Reviewerly/exHarmony huggingface dataset:
Techniques of Parallelization A Survey

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Analyzing trajectories of learning processes through behaviour-based entropy

Over recent decades, it has been asserted that the essence of developmental learning processes is change through learning. However, capturing the essence of change in learning processes remains an open question. To study learning processes, we take up a maze problem and conduct an experiment in which a participant draws a route for the maze problem over 10 sessions. To understand how a participant learns to draw a route, we draw learning curves by plotting, for each session, the number of mazes for which a participant succeeds in drawing correct routes. To analyze the learning process, we introduce a new metric called behaviour-based entropy, which quantifies the extent of how intensively a participant is devoted to drawing a route. A crucial finding is that substantial improvement in performance is preceded by a few sessions (plateau) during which the behaviour-based entropy is quite high. We run a program that simulates drawing of routes, and thereby obtain a learning curve based on the simulation. The resultant learning curves turn out to coincide roughly with the corresponding learning curves based on the experiment, which demonstrates the plausibility of the computational model for the simulation.
Map-based Visualization of Item Spaces for Increasing Transparency and Control in Recommender Systems

Recommender systems (RS) are very common tools designed to help users choose items from a large number of alternatives. While their algorithms are already quite mature in terms of precision, RS cannot unfold their full potential due to a lack of transparency and missing means of control. In this paper we introduce a method aiming at creating recommendations that are comprehensible and controllable by their users while granting an overview over the item domain. To achieve this, the entire item space of a domain is visualized using a map-like interface. Inside, users can express their preferences on which the RS reacts with matching recommendations. To change recommendations, users can alter their preferences expressed, which creates a continuous feedback loop between user and RS. We demonstrate our general method using two prototype applications, located in different item domains and utilizing different forms of visualization and interaction modalities. Empirical user studies with both prototypes show a great potential of our method to increase overview, transparency and control in RS.
Virtual multi-modal object detection and classification with deep convolutional neural networks

In this paper we demonstrate how the post-processing of gray-scale images with algorithms which have a singularity enhancement effect can assume the role of auxiliary modalities, as in the case where an intelligent system fuses information from multiple physical modalities. We show that as in multimodal AI-fusion, “virtual” multimodal inputs can improve the performance of object detection. We design, implement and test a novel Convolutional Neural Network architecture, based on the Faster R-CNN network for multiclass object detection and classification. Our architecture combines deep feature representations of the input images, generated by networks trained independently on physical and virtual imaging modalities. Using an analog of the ROC curve, the Average Recall over Precision curve, we show that the fusion of certain virtual modality inputs, capable of enhancing singularities and neutralizing illumination, improve recognition accuracy.
Image Feature Description Based on Local Intensity Comparison

Due to the poor performance of traditional descriptors to complicated illumination and orientation estimation, this paper presents a method for image feature region description based on local intensity comparison. First, the invariant regions of interest are divided into several subregions according to the intensity order. Then, the descriptor is calculated by comparing the value of local neighboring pixels. This method combines overall information and local information together. The experimental results show that the proposed descriptor is not only invariant to monotonic intensity changes and image rotation, but also robust to many other geometric and photometric transformations.
Extraction of Skull and Face Surfaces from CT Images

Craniofacial reconstruction is a method to estimate the shape of a person's face based on the shape of his skull. The primary data in craniofacial reconstruction are the skull and face surfaces, but often human head data is taken using a CT scanner. The size of the CT image files is enormous, so we need to represent the CT image in a more straightforward format with small file size. In this paper, we propose a method for skull and face surfaces extraction from CT images. This study aims to obtain point clouds with small file size. In general, the method consists of two stages. First, the segmentation of the skull and face to get the outermost contour of the skull and face. Second, the point cloud construction. The proposed method has succeeded in extracting skull and face surfaces. The extraction process produces point clouds with an average file size of 9.1% of the size of the original CT image file.
A Label-Specific Attention-Based Network with Regularized Loss for Multi-label Classification

In a multi-label text classification task, different parts of a document do not contribute equally to predicting labels. Most existing approaches failed to consider this problem. Several methods have been proposed to take this problem into account. However, they just utilized hidden representations of neural networks as input of attention mechanism, not combining with label information. In this work, we propose an improved attention-based neural network model for multi-label text classification, which can obtain the weights of attention mechanism by computing the similarity between each label and each word of documents. This model adds the label information into text representations which can select the most informative words accurately for predicting labels. Besides, compared with single-label classification, the labels of multi-label classification may have some correlations such as co-occurrence or conditional probability relationship. So we also propose a special regularization term for this model, which can help to exploit label correlations by using label co-occurrence matrix. Experimental results on AAPD and RCV1-V2 datasets demonstrate that the proposed model yields a significant performance gain compared to many state-of-the-art approaches.
The History of an Invention: Boris Rosing

This invention is used daily by billions of earthlings. But the name of Boris Lvovich Rosing, a professor at St. Petersburg Institute of Technology, whose 150th birthday is celebrated this year, is not widely known. All modern TVs are still working on the principles invented by Rosing. In his diaries, his student, Vladimir Zvorykin, always pointed out that he had only implemented the idea of Rosing.
Reliable Tracking via Adaptive Correlation Filter and Local Patches

Due to excellent classification performance, tracker based on correlation filter shows excellent performance in the most challenging occasions, such as illumination change, motion blur, and etc. However, these methods are still sensitive to occlusion and fast motion for they rely on the overall spatial construction of the tracked object heavily. In these scenarios, most conventional methods based correlation filter show performance degradation and can lead to eventual tracking failures. To circumvent drift in above complex scenes, we will derive an efficient method via correlation filter and local patches for tracking, which fully make use of the whole and local information of the target jointly. By incorporation the information of local patches into the tracking framework, the method is less sensitive to the cases with deformation, occlusion, and etc. To further improve the discrimination ability of tracker, we integrate the global representation in the tracking framework to fuse multi-channel features in the tracking process, which will improve the tracking performance significantly. Extensive experiments on several tracking benchmark datasets show that our approach achieved superior performance compared to several state-of-the-art methods.
Research on the Structural Effectiveness of Overseas Anti-terrorism Operational Command System Supported by Space Information

Complex network is an important method to analyze the structural effectiveness of Overseas anti-terrorism operational command system supported by space information. Firstly, we expound the command body and its main tasks of overseas anti-terrorism operational command system supported by space information, and demonstrates the complexity of the system; secondly, we discuss the theory, methods and application steps of complex network, define the network performance indicators and calculation formulas; thirdly the method of complex networks is used to model space information support the overseas anti-terrorism operational command system in the current, construct space information to support the overseas anti-terrorism operational command system in the future; finally the complex network parameters and network structure performance indicators are calculated and analyzed. The results show that the future command system has a certain improvement in the timeliness of information flow, synergy, node relevance, and emergence, and the synergy is especially significant, while the node correlation is not improved much.
Beyond the Screen

Long-distance romantic relationships have become prevalent in today's globalized world. Although mainstream communication technologies have provided instant, cheap, and convenient channels for people to communicate at a distance, the emphasis of these technologies is placed on functionality as they are designed for a large variety of end users, rather than providing emotional communication which the author argues is meaningful and necessary for the individuals to maintain ties to their romantic partners who are forced to live physically apart for some reason. The author envisions that there is a gap between understanding the users' needs in research and designing technologies for them in practice. The author's PhD research has been dedicated to bridging this gap by mediating emotional communication for serious long-distance romantic relationships through unconventional artefacts. This manuscript presents an overview of the work thus far.
Using Decision-Making Block of Computer-Based Intelligent Biomedical Avatar for Applied Research in Bioinformatics

A biomedical task in which the definitions and properties of applied research indicators under study in bioinformatics is formalized. A wide range of traditional approaches used for predicting medical time series were reviewed. Advanced algorithms for predicting moments of reversals of biomedical trends based on machine learning tools were investigated as well. The effectiveness of different kinds of approaches was discussed, and related examples are given. An original securities price dynamics trend classification algorithm, based on the use of the sliding window methodology and biomedical avatar, is described. A general scheme of the classification algorithm to identify biomedical market phases is analyzed and results of computer modelling are presented. Selection of initial and resulting metrics is grounded.
An Unsupervised Method for Concept Association Analysis in Text Collections

This paper addresses the challenge of content categorization to support document navigation and retrieval. The work is motivated by the need to categorize all legislation of a country, where the existing metadata for each document is not sufficient for effective categorization, as concepts vary considerably among documents, resulting in highly sparse vector-space models. To address this challenge, we survey recent related work and propose a solution that integrates currently dispersed principles in a new unsupervised knowledge discovery process combining principles from topic modeling and formal concept analysis, thus not requiring prior domain knowledge to be applied in large document collections. The results confirm the potential of the proposed approach.
Reading Assistant: a reciter in your pocket

Our lives revolve around technology. From small calculators to big satellites, technology has become an inseparable part of human life. It can remove obstructions that individuals with different abilities experience in their day-to-day lives; for example, in speaking, reading, writing, and traveling. This chapter shows how technology can assist differently abled people and allow them to be more independent. It discusses a wearable device that combines optical character recognition and text-to-speech technologies to provide audio of content in books and other reading materials. This device will help people hear the books they want to read. This device can benefit blind people, people with weak vision, people suffering from dyslexia, and other differently abled people. It can even act as a device for interactive learning.
Sentiment Analysis Based on LSTM Architecture with Emoticon Attention

Sentiment analysis is one of the most important research directions in natural language processing field. People increasingly use emoticons in text to express their sentiment. However, most existing algorithms for sentiment classification only focus on text information but don’t full make use of the emoticon information. To address this issue, we propose a novel LSTM architecture with emoticon attention to incorporate emoticon information into sentiment analysis. Emoticon attention is employed to use emoticons to capture crucial semantic components. To evaluate the efficiency of our model, we build the first sentiment corpus with rich emoticons from movie review website and we use it as our experiment dataset. Experiments results show that our approach is able to better use emoticon information to improve the performance on sentiment analysis.
Trajectory Optimization of Two Cooperative Aircrafts Based on RHC

Trajectory optimization problem of two cooperative aircrafts in cooperative tactical engagement maneuvering is studied. The optimization problem of air combat for two aircrafts is established based on the analysis of vertical tactical engagement and three different performance measures and terminal constraints are proposed. The receding horizon control (RHC) model and the numerical solution based on simpson-direct-collocation is put forward. In order to improve the online performance, a BP neural network based approximation of the performance measures is proposed. Finally, a simulation shows that this method is feasible for trajectory optimization in cooperative air combat.
Preventing Potential Backdoors in Bike Algorithm

Abstract BIKE suite of algorithms is one of the candidates in NIST call for public-key post-quantum cryptographic algorithms. It is a key-encapsulation mechanism based on QC-MDPC codes with purely ephemeral keys. The security device implementing such an algorithm therefore needs to generate multiple key pairs in its lifetime very efficiently. In our paper we explore the situation where BigBrother-type adversary can subtly corrupt the vendor(s) of the security devices (e.g., by altering the standard algorithms). In our model, BigBrother cannot preload the keys or synchronize the key generator by a covert channel, but is able to learn the secrets of security devices by observing the public execution of the KEM protocols. BigBrother typically obtains the secret through the usage of (masked) weak keys. However, we can also imagine other covert channels embedded into the ephemeral public keys by some unknown algorithm. To prevent these classes of attacks, we propose that the standard should explicitly specify a verifiable algorithm to transform the required key randomness into a set of keys.
Incidence of code smells in the application of design patterns

Design patterns are reusable solutions that can be applied to solve specific problems in software design. Such patterns can be misapplied, though, and give rise to code smells, i.e., fragments in the code that indicate possible design flaws. In this study, we aim to understand how often code smells co-occur with design patterns, as well as to determine the most common co-occurrences. To this end, we identified instances of code smells and design patterns in methods of 25 open source Java projects, by using automated detection tools. We also manually inspected fragments of the projects' source code to gather insight on the relationship between specific pairs of smells and patterns. Among other findings, we found that methods that are part of the Adapter pattern are more likely to contain code smells, especially the Feature Envy smell, although it can be argued that the detection of this smell in this context is a false positive.
DYNAMICS OF VALUE ORIENTATIONS OF STUDENTS OF THE STATE UNIVERSITY OF MANAGEMENT 2001-2018

The results of studies of value orientations of students of the State University of Management for the period 2001-2018 have been presented. Based on the analysis of the dynamics of values, a conclusion has been made about the peculiarities of the transformation of the value consciousness of the students of the State University of Management for the specified period. So, for students of the State University of Management, traditional value orientations are still very significant – the family, self-realization – the significance of these value orientations is increasing. At the same time, the results of a study in 2018 show the increasing importance of the values of “social altruism”.
Searching for Information with Meet and Join Operators

Information Retrieval (IR) is the complex of models, languages, and techniques aimed to retrieve documents containing information relevant to the user’s information needs. Current retrieval technology requires a retrieval model for guaranteeing effective results. While all retrieval models for term-based search bring into play the Boolean logic of sets, a document collection can be searched by themes, instead of terms, using the logic of vector spaces, instead of sets. Indeed, vector spaces may generalize sets by breaking some laws of algebra of sets. The main aim of this chapter is to provide an overview of the state-of-the-art formalism used in IR and explain how the novel model based on themes defined as vector spaces and inspired by quantum operators, such as two lattice operators known as meet and join, can be built upon this formalism.
First workshop on the impact of recommender systems at ACM RecSys 2019

Research in the area of recommender systems is largely focused on the value such a system creates for the users, by helping them finding items they are interested in. This is usually done by learning to rank the recommendable items based on their assumed relevance for each user. The implicit underlying goal often is that this personalization positively affects users in different positive ways, e.g., by making their search and decision processes easier or by helping them discover new things [3].
Optical and Radar Data Analysis for Land Use Land Cover Mapping in Peru

This analysis determined the best individual band and combinations of various numbers of bands for land use land cover mapping for three sites in Peru. The data included Landsat Thematic Mapper (TM) optical data, PALSAR L-band dual-polarized radar, and derived radar texture images. Spectral signatures were first obtained for each site class and separability between classes determined using divergence measures. Results show that the best single band for analysis was a TM band, which was different for each site. For two of the three sites, the second best band was a radar texture image from a large window size. For all sites the best three bands included two TM bands and a radar texture image. The original PALSAR bands were of limited value. Finally upon further analysis it was determined that no more than six bands were needed for viable classification at each study site.
Avatars Expand the Opportunities of Modern Economy

There is no doubt that modern economy will change under the influence of digital technologies. This fact stipulates for the analysis of the possibilities of using new technologies in economic practice, as well as the consequences of this process. In this chapter, the authors show how digital technologies contribute to the harmonization of economic interests at the micro and macro levels and maximize the benefits. The purpose of this chapter is to try to assess the advantages and challenges of introducing digital technologies based on avatars using blockchain technology in various areas of the economy. This approach is the theoretical basis for the development of economic solutions for the implementation of public policy on the development of digital economy.
Pick &amp; merge

Microsoft Windows is the most popular operating system (OS) for personal computers (PCs). With hundreds of millions of users, its app marketplace, Windows Store, is one of the largest in the world. As such, special considerations are required in order to improve online computational efficiency and response times.
Applying the Lesson to More Complex Transactions

Having mastered the application of the arm’s length principle to a range of comparatively simple transactions, this chapter will provide some more advanced guidance on coping with transfer pricing challenges related to management fees (centralized services) as well as financial transactions. The advantages of applying a systematic (sequential) approach that is built on the recommendations of the OECD in the context of the simplified benefit test for management services are illustrated. The reader is provided with a decision tree to guide him through setting up his own allocation mechanism. For financial transactions (loans and cash pooling), the reader is provided with a pragmatic summary of the latest developments in the context of the OECD BEPS project. Concise case studies illustrate the process of choosing an appropriate transfer pricing method as well as conducting exemplary comparability analyses.