Reviewerly/exHarmony huggingface dataset:
Metadata-Based Automatic Query Suggestion in Digital Library Using Pattern Mining

This paper presents a Query Auto-Completion (QAC) framework that aims at assisting users in a digital library to specify their search intent with reduced effort. The proposed system suggests metadata-based facets to users as they specify their queries into the system. In this work, we model the facet-based QAC problem as frequent pattern mining problem where the system aims at leveraging association among different facet combinations. Among several frequent pattern mining algorithms, the present work make use of FP-Growth to discover facet patterns at large-scale. These facet patterns represented in form of association rules are used for online query auto-completion or suggestion. A prototype QAC augmented digital library search system is implemented by considering a limited bibliographic dataset (35K resources) of the National Digital Library of India (NDLI: https://ndl.iitkgp.ac.in ) portal. We perform extensive experiments to measure the quality of query suggestions and QAC augmented retrieval performance. Significant improvement over baseline search system is observed in both the aspects mentioned above.
Student Staff in IT: From “just a job” to a career

How can IT go from “just a job” to a career path for students? Many IT professionals have experienced this transition - this paper explores a range of contributing experiences and circumstances to identify common themes and significant indicators in that transition. Through a case study and student technician surveys and interviews, we will examine the roles that student training, projects, increasing levels of responsibility, and other interactions play in transforming students’ awareness of IT as a career choice rather than simply a work-study position.
Weakly-Supervised Relation Extraction in Legal Knowledge Bases

Intelligent legal information systems are becoming popular recently. Relation extraction in massive legal text corpora such as precedence cases is essential for building knowledge bases behind these systems. Recently, most works have applied deep learning to identify relations between entities in text. However, they require a large amount of human labelling, which is labour intensive and expensive in the legal field. This paper proposes a novel method to effectively extract relations from legal precedence cases. In particular, relation feature embeddings are trained in an unsupervised way. With limited labelled data, the proposed method is shown to effective in constructing a legal knowledge base.
Consistent Offline Update of Suspended Virtual Machines in Clouds

In Infrastructure-as-a-Service clouds, there exist many virtual machines (VMs) that are not used for a long time. For such VMs, many vulnerabilities are often found in installed software while VMs are suspended. If security updates are applied to such VMs after the VMs are resumed, the VMs easily suffer from attacks via the Internet. To solve this problem, offline update of VMs has been proposed, but some approaches have to permit cloud administrators to resume users' VMs. The others are applicable only to completely stopped VMs and often corrupt virtual disks if they are applied to suspended VMs. In addition, it is sometimes difficult to accurately emulate security updates offline. In this paper, we propose OUassister, which enables consistent offline update of suspended VMs. OUassister emulates security updates of VMs offline in a non-intrusive manner and applies the emulation results to the VMs online. This separation prevents virtual disks of even suspended VMs from being corrupted. For more accurate emulation of security updates, OUassister provides an emulation environment using a technique called VM introspection. Using this environment, it automatically extracts updated files and executed scripts. We have implemented OUassister in Xen and confirmed that the time for critical online update was largely reduced.
Generative Model for Probabilistic Inference

Generative models (GMs) such as Generative Adversary Network (GAN) and Variational Auto-Encoder (VAE) have thrived these years and achieved high-quality results in generating new samples. Especially in Computer Vision, GMs have been used in image inpainting, denoising, and completion, which can be treated as the inference from observed pixels to corrupted pixels. However, images are hierarchically structured, which are quite different from many real-world inference scenarios with non-hierarchical features. These inference scenarios contain heterogeneous stochastic variables and irregular mutual dependences. Traditionally they are modeled by Bayesian Network (BN). However, the learning and inference of BN model are NP-hard thus the number of stochastic variables in BN is highly constrained. In this paper, we adapt typical GMs to enable heterogeneous learning and inference in polynomial time. We also propose an extended autoregressive (EAR) model and an EAR with adversary loss (EARA) model and give theoretical results on their effectiveness. Experiments on several BN datasets show that our proposed EAR model achieves the best performance in most cases compared to other GMs. Except for black box analysis, we've also done a serial of experiments on Markov border inference of GMs for white box analysis and give theoretical results.
A Fuzzy Approach to Optimal DC Motor Controller Design

The paper deals with the design of a fuzzy controller for a DC motor based on its fuzzy model. In the first step of the design process the fuzzy model of the DC motor is built on basis of its measured input-output data. This model is then used for the design of fuzzy controller based on finding the optimal input signal sequences that will transfer the system from any point of its state space to the chosen end point based on the selected optimization criterion. The resultant Sugeno type fuzzy controller is of proportional character so in case if zero control deviation is required in steady state, it can be replaced by an integration component. Significant simulations results performed in MATLAB/Simulink have confirmed the correctness of the proposed design method and appropriate dynamic properties, invariance and robustness of the designed fuzzy controller.
Blockchain and Data Protection

The author examines the impact of blockchain and smart contracts on the legal profession. After all, the lawyer is entitled to draft smart contracts. For a few years now, in information technology, the lawyer has promoted the writing of so-called ‘agile’ contracts in connection with projects run by ‘agile’ methods. However, the smart contract, whether described as a contract or simple algorithm, challenges the lawyer by its philosophy (‘code is law’) and by its writers who are no longer jurists but developers. He discusses whether this technology will be ‘killing off’ the legal profession. Before over-hastily assuming this apocalyptic demise of the lawyer, the author suggests to think about the role and the status of the lawyer in his general mission of advice and defense, and considers whether information technology can be a substitute for the lawyer or simply a new tool that could be used.
Correlated Extra Reductions Defeat Fixed Window Exponentiation

The security of modular power algorithm is a very important research topic, which is the core operation of public key cryptography algorithm.Since the first timing attack was public in 1996, the attacker can exploit time differences between specific events to recover a secret key.In 2016, Dugardin took advantage of extra reductions to attack a regular exponentiation algorithm, which did not entirely adapt the fixed window method with Montgomery's algorithm.The central thesis of this paper is that there exists a positive correlation between extra reductions of pre-computation and post-computation when the calculation has the same multiplier factor.In this article, basing on this dependency we present an attack method, and confirm the feasibility and effectiveness of it by conducting simulation experiments.Experimental results verify that the method can effectively attack modular power algorithm.
Image Deblurring using Bi-directional Akamatsu Transform

Out-of-focus is something that can happen when taking digital images. Image deblurring is a method used to improve the focus of objects in the image. In this research, Akamatsu's transformation was proposed for deblurring images. Akamatsu Transformation is a transformation derived from integral and differential transformations. The characteristics of integral transformations are similar to low pass filters, whereas differential transformations produce high frequencies which have the characteristics of the original image, wherein the Akamatsu transformation values are modified at both transformations. This transformation is that it can do fast computing so that it is suitable for use on mobile devices. In this research, Akamatsu transform is applied bi-directionally. In detail this transformation is carried out twice, first done in the direction of the x-axis (horizontal), followed by the transformation of Akamatsu in the direction of the y-axis (vertical). With the bi-directional Akamatsu transform technique, better image reconstruction can be produced because the process of modifying integral and differential values is carried out in two stages. This is evidenced by the evaluation and measurement of the quality of reconstruction in the output image calculated by PSNR.
AutoWaze: Towards Automatic Event Inference in Intelligent Transportation Systems

Traffic monitoring is one of the key challenges in Intelligent Transportation Systems (ITS). In this paper, we propose to build a crowdsourcing application for traffic monitoring. The novelty of the proposed approach is that visual data is collected to enable automatic event inference with the recent advance in Computer Vision. The challenge is that mobile devices are not capable of handling visual task processing in high accuracy. We propose to build a networked system so that mobile devices can offload data via available wireless access interfaces (e.g., 4G LTE, WiFi, DSRC) to edge servers, e.g., GENI Rack. We plan to use the testbed at Kettering University to validate the proposed approach.
Distributed Quantitative Information Flow Evaluation for Service Composition in Clouds

Service composition provides a promising way for the delivery of IT applications to satisfy complex consumers' demands. The qualitative information flow control mechanism based on non-interference provides a solid security assurance on the propagation of customer's private data across multiple service providers. However, strict discipline limits the service availability and may cause a high failure rate on service composition. Therefore, we propose a distributed quantitative information flow evaluation approach for service composition across multiple clouds. The quantitative approach provides us a more precise way to evaluate the leakage and supports the customized disciplines on information flow security for the diverse requirements of different customers. Through the experiments and the proof, the results indicate that our approach can improve the availability of composite service effectively with affordable costs while satisfying the customer's security requirements.
Distinct Sources of a Bovine Blastocyst Digital Image Do not Produce the Same Classification by a Previously Trained Software Using Artificial Neural Network

We develop an online graphical and intuitive interface connected to a server aiming to facilitate access to professionals worldwide that face problems with bovine blastocysts classification. The interface Blasto3Q (3Q is referred to the three qualities of the blastocyst grading) contains a description of 24 variables that are extracted from the image of the blastocyst and analyzed by three Artificial Neural Networks (ANNs) that classifies the same loaded image. The same embryo (i.e., the biological specimen) was submitted to digital image capture by the control group (inverted microscope with 40x of magnification) and to experimental group (stereomicroscope with maximum of magnification plus 4x zoom from the cell phone). The 36 images obtained from control and experimental groups were uploaded on the Blasto3Q. Each image from both sources was evaluated for segmentation and submitted (only if it could be properly or partially segmented) to the quality grade classification by the three ANNs of the Blasto3Q program. In the group control, all the images were properly segmented, whereas 38.9% (07/18) and 61.1% (11/18) of the images from the experimental group, respectively could not be segmented or were partially segmented. The percentage of agreement was calculated when the same blastocyst was evaluated by the same ANN from the two sources (control and experimental groups). On the 54 potential evaluations of the three ANNs (i.e., 18 images been evaluated by the three networks) from the experimental group only 22.2% agreed with evaluations of the control (12/54). Of the remaining 42 disagreed evaluations from experimental group, 21 were unable to be performed and 21 were wrongly processed when compared with control evaluation.
Parallel GF(2<sup>n</sup>) Modular Squarers

Operations over polynomial Galois fields GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ) are employed in a variety of cryptographic systems, such as elliptic curve cryptography (ECC). These operations include multiplication and reduction with respect to an irreducible polynomial modulus. Fast parallel multipliers can be designed at the cost of higher die area. In addition to modular multiplication, ECC employs modular squaring. Certain properties of GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ) polynomials make computation of squares trivial. Modular reduction of these squares can be performed in less time and with less hardware complexity compared to the general multiplication case. In an ECC processor, a dedicated squaring unit can potentially reduce overall latency with minimal hardware cost. A fully parallel polynomial n-bit squarer is presented with O(log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> n) latency, which uses lookup tables to store modular reduction terms. It is compared with and evaluated against a polynomial multiplier of similar design.
Entropy Based Bayes' Rule for Coping Dimensionality Reduction in Predictive Task of Data Mining

An integrated framework is developed in this work for reduction of dimensionality and prediction of the class label of unseen sample by using the best attribute of entropy measure and Bayes predictive rule. Dimensionality reduction is one of the fundamental problems of pre-processing phase of knowledge discovery in databases. Hence, for realizing the importance of dimensionality reduction across domains, a filter like approach through entropy measure is developed in this work. The framework is evaluated thoroughly through a few benchmark datasets obtained from University of California, Irvine(UCI) Machine Learning repository and then compared with canonical Naive-Bayesian classifier.
Lesion Labeling on Mammogram by Combining Object Detection with Structured Report Interpretation

Data labeling plays an important role in many fields. Current approaches perform labeling of work by manual operation results in time consumption. For analysis of mammogram image, people need to mark region of interesting (ROI) of a lesion in advance, such as mass, calcification, architectural distortion, and asymmetry. In this paper, we propose a scheme to automatically label lesion on mammogram images, it is realized by combining object detection with structured report interpretation. Moreover, we develop a user interface to modify the ROIs of the labeled lesions. The experiment results show that the proposed scheme comes to similar performance to the compared approaches in lesion labeling.
Multimodal 3D Human Pose Estimation from a Single Image

In this paper, we propose a method for estimating 3D human pose from a single RGB image. Compared to methods that either provide point estimates for coordinate regression or unimodal predictions of joint locations, our approach predicts joint locations using multimodal distributions. In addition, we apply a data-driven approach to learn the conditional dependencies of the relative positions of joints. Our end-to-end approach takes as input images with either 2D or 3D labels and performs on par or better than the state-of-the-art on the Human3.6M and MPII datasets.
«Divided audience» – a new education model as a reform of the education process in Chinese universities

The article is devoted to the idea of a «divided audience» which was proposed by a Chinese scientist, Professor Chang Xuexin. The author outlines that at the moment, it is used as a new educational model in Chinese universities and it is considered reform of the educational process. The author comes to the conclusion that this model has changed the traditional Chinese pedagogical thought – the role of students has become dominant in teaching. It is practically confirmed by improvements in the quality of education in Chinese universities and is acute topic of discussion among teachers and scientists in China.
Test and analysis of high resolution regional model precipitation forecast based on wavelet

Abstract In order to avoid the double punitive disadvantage of traditional test methods, based on the advantages of wavelet in multi-resolution analysisthe precipitation forecast products of RMAPS (Rapid-refresh Multi-scale Analysis and Prediction System) system are tested objectively. In this study, Haar is used as basis function. The test results show that the hourly precipitation forecast error of the 3km discriminant ratio of the forecast system is less than 15% for the precipitation process with the horizontal scale exceeding 94km. As the wavelet analysis scale decreases, the precipitation error gradually increases.
Training technologies in Ukrainian for Specific Purposes

The purpose of the article is to substantiate the use of training technologies in the process of study rig the subject « Ukrainian for Specific Purposes», find out the role of a teacher in the trainings, provide methodological recommendations for the introduction of training technologies in Ukrainian language classes in higher education institutions. Training technologies in anon creating conditions for self-disclosure of the student’s personality in order to implement his communicative Ukrainian language competencies in the simulated situations of future professional activities. Educational and training sessions have been differentiated. The peculiarities of conducting trainings in academic student groups that consist of homogeneous members acquainted with each other, of the same age, combined in one academic group for mastering one specialty. Such a membership allows reducing its organizational part. Instead, the emphasis is placed on the main part of the training session, where there is active interaction while training certain competence. For each tags of short-term and multiple (cyclical) trainings in the Ukrainian language, methodical recommendations for didactic exercises of some topics of the discipline curriculum in Ukrainian for Specific Purposes classes are provided. Examples of interactive exercises that are effective at the main and final stages of the training session are given. The functions of a teacher at the training session have been analyzed with regard to such terms as instructor, a trainer, a coach, a group leader, an organizer, a moderator, a time manager, an expert, an analyst, a negotiator and an announcer. According to the author of the article, the coach is the most suitable concept that corresponds to the purpose, stages and methodology of conducting a training session. The conclusion has been made on the expediency of introducing training technologies in the conditions of reducing the auditorium time to study the discipline «Ukrainian for Specific Purposes».
The role of innovative technologiesin the process of study in ghumanitarian disciplines and the formation of conflictological culture among students of technical universities

In the article the essence of the concept of conflictual culture of personality and conflictual culture of future specialists of technical special tiesis dis closed. The problems of formation of conflictological culture in technicalhig her educational institutions are considered. The peculiarities and problems of introduction of innovative technologies in the educational process of technical universities during the study of humanitarian disciplines are emphasized. The author considers innovative teaching technologies as effective psychological and pedagogical methods, forms, means, which are based on the cultura land educational aspect, on the traditional achievements of education, which remain effective, but trans formed depending on the time, psychological features of the personand the specialty of the institution and are implemented in the educational the process of a hig her technical education institution for acquiring know ledge, developing skill sands kills for work after graduation and everyday life of a future engineer. The method of work with work books forin dependent work on the discipline «Pedagogy, Psychology and Teaching Methods in Higher School» for the masters of technical universities is describe das one of the examples of innovative technologies. The auth or proposes the definition of a workbook for independent work as a means of learning that contributes to the cognitive, creative and in dependent development of a specialist, help sin mastering know ledge of a certain discipline on the basis of solving non-standard tasks and forms the ability and ability to evaluate their own and others’ capabilities. Work books must function as a basis for understanding the lecture material, improving it sabsorption and forming components of the conflict culture of future special is in technicalspecialties. The main task of the working notebook developed by us is the development of in dependent acquisition of know ledge. Students are offered interesting tasks in the form of unusual questions, filling schemes and selfdevelopments chemes, crossword puzzles, scanwords, problem (pedagogical) situations, which students analyze. Tests and techniques for self-knowledge that have a psychological approach are offered, and students not only have to complete them, but also an alyze what will enable them to get acquainted with the methodology and to deepen their know ledge of psychology and to acquire the skills to apply them in pedagogical activity. Defined purpose, tasks, functions of the workbook. Examples of tasks performed by students during the study of humanitari and is ciplines are given.