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  • A comprehensive survey and analysis of generative models in machine learning
    Comput. Sci. Rev. (IF 7.707) Pub Date : 2020-07-30
    Harshvardhan GM; Mahendra Kumar Gourisaria; Manjusha Pandey; Siddharth Swarup Rautaray

    Generative models have been in existence for many decades. In the field of machine learning, we come across many scenarios when directly learning a target is intractable through discriminative models, and in such cases the joint distribution of the target and the training data is approximated and generated. These generative models help us better represent or model a set of data by generating data in

    更新日期:2020-07-30
  • Localization schemes for Underwater Acoustic Sensor Networks - A Review
    Comput. Sci. Rev. (IF 7.707) Pub Date : 2020-05-18
    Archana Toky; Rishi Pal Singh; Sanjoy Das

    Underwater Acoustic Sensor Networks (UWASNs) connect the resources available in oceans to the rest of the world. The network has a huge amount of sensors which are sparsely deployed and are interconnected to collect information for the applications like target tracking, marine life monitoring, surveillance, and civilian applications. To get meaningful information from the data collected by this network

    更新日期:2020-07-24
  • Big networks: A survey
    Comput. Sci. Rev. (IF 7.707) Pub Date : 2020-05-20
    Hayat Dino Bedru; Shuo Yu; Xinru Xiao; Da Zhang; Liangtian Wan; He Guo; Feng Xia

    A network is a typical expressive form of representing complex systems in terms of vertices and links, in which the pattern of interactions amongst components of the network is intricate. The network can be static that does not change over time or dynamic that evolves through time. The complication of network analysis is different under the new circumstance of network size explosive increasing. In

    更新日期:2020-07-24
  • Julia language in machine learning: Algorithms, applications, and open issues
    Comput. Sci. Rev. (IF 7.707) Pub Date : 2020-05-16
    Kaifeng Gao; Gang Mei; Francesco Piccialli; Salvatore Cuomo; Jingzhi Tu; Zenan Huo

    Machine learning is driving development across many fields in science and engineering. A simple and efficient programming language could accelerate applications of machine learning in various fields. Currently, the programming languages most commonly used to develop machine learning algorithms include Python, MATLAB, and C/C ++. However, none of these languages well balance both efficiency and simplicity

    更新日期:2020-07-24
  • Context Aware Recommendation Systems: A review of the state of the art techniques
    Comput. Sci. Rev. (IF 7.707) Pub Date : 2020-05-20
    Saurabh Kulkarni; Sunil F. Rodd

    Recommendation systems are gaining increasing popularity in many application areas like e-commerce, movie and music recommendations, tourism, news, advertisement, stock markets, social networks etc. Conventional recommendation systems either use content based or collaborative filtering based approaches to model user preferences and give recommendations. These systems usually fail to consider evolving

    更新日期:2020-07-24
  • Graph spanners: A tutorial review
    Comput. Sci. Rev. (IF 7.707) Pub Date : 2020-05-26
    Reyan Ahmed; Greg Bodwin; Faryad Darabi Sahneh; Keaton Hamm; Mohammad Javad Latifi Jebelli; Stephen Kobourov; Richard Spence

    This survey provides a guiding reference to researchers seeking an overview of the large body of literature about graph spanners. It surveys the current literature covering various research streams about graph spanners, such as different formulations, sparsity and lightness results, computational complexity, dynamic algorithms, and applications. As an additional contribution, we offer a list of open

    更新日期:2020-07-24
  • Current trends in consumption of multimedia content using online streaming platforms: A user-centric survey
    Comput. Sci. Rev. (IF 7.707) Pub Date : 2020-05-26
    Przemysław Falkowski-Gilski; Tadeus Uhl

    In its early days, consumption of multimedia content was only possible at a stationary terminal device. The music player was located at home, and had to have a physical drive. Over the last decade, there has been an enormous increase in the number of online streaming platforms. These services enable users to consume rich multimedia content on various devices. Thanks to the widespread and availability

    更新日期:2020-07-24
  • Indoor environment propagation review
    Comput. Sci. Rev. (IF 7.707) Pub Date : 2020-05-29
    H. Obeidat; A. Alabdullah; E. Elkhazmi; W. Suhaib; O. Obeidat; M. Alkhambashi; M. Mosleh; N. Ali; Y. Dama; Z. Abidin; R. Abd-Alhameed; P. Excell

    A survey of indoor propagation characteristics is presented, including different models for path loss, shadowing and fast fading mechanisms, different channel parameters including signal strength, power delay, coherence bandwidth, Doppler spread and angle of arrival. The concepts of MIMO channels are also covered. The study also explores many types of deterministic channel modelling, such as Finite

    更新日期:2020-07-24
  • A systematic mapping study of clone visualization
    Comput. Sci. Rev. (IF 7.707) Pub Date : 2020-06-05
    Muhammad Hammad; Hamid Abdul Basit; Stan Jarzabek; Rainer Koschke

    Knowing code clones (similar code fragments) is helpful in software maintenance and re-engineering. As clone detectors return huge numbers of clones, visualization techniques have been proposed to make cloning information more comprehensible and useful for programmers. We present a mapping study of clone visualization techniques, classifying visualizations in respect to the user goals to be achieved

    更新日期:2020-07-24
  • Motif discovery in networks: A survey
    Comput. Sci. Rev. (IF 7.707) Pub Date : 2020-06-05
    Shuo Yu; Yufan Feng; Da Zhang; Hayat Dino Bedru; Bo Xu; Feng Xia

    Motifs are regarded as network blocks because motifs can be used to present fundamental patterns in networks. Motif discovery is well applied in various scientific problems, including subgraph mining and graph isomorphism tasks. This paper analyzes and summarizes current motif discovery algorithms in the field of network science with both efficiency and accuracy perspectives. In this paper, we present

    更新日期:2020-07-24
  • Integrating blockchain technology into the energy sector — from theory of blockchain to research and application of energy blockchain
    Comput. Sci. Rev. (IF 7.707) Pub Date : 2020-06-09
    Qiang Wang; Min Su

    Blockchain technology has been ushering in nothing short of a decentralized revolution. Distributed/decentralized energy is recognized the best way to ensure energy sustainability in the future. An open question is what promise the integration of blockchain and energy hold for energy future. This paper systematically reviews the theory of blockchain and explores the current status of energy blockchain

    更新日期:2020-07-24
  • Multidisciplinary Pattern Recognition applications: A review
    Comput. Sci. Rev. (IF 7.707) Pub Date : 2020-06-08
    Marina Paolanti; Emanuele Frontoni

    Pattern recognition (PR) is the study of how machines can examine the environment, learn to distinguish patterns of interest from their background, and make reliable and feasible decisions regarding the categories of the patterns. However, even after almost 70 years of research, the design of an application based on pattern recognizer remains an ambiguous goal. Moreover, currently, there are huge volumes

    更新日期:2020-07-24
  • A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability
    Comput. Sci. Rev. (IF 7.707) Pub Date : 2020-06-17
    Xiaowei Huang; Daniel Kroening; Wenjie Ruan; James Sharp; Youcheng Sun; Emese Thamo; Min Wu; Xinping Yi

    In the past few years, significant progress has been made on deep neural networks (DNNs) in achieving human-level performance on several long-standing tasks. With the broader deployment of DNNs on various applications, the concerns over their safety and trustworthiness have been raised in public, especially after the widely reported fatal incidents involving self-driving cars. Research to address these

    更新日期:2020-07-24
  • A review of using object-orientation properties of C++ for designing expert system in strategic planning
    Comput. Sci. Rev. (IF 7.707) Pub Date : 2020-06-23
    Mohsen Ahmadi; Moein Qaisari Hasan Abadi

    Using intelligent expert systems is a necessity for improving the situation of organizations. Since the process of identifying strategy in a strategic plan is time-consuming and costly, the role of expert systems in strategic planning is considerable. Managers utilize expert system to maintain and disseminate knowledge, training, and competition in the organization. Complicated expert system with long

    更新日期:2020-07-24
  • Detection and mitigation of DDoS attacks in SDN: A comprehensive review, research challenges and future directions
    Comput. Sci. Rev. (IF 7.707) Pub Date : 2020-06-20
    Jagdeep Singh; Sunny Behal

    Many security solutions have been proposed in the past to protect Internet architecture from a diversity of malware. However, the security of the Internet and its applications is still an open research challenge. Researchers continuously working on novel network architectures such as HTTP as the narrow waist, Named Data Networking (NDN), programmable networks and Software-Defined Networking (SDN) for

    更新日期:2020-07-24
  • When machine learning meets medical world: Current status and future challenges
    Comput. Sci. Rev. (IF 7.707) Pub Date : 2020-06-20
    Abir Smiti

    Imagine the enormous amounts of data that can be generated in the medical field. Each patient has his own medical record which contains valuable information like patient allergy, chronic diseases and vaccinations. Healthcare can profit from this data when it is properly analyzed. The more data gathered, the more complicated data analytics become, therefore, machine learning can be a very useful solution

    更新日期:2020-07-24
  • Improved node localization using K-means clustering for Wireless Sensor Networks
    Comput. Sci. Rev. (IF 7.707) Pub Date : 2020-06-25
    Salim El Khediri; Walid Fakhet; Tarek Moulahi; Rehanullah Khan; Adel Thaljaoui; Abdennaceur Kachouri

    A power-efficient K-means clustering algorithm for Wireless Sensor Networks (WSN) is proposed. This algorithm aims to manage the consumption of energy by WS nodes and enhance the running time for WSN given space constraints. WS node cluster formation is structured as a sample space partition in k-means for the reason that the radio channel is unstable and the distribution of the nodes is coarse. After

    更新日期:2020-07-24
  • Community detection in node-attributed social networks: A survey
    Comput. Sci. Rev. (IF 7.707) Pub Date : 2020-07-21
    Petr Chunaev

    Community detection is a fundamental problem in social network analysis consisting, roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social graph) with certain social connections (modeled as edges in the social graph) into densely knitted and highly related groups with each group well separated from the others. Classical approaches for community detection usually deal

    更新日期:2020-07-24
  • Development of improved whale optimization-based FCM clustering for image watermarking
    Comput. Sci. Rev. (IF 7.707) Pub Date : 2020-07-18
    Kavitha Soppari; N. Subhash Chandra

    With the growing interest in copyright protection of multimedia, digital watermarking has been introduced and widely researched. The digital image watermarking approaches are generally adopted in the spatial or transform domain. Most of the transform-domain watermarking approaches are based on Discrete Cosine Transforms (DCT) and robust to JPEG lossy compression. Few contributions have been done using

    更新日期:2020-07-24
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