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  • Cost- and Time-Based Data Deployment for Improving Scheduling Efficiency in Distributed Clouds
    Comput. J. (IF 1.077) Pub Date : 2020-09-29
    Chunlin Li; Yihan Zhang; Xiaomei Qu; Youlong Luo

    In recent years, with the continuous development of internet of things and cloud computing technologies, data intensive applications have gotten more and more attention. In the distributed cloud environment, the access of massive data is often the bottleneck of its performance. It is very significant to propose a suitable data deployment algorithm for improving the utilization of cloud server and the

  • A Blockchain-Based Framework for IoT Data Monetization Services
    Comput. J. (IF 1.077) Pub Date : 2020-09-24
    Muhammad Salek Ali; Massimo Vecchio; Fabio Antonelli

    Within internet of things (IoT) research, there is a growing interest in leveraging the decentralization properties of blockchains, towards developing IoT authentication and authorization mechanisms that do not inherently require centralized third-party intermediaries. This paper presents a framework for sharing IoT data in a decentralized and private-by-design manner in exchange for monetary services

  • Efficient Group ID-Based Encryption With Equality Test Against Insider Attack
    Comput. J. (IF 1.077) Pub Date : 2020-09-23
    Yunhao Ling; Sha Ma; Qiong Huang; Ximing Li; Yijian Zhong; Yunzhi Ling

    ID-based encryption with equality test (IBEET) allows a tester to compare ciphertexts encrypted under different public keys for checking whether they contain the same message. In this paper, we first introduce group mechanism into IBEET and propose a new primitive, namely group ID-based encryption with equality test (G-IBEET). With the group mechanism: (1) group administrator can authorize a tester

  • Corrigendum to: Multi-Swarm Cuckoo Search Algorithm with Q-Learning Model
    Comput. J. (IF 1.077) Pub Date : 2020-09-23
    Juan Li; Dan-dan Xiao; Ting Zhang; Chun Liu; Yuan-xiang Li; Gai-ge Wang

    AbstractFunctional encryption (FE) can provide a fine-grained access control on the encrypted message. Therefore, it has been applied widely in security business. The previous works about functional encryptions most focused on the deterministic functions. The randomized algorithm has wide application, such as securely encryption algorithms against chosen ciphertext attack, privacy-aware auditing. Based

  • Transportation Index Computation: A Development Theme Mining-Based Approach
    Comput. J. (IF 1.077) Pub Date : 2020-09-15
    Gang Han; Menggang Li; Yiduo Mei; Deming Li

    In order to comprehensively evaluate the achievements of the 'Belt and Road' in integrated transportation, researchers need to optimize the method of generating evaluation indices and construct the framework structure of the 'Belt and Road' transportation index system. This paper used GDELT database as data source and obtained full text data of English news in 25 countries along ‘the Belt and Road’

  • An Opinion Spread Prediction Model With Twitter Emotion Analysis During Algeria’s Hirak
    Comput. J. (IF 1.077) Pub Date : 2020-09-14
    Ahlem Drif; Khalil Hadjoudj

    Social media is believed to have played a central role in the mobilization of Algerian citizens to peaceful protest against their country’s corrupt regime. Since no one foresaw these protests (called ‘The Revolution of Smiles’ or ‘The Hirak Movement’), this research conducted social media analysis to elicit vital insights about both the intensity of sentiment and the influence of social media on this

  • Retrieving Semantic Image Using Shape Descriptors and Latent-Dynamic Conditional Random Fields
    Comput. J. (IF 1.077) Pub Date : 2020-09-12
    Mahmoud Elmezain; Hani M Ibrahem

    This paper introduces a new approach to semantic image retrieval using shape descriptors as dispersion and moment in conjunction with discriminative classifier model of latent-dynamic conditional random fields (LDCRFs). The target region is firstly localized via the background subtraction model. Then the features of dispersion and moments are employed to k-means clustering to extract object’s feature

  • The Conditional Reliability Evaluation of Data Center Network BCDC
    Comput. J. (IF 1.077) Pub Date : 2020-09-08
    Mengjie Lv; Baolei Cheng; Jianxi Fan; Xi Wang; Jingya Zhou; Jia Yu

    As the number of servers in a data center network (DCN) increases, the probability of server failures is significantly increased. Traditional connectivity is an important metric to measure the reliability of DCN. However, the traditional connectivity of a DCN based on the condition of arbitrary faulty servers is generally lower. Therefore, it is important to increase the connectivity of a DCN by adding

  • Detection of 2D and 3D Video Transitions Based on EEG Power
    Comput. J. (IF 1.077) Pub Date : 2020-09-07
    Negin Manshouri; Mesut Melek; Temel Kayıkcıoglu

    Despite the long and extensive history of 3D technology, it has recently attracted the attention of researchers. This technology has become the center of interest of young people because of the real feelings and sensations it creates. People see their environment as 3D because of their eye structure. In this study, it is hypothesized that people lose their perception of depth during sleepy moments

  • A Machine Learning-Based Model to Evaluate Readability and Assess Grade Level for the Web Pages
    Comput. J. (IF 1.077) Pub Date : 2020-09-07
    Muralidhar Pantula; K S Kuppusamy

    Evaluating readability of web documents has gained attention due to several factors such as improving the effectiveness of writing and to reach a wider spectrum of audience. Current practices in this direction follow several statistical measures in evaluating readability of the document. In this paper, we have proposed a machine learning-based model to compute readability of web pages. The minimum

  • Related Blogs’ Summarization With Natural Language Processing
    Comput. J. (IF 1.077) Pub Date : 2020-09-05
    Niyati Baliyan; Aarti Sharma

    There is plethora of information present on the web, on a given topic, in different forms i.e. blogs, articles, websites, etc. However, not all of the information is useful. Perusing and going through all of the information to get the understanding of the topic is a very tiresome and time-consuming task. Most of the time we end up investing in reading content that we later understand was not of importance

  • Querying Tenuous Group in Attributed Networks
    Comput. J. (IF 1.077) Pub Date : 2020-08-29
    Yang Li; Heli Sun; Liang He; Jianbin Huang; Jiyin Chen; Hui He; Xiaolin Jia

    Finding groups in networks is very common in many practical applications, and most work mainly focus on dense groups. However, in scenarios like reviewer selection or weak social friends recommendation, we need to emphasize the privacy of individuals or minimize the possibility of information dissemination. So the internal relationship between individuals should be as tenuous as possible, but existing

  • S-DolLion-MSVNN: A Hybrid Model for Developing the Super-Resolution Image From the Multispectral Satellite Image
    Comput. J. (IF 1.077) Pub Date : 2020-08-28
    Anil B Gavade; Vijay S Rajpurohit

    Super-resolution offers a new image with high resolution from the low-resolution (LR) image that is highly employed for the numerous remote sensing applications. Most of the existing techniques for formation of the super-resolution image exhibit the loss of quality and deviation from the original multi-spectral LR image. Thus, this paper aims at proposing an efficient super-resolution method using

  • Machine Learning Model for Multi-View Visualization of Medical Images
    Comput. J. (IF 1.077) Pub Date : 2020-08-27
    Nitesh Pradhan; Vijaypal Singh Dhaka; Geeta Rani; Himanshu Chaudhary

    Imaging techniques such as X-ray, computerized tomography scan and magnetic resonance imaging are useful in the correct diagnosis of a disease or deformity in the organ. Two-dimensional imaging techniques such as X-ray give a clear picture of simple bone deformity but fail in visualizing multiple fractures in a bone. Moreover, these lack in providing a multi-angle view of a bone. Three-dimensional

  • A Modern View on Forward Security
    Comput. J. (IF 1.077) Pub Date : 2020-08-24
    Colin Boyd; Kai Gellert

    Forward security ensures that compromise of entities today does not impact the security of cryptographic primitives employed in the past. Such a form of security is regarded as increasingly important in the modern world due to the existence of adversaries with mass storage capabilities and powerful infiltration abilities. Although the idea of forward security has been known for over 30 years, current

  • Work-Efficient Parallel Non-Maximum Suppression Kernels
    Comput. J. (IF 1.077) Pub Date : 2020-08-21
    David Oro; Carles Fernández; Xavier Martorell; Javier Hernando

    In the context of object detection, sliding-window classifiers and single-shot convolutional neural network (CNN) meta-architectures typically yield multiple overlapping candidate windows with similar high scores around the true location of a particular object. Non-maximum suppression (NMS) is the process of selecting a single representative candidate within this cluster of detections, so as to obtain

  • Multichannel Ordered Contention MAC Protocol For Underwater Wireless Sensor Networks
    Comput. J. (IF 1.077) Pub Date : 2020-08-20
    Alak Roy; Nityananda Sarma

    Recent advancement in hardware and the availability of bandwidth open scope for multichannel communication in underwater wireless sensor networks. Utilizing multiple channels for data and control packets in bursty traffic networks can reduce collisions due to several contending nodes. The paper presents a synchronous reservation-based multichannel ordered contention MAC protocol for deep underwater

  • Breast Cancer Diagnosis Using Multi-Stage Weight Adjustment In The MLP Neural Network
    Comput. J. (IF 1.077) Pub Date : 2020-08-19
    Amin Rezaeipanah; Gholamreza Ahmadi

    Breast cancer is the most common kind of cancer, which is the cause of death among the women worldwide. There is evidence that shows that the early detection and treatment can increase the survival rate of patients who suffered this disease. Therefore, this paper proposes an automatic breast cancer diagnosis technique using a genetic algorithm for simultaneous feature selection and parameter optimization

  • Wrapper-Enabled Feature Selection and CPLM-Based NARX Model for Stock Market Prediction
    Comput. J. (IF 1.077) Pub Date : 2020-08-19
    Dattatray P Gandhmal; K Kumar

    The prices in the stock market are dynamic in nature, thereby pretend as a hectic challenge to the sellers and buyers in predicting the trending stocks for the future. To ensure effective prediction of the stock market, the chronological penguin Levenberg–Marquardt-based nonlinear autoregressive network (CPLM-based NARX) is employed, and the prediction is devised on the basis of past and the recent

  • Similarity of Sentences With Contradiction Using Semantic Similarity Measures
    Comput. J. (IF 1.077) Pub Date : 2020-08-19
    M Krishna Siva Prasad; Poonam Sharma

    Short text or sentence similarity is crucial in various natural language processing activities. Traditional measures for sentence similarity consider word order, semantic features and role annotations of text to derive the similarity. These measures do not suit short texts or sentences with negation. Hence, this paper proposes an approach to determine the semantic similarity of sentences and also presents

  • A Genetically Based Combination of Visual Saliency and Roughness for FR 3D Mesh Quality Assessment: A Statistical Study
    Comput. J. (IF 1.077) Pub Date : 2020-08-17
    Anass Nouri; Christophe Charrier; Olivier Lézoray

    In this paper, we present a full-reference quality assessment metric based on the information of visual saliency. The saliency information is provided under the form of degrees associated to each vertex of the surface mesh. From these degrees, statistical attributes reflecting the structures of the reference and distorted meshes are computed. These are used by four comparisons functions genetically

  • An Adaptively Secure Functional Encryption for Randomized Functions
    Comput. J. (IF 1.077) Pub Date : 2020-08-08
    Muhua Liu; Ping Zhang

    Functional encryption (FE) can provide a fine-grained access control on the encrypted message. Therefore, it has been applied widely in security business. The previous works about functional encryptions most focused on the deterministic functions. The randomized algorithm has wide application, such as securely encryption algorithms against chosen ciphertext attack, privacy-aware auditing. Based on

  • KORGAN: An Efficient PKI Architecture Based on PBFT Through Dynamic Threshold Signatures
    Comput. J. (IF 1.077) Pub Date : 2020-08-12
    Murat Yasin Kubilay; Mehmet Sabir Kiraz; Haci Ali Mantar

    During the past decade, several misbehaving certificate authorities (CAs) have issued fraudulent TLS certificates allowing man-in-the-middle (MITM) kinds of attacks that result in serious security incidents. In order to avoid such incidents, Yakubov et al. ((2018) A blockchain-based PKI management framework. NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium, Taipei, Taiwan, April

  • Lost In Translation: Exposing Hidden Compiler Optimization Opportunities
    Comput. J. (IF 1.077) Pub Date : 2020-08-07
    Kyriakos Georgiou; Zbigniew Chamski; Andres Amaya Garcia; David May; Kerstin Eder

    Existing iterative compilation and machine learning-based optimization techniques have been proven very successful in achieving better optimizations than the standard optimization levels of a compiler. However, they were not engineered to support the tuning of a compiler’s optimizer as part of the compiler’s daily development cycle. In this paper, we first establish the required properties that a technique

  • Security Analysis of the First Certificateless Proxy Signature Scheme Against Malicious-But-Passive KGC Attacks
    Comput. J. (IF 1.077) Pub Date : 2020-08-05
    Xi-Jun Lin; Qihui Wang; Lin Sun; Zhen Yan; Peishun Liu

    Recently, Yang et al. proposed the first certificateless proxy signature scheme against malicious-but-passive key generation center (MKGC) attacks. They proved that their scheme can resist the MKGC attacks in the standard model. In this paper, we point out that their scheme cannot achieve this security because the adversary can forge valid signatures.

  • Improved Tactile Perception of 3D Geometric Bumps Using Coupled Electrovibration and Mechanical Vibration Stimuli
    Comput. J. (IF 1.077) Pub Date : 2020-08-05
    Xiaoying Sun; Chen Zhang; Guohong Liu

    At present, the tactile perception of 3D geometric bumps (such as sinusoidal bumps, Gaussian bumps, triangular bumps, etc.) on touchscreens is mainly realized by mapping the local gradients of rendered virtual surfaces to lateral electrostatic friction, while maintaining the constant normal feedback force. The latest study has shown that the recognition rate of 3D visual objects with electrovibration

  • Interpolation Attacks on Round-Reduced Elephant, Kravatte and Xoofff
    Comput. J. (IF 1.077) Pub Date : 2020-08-05
    Haibo Zhou; Rui Zong; Xiaoyang Dong; Keting Jia; Willi Meier

    We introduce an interpolation attack using the Moebius Transform. This can reduce the time complexity to get a linear system of equations for specified intermediate state bits, which is general to cryptanalysis of some ciphers with update function of low algebraic degree. Along this line, we perform an interpolation attack against Elephant-Delirium, a round 2 submission of the ongoing national institute

  • A Cotton Disease Diagnosis Method Using a Combined Algorithm of Case-Based Reasoning and Fuzzy Logic
    Comput. J. (IF 1.077) Pub Date : 2020-08-05
    Yuhong Dong; Zetian Fu; Stevan Stankovski; Yaoqi Peng; Xinxing Li

    In this study, a cotton disease diagnosis method that uses a combined algorithm of case-based reasoning (CBR) and fuzzy logic was designed and implemented. It focuses on the prevention, diagnosis and control of diseases affecting cotton production in China. Conventional methods of disease diagnosis are primarily based on CBR with reference to user-provided symptoms; however, in most cases, user-provided

  • Subgraph Reliability of Alternating Group Graph With Uniform and Nonuniform Vertex Fault-Free Probabilities
    Comput. J. (IF 1.077) Pub Date : 2020-08-05
    Yanze Huang; Limei Lin; Li Xu

    As the size of a multiprocessor system grows, the probability that faults occur in this system increases. One measure of the reliability of a multiprocessor system is the probability that a fault-free subsystem of a certain size still exists with the presence of individual faults. In this paper, we use the probabilistic fault model to establish the subgraph reliability for |$AG_n$|⁠, the |$n$|-dimensional

  • An Energy-Efficient Step-Counting Algorithm for Smartphones
    Comput. J. (IF 1.077) Pub Date : 2020-08-04
    Runze Yang; Jian Song; Baoqi Huang; Wuyungerile Li; Guodong Qi

    Step counting is not only the key component of pedometers (which is a fundamental service on smartphones), but is also closely related to a range of applications, including motion monitoring, behavior recognition, indoor positioning and navigation. Due to the limited battery capacity of current smartphones, it is of great value to reduce the energy consumption of such a popular service. Therefore,

  • Improving Matsui’s Search Algorithm For The Best Differential/Linear Trails And Its Applications For DES, DESL And GIFT
    Comput. J. (IF 1.077) Pub Date : 2020-08-04
    Fulei Ji; Wentao Zhang; Tianyou Ding

    Automatic search methods have been widely used for cryptanalysis of block ciphers, especially for the most classic cryptanalysis methods—differential and linear cryptanalysis. However, the automatic search methods, no matter based on MILP, SMT/SAT or CP techniques, can be inefficient when the search space is too large. In this paper, we propose three new methods to improve Matsui’s branch-and-bound

  • Considering Fine-Grained and Coarse-Grained Information for Context-Aware Recommendations
    Comput. J. (IF 1.077) Pub Date : 2020-08-04
    Yiqin Luo; Yanpeng Sun; Liang Chang; Tianlong Gu; Chenzhong Bin; Long Li

    In context-aware recommendation systems, most existing methods encode users’ preferences by mapping item and category information into the same space, which is just a stack of information. The item and category information contained in the interaction behaviours is not fully utilized. Moreover, since users’ preferences for a candidate item are influenced by the changes in temporal and historical behaviours

  • Dynamic Frequency Scaling of a Single-Core Processor Using Machine Learning Paradigms
    Comput. J. (IF 1.077) Pub Date : 2020-08-04
    Sukhmani K Thethi; Ravi Kumar

    Dynamic frequency scaling (DFS) is one of the most important approaches for on-the-fly power optimization in modern-day processors. Owing to the trend of chip size shrinkage and increasing the complexity of system design, the problem of achieving an efficient DFS depends upon multi-parametric, non-linear optimization. Hence, it becomes extremely important to identify an optimal underclocking frequency

  • Thermal Image-Based Object Classification for Guiding the Visually Impaired
    Comput. J. (IF 1.077) Pub Date : 2020-08-04
    V Nancy; G Balakrishnan

    Thermal sensors are now being an emerging technology in image processing applications such as face recognition, fault detection, object detection and classification, navigation, etc. Owing to its versatility, it has been an influential concern for many researchers recently. Thermal sensors have proficiency of sensing the object heedless of the lighting conditions. Due to this added leverage of thermal

  • Determining Exact Solutions for Structural Parameters on Hierarchical Networks With Density Feature
    Comput. J. (IF 1.077) Pub Date : 2020-07-30
    Fei Ma; Ping Wang

    The problem of determining closed-form solutions for some structural parameters of great interest on networked models is meaningful and intriguing. In this paper, we propose a family of networked models |$\mathcal{G}_{n}(t)$| with hierarchical structure where |$t$| represents time step and |$n$| is copy number. And then, we study some structural parameters on the proposed models |$\mathcal{G}_{n}(t)$|

  • Lemuria: A Novel Future Crop Prediction Algorithm Using Data Mining
    Comput. J. (IF 1.077) Pub Date : 2020-07-30
    M Tamil Selvi; B Jaison

    Agriculture exhibitions an important role in the progression and enlargement of the economy of any country. Prediction of crop yield will be useful for farmers, but it is difficult to predict crop yield because of the climatic factors such as rainfall, soil factors and so on. To tackle these issues, we are implementing a novel algorithm called Lemuria by applying data mining in agriculture especially

  • On Performance Improvement Of Reversible Data Hiding With Contrast Enhancement
    Comput. J. (IF 1.077) Pub Date : 2020-07-25
    Haishan Chen; Junying Yuan; Wien Hong; Jiangqun Ni; Tung-Shou Chen

    Reversible data hiding (RDH) with contrast enhancement (RDH-CE) is a special type of RDH in improving the subjective visual perception by enhancing the image contrast during the process of data embedding. In RDH-CE, data hiding is achieved via pairwise histogram expansion, and the embedding rate can be increased by performing multiple cycles of histogram expansions. However, when embedding rate gets

  • Effective Link Prediction with Topological and Temporal Information using Wavelet Neural Network Embedding
    Comput. J. (IF 1.077) Pub Date : 2020-07-23
    Xian Mo; Jun Pang; Zhiming Liu

    Temporal networks are networks that edges evolve over time, hence link prediction in temporal networks aims at inferring new edges based on a sequence of network snapshots. In this paper, we propose a graph wavelet neural network (TT-GWNN) framework using topological and temporal features for link prediction in temporal networks. To capture topological and temporal features, we develope a second-order

  • On Enabling Attribute-Based Encryption to Be Traceable Against Traitors
    Comput. J. (IF 1.077) Pub Date : 2020-07-23
    Zhen Liu; Qiong Huang; Duncan S Wong

    Attribute-based encryption (ABE) is a versatile one-to-many encryption primitive, which enables fine-grained access control over encrypted data. Due to its promising applications in practice, ABE schemes with high efficiency, security and expressivity have been continuously emerging. On the other hand, due to the nature of ABE, a malicious user may abuse its decryption privilege. Therefore, being able

  • SkySlide: A Hybrid Method for Landslide Susceptibility Assessment based on Landslide-Occurring Data Only
    Comput. J. (IF 1.077) Pub Date : 2020-07-21
    Alev Mutlu; Furkan Goz

    Landslide susceptibility assessment is the problem of determining the likelihood of a landslide occurrence in a particular area with respect to the geographical and morphological properties of the area. This paper presents a hybrid method, namely SkySlide, that incorporates clustering, skyline operator, classification and majority voting principle for region-scale landslide susceptibility assessment

  • Algorithms Based on Path Contraction Carrying Weights for Enumerating Subtrees of Tricyclic Graphs
    Comput. J. (IF 1.077) Pub Date : 2020-07-17
    Yu Yang; Beifang Chen; Guoping Zhang; Yongming Li; Daoqiang Sun; Hongbo Liu

    The subtree number index of a graph, defined as the number of subtrees, attracts much attention recently. Finding a proper algorithm to compute this index is an important but difficult problem for a general graph. Even for unicyclic and bicyclic graphs, it is not completely trivial, though it can be figured out by try and error. However, it is complicated for tricyclic graphs. This paper proposes path

  • Large Universe CCA2 CP-ABE With Equality and Validity Test in the Standard Model
    Comput. J. (IF 1.077) Pub Date : 2020-07-17
    Cong Li; Qingni Shen; Zhikang Xie; Xinyu Feng; Yuejian Fang; Zhonghai Wu

    Attribute-based encryption with equality test (ABEET) simultaneously supports fine-grained access control on the encrypted data and plaintext message equality comparison without decrypting the ciphertexts. Recently, there have been several literatures about ABEET proposed. Nevertheless, most of them explore the ABEET schemes in the random oracle model, which has been pointed out to have many defects

  • Reliability Analysis of Alternating Group Graphs and Split-Stars
    Comput. J. (IF 1.077) Pub Date : 2020-07-16
    Mei-Mei Gu; Rong-Xia Hao; Jou-Ming Chang

    Given a connected graph |$G$| and a positive integer |$\ell $|⁠, the |$\ell $|-extra (resp. |$\ell $|-component) edge connectivity of |$G$|⁠, denoted by |$\lambda ^{(\ell )}(G)$| (resp. |$\lambda _{\ell }(G)$|⁠), is the minimum number of edges whose removal from |$G$| results in a disconnected graph so that every component has more than |$\ell $| vertices (resp. so that it contains at least |$\ell

  • Persistence of Hybrid Diagnosability of Regular Networks Under Testing Diagnostic Model
    Comput. J. (IF 1.077) Pub Date : 2020-07-16
    Guanqin Lian; Shuming Zhou; Eddie Cheng; Jiafei Liu; Gaolin Chen

    Diagnosability is an important metric to fault tolerance and reliability for multiprocessor systems. However, plenty of research on fault diagnosability focuses on node failure. In practical scenario, not only node failures take place but also link malfunctions may arise. In this work, we investigate the diagnosability of general regular networks with failing nodes as well as missing malfunctional

  • Interference and Coverage Modeling for Indoor Terahertz Communications with Beamforming Antennas
    Comput. J. (IF 1.077) Pub Date : 2020-07-16
    Chao-Chao Wang; Wan-Liang Wang; Xin-Wei Yao

    A general framework to investigate the interference and coverage probability is proposed in this paper for indoor terahertz (THz) communications with beamforming antennas. Due to the multipath effects of THz band (0.1–10 THz), the line of sight and non-line of sight interference from users and access points (APs) (both equipped with beamforming antennas) are separately analyzed based on distance-dependent

  • EmailDetective: An Email Authorship Identification And Verification Model
    Comput. J. (IF 1.077) Pub Date : 2020-07-13
    Yong Fang; Yue Yang; Cheng Huang

    Emails are often used to illegal cybercrime today, so it is important to verify the identity of the email author. This paper proposes a general model for solving the problem of anonymous email author attribution, which can be used in email authorship identification and email authorship verification. The first situation is to find the author of an anonymous email among the many suspected targets. Another

  • On the Behaviour of p -Adic Scaled Space Filling Curve Indices for High-Dimensional Data
    Comput. J. (IF 1.077) Pub Date : 2020-07-13
    Patrick Erik Bradley; Markus Wilhelm Jahn

    Space filling curves are widely used in computer science. In particular, Hilbert curves and their generalizations to higher dimension are used as an indexing method because of their nice locality properties. This article generalizes this concept to the systematic construction of |$p$|-adic versions of Hilbert curves based on special affine transformations of the |$p$|-adic Gray code and develops a

  • MeSH-Based Semantic Indexing Approach to Enhance Biomedical Information Retrieval
    Comput. J. (IF 1.077) Pub Date : 2020-07-09
    Hager Kammoun; Imen Gabsi; Ikram Amous

    Owing to the tremendous size of electronic biomedical documents, users encounter difficulties in seeking useful biomedical information. An efficient and smart access to the relevant biomedical information has become a fundamental need. In this research paper, we set forward a novel biomedical MeSH-based semantic indexing approach to enhance biomedical information retrieval. The proposed semantic indexing

  • Non-Malleable Zero-Knowledge Arguments with Lower Round Complexity
    Comput. J. (IF 1.077) Pub Date : 2020-07-09
    Zhenbin Yan; Yi Deng

    Round complexity is one of the fundamental problems in zero-knowledge (ZK) proof systems. Non-malleable zero-knowledge (NMZK) protocols are ZK protocols that provide security even when man-in-the-middle adversaries interact with a prover and a verifier simultaneously. It is known that the first constant-round public-coin NMZK arguments for NP can be constructed by assuming the existence of collision-resistant

  • Incorporating Biterm Correlation Knowledge into Topic Modeling for Short Texts
    Comput. J. (IF 1.077) Pub Date : 2020-07-08
    Kai Zhang; Yuan Zhou; Zheng Chen; Yufei Liu; Zhuo Tang; Li Yin; Jihong Chen

    The prevalence of short texts on the Web has made mining the latent topic structures of short texts a critical and fundamental task for many applications. However, due to the lack of word co-occurrence information induced by the content sparsity of short texts, it is challenging for traditional topic models like latent Dirichlet allocation (LDA) to extract coherent topic structures on short texts.

  • An Approach for the Evaluation and Correction of Manually Designed Video Game Levels Using Deep Neural Networks
    Comput. J. (IF 1.077) Pub Date : 2020-07-08
    Omid Davoodi; Mehrdad Ashtiani; Morteza Rajabi

    In the current state of the video game productions, most of the video game levels are created by the human operators working as level designers. This manual process is not only time-consuming and resource-intensive but also hard to guarantee uniform quality in the contents created by the level designers. One way to address this issue is to use computer-assisted level design techniques. In this paper

  • LUISA: Decoupling the Frequency Model From the Context Model in Prediction-Based Compression
    Comput. J. (IF 1.077) Pub Date : 2020-07-07
    Vinicius Fulber-Garcia; Sérgio Luis Sardi Mergen

    Prediction-based compression methods, like prediction by partial matching, achieve a remarkable compression ratio, especially for texts written in natural language. However, they are not efficient in terms of speed. Part of the problem concerns the usage of dynamic entropy encoding, which is considerably slower than the static alternatives. In this paper, we propose a prediction-based compression method

  • Optimal Slack Stealing Servicing for Real-Time Energy Harvesting Systems
    Comput. J. (IF 1.077) Pub Date : 2020-07-07
    Rola El Osta; Maryline Chetto; Hussein El Ghor

    We consider the problem of real-time scheduling in uniprocessor devices powered by energy harvesters. In particular, we focus on mixed sets of tasks with time and energy constraints: hard deadline periodic tasks and soft aperiodic tasks without deadlines. We present an optimal aperiodic servicing algorithm that minimizes the response times of aperiodic tasks without compromising the schedulability

  • Two-Factor Decryption: A Better Way to Protect Data Security and Privacy
    Comput. J. (IF 1.077) Pub Date : 2020-07-07
    Hui Cui; Russell Paulet; Surya Nepal; Xun Yi; Butrus Mbimbi

    Biometric information is unique to a human, so it would be desirable to use the biometric characteristic as the private key in a cryptographic system to protect data security and privacy. In this paper, we introduce a notion called two-factor decryption (TFD). Informally speaking, a TFD scheme is a variant of the public-key encryption (PKE) scheme. In a TFD scheme, messages are encrypted under public

  • Fast Learning Through Deep Multi-Net CNN Model For Violence Recognition In Video Surveillance
    Comput. J. (IF 1.077) Pub Date : 2020-07-06
    Aqib Mumtaz; Allah Bux Sargano; Zulfiqar Habib

    The violence detection is mostly achieved through handcrafted feature descriptors, while some researchers have also employed deep learning-based representation models for violent activity recognition. Deep learning-based models have achieved encouraging results for fight activity recognition on benchmark data sets such as hockey and movies. However, these models have limitations in learning discriminating

  • Corrigendum to: Congestion Free Transient Plane(CFTP) using bandwidth sharing during link failures in SDN
    Comput. J. (IF 1.077) Pub Date : 2020-07-03
    Muthumanikandan Vanamoorthy; Valliyammai Chinnaiah

    AbstractWith energy consumption in high-performance computing clouds growing rapidly, energy saving has become an important topic. Virtualization provides opportunities to save energy by enabling one physical machine (PM) to host multiple virtual machines (VMs). Dynamic voltage and frequency scaling (DVFS) is another technology to reduce energy consumption. However, in heterogeneous cloud environments

  • The Notion of Transparency Order, Revisited
    Comput. J. (IF 1.077) Pub Date : 2020-07-03
    Huizhong Li; Yongbin Zhou; Jingdian Ming; Guang Yang; Chengbin Jin

    We revisit the definition of transparency order (TO) and that of modified transparency order (MTO) as well, which were proposed to measure the resistance of substitution boxes (S-boxes) against differential power analysis (DPA). We spot a definitional flaw in original TO, which is proved to significantly affect the soundness of TO. Regretfully, MTO overlooks this flaw, yet it happens to incur no bad

  • Enabling Smart City With Intelligent Congestion Control Using Hops With a Hybrid Computational Approach
    Comput. J. (IF 1.077) Pub Date : 2020-07-03
    Sagheer Abbas; Muhammad Adnan Khan; Atifa Athar; Syed Ali Shan; Anwar Saeed; Tahir Alyas

    In a smart city, the subject of the congestion-free traffic has been leading objectives from the past decade, and many approaches are adopted to make congestion-free roads. These approaches and signals at one junction are not inter-linked with the signal at the previous one. Therefore, the traffic flow on the same road and at associative roads is not smooth. The study proposed a model with a hybrid

  • Destination Image Recognition And Emotion Analysis: Evidence From User-Generated Content Of Online Travel Communities
    Comput. J. (IF 1.077) Pub Date : 2020-07-03
    Weidong Huang; Shuting Zhu; Xinkai Yao

    The tourism destination image is an intangible value that enhances the internal and external spiritual value of the region. To improve tourist experiences and provide reference for relevant departments, we applied the GooSeeker web data crawler tool and Python data mining kit to crawl and analyze the representative online tourism community data. We conduct an empirical analysis through data from the

  • Model-Based Comparison of Cloud-Edge Computing Resource Allocation Policies
    Comput. J. (IF 1.077) Pub Date : 2020-07-01
    Lili Jiang; Xiaolin Chang; Runkai Yang; Jelena Mišić; Vojislav B Mišić

    The rapid and widespread adoption of internet of things-related services advances the development of the cloud-edge framework, including multiple cloud datacenters (CDCs) and edge micro-datacenters (EDCs). This paper aims to apply analytical modeling techniques to assess the effectiveness of cloud-edge computing resource allocation policies from the perspective of improving the performance of cloud-edge

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