Reviewerly/exHarmony huggingface dataset:
CongraPlus: Towards Efficient Processing of Concurrent Graph Queries on NUMA Machines

Graph analytics has been routinely used to solve problems in a wide range of real-life applications. Efficiently processing concurrent graph analytics queries in a multiuser environment is highly desirable as we enter a world of edge device oriented services. Existing research, however, primarily focuses on analyzing a single, large graph dataset and leaves the efficient processing of multiple mid-sized graph analytics queries an intriguing yet challenging open problem. In this work, we investigate the scheduling of concurrent graph analytics queries on NUMA machines. We analyze the performance of several graph analytics algorithms and observe that they have diminishing performance returns as the number of processor cores increases. With concurrent graph analytics, such diminishing returns translate to no or even negative performance gains because of increasing contention on shared hardware resources. We also demonstrate the unpredictability of memory bandwidth usage for numerous graph analytics algorithms, which can lead to sub-optimal performance due to its potential to cause severe memory bandwidth contention. Motivated by the above observations, we propose CongraPlus, a NUMA-aware scheduler that intelligently manages concurrent graph analytics queries for better system throughput and memory bandwidth efficiency. CongraPlus collects the memory bandwidth consumption characteristics of graph analytics queries via offline profiling and eliminates memory bandwidth contention by computing the optimal sequence to launch queries. It also avoids computation resource contention by assigning a certain number of processor cores to the individual queries. We implement CongraPlus in C++ on top of the Ligra graph processing framework and test it with judiciously selected graph processing query combinations. Our results reveal that CongraPlus-based schemes improve query throughput by 30 percent compared to the conventional approach. It also exhibits a much better quality of service and scalability.
Towards an Intelligent Data Analysis System for Decision Making in Medical Diagnostics

Artificial neural networks (ANN) are currently massively used in different fields, especially for very complex problems. In this work we propose an approach to use these systems, and in particular the paradigm of the self-organizing map (SOM) in the medical field. The idea is to use this paradigm to develop an intelligent system able of learning to analyze, classify, and visualize multi-parameter objects in a reduced two-dimensional space in the form of object maps. This approach allows for the visual analysis and interpretation of data to reveal the most informative indicators for decision making. The application in the medical field aims to help make a very good diagnosis to make the most relevant decisions in order to provide appropriate treatment depending on the patient’s state.
An MDA Approach Based on UML and ODM Standards to Support Big Data Analytics Regarding Ontology Development

Today, the large increase in the amount of data produced by different sources and the development of technologies to store and analyze them offer many perspectives for the ontology modeling. The creation of domain ontologies will form the basis for application developers to target business professional contexts, however the future of big data will depend on the use of technologies to model ontologies. With that said, many researches recommend the combination of ontologies and big data approaches as the most efficient way to store, extract and analyze data. In this paper, we present a new methodology supporting ontology modeling for the automatic generation of domain ontologies. We propose a transformation from UML class diagrams to ODM models in agreement with the MDA approach. MDA provides opportunities to present ontology artifacts in an intuitive way by defining them in a high level of abstraction using the UML graphical syntax. With the MDA process, the ontology represented as a class diagram will automatically be generated through an ODM metamodel. In this proposal, we founded on an analytical survey. To validate our proposal, we applied it to an e-learning domain ontology.
Market Share Strategies for Cloud Computing Providers

The cloud computing industry is characterized by a small number of dominant providers and a host of small-to-medium providers. As the demand grows, additional providers are expected to enter the market. The need for 24/7 support and competitive pricing allows established providers to maintain defensible industry positions. This paper examines the impact of adopting growth and equilibrium market share strategies by cloud providers. Capacity planning remains a challenging problem given uncertain growth and demand, and providers seeking to pursue different market share strategies must adopt different tactics. Results from a system dynamics simulation indicate that market share growth can be achieved at the cost of overall revenue. A reactive strategy to maintain market share requires more price manipulation, but results in higher overall income. Simulations are conducted for a variety of economic conditions, with a view to providing cloud service providers broad guidance for cloud capacity building strategies.
Mining Top-k Regular High-Utility Itemsets in Transactional Databases

Mining high-utility itemsets is an important task in the area of data mining. It involves exponential mining space and returns a very large number of high-utility itemsets. In a real-time scenario, it is often sufficient to mine a small number of high-utility itemsets based on user-specified interestingness. Recently, the temporal regularity of an itemset is considered as an important interesting criterion for many applications. Methods for finding the regular high utility itemsets suffers from setting the threshold value. To address this problem, a novel algorithm called as TKRHU (Top k Regular High Utility Itemset) Miner is proposed to mine top-k high utility itemsets that appears regularly where k represents the desired number of regular high itemsets. A novel list structure RUL and efficient pruning techniques are developed to discover the top-k regular itemsets with high profit. Efficient pruning techniques are designed for reducing search space. Experimental results show that proposed algorithm using novel list structure achieves high efficiency in terms of runtime and space.
A stochastic model of cyber attacks with imperfect detection

This paper introduces a cyber security model with imperfect detection, in which one attacker launches multiple attacks against the target with adjusted strength based on the previous attacking outcome. Several sufficient conditions leading to the usual stochastic order on the first time to observe a truly compromised target, to observe a successful attack and to compromise the target are developed, respectively. The probability for the target to be truly compromised before observing some number of successful attacks is proved to increase (decrease) in the attacking (defense) strength. Monte Carlo simulations are also conducted to empirically illustrate the theoretical results.
Developing an electronic glove based on fuzzy logic for mobile robot control

An electronic glove is a sensor-equipped glove for detecting changes in the motion or the bending of a finger. The function of an electronic glove can replace the function of a remote control, especially for people with disabilities. In this study, we designed and realized a glove equipped with a f lex sensor on each finger to detect the bending of the finger and used a mobile robot as the plant of the electronic glove. The system works by detecting finger-bending in the right hand and processing it into the commands stop, forward, backward, forward left and forward right. The flex sensor resistance value caused by the finger-bending is used as an input for fuzzy logic control and produces the output to control the mobile robot. The flex sensor is capable of varying values between normal conditions of 27.2 kΩ up to a maximum of 46 kΩ. The lowest speed of the mobile robot is 0.75 m/s, with a maximum speed of 2.17 m/s. The commands from the electronic glove are sent to the mobile robot wirelessly with a maximum distance between electronic glove and the mobile robot of 200 m in an obstacle-free situation. Based on the experimental results, 100% of the orders were executed according to the design. The use of fuzzy logic ensures that every bending change is accompanied by a change in speed, so that mobile robot movements become smoother.
A Novel Wavelet-Based Image Defogging Using Dark Channel Prior and Guided Filter

Recently, fog removal from images and videos has got tremendous importance in image and video processing for object detection, tracking and surveillance system, advance image editing, and many more which are poorly affected by the fog. A lot of works have been proposed so far for removing fog from image and video. It includes different methods like contrast enhancement, local color line model based and many more fog removal technique. In this paper, we propose a novel defogging method where guided filter based dark channel prior (DCP) is applied on low–low (LL) band of discrete wavelet transformation (DWT) coefficient of the intensity rectified image which is obtained after homomorphic filtering. To improve the sharpness of the output, unsharp masking (USM) is applied on high–low (HL) and low–high (LH) bands of the DWT coefficient. Finally, sharp and high contrast output is generated by applying contrast limited adaptive histogram equalization technique (CLAHE) to the inverse transformed image. The proposed method improves the overall quality of the defogged output image with respect to contemporary methods. The measured quality metrics of the proposed method are compared with some recent works. The quantitative and qualitative results confirm the claims.
Acquisition of the Chinese Particle le by L2 Learners: A Corpus-Based Approach

The Chinese particle le (了) has proven to be challenging for L2 learners to acquire, both because it may function as either a perfective aspect marker or a sentence-final modal particle and because its usage is subject to various semantic, syntactic, prosodic, and discourse constraints. Previous research into the development of knowledge of the uses of le and the order of acquisition of its functions and meanings has yielded inconsistent results. Furthermore, due to the limited data available, previous studies have usually employed written corpus data produced by a small number of learners from a specific L1 background. Utilizing the spoken subcorpus of the large-scale Guangwai-Lancaster Chinese Learner Corpus, this study closely examines the uses of le by learners of Chinese from diverse L1 backgrounds as well as the developmental pattern of their acquisition of this particle. Results demonstrate that learners generally use le in speech with a low frequency and a high degree of accuracy. Significant increase in frequency of use is observed between beginner and intermediate learners, while that in accuracy is observed between intermediate and advanced learners. Evidence from the current study does not support a specific acquisition order for the basic functions of le. Learner errors primarily involve overuse of the particle in conjunction with statives and may be largely attributed to learners’ deficient knowledge of the constraints of its usage. Findings of our investigation have useful implications for the instruction of the particle le.
Hardware architecture for an audio fingerprinting system

Collusion-resistant fingerprinting seems to be an effective solution to piracy because it allows media owners to detect any unauthorized copies and trace them back to dishonest users. When fingerprinting techniques are used by a digital media store, where massive distribution of audio and video is common, real-time multi-channel performance is essential. Digital signal processors and multi-core workstations have been used to implement watermarking schemes and they can provide audio signal processing in real-time. In addition, custom hardware architectures offer the possibility of fully exploiting the parallelism inherent in this type of algorithm for more demanding scenarios. We present an efficient hardware implementation of a spread spectrum algorithm-based collusion-resistant fingerprinting system in the modulated complex lapped transform domain. The results of implementing the proposed hardware architecture on a field programmable gate array are presented and discussed.
Improvement of MRI Brain Image Segmentation Using Fuzzy Unsupervised Learning

Background: Magnetic resonance imaging (MRI) plays an important role in clinical diagnosis. The ability of fuzzy c-mean (FCM) algorithm in segmenting MR images has been proven. Some MR images are contaminated with noise. FCM performance is degraded in noisy images. Several efforts are done to overcome this weakness. Objectives: The aim of this study was to propose a new method for MR image segmentation which is more resistant than other methods when noisy MR images are confronted. Materials and Methods: In this study, simulated brain database prepared by BrainWeb was be used for analysis. First FCM and its improvements were analysed and their ability in segmenting noisy MR images were evaluated. Next, knowing that applying genetic algorithm on improver fuzzy c-mean (IFCM) could improve its performance, a new segmentation method was proposed by applying particle swarm optimization on IFCM. Results: The proposed algorithm was applied on some intentionally noise-added MR images. Similarity between the segmented image and the original one was measured using Dice index. Other off-the-shelf algorithms were also tested in the same conditions. The indices were presented together. In order to compare the algorithmsâ performances, the experiments were repeated using different noisy images. Conclusion: The obtained results show that the proposed algorithms have better performance in segmenting noisy MR images than existing methods.
Introducing <i>Quantum Engineering</i>

Quantum EngineeringVolume 1, Issue 1 e6 EDITORIAL Introducing Quantum Engineering Gui-Lu Long, Corresponding Author Gui-Lu Long gllong@tsinghua.edu.cn Tsinghua University, Beijing, China Gui-Lu Long, Tsinghua University, Beijing 100084, China. Email: gllong@tsinghua.edu.cnSearch for more papers by this authorPeter Mueller, Peter Mueller IBM Zurich Research Laboratory, Rüschlikon, SwitzerlandSearch for more papers by this authorJack Patterson, Jack Patterson Wiley, Oxford, UKSearch for more papers by this author Gui-Lu Long, Corresponding Author Gui-Lu Long gllong@tsinghua.edu.cn Tsinghua University, Beijing, China Gui-Lu Long, Tsinghua University, Beijing 100084, China. Email: gllong@tsinghua.edu.cnSearch for more papers by this authorPeter Mueller, Peter Mueller IBM Zurich Research Laboratory, Rüschlikon, SwitzerlandSearch for more papers by this authorJack Patterson, Jack Patterson Wiley, Oxford, UKSearch for more papers by this author First published: 04 February 2019 https://doi.org/10.1002/que2.6Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume1, Issue1March 2019e6 RelatedInformation
Comparing multiple categories of feature selection methods for text classification

Selecting effective features from data sets is a particularly important part in text classification, data mining, pattern recognition, and artificial intelligence. Feature selection (FS) is capable of excluding irrelevant features for the classification task and reducing the dimensionality of data sets, which help us better understand data. Through FS selection, the performance of machine learning techniques is improved, and computation requirement is minimized. Thus far, a large number of FS methods have been proposed, whereas the most practically effective one has not been found. Although it is conceivable that different categories of FS methods follow different criteria for evaluating variables, rare studies have focused on evaluating various categories of FS methods. This article first lists thirteen superior FS methods under five different categories and focuses on evaluating and comparing the effectiveness and general versatility of these methods. The thirteen FS methods were ranked using rank aggregation method. Subsequently, the best five FS methods were elected to perform multi-class classifications. Support vector machine served as the classifier. Different languages, different numbers of selected features, and different performance measures were employed to measure the effectiveness and general versatility of these methods together. The analysis results suggest that Mahalanobis distance is the best method on the whole.
Requirements Engineering in the New Product Development Process: A Structured Literature Review

The New Product Development Process (NDP) is noticed as a critical factor to maintain the competitiveness in organizations. New products allow us to focus on taking a competitive position. Also, the first and most important reason for any new product development is to provide new value to the customer. In this sense, the Requirements Engineering (RE) approach plays a vital role within the New Product Development Process (NDP) lifecycle, once the product performance and its acceptability on the market depend on how RE is integrated into the product development. In this way, this paper aims to identify the proposed solutions and trends regarding RE, through a literature review based on studies produced in the last five years. Hence, the existing gaps in the literature about this theme are presented and analyzed. In order to achieve these objectives, the present study was based on ProKnow-C process (Knowledge Development Process — Constructivist).
Adaptive background mixture model with spatio-temporal samples

Abstract Background modeling is a popular issue in computer vision society during the past few decades. We analyze the usual pixel-level approach and build an adaptive background mixture model with spatio-temporal samples called STS. These samples are described based on a joint domain-range formulation and the pixel is classified with a weight marginal probability density function. On-line update including proximal sample selection, parameter learning, and neighbor update are used to regulate the model to adapt to varying scenes. We perform a bootstrap modeling experiment, and results show that our method outperforms other widely adopted techniques which use only intensity or region description as pixel feature with higher F-measure on most datasets. And our method is a matched rival to combination methods which use both pixel intensity and region description.
A Heuristic Qubit Placement Strategy for Nearest Neighbor Realization in 2D Architecture

Recently, quantum computing has received massive attention of the researchers due to the advantages it offers in solving some problems efficiently compared to conventional computing. But there are several design challenges that need to be satisfied for various quantum technologies to perform reliable quantum operation. One such essential requirement demands to maintain the neighborhood organization of the operating qubits, referred to as the nearest neighbor (NN) constraint. This can be settled through insertion of SWAP gates which helps to synthesize a NN-compliant design by exchanging the positions of the qubits. Consequently, the cost overhead of the circuit enhances due to SWAP gate insertion as the number of gates in the circuit increases, thereby exploitation of various approaches to address this issue has turned out to be essential of late. In this regard, here we have introduced an improved heuristic design approach for the synthesis of a two dimensional NN-compliant circuit with reduced cost overhead. The approach has been executed in three phases of qubit selection, qubit placement and SWAP gate insertion. Initially, the qubits have been chosen for placement based on some cost metric estimation and then organized them on the grid locations in a specific order in the second phase. Lastly, SWAP gate insertion phase has been exercised to make qubits adjacent. It has been observed that our method has performed comparatively better than the previous works after carrying out experimental evaluations over a wide range of benchmark specifications.
No More than What I Post: Preventing Linkage Attacks on Check-in Services

With the flourishing of location based social networks, posting check-ins has become a common practice to document one's daily life. Users usually do not consider check-in records as violations of their privacy. However, through analyzing two real-world check-in datasets, our study shows that check-in records are vulnerable to linkage attacks. To address this problem, we design a partition-and-group framework to integrate the information of check-ins and additional mobility data to attain a novel privacy criterion - kt, l-anonymity. It ensures adversaries with arbitrary background knowledge cannot use check-ins to re-identify users in other anonymous datasets or learning unreported mobility records. The proposed framework achieves favorable performance against state-of-art baseline in terms of improving check-in utility by 24% ~ 57% while providing stronger privacy guarantee at the same time. We believe this study will open a new angle in attaining both privacy-preserving and useful check-in services.
Structural Setting of Western Mali Insights from Magnetic Data Analysis

Within the Western African Craton, the Kedougou-Kenieba Inlier lodges the Senegalo-Malian Shearing-Zone, with its secondary structures, control the gold mineralization. The magnetic method is of great interest in structures determination and characterization. Many techniques were applied to the airborne magnetic data (Field Residual, Reduction to Equator, Derivations, and Euler Deconvolution) in order to reveal magnetic structures related to mineralization. Lineaments of different directions (N–S, NE–SW, NW–SE and E–W) have been identified and coincided with the known faults and intermediate to basic magmatic dykes. These lineaments have low magnetic intensity signatures, generally negative. A large NE–SW overlapping corridor contains the majority of the Artisanal Mining Sites testifying the controllability of gold deposits by the NE–SW structures. Euler solution led to a brief estimate of the depth of different structures. This led to propose additional targets so as to help exploration activities in Western Mali.
An Attribute-Based Assured Deletion Scheme in Cloud Computing

With the rapid development of cloud computing, it has been increasingly attractive for individuals and groups to store and share data via cloud storage. Once stored in the third-party cloud storage service providers, the privacy and integrity of outsourced data should be attached with more attention as a challenging task. This article presents the attribute-based assured deletion scheme (AADS) which aims to protect and assuredly delete outsourced data in cloud computing. It encrypts outsourced data files with standard cryptographic techniques to guarantee the privacy and integrity, and assuredly deletes data upon revocations of attributes. AADS could be applied to solve important security problems by supporting fine-grained attribute-based policies and their combinations. According to the comparison and analysis, AADS provides efficient data encryption and flexible attribute-based assured deletion for cloud-stored data with an acceptable concession in performance cost.