Reviewerly/exHarmony huggingface dataset:
Distributed Storage Hash Algorithm (DSHA) for File-Based Deduplication in Cloud Computing

Increasing volume of digital data in cloud storage demands more storage space and efficient technique to handle these data. Duplicate is unavoidable while handling huge volume of data. Data Deduplication is an efficient approach in cloud storage environment that utilizes different techniques to deal with duplicate data. Existing systems generate the hash value by using any kind of cryptographic hash algorithms such as MD5 or Secure hash algorithms to implement the De-duplication approach. These algorithms produce fixed length of 128 bit or 160 bit as output respectively in order to identify the presence of duplication. So, an additional memory space is used to store this hash value. In this paper, an efficient Distributed Storage Hash Algorithm (DSHA) has been proposed to lessen the memory space occupied by the hash value which is utilized to identify and discard redundant data in cloud. Experimental analysis shows that the proposed strategy, reduces memory utilization of hash value and improves data read/write performance.
Determination of accuracy value in id3 algorithm with gini index and gain ratio with minimum size for split, minimum leaf size, and minimum gain

Abstract A process that explains and functions to distinguish data classes is called Classification. The use of Gain Ratio in ID3 algorithm is very influential on accuracy compared to the Gini Index, and if the higher the determination of the minimum size of split, the minimum leaf size and the minimum gain, the accuracy results will be greater in the gini index and gain ratio, but from both methods there is an accuracy difference of 27% for each accuracy test. In determining the minimum size of split 2, the minimum leaf size 2 and the minimum gain 0.4 with the value of accuracy at the gain ratio of 85.53% and gini index 58.67%. The minimum size of split 6, the minimum leaf size 3 and the minimum gain 0.8 with accuracy values at gain ratio 64.67% and gini index 60.67%. Then the minimum size of split 12, the minimum leaf size 6 and the minimum gain of 0.16 with the value of accuracy at the gain ratio of 86.00% and the index of 68.00%. While the minimum size of split 48, the minimum leaf size 24 and the minimum gain of 0.64 with the value of accuracy at the gain ratio of 95.33% and the index value of 72.00%. Then the Gain ratio which produces the highest accuracy value compared to the gini index.
Combined group method of data handling models using artificial bee colony algorithm in time series forecasting

Time series forecasting is an important area of forecasting which has gained many attentions from various research areas. In line with its popularity, various models have been introduced for the purpose of producing accurate time series forecasts. Nevertheless, it is difficult to find an ideal model as there is no model that can perform best for all types of data. Recently, combination of forecasts has gained immense popularity in the time series forecasting area. Among the well-known combination technique is the weighted-based approach, where appropriate weights are given based on the performance of each individual model. In this paper, a robust methodology for accurate time series forecasting based on Artificial Bee Colony (ABC) algorithm and four Group Method of Data Handling (GMDH) models, namely GMDH-Polynomial, GMDH-Radial Basis Function, GMDH-Sigmoid and GMDH-Tangent are proposed. In this methodology, the first step was to develop forecasts using individual GMDH models. In the second step, weights for each individual GMDH were calculated heuristically using ABC algorithm. In the final step, the results were aggregated to form a new forecast. For the purpose of evaluating the performance of the proposed model, the model was applied to a real time series data which is the monthly tourist arrivals from Singapore to Malaysia from year 2000 to 2017. Based on the empirical results, the application of nonlinear transfer function such as Tangent function has the potential to improve the performance of GMDH model, while the other functions such as Sigmoid function produced a forecast which is worse than the conventional GMDH-Polynomial. However, the combination of GMDH models using ABC managed to outperform all the individual models. The empirical finding demonstrated the efficiency of Combined GMDH model in time series forecasting, as well as the applicability of implementing optimization algorithm to find appropriate weights for the individual models.
Asymmetric image optical encryption under spatially incoherent illumination

In this paper new method of asymmetric optical encryption is presented. It is based on the double encryption using spatially incoherent illumination: to obtain a ciphertext a plaintext is encoded twice, first time by sender, second time—by recipient. This way no exchange of encryption keys is required. Two variants of this method were analyzed: one in which both encryption procedures are implemented optically and another one in which the first operation is done optically and the second one—numerically. Both variants were numerically simulated and the latter one was implemented optically. Results of numerical and optical experiments demonstrate high quality of decrypted images (decoding error 0.19), thus confirming usability of proposed method.
EC4: ECN and Credit-Reservation Converged Congestion Control

Bursty traffic and thousands of concurrent flows incur inevitable congestion in data center networks (DCNs) and then affect the overall performance. Various transport protocols are developed to mitigate the network congestion, including reactive and proactive protocols. Reactive schemes to handling congestion after congestion arises are common to current DCNs. However, with the growth of scale and link speed, reactive schemes such as DCTCP encounter the significant problem of slow responding to congestion. On the contrary, proactive protocols are designed to avoid congestion, and they have the advantages of zero data loss, fast convergence and low buffer occupancy (e.g., credit-reservation protocols). But in actual deployment scenario, it is hard to guarantee one protocol to be deployed in every server at one time. When credit-reservation protocol is deployed to DCNs step-by-step, the network is converted to multi-protocol state and faces the following fundamental challenges: (i) unfairness, (ii) high bu er occupancy, and (iii) heavy tail delay. Therefore, we propose EC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> , which is for converging ECN-based and credit-reservation protocols with minimal modification. To the best of our knowledge, EC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> is the first to address how to harmonize proactive and reactive congestion control. Targeting the common ECN-based protocol-DCTCP, EC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> leverages the Forward Explicit Congestion Notification (FECN) to deliver realtime congestion information and redefines feedback control. After evaluation, the results show that EC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> e ectively addresses the unfair link allocation. Furthermore, even workloads at 0.6 does not cause buffer overflow, thus largely eliminating the timeouts problem.
A family of constacyclic codes over F_{p^m}[v,w]/ &lt; v^{2}-1,w^{2}-1,vw-wv&gt;

No AccessA family of constacyclic codes over 𝔽pm [υ, w]/⟨υ2 − 1, w2 − 1, υw − wυ⟩Habibul Islam, Ram Krishna Verma and Om PrakashHabibul IslamDepartment of Mathematics, Indian Institute of Technology Patna, Patna - 801106, India, Ram Krishna VermaDepartment of Mathematics, Indian Institute of Technology Patna, Patna - 801106, India and Om PrakashDepartment of Mathematics, Indian Institute of Technology Patna, Patna - 801106, IndiaPublished Online:October 13, 2020pp 198-210https://doi.org/10.1504/IJICOT.2020.110677PDF ToolsAdd to FavouritesDownload CitationsTrack Citations Share this article on social mediaShareShare onFacebookTwitterLinkedInReddit AboutAbstractIn this article, for any odd prime p, we study the constacyclic and skew constacyclic codes of arbitrary length over R = 𝔽pm [υ, w]/⟨υ2 − 1, w2 − 1, υw − wυ⟩. It is shown that the Gray images of constacyclic and skew constacyclic codes of length n over R are quasi-twisted and skew quasi-twisted codes, respectively over 𝔽pm of length 4 n with index 4. Also, the structures of constacyclic and skew constacyclic codes are obtained by decomposition method.Keywordsconstacyclic code, skew constacyclic code, Gray map, self-dual code Previous article Next article FiguresReferencesRelatedDetails Volume 5Issue 3-42020 ISSN: 1753-7703eISSN: 1753-7711 HistoryPublished onlineOctober 13, 2020 Copyright © 2020 Inderscience Enterprises Ltd.Keywordsconstacyclic codeskew constacyclic codeGray mapself-dual codeAuthors and AffiliationsHabibul Islam1 Ram Krishna Verma2 Om Prakash3 1. Department of Mathematics, Indian Institute of Technology Patna, Patna - 801106, India2. Department of Mathematics, Indian Institute of Technology Patna, Patna - 801106, India3. Department of Mathematics, Indian Institute of Technology Patna, Patna - 801106, IndiaPDF download
Recognition of images of Korean characters using embedded networks

Despite the significant success in the field of text recognition, complex and unsolved problems still exist in this field. In recent years, the recognition accuracy of the English language has greatly increased, while the problem of recognition of hieroglyphs has received much less attention. Hieroglyph recognition or image recognition with Korean, Japanese or Chinese characters have differences from the traditional text recognition task. This article discusses the main differences between hieroglyph languages and the Latin alphabet in the context of image recognition. A light-weight method for recognizing images of the hieroglyphs is proposed and tested on a public dataset of Korean hieroglyph images. Despite the existing solutions, the proposed method is suitable for mobile devices. Its recognition accuracy is better than the accuracy of the open-source OCR framework. The presented method of training embedded net bases on the similarities in the recognition data.
Hybrid Sense Classification Method for Large-Scale Word Sense Disambiguation

Word sense disambiguation (WSD) is a task of determining a reasonable sense of a word in a particular context. Although recent studies have demonstrated some progress in the advancement of neural language models, the scope of research is still such that the senses of several words can only be determined in a few domains. Therefore, it is necessary to move toward developing a highly scalable process that can address a lot of senses occurring in various domains. This paper introduces a new large WSD dataset that is automatically constructed from the Oxford Dictionary, which is widely used as a standard source for the meaning of words. We propose a new WSD model that individually determines the sense of the word in accordance with its part of speech in the context. In addition, we introduce a hybrid sense prediction method that separately classifies the less frequently used senses for achieving a reasonable performance. We have conducted comparative experiments to demonstrate that the proposed method is more reliable compared with the baseline approaches. Also, we investigated the adaptation of the method to a realistic environment with the use of news articles.
Single-sample augmentation framework for training Viola-Jones classifiers

In this paper we present a single-sample augmentation framework. The key idea of the framework consists of synthesizing a positive training set from a single natural sample using relevant geometric and pixel intensity transforms. The efficiency of the proposed framework has been demonstrated solving round seal stamp detection problem using Viola-Jones approach on the public “SPODS” dataset. The mentioned image transformations make it possible to simulate different orientation of the stamps, color differences, and distortions caused by stamping process and document aging. The proposed framework can be applied to training various machine learning algorithms for solving computer vision and computed tomography problems.
Supporting Interview Analysis with Autocoding

Interview analysis is a technique employed in qualitative research.Researchers annotate (code) interview transcriptions, often with the help of Computer-Assisted Qualitative Data Analysis Software (CAQDAS).The tools available today largely replicate the manual process of annotation.In this article, we demonstrate how to use natural language processing (NLP) to increase the reproducibility and traceability of the process of applying codes to text data.We integrated an existing commercial machine-learning (ML) based concept extraction service into an NLP pipeline independent of domain specific rules.We applied our prototype in three qualitative studies to evaluate its capabilities of supporting researchers by providing recommendations consistent with their initial work.Unlike rule based approaches, our process can be applied to interviews from any domain, without additional burden to the researcher for creating a new ruleset.Our work using three example data sets shows that this approach shows promise for a real-life application, but further research is needed.
Prose, Verse and Authorship in <i>Dream of the Red Chamber</i>: A Stylometric Analysis

In this study, we provide a quantitative analysis of prose and verse in the classical Chinese novel, Dream of the Red Chamber (DRC), and discuss the implications for the disputed authorship of the novel. Firstly, we examine the amount of verse in across the chapters of DRC, and compare the style of the verse and prose portions of DRC. Secondly, a Principal Component Analysis (PCA) of DRC is performed based on the prose portions of the novel. Lastly, we discuss the implications of our experimental results for authorship attribution as well as descriptive stylistic analysis of DRC. Our authorial analysis largely confirms the findings of some previous studies that the novel has two authors. Meanwhile, stylistic analyses of the prose portions of the novel yield new and interesting results, which demonstrates that stylometric tools can be used to facilitate descriptive studies of classical Chinese literature.
Performance Analysis and Mitigation of Virtual Machine Server by using Naive Bayes Classification

Evolution of virtualization in data center is fast and is being widely used in the world. Usage of virtual machine (VM) in data center (DC) cannot be aside from problems such as: Operating System (OS) problem, virtual network, memory and CPU utilizations. Besides of those problems, utilization of VM can be found in cloud computing technology to make more efficient and its performance should be good entirely. Problem in VM is very complex. It can be found in OS, application and physical server. To keep it in good performance, operational engineer should operate VM monitoring system in order to keep VM run well. This research will use several methods, such as: fuzzy Mamdani, holdout validation and naive Bayes. These methods will then create decision making for VM performance condition.
LS-RQ: A Lightweight and Forward-Secure Range Query on Geographically Encrypted Data

In the era of cloud computing, to achieve convenient location-based service (LBS), consumers such as users, companies, and organizations prefer subcontracting massive geographical data to public clouds after encryption for privacy and security. However, numerous harmful cyber-attacks happen on those public clouds in an unpredicted and hourly manner. To alleviate those concerns, various secure query schemes on the encrypted data have been proposed in the literature. As a fundamental query of LBSs, forward-secure range query has not been well investigated. To address this issue, we propose a lightweight and forward-secure range query (LS-RQ) on geographically encrypted data, which soundly balances between security and efficiency. Promisingly, we design an index mechanism to manage geographical data on the public clouds, while not compromising the privacy of data. Moreover, our LS-RQ schemes provide a convenient approach to range query on geographically encrypted data on-the-fly. We also rigorously prove that LS-RQ is forward-secure. Finally, extensive experimental studies are performed on both real and synthetic datasets. By observation, our LS-RQ schemes are highly efficient in realistic environments. Particularly, on encrypted datasets with about 1000000 geographical data, our solution to secure range query takes strictly less than a second.
Outcome Correlation in Graph Neural Network Regression.

Graph neural networks aggregate features in vertex neighborhoods to learnvector representations of all vertices, using supervision from some labeledvertices during training. The predictor is then a function of the vectorrepresentation, and predictions are made independently on unlabeled nodes. Thiswidely-adopted approach implicitly assumes that vertex labels are independentafter conditioning on their neighborhoods. We show that this strong assumptionis far from true on many real-world graph datasets and severely limitspredictive power on a number of regression tasks. Given that traditionalgraph-based semi-supervised learning methods operate in the opposite manner byexplicitly modeling the correlation in predicted outcomes, this limitation maynot be all that surprising. Here, we address this issue with a simple and interpretable framework thatcan improve any graph neural network architecture by modeling correlationstructure in regression outcome residuals. Specifically, we model the jointdistribution of outcome residuals on vertices with a parameterized multivariateGaussian, where the parameters are estimated by maximizing the marginallikelihood of the observed labels. Our model achieves substantially boosts theperformance of graph neural networks, and the learned parameters can also beinterpreted as the strength of correlation among connected vertices. To allowus to scale to large networks, we design linear time algorithms forlow-variance, unbiased model parameter estimates based on stochastic traceestimation. We also provide a simplified version of our method that makesstronger assumptions on correlation structure but is extremely easy toimplement and provides great practical performance in several cases.
Experimental study and numerical model adequacy assessment of hull structure dynamic response subject to underwater explosion

The purpose of this study was to establish a model adequacy assessment method for dynamic response of hull structure subjected to underwater explosion. A numerical model is established and experiments are carried out to assess the shock response of the hull structure. Experiment data processing method suitable for this study is proposed. The numerical calculation results and the processed experimental data are first compared graphically to initially verify the accuracy of the numerical model. Subject matter expert score for qualitative analysis is obtained from the comparison of time series and the quantitative validation metric is calculated. Four validation metrics are introduced to represent the different time and frequency domain characteristics of the signals. Probabilistic neural network is used to establish the relationship between subject matter expert score and validation metric. The total probability formula is used to synthesize the overall model adequacy score of the model.
Texture Analysis on Side Scan Sonar images using EMD, XCS-LBP and Statistical Co-occurrence

Underwater vehicle captured images can be degraded in quality due to the underwater environment. To overcome this, image enhancement method such as Empirical Mode Decomposition (EMD) and Xtended Central Symmetric Local Binary Pattern (XCS-LBP) are applied on different shades of Side Scan Sonar (SSS) to extract the texture feature. These features can be evaluated using a statistical measure technique called Gray Level Co-occurrence Matrix (GLCM). Finally, it is concluded that the recommended EMD with XCS-LBP has shown superior results compared to other algorithms.
Marker controlled superpixel nuclei segmentation and automatic counting on immunohistochemistry staining images

Abstract Motivation For the diagnosis of cancer, manually counting nuclei on massive histopathological images is tedious and the counting results might vary due to the subjective nature of the operation. Results This paper presents a new segmentation and counting method for nuclei, which can automatically provide nucleus counting results. This method segments nuclei with detected nuclei seed markers through a modified simple one-pass superpixel segmentation method. Rather than using a single pixel as a seed, we created a superseed for each nucleus to involve more information for improved segmentation results. Nucleus pixels are extracted by a newly proposed fusing method to reduce stain variations and preserve nucleus contour information. By evaluating segmentation results, the proposed method was compared to five existing methods on a dataset with 52 immunohistochemically (IHC) stained images. Our proposed method produced the highest mean F1-score of 0.668. By evaluating the counting results, another dataset with more than 30 000 IHC stained nuclei in 88 images were prepared. The correlation between automatically generated nucleus counting results and manual nucleus counting results was up to R2 = 0.901 (P &amp;lt; 0.001). By evaluating segmentation results of proposed method-based tool, we tested on a 2018 Data Science Bowl (DSB) competition dataset, three users obtained DSB score of 0.331 ± 0.006. Availability and implementation The proposed method has been implemented as a plugin tool in ImageJ and the source code can be freely downloaded. Supplementary information Supplementary data are available at Bioinformatics online.
Effectively Using Machine Learning to Expedite System Level Test Failure Debug

In this contribution, a machine learning based algorithm to classify system level test failures is proposed. A system level test failure is first modeled as a point in a multidimensional feature space. Then, such failure is classified into a pre-determined failure class, using the multi-class Support Vector Machine classifier, via the one-versus-one approach. When the proposed algorithm is automatically applied to a population of failing system level test failures, defect part per million failure trends can be produced, and used to prioritize debug activities. The proposed algorithm was successfully implemented in the latest Intel <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Xeon <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> 14nm product line, with a classification accuracy of 80% and an average classification time of 20 seconds.
A Multi-agent Environment Acting as a Personal Tourist Guide

Despite Bulgaria’s unique geographical location and its possession of one of the oldest cultures in Europe and the world, our cultural heritage is relatively unknown outside the country. At present, modern information and communication technologies are not used in their full potential for its promotion and advertising. This article presents an intelligent tourist guide which can support tourists during their visits to cultural and historical sites. Furthermore, the guide takes into account various factors, such as the tourists’ preferences, location, time available, and the presence and location of cultural and historical objects in the area, in order to propose virtual or real cultural and historical routes. Tourists can choose one of the two available options to implement a route—a virtual tour or a real walk. A typical scenario demonstrates the usage of the tourist guide.