Reviewerly/exHarmony huggingface dataset:
Incorporating Semantic Word Representations into Query Expansion for Microblog Information Retrieval

Microblog information retrieval has attracted much attention of researchers to capture the desired information in daily communications on social networks. Since the contents of microblogs are always non-standardized and flexible, including many popular Internet expressions, the retrieval accuracy of microblogs has much room for improvement. To enhance microblog information retrieval, we propose a novel query expansion method to enrich user queries with semantic word representations. In our method, we use a neural network model to map each word in the corpus to a low-dimensional vector representation. The mapped word vectors satisfy the algebraic vector addition operation, and the new vector obtained by the addition operation can express some common attributes of the two words. In this sense, we represent keywords in user queries as vectors, sum all the keyword vectors, and use the obtained query vectors to select the expansion words. In addition, we also combine the traditional pseudo-relevance feedback query expansion method with the proposed query expansion method. Experimental results show that the proposed method is effective and reduces noises in the expanded query, which improves the accuracy of microblog retrieval.
A Design of a Scheduling System for an Unmanned Aerial Vehicle (UAV) Deployment

Deriving a detailed schedule for an unmanned aerial vehicle (UAV) deployment involves a wide range of scheduling problems, where solving them as a whole is intractable. Moreover, uncertainties in operational environments of the UAV deployment bring a gap between models assumed for the scheduling problems and an actual system. Motivated by the facts, we propose a conceptual design of a scheduling system for a UAV deployment. Specifically we first introduce fundamental scheduling problems for UAV deployments and specify dependencies between them. A design for a scheduling system, which sequentially solves the scheduling problems and evaluates the solutions using a simulation model, is then proposed. Finally, a prototype of the scheduling system implemented to demonstrate the feasibility of the design is described.
A TCP Incast Avoidance Method Based on Retransmission Requests from a Client

When a standard TCP implementation using minimum retransmission timeout (RTOmin) of 200 ms is used in distributed file systems in data centers, a well-known throughput degradation called TCP Incast occurs, because 200 ms is too large as RTOmin in data centers. In order to avoid TCP Incast, a TCP implementation using a much smaller RTOmin attained by a fine-grained kernel timer has been proposed. However, throughput still decreases in the implementation when the number of servers in distributed file systems is large. In this paper, we propose an improvement method using special acknowledgement packets which enable a client to request servers to retransmit packets in a better timing. Numerical examples obtained by extensive simulation runs show the effectiveness of the improved method.
Russian Text Vectorization: An Approach Based on SRSTI Classifier

This paper presents an approach to Russian text vectorization based on SRSTI classifier. Our approach is based on using SRSTI categories as vector space dimensions. The categories are defined by lists of keywords. We explain our choice of SRSTI as a basis for vector space. We describe the keywords selection process, as well as vector calculation and comparison algorithm. We apply developed algorithm to marked-up SRSTI texts and user social profiles. We also suggest approaches to vector space improvement and evaluate them.
Quotation Extraction from Indonesian Online News

One of the core news is the statement of the informants. The statement can be expressed in the form of a direct or indirect quotation. The quotation can be done by means of the extraction of quotation in the news texts. The quotation extraction process can be initiated through the introduction of quotation sentences followed by the introduction about the informants. The introduction of the indirect quotation sentence is done by using the machine learning approach with the Support Vector Machine algorithm, while the direct one is done using the regular expression approach. The process of informant introduction uses the Named Entity Recognition. The proposed quotation extraction process was able to result in the accuracy of 90,76%, precision of 91,22% and recall of 90,74%. The result of the quotation extraction process can be developed to analyze the news sentiments as the news as a media to deliver the information should have a neutral sentiment. The sentiment to the news can be analyzed from the statement by the informant.
Synthesis of Optimal Multivariable Robust Systems of Stochastic Stabilization of Moving Objects

The article is aimed at creating algorithmic support for the software and technical complex of dynamic design of optimal multivariable systems of stochastic stabilization of moving objects with unspecified dynamics by substantiating the choice of the basic algorithm for synthesis of optimal structures of these systems and their adaptation for application in conditions of a limited word size of a computer with the possibility to verify results. The verification of the results of the computational procedures of the new developed algorithm for the synthesis of the stochastic stabilization system is to determine the relative errors of the calculations.
Blockchain-Based Money as the Ultimate Challenge to Sovereignty

The article considers the radical challenge that blockchain, and in particular the blockchain-based cryptocurrency Bitcoin, poses to state sovereignty. If blockchain ever succeeds to be adopted on a large scale, Bitcoin, or any other permissionless blockchain-based cryptocurrency for that matter, is a direct threat to one of the key tenets of sovereignty: the monopoly over money. Without this traditional monopoly, states will not be able to exist as they have so far. Building on this premise, the article argues that blockchain-based money is currently posing a serious challenge to state sovereignty and could therefore reshape public law. This article also contends that Bitcoin in particular might collapse for technical reasons. However, if Bitcoin proves to be resilient enough to resist ongoing legal challenges, then the very “ basic norm ” of our legal systems will arguably change.
Attitudes Towards English Language Learning Apps from Korean and Chinese EFL Students

The purpose of study was to investigate university students’ attitudes towards English language learning apps and examine whether there is any significant difference in their attitudes depending on their personal variables such as their gender, major, grade, nationality, and so on. This study also explored students’ perceptions of English language learning apps that they have used. Participants were college students living in Korea and China. Data were collected by means of a questionnaire and an interview. Results showed that both Korean and Chinese students generally held positive attitudes towards English language learning apps, although Chinese students showed more positive attitudes than Koreans. The main reasons that they liked to use English language learning apps were their convenience, ubiquity, and rich resources. On the other hands, it was revealed that the lack of communication and feedback was the main problem of using such apps. Both Korean and Chinese students stated that it was an effective way to use English language learning apps to practice pronunciation, vocabulary, listening, and reading skills. Findings of this research suggest that English language learning apps should be developed to inspire users/students’ learning interest and foster learner autonomy.
Dynamic monitoring of software use with recurrent neural networks

User interaction with a software may be formalized as a sequence of actions. In this paper we propose two methods – based on different representations of input actions – to address two distinct industrial issues: next action prediction and software crash risk detection. Both methods take advantage of the recurrent structure of Long Short Term Memory neural networks to capture dependencies among our sequential data as well as their capacity to potentially handle different types of input representations for the same data. Given the history of user actions in the interface, our first method aims at predicting the next action. The proposed recurrent neural network outperforms state-of-the-art proactive user interface algorithms with standard one-hot vectors as inputs. Besides, we propose to feed the LSTM with actions embeddings. This continuous representation performs better than one-hot encoded vector LSTM and its lower dimension reduces at the same time the computational cost. Using the same data set, the second method aims at crash risk detection. To address this task, we propose to use feature vectors composed of actions with above average crash probabilities as inputs of the LSTM – with the idea to take advantage of its ability to learn relevant past information to detect crash patterns. The method outperforms state-of-the-art sequence classification methods. Our approaches are demonstrated on medical imaging software logs from ten different hospitals worldwide, though they might be applied to various user interfaces in a wide range of applications.
Text-Based Spam Tweets Detection Using Neural Networks

Social media platform plays a major role in everyone’s day-to-day life activities. Twitter is one of the vast growing platforms but it is also subjected to attacks such as Spamming and Combat Twitter attacks. The spamming is the use of the system to send an unsolicited message, especially the advertisement, sending messages repeatedly on same site which leads to major loss for customers and organization. In literature, the existing techniques for detecting the twitter spam text tweet suffer due to an issue such as limited work performance and data sets which leads to inefficiency of system. In order to solve these problems, we proposed a framework to detect the text-based spam tweets using Naive Bayes Classification algorithm and Artificial Neural Network. Performance study of these two algorithms shows that Artificial Neural Network performs better than Naive Bayes Classification algorithm.
Sentiment Analysis on Aadhaar for Twitter Data—A Hybrid Classification Approach

The Aadhaar project is launched by the Government of India to improve social security benefits which is the world largest individual identity project. Nowadays, social media is swarming with an opinion on Aadhaar. This paper presents a hybrid model of machine learning classifier to identify the opinion of individuals on Aadhaar data constraining analysis to the aspect “Enrolment Process”, “mAadhaar”, “CustomerCare” and “Security” of Twitter. In the proposed model, the concept of machine learning algorithms (naïve Bayes, support vector machine and logistic regression) with long short-term memory (LSTM) network has been combined to maximize the classification accuracy. The designed model incorporates the concept of accuracy based on the best feature subset selection to analyse the individual opinion on Aadhaar. Further, a result comparison of the hybrid model with the base model has been carried out and it has been found that the accuracy of hybrid approach is better than the base classification model.
An exploratory study of one-shot learning using Siamese convolutional neural network for histopathology image classification in breast cancer from few data examples

Convolutional Neural Networks have been successfully used in several tasks in the last decade, but this kind of supervised method requires a large number of annotated examples to obtain good results. However, in biomedical domains, such as digital pathology, the amount of annotated samples is very reduced and imbalanced. On the other hand, models trained using supervised methods need to be retrained when a new class (with its samples) is introduced. This process is computationally expensive and can be difficult when you don’t have a lot of examples for the same class. In order to address these issues, there are novel approaches that use a few number of annotated examples to achieve an acceptable or good generalization capability of the model. One of these methods is One-shot Learning, which is intended to classify a set of data from different classes from one or a few number of annotated examples, and which allows to incorporate new data from new classes without re-training the model. This work explores a method to classify samples of tissues in breast cancer histopathology images by means of similarity between pairs of image samples using a Siamese Convolutional Neural Networks, which achieved a 90.83% of accuracy test.
Measuring book impact via content-level academic review mining

Purpose As for academic papers, the customary methods for assessing the impact of books are based on citations, which is straightforward but limited to the coverage of databases. Alternative metrics can be used to avoid such limitations, such as blog citations and library holdings. However, content-level information is generally ignored, thus overlooking users’ intentions. Meanwhile, abundant academic reviews express scholars’ opinions on books, which can be used to assess books’ impact via fine-grained review mining. Hence, this study aims to assess books’ use impacts by conducting content mining of academic reviews automatically and thereby confirmed the usefulness of academic reviews to libraries and readers. Design/methodology/approach Firstly, 61,933 academic reviews in Choice: Current Reviews for Academic Libraries were collected with three metadata metrics. Then, review contents were mined to obtain content metrics. Finally, to identify the reliability of academic reviews, Choice review metrics and other assessment metrics for use impact were compared and analysed. Findings The analysis results reveal that fine-grained mining of academic reviews can help users quickly understand multi-dimensional features of books, judge or predict the impacts of mass books, so as to provide references for different types of users (e.g. libraries and public readers) in book selection. Originality/value Book impact assessment via content mining can provide more detail information for massive users and cover shortcomings of traditional methods. It provides a new perspective and method for researches on use impact assessment. Moreover, this study’s proposed method might also be a means by which to measure other publications besides books.
Double-Arc Parallel Coordinates and its Axes re-Ordering Methods

Abstract The Parallel Coordinates Plot (PCP) is a popular technique for the exploration of high-dimensional data. In many cases, researchers apply it as an effective method to analyze and mine data. However, when today’s data volume is getting larger, visual clutter and data clarity become two of the main challenges in parallel coordinates plot. Although Arc Coordinates Plot (ACP) is a popular approach to address these challenges, few optimization and improvement have been made on it. In this paper, we do three main contributions on the state-of-the-art PCP methods. One approach is the improvement of visual method itself. The other two approaches are mainly on the improvement of perceptual scalability when the scale or the dimensions of the data turn to be large in some mobile and wireless practical applications. 1) We present an improved visualization method based on ACP, termed as double arc coordinates plot (DACP). It not only reduces the visual clutter in ACP, but use a dimension-based bundling method with further optimization to deals with the issues of the conventional parallel coordinates plot (PCP). 2)To reduce the clutter caused by the order of the axes and reveal patterns that hidden in the data sets, we propose our first dimensional reordering method, a contribution-based method in DACP, which is based on the singular value decomposition (SVD) algorithm. The approach computes the importance score of attributes (dimensions) of the data using SVD and visualize the dimensions from left to right in DACP according the score in SVD. 3) Moreover, a similarity-based method, which is based on the combination of nonlinear correlation coefficient and SVD algorithm, is proposed as well in the paper. To measure the correlation between two dimensions and explains how the two dimensions interact with each other, we propose a reordering method based on non-linear correlation information measurements. We mainly use mutual information to calculate the partial similarity of dimensions in high-dimensional data visualization, and SVD is used to measure global data. Lastly, we use five case scenarios to evaluate the effectiveness of DACP, and the results show that our approaches not only do well in visualizing multivariate dataset, but also effectively alleviate the visual clutter in the conventional PCP, which bring users a better visual experience.
Innovating Pedagogy 2020: Open University Innovation Report 8

This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation.This eighth report, produced by The Open University in collaboration with the National Institute for Digital Learning (NIDL) in Ireland, describes ten innovations that have the potential to influence education in the coming years.
A code for square permutations and convex permutominoes

In this article we consider square permutations, a natural subclass ofpermutations defined in terms of geometric conditions, that can also bedescribed in terms of pattern avoiding permutations, and convex permutoninoes,a related subclass of polyominoes. While these two classes of objects arisedindependently in various contexts, they play a natural role in the descriptionof certain random horizontally and vertically convex grid configurations. We propose a common approach to the enumeration of these two classes ofobjets that allows us to explain the known common form of their generatingfunctions, and to derive new refined formulas and linear time random generationalgorithms for these objects and the associated grid configurations.
Open Challenge for Correcting Errors of Speech Recognition Systems

The paper announces the new long-term challenge for improving the performance of automatic speech recognition systems. The goal of the challenge is to investigate methods of correcting the recognition results on the basis of previously made errors by the speech processing system. The dataset prepared for the task is described, evaluation criteria are presented and baseline solutions of the problem are proposed.
EUROPEAN ACCREDITATION AGENCY’S VIEW ON KAZAKHSTAN’S ENGINEERING AND INFORMATION TECHNOLOGY HIGHER EDUCATION

In the era of digital economy, there is an urgent need in highly qualified engineering and Information Technology (IT) specialists in Kazakhstan. The aim of the research is to analyse the needs of higher education in the field of engineering and IT in Kazakhstan underpinning elaboration of implications for higher education in the field of engineering and IT in Kazakhstan. The empirical study was carried out in October 2019. Exploratory study was implemented. Interpretive paradigm was employed in the present work. Non-structured interview served as the basis for data collection. Content analysis was performed for data analysis. Findings of theoretical analysis allow extending three-component model on needs analysis, namely experts’ perspective, educators’ perspective, and students’ perspective by the fourth component, namely community perspective. Implications for higher education in Kazakhstan are formulated as following: universities delivering higher education in the field of engineering and IT in Kazakhstan need to balance academic and practical components in their study programmes; interaction between educators and students, on the one side, as well as enterprises, companies and industry, on the other side, should be enhanced in higher education in the field of engineering and IT in Kazakhstan. Directions of future work are proposed.
Entanglement Detection via Direct-Sum Majorization Uncertainty Relations

Abstract In this paper we investigate the relationship between direct-sum majorization formulation of uncertainty relations and entanglement, for the case of two observables. Our primary results are entanglement detection methods based on direct-sum majorization uncertainty relations. These detectors provide a set of sufficient conditions for detecting entanglement whose number grows linearly with the dimension of the state being detected.
Design of the Intelligent Internship Management Platform for Vocational Colleges

The decentralized internship is still the main form of internships in vocational colleges. Many reforms have been carried out to improve the effectiveness and efficiency of decentralized internship management, but there are still many shortcomings need to be fixed. Focus on the characteristic of decentralized internships, we introduced voiceprint recognition, speech recognition, mobile phone location and virtual electric fence technologies to develop the intelligent internship management platform, which can help full-time teachers of vocational colleges to realize the long-distance precisely internship management. Practice proved that our intelligent platform can definitely improve the attendance of interns, enhance the talent training effectiveness, ensure interns' safety, reduce the management costs and reduce teachers' labor intensity.
Cybersecurity Framework for Requirements of Repair, Update, and Renovation in Industry 4.0

In order to capture technological progress and industrial development, organizations need some transformation and change. Therefore, device exchange to existing systems, adding new technological devices, update to software systems are performed. In addition, product replacement and software downloads can be performed during the elimination of faults in the existing system. This repair, update, and renewal of industrial systems require possible cybersecurity requirements. In order to implement cybersecurity in a healthy way, it needs to be accepted from some stages and systems. These stages and systems may vary depending on the models and technologies of the systems in industry 4.0. In this study, cyber systems used in industry are discussed and the cybersecurity framework is proposed for repairing, updating and renewing systems in general. They claim that the given framework will be a reference to cybersecurity requirements in industry 4.0.
Project Portfolio Risk Response Selection Using Bayesian Belief Networks

Risk identification, impact assessment, and response planning constitute three building blocks of project risk management. Correspondingly, three types of interactions could be envisioned between risks, between impacts of several risks on a portfolio component, and between several responses. While the interdependency of risks is a well-recognized issue, the other two types of interactions remain unacknowledged in the risk response planning literature. This research suggests a Bayesian belief network for modeling portfolio risks, their impacts, and responses. There are three kinds of nodes in this network: nodes representing portfolio risks, nodes corresponding to risk impacts on each objective of each portfolio component, and nodes showing response actions. The problem is to decide which responses are to be selected. For this purpose, an optimization model is proposed that minimizes the sum of both residual risk effects on portfolio component objectives and response implementation costs. Subsequently, a genetic algorithm is introduced to solve the model. A simple portfolio instance is also provided to illustrate the proposed model.