Reviewerly/exHarmony huggingface dataset:
Interactive Visualization of Ontology-Based Conceptual Domain Models in Learning and Scientific Research

TheLitovkin, Dmitry paper presents an approach to knowledge transferring and sharingAnikin, Anton on the baseKultsova, Marina of the semantic link network (SLN) representing expert knowledge in the explicit form. To provide an efficient SLN understanding, it is represented with a geometric graph which can be interactively visualized using a combination of the appropriate visualization methods. The coupling of these methods allows getting a different level of details of the SLN visualization in accordance with the user needs. The proposed approach is planned being implemented in the knowledge management system for learning and scientific research.
A Multivariate Method for Group Profiling Using Subgroup Discovery

We propose in this paper a new method for Group Profiling based on Subgroup Discovery. Group Profiling is the process of constructing descriptive profiles for communities in social networks. Traditional methods for Group Profiling often return a set of univariate descriptors. By searching for the best univariate descriptors, these methods neglect possible interactions between them that could enhance the overall community description. Moreover, these methods do not control for coverage of descriptions. This imposes a severe limitation on the significance of results since a description may represent only a small fraction of a community. Here we investigate how the problem of Group Profiling can be modeled as a Subgroup Discovery (SD) task. We propose a new method based on SD for finding multivariate community descriptions with high coverage. We assess the quality of our proposal by finding descriptions for communities found in a real-world co-authorship network of scientific articles from Arxiv. Our experiments highlight that there is a compromise between the quality and coverage of descriptions. The experiments also show that our method improves on traditional univariate approaches, returning better descriptions both in terms of quality and coverage.
Stochastic modeling of a technological process using histogram computation

Abstract The focus of research is a discrete-event simulation model of the production process, including many technological operations, the time duration of which should be considered as a random function of several discrete variables. It is assumed that a limited amount of available statistics allows us to construct estimating distribution functions of the random function concerned only for individual values of its arguments. The estimates obtained in this way are suggested in order to be used to form the estimating distribution functions of the random function for any values of its arguments by the methods of histogram arithmetic. Results of the use of the proposed approach, implemented within a computer simulation model of a transport technological process are considered.
G-HAPNet: A Novel Structure for Single Image Super-Resolution

Recent investigations on single image super-resolution (SISR) have progressed with the development of deep convolutional neural networks (DCNNs). However, increasingly complex network designs cause huge computational budgets. Therefore, a more efficient structure for SISR task is desirable. In this report, we propose a novel structure, called G-HAPNet. Specifically, the group-hierarchical atrous pyramid block (G-HAPB) is built to package as a general block for deeper network constitution. Firstly, the original features are expanded and grouped in channel. Then, a atrous pyramid is constructed to extract multi-scale features from corresponding channels. Besides, we introduce hierarchical grouping aggregation (HGA) which includes forward aggregation and backward aggregation by skip connections so that we can achieve hierarchical fusion and information guidance among multi-scale features. Extensive experiments demonstrate that with the same level depth and computational budgets, our proposed G-HAPNet has better performance than state-of-the-art methods on both synthetic datasets and real-world dataset, which indicates our G-HAPNet is a more efficient and practical structure for SISR.
Community Based Node Embeddings for Networks

Network embedding has got enormous attention in recent past for their wide range of applications across different types of networks. This paper mainly includes a simple and novel model which is used for better node embeddings with respect to community detection in social networks. We use existing algorithms (mainly community detection algorithm) and Representation Learning (RL) techniques to find better embeddings that assist in better community detection.
Blockchain Based Integrated Authentication System

Due to the development of hardware and the development and optimization of network, the 4th industrial revolution has grown up with information and communication technology, and new technological innovations such as artificial intelligence, internet of things, quantum computing and blockchain are emerging as core driving force. The Fourth Industrial Revolution is developing towards the connection, sharing and decentralization of nodes. It is the blockchain that includes all three characteristics. The blockchain is used in many industrial fields based on the characteristics However, there is a lack of research on the integrated authentication management blockchain. In this paper, we propose an integrated authentication management blockchain using blockchain. The existing authentication system is constituted independently, and the user must suffer inconvenience of generating redundant data in a system requiring authentication. In this paper, personal information is integrated and stored in a blockchain, personal information is protected by zero-knowledge proofs, and fast response speed can be provided.
RAMP Shapes: Declarative RDF $$\leftrightarrow $$ ADT Mapping

With the broad availability of Linked Data efficient and convenient programmatic access to semantic graph data within programming languages is important for using the data in applications. To this end, we present RAMP (RDF Algebraic Data Type Mapping), a type construction language, specification and an implementation of mapping operations between RDF graphs and structured data types. RAMP is based on algebraic data types, and aims to overcome limitations of existing approaches for example regarding the set of supported language constructs, or the ability to generalize over a different programming languages. At the same time, RAMP focuses on providing computationally efficient mapping operations.
Arab Handwriting Character Recognition Using Deep Learning

Recent work has shown that neural networks have great potential in the field of handwriting recognition. The advantage of using this type of architecture, besides being robust, is that the network learns the characteristic vectors automatically thanks to the convolution layers. We can say that it creates intelligent filters. In this article we study deep learning in the field of Arab handwritten character in order to have a better understanding of its functioning. In this paper we present the work we have done on convolutional neural networks. First, we explain the theoretical aspects of neural networks, then we present our experimental protocols and we comment on the results obtained.
Measuring linearity by area-length of convex hull

Effective linearity measures are useful in image processing. Sets of points with relatively high linear pattern are taken as support region. In this work, we introduce a linearity measure known as convex hull area-length ratio to obtain support region for boundary points of an image. The measure is invariant under transformations such as scaling, rotation and translation. The algorithm is using simple computation and resistant to protrusion in data set. Dominant point detection is the extension application after the support region determination. The capability of the measure is presented graphically with boundary points of images.
Overview of Data Deduplication Technology in a Cloud Storage Environment

Data deduplication technology involves improving the data storage efficiency while storing and managing large amounts of data. Data deduplication reduces storage requirements by determining whether replicated data is being added to the storage, and thereby omitting the data upload if the same data already exists. Data deduplication requires data confidentiality and integrity when being applied in a cloud storage environment, and therefore various security measures such as encryption are needed. However, common encryption technologies generally cannot be applied simultaneously with data deduplication owing to the inherent inability to transform the source data. Various studies have been conducted for addressing the said issues. This paper discusses the basic environment of data deduplication technology. It also analyzes and compares multiple proposed techniques for addressing security threats.
From start-ups to governments

In this paper, we investigate the realities, challenges and socio-economic implications of blockchain technology adoption from the ecosystem perspective, mainly the public and private sectors stakeholders, and entrepreneurs and start-ups. A holistic, theoretical framework is proposed as well as strategies to overcome the challenges and accelerate the pace of the adoption.
Side-Channel Analysis for Searchable Encryption System and its Security Evaluation

Searchable encryption will become more important as medical services intensify their use of big data and artificial intelligence. To use searchable encryption safely, the resistance of terminals with embedded searchable encryption to illegal attacks (tamper resistance) is extremely important. This study proposes a searchable encryption system embedded in terminals and evaluate the tamper resistance of the proposed system. This study also proposes attack scenarios and quantitatively evaluates the tamper resistance of the proposed system by performing experiments following the proposed attack scenarios.
Creating Audio Games Online with a Browser-Based Editor

Play has been identified as a fundamental human desire (see, e.g., Huizinga's seminal work on "Homo Ludens"). To little surprise then, people have also used sound in play and to create games. Since the advent of the personal computer, the genre of audio games invites sighted and visually impaired people alike to play interactive computer games solely based on sound renderings. While audio games are popular, especially among blind people, there is a lack of development tools to support audio game design and to foster further growth of this genre. For this reason, we demonstrate a browser-based audio game editor that we have developed over the last year or so, drawing on the experience and needs of seven long-term audio gamers. To the best of our knowledge, it is the first application or tool of its kind. Its key features are easy usage (including instant game play and sound rendering) and open source development to increase sustainability and possible impact.
Image Filter Selection, Denoising and Enhancement Based on Statistical Attributes of Pixel Array

The choice of image filters inKurama, Vihar computer vision has a significant effect on the imageSridevi, T. reconstruction and feature extraction. Currently, the most filters are used to enhance images for human consumptions, programmed operations and to reduce the noise, frequency levels in the image. Though it is hard to select an optimal set of filters for a given series of images, in this work, we propose to choose the best assortment of different filters for a given image as the input. By generating the pixel array of the input image, we compute all the image attributes such as RGB colour mean, variance, mean squared error and signal-to-noise ratio values of the input image and then compare with the same, once the filter is applied. We verify the effectiveness of the filters by conducting an empirical evaluation with best-discovered traits.
Hypergraph Regularized GM-pLSA: A Model to Learn the Latent Semantic Attributes

Semantic attributes are proven to help improve the performance of a series of applications in the field of computer vision. The semantic attributes are usually defined by humans and labeled manually, but whether these attributes can discriminate the images or videos well should be doubted, because these attributes are not optimized. In order to solve this problem, we proposed a new model named HRGM-pLSA to learn the latent semantic attributes. Our model employs GM-pLSA to learn the latent topics in the images. To utilize the prior knowledge of predefined attributes, we use the hypergraph to present the complex correlation of the attributes in the images. We construct a regularization term of a hypergraph and integrate it into the GM-pLSA to make the learned latent semantic attributes get better performance compared to primary attributes. In this paper, we evaluate the quality of attributes in a practical application: image retrieval. The performance of our model in the application demonstrates the value of our model.
Learning Network Representation via Ego-Network-Level Relationship

Network representation, aiming to map each node of a network into a low-dimensional space, is a fundamental problem in the network analysis. Most existing works focus on the self-level or pairwise-level relationship among nodes to capture network structure. However, it is too simple to characterize the complex dependencies in the network. In this paper, we introduce the theory of the ego network and present an ego-network-level relationship. Then a deep recurrent auto-encoder model is proposed to preserve the complex dependencies in each ego network. In addition, we present two strategies to solve the sparsity problem. Finally, we conduct extensive experiments on three real datasets. The experimental results demonstrate that the proposed model can well preserve network structure and learn a good network representation.
Multiobjective Optimization Using Evolutionary Algorithms in Agile Teams Allocation

Ensuring that the team meets project requirements is essential to ensure the software quality available in the market and the success of the project. In this context, this study evaluated three algorithms for optimizing software engineering problems: NSGAII, SPEA2 and MOCell, in order to support project managers in the composition of agile software development teams. These algorithms were tested in an experiment carried out in a software development company and evaluated in four projects recently executed by the company. The approach considered the characteristics of the project activities, available human resources, human resource profile, project constraints (scope and time for execution) and constraints established by the organization. The algorithms returned solutions with the number of resources needed to carry out the project, as well as resources such as more project qualification, lower cost, and productivity adequate for the term established by the client. The results showed that the three algorithms evaluated presented consistent performances. The NSGAII and SPEA2 had very similar results and behavior, whereas the MOCell presented a better performance in the computational effort and needed a larger population for its saturation.
Localization of Autonomous Mobile Robot by Integration of Multiple Sensor Information based on Sensor Information Reliability Map

We have proposed a localization method of an autonomous mobile robot using multiple sensor information in order to realize robust localization. This method is based on Particle Filter, and bias probability distributions are detected and eliminated before integration of probability distributions for localization. However, the calculation cost is enormous because it is proportional to the number of sensors and particles. In order to reduce the calculation cost for localization, we propose a new localization method using the reliability map of sensor information which is generated based on selection results of sensor information in the previous method in this paper. The usefulness of the proposed method is discussed with some experimental results with a real robot in various real environments.
Smart Physical Intruder Detection System for Highly Sensitive Area

In this ever-growing world of automation and digitization, where data is a pivotal element for the growth of every individual, institution, and organization, whether digital or physical, data could also be the reason for destruction, if acquired by an antagonist through unconventional access. Data is a very sensitive point in all the domains ranging from an individual’s personal space to tactical military centers such as defense institutions, military matters, financial institutions, hospitals, and educational institutions. Thus, it is necessary to protect the data from intruders. Physical Intruder Detection is equally important as the detection of intrusion in computer networks. Though the later is always digital and without manual intervention. Physical Intruder Detection can be either digital or done manually. The paper presents a system for an enclosed area, based on IoT and supported by Digital Image Processing, to capture any Physical Intruder who breaches the security system and alert the rightful person regarding the intrusion. The approach uses the PIR motion sensor to detect any suspicious activity, turn on the webcam and with the help of Face Recognition System using Digital Image Processing, recognizes whether it is the rightful person or not. If it is an Intruder, then the webcam will start recording the activity of the Intruder and send a text message as well as an email to the system owner alerting him/her/them about the intrusion. A link to this live feed to the system owner is also attached to the alert message and mail. This Intruder Detection System is energy efficient as well because the webcam will be turned on only when the motion sensor detects any suspicious activity.
A Multi-attribute Decision-making Optimization Algorithm Based on Conflict Evidence Fusion and Cloud Model

Abstract To eliminate fuzziness and uncertainty of linguistic comments in multi-attribute decision-making, this paper introduces D-S evidence theory into cloud model assessment. Golden section method is employed to convert experts’ linguistic comments into cloud decision-making matrix, and then criterion clouds of different levels in comment set are taken as the reference to determine the membership to each level of assessment, so as to construct the basic probability allocation function (mass function) for different experts in respect of different attributes in different schemes. Thereafter, conflict coefficient, Jousselme distance, and Pignistic probability distance are introduced on the basis of D-S evidence theory to define evidence conflict measure. By calculating evidence reliability and relative expert weight, mass functions for experts are modified and fused. In the end, mass functions of different schemes are fused on the basis of attribute weight, and compared with the mass functions of ideal cloud and negative ideal cloud. Therefore, optimal scheme will be determined by comparing average closeness.
Some Fuzzy Inference Processes in Picture Fuzzy Systems

Dealing with uncertain and linguistic information has been always a big problem in the areas of computational intelligence and artificial intelligence. Fuzzy inference mechanism is one of the common approaches to handling uncertain and linguistic information. The conventional fuzzy inference rule is expressed in the form of "if-then" rule: "IF X is A THEN Y is B", where A and B represent fuzzy sets in universes U, V. Since 2013, Cuong and Kreinovich [7] proposed Picture Fuzzy Sets (PFS), the interest in FPS continues to grow and the research area continues to expand, especially picture fuzzy logic and picture fuzzy inference. PFS is a direct expansion of FS, it enhances the flexibility and efficiency of FS. This paper has presented some new classes of operators in the implication and generalize the Inference of Compositional Rule on the Picture Fuzzy Logic (PFL) setting.
High Performance Application Specific Stream Architecture for Hardware Acceleration of HOG-SVM on FPGA

Conventional sequential processing on software with a general purpose CPU has become significantly insufficient for certain heavy computations due to the high demand of processing power to deliver adequate throughput and performance. Due to many reasons a high degree of interest could be noted for high performance real time video processing on embedded systems. However, embedded processing platforms with limited performance could least cater the processing demand of several such intensive computations in computer vision domain. Therefore, hardware acceleration could be noted as an ideal solution where process intensive computations could be accelerated using application specific hardware integrated with a general purpose CPU. In this research we have focused on building a parallelized high performance application specific architecture for such a hardware accelerator for HOG-SVM computation implemented on Zynq 7000 FPGA. Histogram of Oriented Gradients (HOG) technique combined with a Support Vector Machine (SVM) based classifier is versatile and extremely popular in computer vision domain in contrast to high demand for processing power. Due to the popularity and versatility, various previous research have attempted on obtaining adequate throughput on HOG-SVM. This research with a high throughput of 240FPS on single scale on VGA frames of size 640x480 out performs the best case performance on a single scale of previous research by approximately a factor of 3-4. Further it's an approximately 15x speed up over the GPU accelerated software version with the same accuracy. This research has explored the possibility of using a novel architecture based on deep pipelining, parallel processing and BRAM structures for achieving high performance on the HOG-SVM computation. Further the above developed (video processing unit) VPU which acts as a hardware accelerator will be integrated as a co-processing peripheral to a host CPU using a novel custom accelerator structure with on chip buses in a System-On-Chip (SoC) fashion. This could be used to offload the heavy video stream processing redundant computations to the VPU whereas the processing power of the CPU could be preserved for running light weight applications. This research mainly focuses on the architectural techniques used to achieve higher performance on the hardware accelerator and on the novel accelerator structure used to integrate the accelerator with the host CPU.