortia, pricing models of the publishers give new opportunities for libraries to provide instant access to information. Consortium, formed by a group of libraries, is a unique program to facilitate electronic access to scholarly databases and journals. The beneficiaries will be faculty, researchers, students and neighbor institutes engaged in pursuing higher education. Consortia will minimize the financial burden and pave the way for an enormous amount of saving of time, money, and manpower.
Document Distance Metric Learning in an Interactive Exploration Process

Visualization of inter-document similarities is widely used for the exploration of document collections and interactive retrieval. However, similarity relationships between documents are multifaceted and measured distances by a given metric often do not match the perceived similarity of human beings. Furthermore, the user's notion of similarity can drastically change with the exploration objective or task at hand. Therefore, this research proposes to investigate online adjustments to the similarity model using feedback generated during exploration or exploratory search. In this course, rich visualizations and interactions will support users to give valuable feedback. Based on this, metric learning methodologies will be applied to adjust a similarity model in order to improve the exploration experience. At the same time, trained models are considered as valuable outcomes whose benefits for similarity-based tasks such as query-by-example retrieval or classification will be tested.
Peculiarity Classification of Flat Finishing Motion Based on Tool Trajectory by Using Self-organizing Maps Part 2: Improvement of Clustering Performance Based on Codebook Vector Density

The paper reports an improvement of an unsupervised classification system for learner peculiarities of flat finishing motion of an iron file in skill training. The system classifies and visualizes peculiarities of learners’ tool motion effectively by using a torus type Self-Organizing Maps (SOM). An automatic clustering based on codebook density of SOM helps skill trainers to grasp learners’ peculiarities distribution easily. In this paper, we focus on the classification improvement of the SOM on the viewpoint of the clustering. Classification performance is improved by comparing different learning schedules of the SOM. Effectiveness of the improvement is evaluated with measured data of an expert and sixteen learners.
Emergent technology as a tool in enhancing the teaching learning process: An assessment

One of the changes that have occurred in the 21 st Century learning classroom is the use of technology.This research is a descriptive study that aimed to assess on the use of technology as a tool in enhancing the teaching learning process of classroom teachers.The study is limited to the use of technology as a tool in enhancing the teaching learning process.Likewise, only teachers in the Basic Education were used as respondents of the study.The researcher limited the study to one hundred teachers to answer the survey questionnaire.The data gathered were statistically treated using frequency distribution, percentage, mean and standard deviation.It revealed that the emergent technology as a tool in enhancing the teaching learning process is extensive because the school compliments the needs of the teachers in the use of technology towards effective teaching and in terms of availability of technology and teacher recognition in enhancing teaching-learning process in the classroom.Effectiveness of the utilization of technology in the teaching readiness of teachers is also extensive.The use of computer technology in the classroom showed an improvement to student learning of concepts which is very extensive as verbal interpretation.Knowing the perspective of the challenging role and skills of teachers is to have an effective teaching especially when integrating technology in their 21st Century classroom.The researcher hopes that this will serve as an inspiration for teachers to come up with similar materials which are relevant to the unique needs of the future teachers.
Performance Evaluation Process and Express Decision

The Performance Evaluation Process (PEP) is a structured technique specifically designed to support an individual with making personal and quick decisions in complex problems, in which the factors of uncertainty and risk are both present, and in which individual biases and emotional factors may be an important influence. PEP is based on a self-regulation model of a decision-making process developed within the systemic-structural activity theory. PEP regulates two concurrently running processes in decision-making – the formation of a mental model and the formation of the level of motivation by using two general regulators: the factor of significance and factor of difficulty. The factor of difficulty provides feedback control, while the factor of significance provides feedforward control. Formation of a mental model is provided dynamically by constructing a decision hierarchy. Formation of the level of motivation is provided recursively by using the IL-Frame. When evaluating outcomes, the IL-Frame operates with four general criteria of success. Express Decision (ED), a progressive mobile web application, is guided by PEP to provide decision-making support.
Conclusion and Further Remarks

This book covered the essential concepts and fundamental algorithms of quantum computing. The reader that followed the book until the end should be able to follow the specialized literature and keep learning more advanced topics of research. In this final chapter, we suggest topics for further studies.
Comparing ELM with SVM in the Field of Sentiment Classification of Social Media Text Data

Machine learning has been used in various fields with thousands of applications. Extreme learning machine (ELM), which is the most recently developed machine learning algorithm, has become increasingly popular for its good generalization ability. However, it has been relatively less applied to the domain of social media. Support Vector Machine (SVM), another popular learning-based algorithm, has been applied for sentiment classification of social media text data and has obtained good results. This paper investigates and compares the capabilities of these two learning-based methods in the field of sentiment classification of social media. The results indicate that SVM can obtain good performance for analyzing small datasets, while for large datasets, ELM performs better than SVM. This research also indicates that ELM has the potential application in the domain of social media analysis.
An Exploratory Study to Understand the Phenomena of Eye-Tracking Technology

Technology has played a pivotal role in revolutionizing the formative aspects of learning and teaching in the current digital age. Due to technology, there is an expectation of having customized medicine, customized interaction, and customized formative communication instead of traditional mass reporting approaches. Formative assessment within higher education teaching and learning environments are no exception to such an approach in the 21st century digital environment. Eye-tracking technology in recent years has provided an insight to understand the human eye movements and concentration patterns, which has application in education. Eye-tracking can be used to examine the processes of individuals in their learning to establish how learning contents are delivered and perceived by all involved (e.g., teaching staff, students, and markers). This chapter proposes that critical and specific information from eye-tracking software can lead to tailored educational content to accommodate, customize, and optimize the unique learning methods for an individual student as per their learning habits. This chapter describes the available eye-tracking technologies and their application in educational processes.
Operational Algorithms for Separable Qubit X States

This work motivates and applies operational methodology to simulation of quantum statistics of separable qubit X states. Three operational algorithms for evaluating separability probability distributions are put forward. Building on previous findings, the volume function characterizing the separability distribution is determined via quantum measurements of multi-qubit observables. Three measuring states, one for each algorithm are generated via (i) a multi-qubit channel map, (ii) a unitary operator generated by a Hamiltonian describing a non-uniform hypergraph configuration of interactions among 12 qubits, and (iii) a quantum walk CP map in a extended state space. Higher order CZ gates are the only tools of the algorithms hence the work associates itself computationally with the Instantaneous Quantum Polynomial-time Circuits (IQP), while wrt possible implementation the work relates to the Lechner-Hauke-Zoller (LHZ) architecture of higher order coupling. Finally some uncertainty aspects of the quantum measurement observables are discussed together with possible extensions to non-qubit separable bipartite systems.
On the Role of Cost-Sensitive Learning in Imbalanced Data Oversampling

Learning from imbalanced data is still considered as one of the most challenging areas of machine learning. Among plethora of methods dedicated to alleviating the challenge of skewed distributions, two most distinct ones are data-level sampling and cost-sensitive learning. The former modifies the training set by either removing majority instances or generating additional minority ones. The latter associates a penalty cost with the minority class, in order to mitigate the classifiers’ bias towards the better represented class. While these two approaches have been extensively studied on their own, no works so far have tried to combine their properties. Such a direction seems as highly promising, as in many real-life imbalanced problems we may obtain the actual misclassification cost and thus it should be embedded in the classification framework, regardless of the selected algorithm. This work aims to open a new direction for learning from imbalanced data, by investigating an interplay between the oversampling and cost-sensitive approaches. We show that there is a direct relationship between the misclassification cost imposed on the minority class and the oversampling ratios that aim to balance both classes. This becomes vivid when popular skew-insensitive metrics are modified to incorporate the cost-sensitive element. Our experimental study clearly shows a strong relationship between sampling and cost, indicating that this new direction should be pursued in the future in order to develop new and effective algorithms for imbalanced data.
Clustering Data in Secured, Distributed Datasets

The massive growth in data generation and collection has brought to the forefront the necessity to develop mechanized methods to analyze and extract information from them. Data clustering is one of the fundamental modes to discover new insights from data. However, high dimensional data has its own challenges where many conventional clustering algorithms fails either in accuracy or scalability. To further complicate the issue, distinct subsets of sensitive data may reside in geographically separated locations with the sensitive nature of the data preventing (or inhibiting) its access for mechanized analysis. Thus, methods to discover information from the collective whole of these secured, distributed data sets that also preserves the integrity of the data must be found. In this paper we develop and assess a distributed algorithm that can cluster geographically separated data while simultaneously preserving the strict privacy requirements of non sharing of protected high dimensional data. We implement our algorithm on the distributed map-reduce based platform Spark and demonstrate its performance by comparing it to the standard data clustering algorithms.
Introduction to Imbalanced Data

An imbalance of sample sizes among class labels makes it difficult to obtain high classification accuracy in many scientific fields, including medical diagnosis, bioinformatics, biology, and fisheries management. This difficulty is referred to as “class imbalance problem” and is considered to be among the 10 most important problems in data mining research. This topic has also been widely discussed in several machine learning workshops. The critical feature of the imbalance problem is that it significantly degrades the performance of standard classification methods, which implicitly assume balanced class distributions and equal costs of misclassification for each class. Hence, new strategies are required for mitigating such imbalances, based on resampling techniques, modification of the classification algorithms, adjustment of weights for class distributions, and so on.
Automating Automated Reasoning

The vision of automated support for the investigation of logics, proposed decades ago, has been implemented in many forms, producing numerous tools that analyze various logical properties (e.g., cut-elimination, semantics, and more). However, full ‘automation of automated reasoning’ in the sense of automatic generation of efficient provers has remained a ‘holy grail’ of the field. Creating a generic prover which can efficiently reason in a given logic is challenging, as each logic may be based on a different language, and involve different inference rules, that require different implementation considerations to achieve efficiency, or even tractability. Two recently introduced generic automated provers apply different approaches to tackle this challenge. $${\text {MetTeL}}$$ , based on the formalism of tableaux, automatically generates a prover for a given tableau calculus, by implementing generic proof-search procedures with optimizations applicable to many tableau calculi. $${\text {Gen2sat}}$$ , based on the formalism of sequent calculi, shifts the burden of search to the realm of off-the-shelf SAT solvers by applying a uniform reduction of derivability in sequent calculi to SAT. This paper examines these two generic provers, focusing in particular on criteria relevant for comparing their performance and usability. To this end, we evaluate the performance of the tools, and describe the results of a preliminary empirical study where user experiences of expert logicians using the two tools are compared.
A blockchain-based smart contracts platform to competency assessment and validation

During last years, several competency management systems (CMSs) have been proposed to support the acquisition, allocation, and improvement of competencies. However, competency information and associated proofs are still not tracked and shared in a trustworthy and immutable way. In this context, blo ckchain technology provides a prominent manner to keep track of competencies and achievements and to ensure their sharing in a secure and transparent manner. Particularly, given the decentralized nature of immutable and distributed ledgers enabled by blockchain, the potential for using this revolutionary new technology for lifelong competency tracking and assessment is tremendous. In this paper, a functional architecture using smart contracts and blockchain is proposed to support competency tracking and assessment in learning networks. Thus, by implementing the proposed architecture, all the different stakeholders involved can be connected, namely, learners, authors, and assessors. Besides, a full traceability of acquired proofs of competencies and competency profiles is warranted, while ensuring their authenticity and integrity.
From Object-Oriented to Workflow: Refactoring of OO Applications into Workflows for an Efficient Resources Management in the Cloud

Cloud Computing is a technology that provides to customers computing/storage resources as services delivered through the internet. Its main characteristics are its elastic nature and its payment model (pay-as-you-go). In order to run applications in the cloud while using its resources efficiently, and thus reducing their usage costs, one of the requirements related to this type of environment is to perform dynamic configurations of these applications. Nevertheless, to dynamically configure applications determining the utilized resources, additionally to when and where they are used is needed. Workflows allow doing this. As a matter of fact, several works aiming to reduce execution costs in the cloud are based on workflows. Unlike them, the architecture of OO applications explicitly expresses little or no behavioral (temporal) information. Therefore, running an OO application in the cloud requires deploying the whole application, and thus all its used resources need to be allocated during its entire execution time. With the goal of reducing execution costs of OO applications in the cloud, we propose a re-engineering process. The process aims to restructure these applications from OO architectural style to workflow style. However, in this paper, we concentrate only on the first step of the process, which aims to generate a workflow from OO source code.
A Lemmatizer Tool for Assamese Language

Word Sense Disambiguation (WSD) requires sense tagged corpora. Words in a corpus appear in inflected or morphed forms. Sense tagging can only be done with words in their root or lemmatized forms. Similarly for Part of Speech Tagging (POS), the words in a corpus are required to be available in their root forms. A WordNet, which is a lexical knowledge base, consists of words in their lemmatized forms. In this paper we present a scheme whereby a trie is constructed from words in the Assamese WordNet. An input word, which maybe in inflected form, is compared against an entry in the trie by searching the trie with the input word as key and following the principle of longest prefix match, the root form of an inflected word may be obtained. When there is a mismatch between an input word and an entry in the trie, a rule based morphological analyzer provides the lemmatized form of the inflected word.
Implementation of Fuzzy Inference System for Classification of Dengue Fever on the villages in Malang

Dengue fever is a disease that must be watched out and early preventive measures are taken so the spread of this disease can be reduced. An early preventive dengue fever can be controlled by a mathematical model. The article proposes a model of fuzzy inference system with optimal fuzzy rule bases generated by fuzzy c-means and optimized by ordinary least square (OLS). The developing system is done by forming a data structure in the form of input-output pairs. Factors that influence the number of dengue fever cases are used as the system input and the number of dengue fever cases as the system output. Based on the input-output pairs, the fuzzy rule bases is generated by using the fuzzy c-means method. The consequent part of the rule bases is optimized by the OLS method to produce the optimal rule bases. The resulted system is able to predict the level of dengue fever in the villages with an accuracy of 90% and can be used to predict the level of dengue fever in a village by inputting factors that influence the level of dengue fever.
IUID in Theory—Scientific Research

In this chapter, the state of research in the field of the development of intercultural user interfaces and the connection between culture and HMI is presented and some main research questions regarding the culture-centered HMI designs are categorized. A look at the connection between theory and practice, then, opens the way to an explanatory model for culture-dependent human–machine interaction relevant to IUID. Both theoretical knowledge and the results of empiricalProblems empirical studies are used. Finally, the first possible model is presented from which recommendations for the development of intercultural user interfaces can be derived.
An Image-Based Encoding to Record and Track Immersive VR Sessions

Capturing and tracking immersive VR sessions performed through HMDs in public spaces, may offer valuable insights on users propensities and spatial affordances. Large collected records can be exploited to analyze or fine-tune locomotion models for time-constrained experiences. The transmission or streaming of such data over the web to analysts or professionals in distance learning field although, can be challenging due to network bandwidth or involve computationally intensive decoding routines. This work investigates compact encoding models to volumetrically absorb user states and propensities during running VR sessions, using image-based encoding approaches. We focus on quantization methods and data layouts to smoothly record immersive sessions and how they compare to standard approaches in terms of storage and spatio-temporal accuracy. Qualitative and quantitative results obtained from public exhibits are presented in order to validate the encoding model.
Pseudorandom Functions from LWE: RKA Security and Application

Pseudorandom Functions (PRF) is a basic primitive in cryptography. In this paper, we study related key attacks (RKA) with which the adversary is able to choose function $$\phi $$ and observe the behavior of the PRF under the modified secret key $$\phi (k)$$ . We focus on the PRF from the Learning with Errors (LWE) assumption by Banerjee and Peikert in CRYPTO 2014. We prove that the PRF is secure against unique-input key shift attacks and restricted affine attacks. After that, we use this RKA-secure PRF to construct a robustly reusable fuzzy extractor, which enjoys higher efficiency and better error correction rate.
Task Scheduling of GPU Cluster for Large-Scale Data Process with Temperature Constraint

With the development of GPU general-purpose computing, GPU heterogeneous cluster has become a widely used parallel processing solution for Large-scale data. Considering temperature management and controlling becomes a new research topic in high-performance computing field. A novel task scheduling model for GPU cluster with temperature limitation was built to balance the heat distribution and prevent the temperature hotspots occur. The scheduling index was introduced by combining the utilization of GPU and temperature. And the state matrix was designed to monitor the GPU cluster and provided status information for scheduler. When the temperature exceeds specific threshold value, the scheduler can improve the speed of fans to reduce the temperature. The experimental results show that the proposed scheduler can balance the heat distribution and prevent the temperature hotspots. Compared with the benchmark scheduling model, the loss of scheduling performance is in the acceptable range.