Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Intelligent Library System Based on Big data

Abstract In most traditional library management systems, readers have to find their interest books through the name or search approximate content from library, which results in wasting time. With the help of big data technology, we construct the intelligent library system using SSM framework and Hadoop framework. A recommendation approach combined co-filtering with content-based recommendations, is introduced to make better recommend efficiency. The system has the intelligent ability of self-learning. For historical lending books data, it uses the Spark MLlib machine learning algorithm library to build clustering or classification models. On the other hand, the system uses Kafka and SparkStreaming to process real-time data. System simulation shows that it has high recommendation accuracy and good timeliness.
Scientific Research Work in the Central Libraries of the Russian Federation: Legal Basis, Organization Forms and Key Problems

The purpose of this article is to identify organizational problems of conducting scientific research work (R&amp;D) by the regional libraries and the possibility of applying the experience of national libraries in the organization of science, taking into account the requirements for scientific research institutions. The article analyzes the legal and organizational grounds for conducting scientific research work by libraries and the experience of national libraries of Russia. The author indicates that the role of leading scientific institutions in the library sphere is assigned to the National Library of Russia and The Russian State Library. The article presents comprehensive analysis of the state of research activities of the central libraries of the subjects of the Russian Federation (CL of RF subjects) on the basis of reflection of this work in the normative legal acts of regional level, governing library operations, and in the regulations of the libraries. Specialized scientific research divisions are studied based on the action plans, public reports, etc. Conducting scientific research in library is one of the determinative types of library activities. The results of studies of central libraries are recognized both at the regional and federal levels and proved by the winners of the All-Russian competition of scientific works on library science, bibliography and book science, the All-Russian competition “The best professional book of the year”, the All-Russian competition “Library analytics”, the All-Russian competition of library innovation and others. The author notes that the identified scientific potential of the CL of the RF subjects is not fully used. The paper concludes that there is insufficient attention to this area of activity from federal authorities and libraries — to the methodical and coordination centres of federal level. The article outlines the prospects and directions for solving the issue of validity and significance of scientific research conducted by regional libraries. The author points out the need for methodological support by national libraries.
Recurrent Networks for Guided Multi-Attention Classification

Attention-based image classification has gained increasing popularity in recent years. State-of-the-art methods for attention-based classification typically require a large training set and operate under the assumption that the label of an image depends solely on a single object (i.e. region of interest) in the image. However, in many real-world applications (e.g. medical imaging), it is very expensive to collect a large training set. Moreover, the label of each image is usually determined jointly by multiple regions of interest (ROIs). Fortunately, for such applications, it is often possible to collect the locations of the ROIs in each training image. In this paper, we study the problem of guided multi-attention classification, the goal of which is to achieve high accuracy under the dual constraints of (1) small sample size, and (2) multiple ROIs for each image. We propose a model, called Guided Attention Recurrent Network (GARN), for multi-attention classification. Different from existing attention-based methods, GARN utilizes guidance information regarding multiple ROIs thus allowing it to work well even when sample size is small. Empirical studies on three different visual tasks show that our guided attention approach can effectively boost model performance for multi-attention image classification.
ENHANCEMENT OF TEXT BASED EMOTION RECOGNITION PERFORMANCES USING WORD CLUSTERS

Human Computer Interaction (HCI) researches the use of computer technology mainly focused on the interfaces between human users and computers. Expression of emotion comprises of challenging style as it is produced with plaint text and short messaging language as well. This research paper investigates on the overview of emotion recognition from various texts and expresses the emotion detection methodologies applying Machine Learning Approach (MLA). This paper recommends resolving the problem of feature meagerness, and largely improving the emotion recognition presentation from short texts by achieving the three aims: (I) The representing short texts along with word cluster features, (II) Presenting a narrative word clustering algorithm, and (iii) Making use of a new feature weighting scheme of the Emotion classification. Experiments were performed for the classifying the emotions with different features and weighting schemes, on the openly available dataset. We have used the word clusters in place of unigrams as features, the micro-averages of accuracy have been found to be enhanced by more than three percentage, which suggests that the overall accuracy value of the text emotion classifier has been improved. All the macro-averages were enhanced by more than one percentage, which suggests that the word cluster feature can advance the generalization potential of the emotion classifier. The experimental results suggest that the text words cluster features and the proposed weighting scheme can moderately resolve the problems of the emotion recognition performance and the feature sparseness.
Combining RTT and ECN for RoCEv2 Protocol

Remote direct memory access (RDMA) has the advantages of direct user-level access to HW, asynchronous communication, etc. RoCEv2 protocol enables RDMA technology to be used in large-scale data centers over Ethernet. It provides high throughput and low latency for data center, but it faces the problem of network congestion caused by RoCEv2 data flow. Efficient congestion control strategy plays an important role in the performance of RoCEv2 based data center network. At present, the network congestion control algorithms for RoCEv2 protocol are mainly based on ECN marked DCQCN and RTT based TIMELY. ECN and RTT have different characteristics and advantages. ECN is more effective to prevent packet losses; whereas RTT controls end-to-end queuing delay better. In this paper, a new algorithm RTT_DCQCN is presented for congestion control which is based on DCQCN. In RTT_DCQCN, the CNP generation mechanism and rate adjustment strategy are optimized; and the scheme of introducing RTT signal into DCQCN is proposed, in which the congestion degree is judged by using ECN and RTT together. The RoCEv2 protocol is implemented on ns-3 network simulation platform. The simulation experiment on ns-3 proved that RTT_DCQCN is valid for the aim of improving stability, fairness and throughput of the RoCEv2 data flows.
Website words matter: an analysis of business schools' online brand personalities

This study investigates how a business school website informs a prospective student about the school's brand personality. Using a content analytical approach, words were extracted from business school websites in one major US market. The results of this research demonstrated the utility of a dictionary-based approach to identifying brand personality, and that within the sample, business schools at public universities signalled more competence, while those at private universities exhibited more excitement and sincerity. Also, higher enrolment business schools portrayed themselves as more competent, while lower enrolment schools described themselves as more sincere. Interestingly, there was no difference between AACSB-accredited and non-AACSB schools on the brand personality dimension, competence. Implications and directions for future research are discussed in detail.
Multi-head Guarded Existential Rules Over Fixed Signatures

Guarded existential rules form a robust rule-based language for modelling ontologies. The central problem of ontology-based query answering, as well as the notion of polynomial combined rewritability, have been extensively studied during the last years for this formalism. However, the relevant setting where the underlying signature is considered to be fixed is far from being well understood. All the existing results on ontology-based query answering and polynomial combined rewritability assume rule heads with one atom, while existential rules in real ontologies are typically coming with multi-heads consisting of several atoms. We aim to fill this gap.
An Efficient Crowd Estimation Method Using Convolutional Neural Network with Thermal Images

Crowd estimation, or the task of determining the number of people in a certain area with a high density, has recently regained much attention as the development of Convolutional Neural Network (CNN) based methods. Different from other common techniques that merely estimate the number of people under relatively simple scene, crowd estimation by CNN has capability to handle the high density of people in the image. However, most of the CNN-based crowd estimation methods devote to analyze visible image, which typically results in privacy invasion for the common public. To address these problems, this paper proposes a crowd estimation algorithm that only uses thermal image as input. The proposed method is based on the fact that there typically exists significant temperature difference between the human face and background, which could be easily distinguished with luminance of raw imagery. Additionally, the utilization of thermal images can easily avoid background interface like counting pictures of faces on clothes or posters, which simplified the model complexity in data fitting. With a lightweight CNN framework, the presented method could obtain high accuracy, low computational cost, and privacy protection. Detailed experiments and a base-line image processing mechanism are implemented to demonstrate the effectiveness and accuracy of the proposed method.
Unilateral Exposure to Mass Media: Non-Communicative Person

The article discusses the forms and ways of the impact of modern digital media on people, groups, and society as a whole. The unilateral communication effect on a person is emphasized. The accent is made on the transmission model of information dissemination, taking into account the formation of its ritualized form. The author pays his particular attention to the status and role of an individual in interaction with mass media; provides arguments about the exclusion of a person from the communication dimension. The activity of modern digital media structures is substantiated as social constructors of reality. The researcher shows the technological and visual possibilities of mass media in the creation of artificial, illusory simulated models of the world perceived by a person as objective, authentic, and real. Mass media are presented as an integral social institution of modern society, operating autonomously, based on its own rules and norms, technological and content principles, conditions of network communication, digitalization, and mediatization of society. The article presents the concepts of theoretical comprehension of mass media as a self-sufficient autopoietic system of N. Luhmann, and consideration of media communication implementation as a non-communication of J. Baudrillard. The author stresses the role of spectacle and visuality in the process of TV influence on the subjects, in the construction of images and models of the world, comparable with the already available information consumers pictures of the world. The contradictory and ambiguous conceptual assessments of the activity and influence of mass media on the modern man are emphasized.
Noninterference specifications for secure systems

This paper presents an analysis of noninterference specifications used in a range of formally verified systems. The main findings are that these systems use distinct specifications and that they often employ small variations, both complicating their security implications. We categorize these specifications and discuss their trade-offs for reasoning about information flows in systems.
Falling Detection Research Based on Elderly Behavior Infrared Video Image Contours Ellipse Fitting

Throughout the world, the proportion of the elders in the total population is increasing dramatically, and home-based care has become the most important form of old-age care. Falling is the most common cause of accidents among the elders at home that poses a huge threat to their health and lives. In order to protect the privacy of the elders an accidental falling detection algorithm for the elders in the home has been proposed in this paper. First, contour-based infrared motion video images are used instead of high-definition cameras to collect the elderly behaviors to protect their privacy. Second, ellipse fitting is performed on the infrared video images of the five behaviors including standing, sitting, squatting, bending and falling. The five geometric characteristic variables of the contour-fitting ellipses including the number of ellipses, centroid positions, ellipsoidal areas, horizontal inclinations and long-short axis ratios of the images, have been extracted. Next, an LSTM model is established using the above variables as inputs for feature extraction and classification. Finally, infrared video images of different types of active behaviors of the elders aged from 50 to 70 years have been selected as IFD database for classification detection. Sixty percent of the IFD images are used as training datasets, and 40% of the IFD images are used as test datasets, and compared with the classification detection of URFD datasets which contains optical RGB HD video images of the different behaviors. The experimental results show the effectiveness of the algorithm proposed in this paper which combines the contour ellipse fitting of the infrared video images and the LSTM feature extraction. The average correct classification rate of the normal and falling down behaviors of the elders is above 95%, which is comparable to the optical RGB datasets. The precision of behavior recognition can effectively protect the privacy of the elders, and provide protection for the accidental falling detection of the elders living alone.
Same Point Composable and Nonmalleable Obfuscated Point Functions

A point obfuscator is an obfuscated program that indicates if a user enters a previously stored password. A digital locker is stronger: outputting a key if a user enters a previously stored password. The real-or-random transform allows one to build a digital locker from a composable point obfuscator (Canetti and Dakdouk, Eurocrypt 2008). Ideally, both objects would be nonmalleable, detecting adversarial tampering. Appending a non-interactive zero knowledge proof of knowledge adds nonmalleability in the common random string (CRS) model. Komargodski and Yogev (Eurocrypt, 2018) built a nonmalleable point obfuscator without a CRS. We show a lemma in their proof is false, leaving security of their construction unclear. Bartusek, Ma, and Zhandry (Crypto, 2019) used similar techniques and introduced another nonmalleable point function; their obfuscator is not secure if the same point is obfuscated twice. Thus, there was no composable and nonmalleable point function to instantiate the real-or-random construction. Our primary contribution is a nonmalleable point obfuscator that can be composed any polynomial number of times with the same point (which must be known ahead of time). Security relies on the assumption used in Bartusek, Ma, and Zhandry. This construction enables a digital locker that is nonmalleable with respect to the input password. As a secondary contribution, we introduce a key encoding step to detect tampering on the key. This step combines nonmalleable codes and seed-dependent condensers. The seed for the condenser must be public and not tampered, so this can be achieved in the CRS model. The password distribution may depend on the condenser’s seed as long as it is efficiently sampleable. This construction is black box in the underlying point obfuscation. Nonmalleability for the password is ensured for functions that can be represented as low degree polynomials. Key nonmalleability is inherited from the class of functions prevented by the nonmalleable code.
Ethics as a Motivation Indicator in Second Language Vocational Digital Teaching

The non-selective second language course at vocational colleges and universities makes teachers strive at fostering students' motivation to learn by choosing from a variety of enhancing factors.Teacher's personality and skills if they comply with pedagogical ethics are considered to be inspiring for students to learn.The aim of this piloting study was to collect empirical data and ascertain the actual motivational power of ethical teaching skills when organizing and conducting autonomous learning activities for college students on the example of educational computer games.The ethical teaching skills applied when creating and using computer games as out-ofclassroom voluntary activities were conceptualized.Their possible influence on learning motivation of college students was analysed with the use of oral and written feedback procedures, and statistical data retrieved from Metrika.Yandex and students' certificates submitted.This allowed to determine five specific groups of students valuing the activity, which in their turn were united into two larger formations: students with distinct intrinsic motivation in the activities-related areas (60%) and socially and psychologically responsive students (40%).A possible method to evaluate statistically the efficiency of some ethical skills specified is suggested, whereas the other skills need further research under modified parameters.Thus, ethical teaching skills are effective in the case the college students possess an intrinsic motivation in an area actualized by an activity suggested.Therefore, they are unlikely to be regarded as an independent motivator, but the indicator of a particular intrinsic motivation characteristic to a student.
Lean IT Transformation Plan for Information Systems Development

Information systems development (ISD) is prone to failure, which can be defined as a time-consuming and costly phenomenon that provides value that is not directly appealing to clients.While ISD can be enhanced using various tools, models, and frameworks, failures related to ISD remain to be evident and costly.These failures are related to human, organizational, and technological factors and waste in ISD.This study identifies the information system (IS) success criteria and factors that contribute to ISD waste.A qualitative case study was conducted for an ICT research unit by using interview, observation, and document analysis techniques as a means of analyzing the IS success criteria, leanness level, and waste.Findings show that lean IT approaches and IS success criteria can be combined to develop a holistic transformation plan for organizational ISD.This transformation plan can potentially assist IS developers deliver high-value IS while driving organizational growth towards the fourth industrial revolution.
Text Summarization Challenge: An Evaluation Program for Text Summarization

Nanba, Hidetsugu Hirao, Tsutomu Fukushima, Takahiro Okumura, ManabuIn Japan, the Text Summarization Challenge (TSC), the first text summarization evaluation of its kind, was conducted in 2000–2001 as a part of the NTCIR (NII-NACSIS Test Collection for IR Systems) Workshop. The purpose of the workshop was to facilitate collecting and sharing text data for summarization by researchers in the field and to clarify the issues of evaluation measures for summarization of Japanese texts. After that, TSC has been held every 18 months as a part of the NTCIR project. In this chapter, we describe our TSC series, the data used, and the evaluation methods for each task, and the features of TSC evaluation.
Recreating Imagination and Self-Regulation as Means of Mathematical Thinking Development in Inclusive Education

The relevance of this article is determined by the need to study new approaches to the organization of the study of mathematics in inclusive education, explore recreational imagination, as well as self-regulation for the development of the learners’ ability to explain in detail their actions, find and correct mistakes on their own and thus develop their mathematical thinking. Today the needs of society to improve the quality of modern education along with the increasing volume of information that modern students must continuously process make the problem of developing mathematical thinking even more acute. The purpose of the study is to research the relationship between recreating imagination, mental self-regulation and mathematical thinking in students with mental retardation. Research methods included observation, psychological and pedagogical experiment, psychodiagnostic tests, and diagnostic tasks. Mathematical and statistical methods were also used (the Pearson correlation coefficient reflecting the degree of correlation between changes in two variables, where the changes in one of them correspond to the changes in the other), as well as interpretive methods. Research result: the article examines the relationship of mental self-regulation, recreating imagination and mathematical thinking in students with mental retardation and confirms the interrelationship of these processes using methods of mathematical statistics in psychology. It also discusses the complex of correctional and developmental exercises aimed at the development of recreating imagination and offers the means of developing mental self-regulation for the further development of the learners’ mathematical abilities. The data obtained in this research can be used in social psychology, pedagogy, developmental psychology, inclusive education, as well as for further theoretical development of this area and find application in the broad practice of inclusive education together with an established and experienced system of tasks.
Preparing Non-English Texts for Computational Analysis

Most methods for computational text analysis involve doing things with “words”: counting them, looking at their distribution within a text, or seeing how they are juxtaposed with other words. While there’s nothing about these methods that limits their use to English, they tend to be developed with certain assumptions about how “words” work – among them, that words are separated by a space, and that words are minimally inflected (i.e. that there aren’t a lot of different forms of a word). English fits both of these assumptions, but many languages do not. This tutorial covers major challenges for doing computational text analysis caused by the grammar or writing systems of various languages, and ways to overcome these issues.