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  • Gravitational search algorithm based on multiple adaptive constraint strategy
    Computing (IF 2.044) Pub Date : 2020-06-29
    Jingsen Liu, Yuhao Xing, Yixiang Ma, Yu Li

    In order to improve the convergence speed and optimization accuracy of gravitational search algorithm, the improved gravitational algorithm with dynamically adjusting inertia weight and trend factors of speed and position is proposed. This kind of algorithm with dynamic inertia weight improves the updating way of particle mass. Moreover, the mass change has a nonlinear decreasing trend and improves

  • GPU-based matrix-free finite element solver exploiting symmetry of elemental matrices
    Computing (IF 2.044) Pub Date : 2020-06-24
    Utpal Kiran, Sachin Singh Gautam, Deepak Sharma

    Matrix-free solvers for finite element method (FEM) avoid assembly of elemental matrices and replace sparse matrix-vector multiplication required in iterative solution method by an element level dense matrix-vector product. In this paper, a novel matrix-free strategy for FEM is proposed which computes element level matrix-vector product by using only the symmetric part of the elemental matrices. The

  • Efficient service discovery in mobile social networks for smart cities
    Computing (IF 2.044) Pub Date : 2020-06-10
    Yonghong Guo, Lu Liu, John Panneerselvam, Rongbo Zhu

    Mobile social networks (MSNs) play an important role in the process of the development of smart cities. Citizens can interact and engage with services provided by MSNs. Smart city services enhance their quality of life. With the popularity of smart phones, mobile social activities have become an important component of citizens’ daily life. People can post their social contents to their remote friends

  • Uniform reliable broadcast in anonymous distributed systems with fair lossy channels
    Computing (IF 2.044) Pub Date : 2020-06-08
    Jian Tang, Mikel Larrea, Sergio Arévalo, Ernesto Jiménez

    Uniform reliable broadcast (URB) is an important abstraction in distributed systems, offering delivery guarantee when spreading messages between processes. Informally, URB guarantees that if a process (correct or not) delivers a message m, then all correct processes deliver m. This abstraction has been extensively investigated in distributed systems where all processes have unique identifiers. Furthermore

  • Software-driven big data analytics
    Computing (IF 2.044) Pub Date : 2020-06-05
    Rajiv Ranjan, Zheng Li, Massimo Villari, Yan Liu, Dimitrios Georgeakopoulos

    Data analytics is the crucial step to reveal essential values of datasets and complete the value chain of big data. In practice, both the hardware infrastructure and the software stack play a fundamental role in big data analytics (BDA). Unfortunately, it is evident that a disproportionately larger amount of effort is being invested in the hardware infrastructure development over the software stack

  • An adaptive workload-aware power consumption measuring method for servers in cloud data centers
    Computing (IF 2.044) Pub Date : 2020-05-27
    Weiwei Lin, Yufeng Zhang, Wentai Wu, Simon Fong, Ligang He, Jia Chang

    As cloud computing technologies and applications develop rapidly in recent years, the quantity and size of cloud datacenters have been ever-increasing, making the overconsumption of energy in datacenters become a widespread concern. To reduce the energy cost by servers, we must first build an accurate power model to achieve flexible, device-free power consumption measuring. However, most of the previous

  • An approach to dynamically assigning cloud resource considering user demand and benefit of cloud platform
    Computing (IF 2.044) Pub Date : 2020-05-26
    Zhongsheng Qian, Xiaojin Wang, Xiangyu Liu, Xiaoxin Xie, Tao Song

    Cloud computing, with the features of flexible resource assignment, timely on-demand service and transparent by-quantity pricing, has been widely applied recently. As a new business service model, cloud platform must be capable of satisfying user demand and enhancing quality of service. Therefore, an excellent resource scheduling scheme is requisite to improve the working efficiency of cloud platform

  • A novel blockchain based framework to secure IoT-LLNs against routing attacks
    Computing (IF 2.044) Pub Date : 2020-05-25
    Rashmi Sahay, G. Geethakumari, Barsha Mitra

    Routing attacks in the Internet of Things environment (IoT) can result in degraded network performance and often denial of service. Low Power and Lossy Network (LLN) is that segment in IoT which comprises constrained devices like sensors and RFIDs. IPv6 Routing Protocol over Low Power and Lossy Networks (RPL) is the standard routing protocol proposed by IETF for routing in IoT-LLNs. RPL efficiently

  • QoS evaluation model based on intelligent fuzzy system for vehicular ad hoc networks
    Computing (IF 2.044) Pub Date : 2020-05-19
    Abir Mchergui, Tarek Moulahi, Salem Nasri

    Supporting the Quality of Service (QoS) for broadcasting techniques in Vehicular Ad hoc NETetworks (VANETs) is a primordial concern. Qualitatively, QoS is an aspect reflecting the network performance, but quantitatively, it is a function of multiple parameters such as end-to-end delay, packet loss ratio and overhead. These parameters are changing over time and according to adopted protocols. Therefore

  • LSTM based link quality confidence interval boundary prediction for wireless communication in smart grid
    Computing (IF 2.044) Pub Date : 2020-05-18
    Wei Sun, Pengyu Li, Zhi Liu, Xue Xue, Qiyue Li, Haiyan Zhang, Junbo Wang

    The smart grid will play an important role in the future city to support the diversified energy supply. Wireless communication, the most cost-effective alternative to the traditional wire-lines, promises to provide ubiquitous bi-direction information channel for smart grid devices. However, due to the complex environment that smart grid devices located in, the wireless link is easily been interfered

  • POF-SVLM: pareto optimized framework for seamless VM live migration
    Computing (IF 2.044) Pub Date : 2020-05-16
    Chetan Dhule, Urmila Shrawankar

    Live migration helps to achieve resource consolidation and fault tolerance. It transfers VM storage together with VM memory and CPU status. During migration, a dirty page rate also delays the period of live migration, and it affects the performance of migration by increasing migration time, network bandwidth consumption, CPU processing overheads and application downtime. Experimental results after

  • A cache-based method to improve query performance of linked Open Data cloud
    Computing (IF 2.044) Pub Date : 2020-05-14
    Usman Akhtar, Anita Sant’Anna, Chang-Ho Jihn, Muhammad Asif Razzaq, Jaehun Bang, Sungyoung Lee

    The proliferation of semantic big data has resulted in a large amount of content published over the Linked Open Data (LOD) cloud. Semantic Web applications consume these data by issuing SPARQL queries. One of the main challenges faced by querying the LOD web cloud on account of the inherent distributed nature of LOD is its high search latency and lack of tools to connect the SPARQL endpoints. In this

  • Efficient offloading schemes using Markovian models: a literature review
    Computing (IF 2.044) Pub Date : 2020-05-10
    Mohammad Masdari, Hemn Khezri

    The increasing demand for new mobile applications puts a heavy demand for more processing power and resources in smart mobile devices (SMD). Offloading is a promising solution for these issues which tries to move data, code, or computation from the SMDs to the remote or nearby resourceful servers. To increase the effectiveness of the offloading process and make better decisions, various stochastic

  • A multi-objective load balancing algorithm for virtual machine placement in cloud data centers based on machine learning
    Computing (IF 2.044) Pub Date : 2020-05-06
    Arezoo Ghasemi, Abolfazl Toroghi Haghighat

    Cloud computing provides utility computing in which clients pay the cost according to their demands and service use. There are some challenges to this technology. One of these issues in data centers is virtual machine (VM) placement so that mapping of these VMs to hosts is executed for a variety of objectives such as load balancing, reducing energy consumption, increasing resource utilization, shortening

  • An approach to evaluate trust and reputation of things in a Multi-IoTs scenario
    Computing (IF 2.044) Pub Date : 2020-05-05
    Domenico Ursino, Luca Virgili

    In the past research, trust and reputation have been investigated for communities of people, for organizations and for multi-agent systems. However, in the last few years, things are becoming increasingly relevant in the Internet scenario and, at the same time, increasingly complex. As a matter of fact, the term “Internet of Things” (hereafter, IoT) is becoming more and more common in both the scientific

  • Feature selection and evaluation for software usability model using modified moth-flame optimization
    Computing (IF 2.044) Pub Date : 2020-05-05
    Deepak Gupta, Anil K. Ahlawat, Arun Sharma, Joel J. P. C. Rodrigues

    This paper introduces a nature-inspired optimized algorithm called modified moth-flame optimization (MMFO) for usability feature selection. To determine quality of software usability plays a significant role. This model contains various usability factors that are divided into several features, which have some characteristics, thus making a hierarchical model. Here, the authors have introduced MMFO

  • An energy-aware clustering and two-level routing method in wireless sensor networks
    Computing (IF 2.044) Pub Date : 2020-05-05
    Aram Mosavifard, Hamid Barati

    Wireless sensor networks (WSN) are consisted of several sensor nodes scattered in an area to gather data from their ambient environment and send it to base station (BS). The energy of nodes in WSNs is limited. One of the most significant issues in WSNs is reducing the energy consumption of nodes, which leads to increased network lifetime. One method to reduce energy consumption in WSNs is energy-efficient

  • An energy-efficient power management for heterogeneous servers in data centers
    Computing (IF 2.044) Pub Date : 2020-04-06
    Qiang Wang, Haoran Cai, Qiang Cao, Fang Wang

    Power management for heterogeneous servers has been playing a key role in improving energy efficiency in data centers. Running latency-critical web services on such scenario is still challenging due to the overheads of task transition between such servers. In this paper, we present a runtime power management system, Montgolfier, which is built on a latency-aware feedback control mechanism. It consolidates

  • Enhanced time-aware QoS prediction in multi-cloud: a hybrid k-medoids and lazy learning approach (QoPC)
    Computing (IF 2.044) Pub Date : 2019-10-10
    Amin Keshavarzi, Abolfazl Toroghi Haghighat, Mahdi Bohlouli

    Cloud service providers should be able to predict the future states of their infrastructure in order to avoid any violation of Service Level Agreement. This becomes more complex when vendors have to deal with services from various providers in multi-clouds. As a result, QoS prediction can significantly support service providers in a better understanding of their resources future states. Users should

  • Software system design based on patterns for Newton-type methods
    Computing (IF 2.044) Pub Date : 2019-09-10
    Ricardo Serrato-Barrera, Gustavo Rodríguez-Gómez, Julio César Pérez-Sansalvador, Saúl Pomares-Hernández, Leticia Flores-Pulido, Antonio Muñoz

    A wide range of engineering applications use optimization techniques as part of their solution process. The researcher uses specialized software that implements well-known optimization techniques to solve his problem. However, when it comes to develop original optimization techniques that fit a particular problem the researcher has no option but to implement his own new method from scratch. This leads

  • Deployment of an aerial platform system for rapid restoration of communications links after a disaster: a machine learning approach
    Computing (IF 2.044) Pub Date : 2019-11-21
    Faris A. Almalki, Marios C. Angelides

    Having reliable telecommunication systems in the immediate aftermath of a catastrophic event makes a huge difference in the combined effort by local authorities, local fire and police departments, and rescue teams to save lives. This paper proposes a physical model that links base stations that are still operational with aerial platforms and then uses a machine learning framework to evolve ground-to-air

  • SVM ensembles for named entity disambiguation
    Computing (IF 2.044) Pub Date : 2019-08-21
    Amal Alokaili, Mohamed El Bachir Menai

    The enormous quantity of digital data necessitates automation, which among other things can help link unstructured to structured data. Such a task requires a systematic approach of mapping entity mentions (e.g., person, location) to corresponding entries in a Knowledge Base. This area of research is rapidly evolving at a breathtaking pace, which has led to the popularization of the Named Entity Disambiguation

  • Long range dependence in cloud servers: a statistical analysis based on Google workload trace
    Computing (IF 2.044) Pub Date : 2020-01-07
    Shaifu Gupta, A. D. Dileep

    Analysis and characterization of cloud workloads provides crucial information for designing optimal resource management policies. In this work, we propose to analyse long range dependence nature of cloud resource workloads. Long range dependence is a phenomenon widely studied in Ethernet and Internet traffic. But there is a dearth of works that analyse long range dependence in cloud workloads. In this

  • A probabilistic model for assigning queries at the edge
    Computing (IF 2.044) Pub Date : 2019-11-18
    Kostas Kolomvatsos, Christos Anagnostopoulos

    Data management at the edge of the network can increase the performance of applications as the processing is realized close to end users limiting the observed latency in the provision of responses. A typical data processing involves the execution of queries/tasks defined by users or applications asking for responses in the form of analytics. Query/task execution can be realized at the edge nodes that

  • A green energy consumption policy of Bluetooth mobile devices for smart cities
    Computing (IF 2.044) Pub Date : 2019-10-30
    Hui Ye, FangMin Li, ZhiXiong Liu, XuDong Deng

    Green computing mode becomes a social concern in recently years. Consequently people have higher demand for Bluetooth device with lower power consumption. In this paper, a green energy consumption policy (ECP) of battery of Bluetooth devices during data transmission was put forward. ECP extracted detail information from four key configuration parameters during Bluetooth data transmission, namely, transmission

  • A review of CUDA optimization techniques and tools for structured grid computing
    Computing (IF 2.044) Pub Date : 2019-07-26
    Mayez A. Al-Mouhamed, Ayaz H. Khan, Nazeeruddin Mohammad

    Recent advances in GPUs opened a new opportunity in harnessing their computing power for general purpose computing. CUDA, an extension to C programming, is developed for programming NVIDIA GPUs. However, efficiently programming GPUs using CUDA is very tedious and error prone even for the expert programmers. Programmer has to optimize the resource occupancy and manage the data transfers between host

  • Implementation of an energy saving cloud infrastructure with virtual machine power usage monitoring and live migration on OpenStack
    Computing (IF 2.044) Pub Date : 2020-04-01
    Chao-Tung Yang, Tsung-Yueh Wan

    Cloud computing is Internet-based computing which requires more physical machines and consumes a large amount of power. By this means, that will reduce the profit of the service providers and harm the environment. How to effectively handle the power consumption of cloud computing has been an issue in recent years. When making a large number of operations, and power consumption cannot be underestimated

  • MDPCluster: a swarm-based community detection algorithm in large-scale graphs
    Computing (IF 2.044) Pub Date : 2020-01-11
    Mahsa Fozuni Shirjini, Saeed Farzi, Amin Nikanjam

    Social network analysis has become an important topic for researchers in sociology and computer science. Similarities among individuals form communities as the basic constitutions of social networks. Regarding the importance of communities, community detection is a fundamental step in the study of social networks typically modeled as large-scale graphs. Detecting communities in such large-scale graphs

  • An approach to merge collaborating processes of an inter-organizational business process for artifact lifecycle synthesis
    Computing (IF 2.044) Pub Date : 2019-11-30
    Jyothi Kunchala, Jian Yu, Sira Yongchareon, Chengfei Liu

    Artifact-centric approach to business process modeling has received considerable attention for elevating data logic to the same level as the process flow logic. With the emergence of this modeling paradigm, several recent works have focused on synthesizing the indispensable lifecycles of key business entities called artifacts from the standalone activity-centric processes. However, synthesizing artifact

  • Strategies for array data retrieval from a relational back-end based on access patterns
    Computing (IF 2.044) Pub Date : 2020-03-30
    Andrej Andrejev, Kjell Orsborn, Tore Risch

    Multidimensional numeric arrays are often serialized to binary formats for efficient storage and processing. These representations can be stored as binary objects in existing relational database management systems. To minimize data transfer overhead when arrays are large and only parts of arrays are accessed, it is favorable to split these arrays into separately stored chunks. We process queries expressed

  • Long-term real time object tracking based on multi-scale local correlation filtering and global re-detection
    Computing (IF 2.044) Pub Date : 2020-03-30
    Qi Zhao, Boxue Zhang, Wenquan Feng, Zhiying Du, Hong Zhang, Daniel Sun

    This paper investigates long-term visual object tracking which is a complex problem in computer vision community and big data analysis, due to the variation of the target and the surrounding environment. A novel tracking algorithm based on local correlation filtering and global keypoint matching is proposed to solve problems occurred during long-term tracking such as occlusion, target-losing, etc.

  • Performance analysis of the ubiquitous and emergent properties of an autonomic reflective middleware for smart cities
    Computing (IF 2.044) Pub Date : 2020-03-30
    Jose Aguilar, M. Jerez, M. Mendonça, M. Sánchez

    One of the biggest challenges in a Smart City is how to describe and dispose of the enormous and multiple sources of information, and how to share and merge it into a single infrastructure, in a timely and correct manner. A Smart City requires computational platforms, which allow the interconnection of multiple and embedded systems, such that the technology is integrated with people, and can respond

  • Enabling distributed intelligence for the Internet of Things with IOTA and mobile agents
    Computing (IF 2.044) Pub Date : 2020-03-27
    Tariq Alsboui, Yongrui Qin, Richard Hill, Hussain Al-Aqrabi

    It is estimated that there will be approximately 125 billion Internet of Things (IoT) devices connected to the Internet by 2030, which are expected to generate large amounts of data. This will challenge data processing capability, infrastructure scalability, and privacy. Several studies have demonstrated the benefits of using distributed intelligence (DI) to overcome these challenges. We propose a

  • A novel scalable representative-based forecasting approach of service quality
    Computing (IF 2.044) Pub Date : 2020-03-27
    Hamdi Yahyaoui, Hala S. Own, Ahmed Agwa, Zakaria Maamar

    Several approaches to forecast the service quality based on its quality of service (QoS) properties are reported in the literature. However, their main disadvantage resides in their limited scalability. In fact, they elaborate a forecasting model for each quality attribute per service, which cannot scale well for large or even medium size datasets of services. Accordingly, we propose a novel scalable

  • Fusing attack detection and severity probabilities: a method for computing minimum-risk war decisions
    Computing (IF 2.044) Pub Date : 2020-03-27
    Vaughn H. Standley, Frank G. Nuño, Jacob W. Sharpe

    State actors can minimize the risk of combat deaths by making decisions consistent with a likelihood ratio test that fuses attack detection data with prior war probabilities. The power-law, which has for decades been used to model the distribution of combat fatalities, is invalid as a probability because the mean is divergent. An investigation of Correlates of War data reveal that combat fatalities

  • SAT-based models for overlapping community detection in networks
    Computing (IF 2.044) Pub Date : 2020-03-23
    Said Jabbour, Nizar Mhadhbi, Badran Raddaoui, Lakhdar Sais

    Communities in social networks or graphs are sets of well-connected, overlapping vertices. Network community detection is a hot research topic in social, biological and information networks analysis. The effectiveness of a community detection algorithm is determined by accuracy in finding the ground-truth communities. In this article, we present two models to detect overlapping communities in large

  • Ontology-based discovery of time-series data sources for landslide early warning system
    Computing (IF 2.044) Pub Date : 2019-06-13
    Jedsada Phengsuwan, Tejal Shah, Philip James, Dhavalkumar Thakker, Stuart Barr, Rajiv Ranjan

    Abstract Modern early warning system (EWS) requires sophisticated knowledge of the natural hazards, the urban context and underlying risk factors to enable dynamic and timely decision making (e.g., hazard detection, hazard preparedness). Landslides are a common form of natural hazard with a global impact and closely linked to a variety of other hazards. EWS for landslides prediction and detection relies

  • Research on statistical machine translation model based on deep neural network
    Computing (IF 2.044) Pub Date : 2019-08-21
    Ying Xia

    Abstract With the increase of translation demand, the advancement of information technology, the development of linguistic theories and the progress of natural language understanding models in artificial intelligence research, machine translation has gradually gained worldwide attention. However, at present, machine translation research still has problems such as insufficient bilingual data and lack

  • Fast fingerprints construction via GPR of high spatial-temporal resolution with sparse RSS sampling in indoor localization
    Computing (IF 2.044) Pub Date : 2019-05-02
    Haojun Ai, Kaifeng Tang, Weiyi Huang, Sheng Zhang, Taizhou Li

    Abstract Effective indoor localization largely relies on the fingerprint database (model) of Received Signal Strength (RSS) in connection with Radio Frequency sources, such as the most widely used Bluetooth Low Energy (BLE) iBeacons. RSSs exhibit significant random variations in both the spatial and temporal domains. It is a notoriously onerous and challenging task to construct the fingerprint database

  • Study on text representation method based on deep learning and topic information
    Computing (IF 2.044) Pub Date : 2019-09-06
    Zilong Jiang, Shu Gao, Liangchen Chen

    Abstract Deep learning provides a new modeling method for natural language processing. In recent years, it has been applied in language model, text classification, machine translation, sentiment analysis, question and answer system, word distributed representation, etc., and a series of theoretical research results have been obtained. For the text representation task, this paper studies the strategy

  • English speech recognition based on deep learning with multiple features
    Computing (IF 2.044) Pub Date : 2019-08-26
    Zhaojuan Song

    Abstract English is one of the widely used languages, with the shrinking of the global village, the smart home, the in-vehicle voice system and voice recognition software with English as the recognition language have gradually entered people’s field of vision, and have obtained the majority of users’ love by the practical accuracy. And deep learning technology in many tasks with its hierarchical feature

  • Towards emotion-sensitive learning cognitive state analysis of big data in education: deep learning-based facial expression analysis using ordinal information
    Computing (IF 2.044) Pub Date : 2019-05-05
    Ruyi Xu, Jingying Chen, Jiaxu Han, Lei Tan, Luhui Xu

    Abstract The boom of big data in education has provided an unrivalled opportunity for educators to evaluate the learners’ cognitive state. However, most existing cognitive state analysis methods focus on attention, ignoring the roles of emotion in human learning. Therefore, this study proposes an emotion-sensitive learning cognitive state analysis framework, which automatically estimates the learners’

  • An optimized cognitive-assisted machine translation approach for natural language processing
    Computing (IF 2.044) Pub Date : 2019-07-12
    Abdulaziz Alarifi, Ayed Alwadain

    Abstract Currently, computer-aided machine translation (MT) processes play a significant role in natural language processing used to translate a specified language into another language like English to Spanish, Latin to French. During the translation process, and particularly during phrase composition, MT systems may exhibit several issues, including failure to produce high quality translations, increased

  • RNN-based signal classification for hybrid audio data compression
    Computing (IF 2.044) Pub Date : 2019-03-26
    Weiping Tu, Yuhong Yang, Bo Du, Wanzhao Yang, Xiong Zhang, Jiaxi Zheng

    Abstract Audio data are a fundamental component of multimedia big data. Switched audio codec has been proved to be efficient for compressing a large range of audio signals at low bit rates. However, coding quality strongly relies on an exact classification of the input signals. Two coding mode selection methods are adopted in AMR-WB+, the state-of-the-art switched audio coder. The closed-loop method

  • A mosaic method for multi-temporal data registration by using convolutional neural networks for forestry remote sensing applications
    Computing (IF 2.044) Pub Date : 2019-04-11
    Yi Zeng, Zihan Ning, Peng Liu, Peilei Luo, Yi Zhang, Guojin He

    Abstract Image registration is one of the most important processes for the generation of remote sensing image mosaics. This paper focuses on the special problems related to remote sensing data registration, and multi-temporal data mosaic applications in the domain of forestry. It proposes an image registration method based on hierarchical convolutional features, and applies it to improve the efficiency

  • Reusing artifact-centric business process models: a behavioral consistent specialization approach
    Computing (IF 2.044) Pub Date : 2020-02-22
    Sira Yongchareon, Chengfei Liu, Xiaohui Zhao

    Abstract Process reuse is one of the important research areas that address efficiency issues in business process modeling. Similar to software reuse, business processes should be able to be componentized and specialized in order to enable flexible process expansion and customization. Current activity/control-flow centric workflow modeling approaches face difficulty in supporting highly flexible process

  • Failure prediction of tasks in the cloud at an earlier stage: a solution based on domain information mining
    Computing (IF 2.044) Pub Date : 2020-02-19
    Chunhong Liu, Liping Dai, Yi Lai, Guibing Lai, Wentao Mao

    Abstract In a large-scale data center, it is vital to precisely recognize the termination statuses of applications at an early stage. In recent years, many machine learning techniques have been applied to this issue, which is beneficial for optimizing the scheduling policy and improving the efficiency of resource utilization. However, if the application’s dynamic information is insufficient at the

  • A novel semi fragile watermarking technique for tamper detection and recovery using IWT and DCT
    Computing (IF 2.044) Pub Date : 2020-02-13
    Nandhini Sivasubramanian, Gunaseelan Konganathan

    A novel semi fragile watermarking technique using integer wavelet transform (IWT) and discrete cosine transform (DCT) for tamper detection and recovery to enhance enterprise multimedia security is proposed. In this paper, two types of watermark are generated which are namely the authentication watermark and recovery Watermark. The Watermarked Image is formed by embedding the authentication watermark

  • Automatic hate speech detection using killer natural language processing optimizing ensemble deep learning approach
    Computing (IF 2.044) Pub Date : 2019-08-01
    Zafer Al-Makhadmeh, Amr Tolba

    Abstract Over the last decade, the increased use of social media has led to an increase in hateful activities in social networks. Hate speech is one of the most dangerous of these activities, so users have to protect themselves from these activities from YouTube, Facebook, Twitter etc. This paper introduces a method for using a hybrid of natural language processing and with machine learning technique

  • Method, formalization, and algorithms to split topology models for distributed cloud application deployments
    Computing (IF 2.044) Pub Date : 2019-04-27
    Karoline Saatkamp, Uwe Breitenbücher, Oliver Kopp, Frank Leymann

    Abstract For automating the deployment of applications in cloud environments, a variety of technologies have been developed in recent years. These technologies enable to specify the desired deployment in the form of deployment models that can be automatically processed by a provisioning engine. However, the deployment across several clouds increases the complexity of the provisioning. Using one deployment

  • Effective clustering protocol based on network division for heterogeneous wireless sensor networks
    Computing (IF 2.044) Pub Date : 2019-09-13
    Wided Abidi, Tahar Ezzedine

    Abstract The major challenge in wireless sensor networks is to reduce energy consumption and increase the lifetime of the network. In this paper, we propose an effective protocol to address this issue. In fact, our proposed protocol is based on first inserting heterogeneous nodes in the network, then dividing the network to regions. And finally, the selection of the Cluster Head (CH) is carried out

  • Cost-driven workflow scheduling on the cloud with deadline and reliability constraints
    Computing (IF 2.044) Pub Date : 2019-07-05
    Samaneh Sadat Mousavi Nik, Mahmoud Naghibzadeh, Yasser Sedaghat

    Abstract Clouds are becoming an effective platform for scientific workflow applications. In the meantime, Cloud computing structures are moving towards being more heterogeneous. In heterogeneous service-oriented systems, managing the reliability of resources (e.g., processors and communication networks) is widely identified as a critical issue due to processor and communication failures affecting user

  • Data quality and the Internet of Things
    Computing (IF 2.044) Pub Date : 2019-07-30
    Caihua Liu, Patrick Nitschke, Susan P. Williams, Didar Zowghi

    Abstract The Internet of Things (IoT) is driving technological change and the development of new products and services that rely heavily on the quality of the data collected by IoT devices. There is a large body of research on data quality management and improvement in IoT, however, to date a systematic review of data quality measurement in IoT is not available. This paper presents a systematic literature

  • Interpretation and automatic integration of geospatial data into the Semantic Web
    Computing (IF 2.044) Pub Date : 2019-02-13
    Claire Prudhomme, Timo Homburg, Jean-Jacques Ponciano, Frank Boochs, Christophe Cruz, Ana-Maria Roxin

    Abstract In the context of disaster management, geospatial information plays a crucial role in the decision-making process to protect and save the population. Gathering a maximum of information from different sources to oversee the current situation is a complex task due to the diversity of data formats and structures. Although several approaches have been designed to integrate data from different

  • Quality attributes use in architecture design decision methods: research and practice
    Computing (IF 2.044) Pub Date : 2019-10-01
    Ioanna Lytra, Carlos Carrillo, Rafael Capilla, Uwe Zdun

    Abstract Over the past 10 years software architecture has been perceived as the result of a set of architecture design decisions rather than the elements that form part of the software design. As quality attributes are considered major drivers of the design process to achieve high quality systems, the design decisions that drive the selection and use of specific quality properties and vice versa are

  • Reliability aware scheduling of bag of real time tasks in cloud environment
    Computing (IF 2.044) Pub Date : 2019-08-10
    Chinmaya Kumar Swain, Neha Saini, Aryabartta Sahu

    Abstract Cloud environment uses data center with a huge number of computational resources, and the probability of failing any of the resources increases with scale. Failures cause unavailability of services, which affects the reliability of the system. It is essential to consider the reliability issue for application deployment in the cloud, considering the failure of the resources. In this work, we

  • How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm
    Computing (IF 2.044) Pub Date : 2018-12-05
    Denis Kotkov, Jari Veijalainen, Shuaiqiang Wang

    Abstract Most recommender systems suggest items that are popular among all users and similar to items a user usually consumes. As a result, the user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected i.e., serendipitous items. In this paper, we propose

  • A social network for supporting end users in the composition of services: definition and proof of concept
    Computing (IF 2.044) Pub Date : 2020-02-06
    Pedro Valderas, Victoria Torres, Vicente Pelechano

    Abstract Nowadays, end users are surrounded by plenty of services that are somehow supporting their daily routines and activities. Involving end users into the process of service creation can allow end users to benefit from a cheaper, faster, and better service provisioning. Even though we can already find tools that face this challenge, they consider end users as isolate individuals. In this paper

  • A system for effectively predicting flight delays based on IoT data
    Computing (IF 2.044) Pub Date : 2020-02-06
    Abdulwahab Aljubairy, Wei Emma Zhang, Ali Shemshadi, Adnan Mahmood, Quan Z. Sheng

    Abstract Flight delay is a significant problem that negatively impacts the aviation industry and costs billion of dollars each year. Most existing studies investigated this issue using various methods based on historical data. However, due to the highly dynamic environments of the aviation industry, relying only on historical datasets of flight delays may not be sufficient and applicable to forecast

  • Data reduction in sensor networks based on dispersion analysis
    Computing (IF 2.044) Pub Date : 2020-02-05
    Janine Kniess, Samuel Oliveira

    Wireless sensor networks are commonly used to collect observations of real-world phenomena at regular time intervals. Sensor nodes rely on limited power sources, and some studies indicate that the main source of energy consumption is related to data transmissions. In this paper, we propose an approach to reduce data transmissions in sensor nodes based on data dispersion analysis. This approach aims

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