Reviewerly/exHarmony huggingface dataset:
Modular Rook (MR): A Modular Data Center Network Architecture Based on Rook’s Graph

Data center networking isDas, Riju becoming more challenging day byMedhi, Nabajyoti day in today’s world of cloud computing. Due to fast technical innovation and vendor diversity, heterogeneity widely exists among data center networks (DCN). Increasing service requirements by industries or organizations requires expansion of the data center network. In the construction of the mega data centers, shipping container-based modular networks are used as basic building blocks. In this paper, we propose Modular Rook (MR) Network and a new modular data center (MDC) network design using Rook’s graph for inter-container network. Modular Rook (MR) combines the advantages of both structured inter-container network and heterogeneous container networks. Modular Rook (MR) is found to possess brilliant network properties such as low network diameter and high bisection bandwidth with a fewer number of switches and links as compared to other DCN topologies.
A model-free soft classification with a functional predictor

Class probability is a fundamental target in classification that contains complete classification information. In this article, we propose a class probability estimation method when the predictor is functional. Motivated by Wang et al. (Biometrika, 95, 149-167, 2007), our estimator is obtained by training a sequence of functional weighted support vector machines (FWSVM) with different weights, which can be justified by the Fisher consistency of the hinge loss. The proposed method can be extended to multiclass classification via pairwise coupling proposed by Wu et al. (Journal of Machine Learning Research, 5, 975-1005, 2004). The use of FWSVM makes our method model-free as well as computationally efficient due to the piecewise linearity of the FWSVM solutions as functions of the weight. Numerical investigation to both synthetic and real data show the advantageous performance of the proposed method.
Neural networks modification for solving the traffic signs detection problem

Abstract The paper deals with the implementation of the traffic signs detection model on the deep neural network basis. The complexity of the problem lies in the detected objects size. The paper considers the use of modern approaches to the deep neural networks Mobile Net v2 and Tiny YOLO v3 implementation to solve the detection problem in real time. Also, a modification of the considered networks is proposed, which allows increasing the Average Precision index by more than 20%. For the networks training GPGPU NVIDIA 1080 and a signs images set called Russian traffic sign images dataset - RTSD which includes more than 15,000 frames for training and more than 3,000 frames for carrying out testing were used.
The methodology of skills space forming development and testing. Synthesis and hypotheses

This paper discusses the possibility of development and testing a special methodology for the formation of a dynamic competence space. The main object of study is a student. The proposed methodology can take into account the risks of an erroneous assessment of the competences of an object, and it is also quickly adaptable and scalable. This paper presents a synthesis of methodology. Seven hypothetical ways of assessing and constructing skills space are considered.
Binary Adjacent Set Occurrence for a Simple Texture Analysis in Binary Image

Texture analysis is one of the common methods that can be used in image classification or automated identification tasks. With the rising of IoT and microcontroller based devices which come with limited computational capabilities, choosing a simple but efficient and effective image analysis method is one of the prominent key factors in the implementation. This paper provides an alternative of simple texture analysis descriptor using binary-adjacent statistical approach. The descriptor intended to approximate coarseness or spatial distribution in the binary image using the scale-invariant feature. By scaling, it gives benefits in analyzing samples with different dimension or aspect as long as it comes from a similar region ratio. The aim is to reduce analysis complexity and memory requirement while maintaining its usability and portability. In the similarity and discrimination test, it is still able to represent better result at low scale-level compared to frequency filters of the Fourier transform method.
Noise Bias Compensation for Reconstructed HDR Images in Two Layer Coding

We propose a two-layer coding method for HDR images with noise bias compensation (NBC). Although the mean of image noise is assumed to be zero originally, it becomes nonzero (that is, noise bias is present) after tone mapping, because of the nonlinearity of tone mapping. In the conventional two-layer coding, the reconstructed LDR image is decoded using a lossy encoder of a base layer that includes noise; the noise is included in an HDR image that is inverse tone mapped from a reconstructed LDR image with noise bias. We aim to improve the quality of reconstructed HDR images by compensating for noise bias. NBC classifies pixels in a noisy image into several subsets according to the pixel value after tone mapping, and compensates for the pixel value in each subset with a compensation value determined in advance. The compensation values are determined by using all pixel values in the LDR image that is input to the base layer, in addition to those in the HDR image that is inversely tone mapped from the reconstructed LDR image. We perform NBC following inverse tone mapping for the reconstructed LDR image. Experimental results show the effectiveness of the proposed method.
A Tamper-Proof Digital Archiving Scheme Based on Blockchain

Every society has given a lot of efforts to preserve knowledge for its heritage and the activity is still going on in modern society. The preservation of knowledge in modern society mainly depends on digital technology, unlike previous eras. Digital data is very useful in many respects such as easy transfer or outstanding reusability, but it is not adequate for data preservation because it can be easily modified, vanished, or replaced without notice. In order to resolve this issue, we propose a tamper-proof digital archiving scheme with Blockchain which protects archived data from malicious counterfeiting and guarantees the fidelity of archived data. In the evaluation, the system shows that it is robust to possible forgeries.
Scheduling Policies for Minimizing Job Migration and Server Running Costs for Cloud Computing Platforms

We propose job scheduling algorithms to minimize job migration and server running costs in cloud computing platforms offering Infrastructure as a Service. We first consider algorithms that assume knowledge of job-size on arrival of jobs. We characterize the optimal cost subject to system stability. We develop a drift-plus-penalty framework based algorithm that can achieve optimal cost arbitrarily closely. Specifically this algorithm yields a trade-off between delay and costs. We then relax the job-size knowledge assumption and give an algorithm that uses readily offered service to the jobs. We show that this algorithm gives order-wise identical cost as the job size based algorithm. We illustrate the performance of the proposed algorithms and compare these to the existing algorithms via simulation.
Development An Android Based Pemeriksaan 5 Menit (P5M) Information System

P5M is a readiness inspection activity such as the attributes and attendance of students before practicing. Students whose attributes are incomplete will be recorded in P5M sheet. Each attribute has a violation point or hour minus. P5M data, will recapitulate the number of hours minus. Constraints that occur in the Astra Manufacturing Polytechnic, namely the filling process of P5M sheets is still manual. This causes the time required to recapitulate minus hours to be long, which is on average 5 minutes. This causes the process of renewing the number of hours minus students and the process of paying hours minus being hampered. To overcome this, an Android-based P5M information system was made. This information system was developed using the Extreme Programming methodology and the MySQL database. With the P5M information system, it can eliminate the process of recapitulation of minus hours and reduce paper usage.
A Parallel Face Detection Method using Genetic &amp; CRO Algorithms on Multi-core Platform

Face recognition is a well-known biometric method that used in many applications for authentication and identification. The original face recognition scheme takes face image, extract its features and store it as a vector in the database. The saved vector is then compared with the input image by comparing features to recognize it. Many methods had been proposed before to achieve that and to increase the level of identification accuracy. This paper proposes a new method by using a meta-heuristic algorithm Genetic and Chemical Reaction Optimization algorithms, both are implemented in parallel using multicore platform. The aim is to increase accuracy of image matching with less error rate and increase performance of the system in terms of speedup.
Evaluation of Adaptive Bitrate Selection Algorithms for MPEG DASH

Dynamic Adaptive Streaming over Hypertext Transfer protocol- HTTP (DASH) is a video streaming technique used for providing the end user with the best probable quality of claimed video sequence. HTTP based standard called MPEG DASH defines a way of notifying the user about different transport streams (TS) with different quality levels and gives information about the most suitable transmission path. MPEG DASH is used for encoding every video sequence into continuous segments of distinct alternatives: different resolutions, bit-rates, and quality levels. Based on present network conditions, an adaptive bitrate selection (ABR) algorithm at the client side selects the bitrate for each segment. In this paper, we evaluated three published ABR algorithms in a streaming client prototype based on the MPEG DASH standard. In the evaluation procedure, all algorithms were tested using three different video sequences from available MPEG DASH dataset. We determined the spatial and temporal information of all tested video sequences and their influence on PSNR (Peak Signal to Noise Ratio) values. Furthermore, video quality levels were analyzed in five testing scenarios in terms of different network conditions. Obtained results show that ABR algorithms that consider users experience and segment size are less sensitive to changing network conditions. The spatial activity has a high influence on the video quality and can be useful as a parameter in DASH dataset preparation in order to improve adaptation algorithms.
GrabView: A Scalable Street View System for Images Taken from Different Devices

In the last decade, many researchers and applications focused on street view systems' geo-spatial and multimedia content. However, most street view systems have high data collection costs and have out of date content in certain regions. An efficient and effective street view system reflecting real world multimedia content on a weekly basis has been an elusive goal. We present GrabView, a street view system that: · Uses a data capture during ride sharing service trips model. · Collects up-to-date geo-referenced multimedia content atlow cost. · Processes both the multimedia and geo-sensor content. · Serves a navigation and browsing user experience equivalent to that from dedicated mapping vehicles. Grab ride hailing service vehicles collect most of GrabView's geo-sensor data and multimedia content. This results in much better up-to-the-minute road network coverage at no extra cost.
A Comparison of Weighted Support Vector Machine (WSVM), One-Step WSVM (OWSVM) and Iteratively WSVM (IWSVM) for Mislabeled Data

Labeling error can occur for various reasons such as the subjective nature of the labeling task, the lack of information to determine the true label of a given example and data entry error. Labeling errors were categorized as mislabeled, unlabeled, partially labeled, incompletely labeled and illegible label. In this study, the focus will be on mislabeled data. The problem of dealing with mislabeled data and in particular of constructing a classifier from such data has been approached from a number of different directions. Therefore, developing learning algorithms that effectively and efficiently deal with mislabeled data is a great practical importance and key aspect in machine learning. Support Vector Machine (SVM) has been widely accepted to be one of the most effective techniques in machine learning algorithms. One of the main drawbacks of SVM is it depends on only a small part of the data points (support vectors) and it treats all training data of a given class equally. To address this problem, one of the solution is the Weighted Support Vector Machines (WSVM). Wu & Liu proposed two different WSVM namely one-step WSVM (OWSVM) and iteratively WSVM (IWSVM). In this paper, a comparison of Weighted Support Vector Machine (WSVM), One-step WSVM (OWSVM) and Iteratively WSVM (IWSVM) for mislabeled data has been done to see the classification accuracy of each of the method. The three methods were compared based on correctly labeled, mislabeled data, data within margin, mislabeled data within margin and classification accuracy for eight KEEL repository datasets using 20% noise in training data. Based on the experimental results, the performance of OWSVM is better than both WSVM and IWSVM based on the correctly labeled, mislabeled data, data within margin, mislabeled data within margin and classification accuracy.
Spectral geometric features for shapes

This chapter introduces a new method for extracting salient features from surfaces in the Laplace–Beltrami spectral domain instead of usual spatial domains. The local shape descriptor of a feature point is the Laplace–Beltrami spectrum of the spatial region associated to the points which are stable and distinctive. The defined salient spectral geometric features are invariant to spatial transforms such as translation, rotation, and scaling as well as isometric deformations and mesh triangulations. Hence, both global and partial matching can be achieved with these salient features. We demonstrate the effectiveness of the method with many experiments and applications in this chapter.
Multi-objective Minimal Time Optimal Control Based Path Planning of Mobile Robots

This paper proposes a novel approach to solve the minimal time multi-robot path planning problem. Firstly, the multi-robot path planning problem is described by a multi-objective minimal time optimal control model. In this process, we mainly study the mathematical description of the trajectory paths without hitting with obstacles and colleagues. Secondly, in order to obtain the numerical solution of the optimal control problem, the model is transformed into multiple single-objective mixed integer programming problems (MSOMIP) by using discretization and lexicographic method. Finally, an improved particle swarm optimization-genetic algorithm (PSO-GA) algorithm is proposed to solve the MSOMIP. The optimal path planning scheme and the corresponding analysis are presented at last.
Synthesis of Quantifier-Free First-Order Sentences from Noisy Samples of Strings

We present a method to extract knowledge in terms of quantifier-free first-order sentences in disjunctive normal form from noisy samples of classified strings. We show that the problem to find such a sentence is NP-complete, and our approach for solving it is based on a reduction to the Boolean satisfiability problem. Moreover, our method bounds the number of conjunctive clauses and the maximum number of literals per clause since sentences with few clauses and few literals per clause are easier to interpret. As the logic we are considering defines exactly the class of locally threshold testable (LTT) languages, our results can be useful in grammatical inference when the goal is to find a model of an LTT language from a sample of strings. This paper also presents an experimental comparison of our method against a SAT-based technique for the problem of learning deterministic finite automata, which is a well-known problem in grammatical inference.
Multi-rotor UAV Track Planning Based on Improved Artificial Potential Field

An improved artificial potential field (APF) method is proposed to solve the multi-rotor UAV flight path planning problem in this paper. Firstly, the grid method is adopted to build the environment model. After that, the A* algorithm is for generating the global path, and performs optimization for the node on the global path. Finally, the potential function of the traditional APF method is improved that an attraction force made by the global path to the multi-rotor drone is introduced, so the UAV can move guided by both obstacle and target together. Simulation results show that the proposed method can effectively correct the defects and deficiencies of the traditional APF method. The trajectory planning quality of multi-rotor UAVs in static and dynamic environments can be improved with the combination of the global advantages of A* algorithm and the APF method based real-time obstacle avoidance capability.
Video Synchronization and Alignment Using Motion Detection and Contour Filtering

The proposed method presents a proficient abandoned functioning of a video synchronization and alignment using motion detection and contour filtering, based on various flat dimensionality frame matching techniques. In the proposed system, motion detection algorithm is used to detect only the motion of the objects and Contour filtering algorithm is used to recognize the objects based on its color. The algorithms are implemented in Java language, which facilitates prototyping using open source library. The application of video alignment includes the detection of objects such as vehicles, which is used in Advance Driver Assistance System (ADAS) and also in video surveillance system for traffic monitoring. The motion detection algorithm is used in CCTV surveillance for detecting terrorist threats. The contour filtering algorithm is implemented in medical examinations. The proposed system is tested on live datasets and obtained a good change detection between the frames. Video synchronization and alignment algorithms had been developed in the earlier years for plain datasets using static cameras. Compared to the algorithms developed in the initial stages, the proposed system provides a better efficiency to speedup the application by optimizing the algorithms, recuperating the data locality and also executing the various modules of the application. The proposed system has obtained a chronological speed up result of 12.39x factor when compared to the existing methods, when processed with the testing dataset with the video resolution of 240 * 320, with 30 frames per second using high definition cameras. The results obtained are further processed to run the embedded CPU applications and GPU processors.
On cyclic codes of length 8ps over Fpm + uFpm

In this paper, we establish the algebraic structure of all cyclic codes and their duals of length 8ps over the chain ring Fpm + uFpm by considering three cases: pm ≡ 1 (mod8), pm ≡ 5 (mod8) and pm ≡ 3 (mod4). We also find out the number of codewords in each of these cyclic codes. Besides this, we list some self-dual cyclic codes of length 8ps over Fpm + uFpm. Also we determine μ-constacyclic codes length 8ps over Fpm + uFpm by establishing a one to-one correspondence between cyclic and μ-constacyclic codes.
Fraclet Based Deblurring Method Implemented on Pandaboard (Rev A6)

This paper has proposed the solution of deblurring problem in power insulator images which is a crucial step to be performed before carrying out the fault detection in insulators. The proposed method is based on the decomposition of blurred images using newly designed fractionally delayed wavelet filters (named as fraclets) followed by thresholding of the image coefficients. The reconstructed image effectively removes the blur irrespective of type of blur, e.g., median blur, average blur, bilateral blur and Gaussian blur. The technique has also been implemented on hardware, i.e., Pandaboard (Rev A6) with images captured in laboratory. Two image metrics, i.e., PSNR and SSIM have been considered for quantitatively evaluating the performance of proposed method.
Selection of appropriate architecture and parameters of neural network for images recognition and classification

Abstract In this article the image recognition and classification problem is considered. Further, there will be suggestion of the new way of using a convolutional neural network to solve the considering problem in an example of a particular task, such as the recognition of ischemic stroke on magnetic resonance images. Moreover, it is considering reasons of choosing the image classification and making arguments obtained by a literary analysis of studies affecting the task. Authors have investigated different neural networks architectures and the accuracy depending on training speed, numbers of layers, numbers of epochs and mini sample size. Experiments result is presented in the table form. During the studying, magnetic resonance images of different diseases having same signals with a considering pathology in diffusion-weighted images format has been selected for the training of the convolutional neural network and for getting the most favourable result. In the result, a suitable architecture with defined parameters has been selected to get the highest accuracy.
Identifying Structural Hole Spanners in Social Networks via Graph Embedding

Information Propagation between different communities in social networks are often through structural hole spanners, which makes that detecting structural hole spanners in social networks has received great attention in recent years. Existing SH spanners detection methods usually rely on graph theory knowledge. However, these methods have obvious drawbacks of poor performance and expensive computation for largescale networks. In this work, we propose a novel solution to identify SH spanner based on graph embedding learning. Considering the special topological nature of SH spanner in the network, we design a deep embedding learning method for detecting SH spanner. Extensive experimental results on real world networks demonstrate that our proposed method outperforms several state-of-the-art methods in the SH spanner identification task in terms of several metrics.
Automating News Summarization with Sentence Vectors Offset

Text summaries consist of short versions of texts that convey their key aspects and help readers understand the gist of such texts without reading them in full. Generating such summaries is important for users who must sift through ever-increasing volumes of the content generated on the web. However, generating high-quality summaries is time-consuming for humans and challenging for automated systems, since it involves understanding the semantics of the underlying texts in order to extract key information. In this work, we develop an extractive text summarization method using vector offsets, which we show empirically to be able to summarize texts from an Internet news corpus with an effectiveness competitive with state-of-the-art extractive techniques.
Arabic Text Semantic Graph Representation

Semantic representing of Arabic text can facilitate several language processing applications. It reflects the meaning of the text as it is understood by humans. Semantic graphs can be used to enhance the performance of several natural language processing applications such as question answering and textual entailment. This paper proposed a graph-based Arabic text semantic representation model. The proposed model aims to represent the meaning of Arabic sentences as a rooted acyclic graph. Most of the works on semantic representation have focused on the English language. Furthermore, not much work considered and focused on the Arabic language. In this paper, the proposed model dedicated to the Arabic language and considers its features and challenges.