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  • Enabling distributed intelligence for the Internet of Things with IOTA and mobile agents
    Computing (IF 2.063) Pub Date : 2020-03-27
    Tariq Alsboui, Yongrui Qin, Richard Hill, Hussain Al-Aqrabi

    Abstract 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 novel scalable representative-based forecasting approach of service quality
    Computing (IF 2.063) Pub Date : 2020-03-27
    Hamdi Yahyaoui, Hala S. Own, Ahmed Agwa, Zakaria Maamar

    Abstract 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

  • Fusing attack detection and severity probabilities: a method for computing minimum-risk war decisions
    Computing (IF 2.063) Pub Date : 2020-03-27
    Vaughn H. Standley, Frank G. Nuño, Jacob W. Sharpe

    Abstract 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 2.063) Pub Date : 2020-03-23
    Said Jabbour, Nizar Mhadhbi, Badran Raddaoui, Lakhdar Sais

    Abstract 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

  • Ontology-based discovery of time-series data sources for landslide early warning system
    Computing (IF 2.063) 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 2.063) 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 2.063) 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 2.063) 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 2.063) 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 2.063) 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 2.063) 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 2.063) 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 2.063) 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 2.063) 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 2.063) 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 2.063) Pub Date : 2020-02-13
    Nandhini Sivasubramanian, Gunaseelan Konganathan

    Abstract 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

  • Automatic hate speech detection using killer natural language processing optimizing ensemble deep learning approach
    Computing (IF 2.063) 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 2.063) 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 2.063) 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 2.063) 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 2.063) 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 2.063) 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 2.063) 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 2.063) 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 2.063) 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 2.063) 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 2.063) 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 2.063) Pub Date : 2020-02-05
    Janine Kniess, Samuel Oliveira

    Abstract 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

  • Dynamic event type recognition and tagging for data-driven insights in law-enforcement
    Computing (IF 2.063) Pub Date : 2020-01-31
    Shayan Zamanirad, Boualem Benatallah, Moshe Chai Barukh, Carlos Rodriguez, Reza Nouri

    Abstract In law enforcement, investigators are typically tasked with analyzing large collections of evidences in order to identify and extract key information to support investigation cases. In this context, events are key elements that help understanding and reconstructing what happened from the collection of evidence items. With the ever increasing amount of data (e.g., e-mails and content from social

  • Characterizing user behavior in journey planning
    Computing (IF 2.063) Pub Date : 2020-01-29
    Ludovico Boratto, Matteo Manca, Giuseppe Lugano, Marián Gogola

    Abstract Journey planners support users in the organization of their trips, by presenting them results with multimodal solutions. While the benefits for the users are straightforward, other stakeholders (such as transport operators and planners) might benefit from understanding how users behave. In this paper, we analyze and characterize user behavior in journey planners, with the aim of getting insights

  • Recommendations from cold starts in big data
    Computing (IF 2.063) Pub Date : 2020-01-29
    David Ralph, Yunjia Li, Gary Wills, Nicolas G. Green

    Abstract This paper examines the challenging problem of new user cold starts in subset labelled and extremely sparsely labelled big data. We introduce a new Isle of Wight Supply Chain (IWSC) dataset demonstrating these characteristics. We also introduce a new technique addressing these challenges, the Transitive Semantic Relationships (TSR) model, which infers potential relationships from user and

  • Nash equilibrium based replacement of virtual machines for efficient utilization of cloud data centers
    Computing (IF 2.063) Pub Date : 2020-01-16
    Hammad ur Rehman Qaiser, Gao Shu

    Abstract Workload uncertainty has been increased with the integration of the Internet of Things to the computing grid i.e. edge computing and cloud data centers. Therefore, efficient resource utilization in cloud data centers become more challenging. Dynamic consolidation of virtual machines on optimal number of processing machines can increase the efficiency of resource utilization in cloud data centers

  • Training ensembles of faceted classification models for quantitative stock trading
    Computing (IF 2.063) Pub Date : 2020-01-11
    Luca Cagliero, Paolo Garza, Giuseppe Attanasio, Elena Baralis

    Forecasting the stock markets is among the most popular research challenges in finance. Several quantitative trading systems based on supervised machine learning approaches have been presented in literature. Recently proposed solutions train classification models on historical stock-related datasets. Training data include a variety of features related to different facets (e.g., stock price trends,

  • On privacy-aware eScience workflows
    Computing (IF 2.063) Pub Date : 2020-01-11
    Khalid Belhajjame, Noura Faci, Zakaria Maamar, Vanilson Burégio, Edvan Soares, Mahmoud Barhamgi

    Abstract Computing-intensive experiments in modern sciences have become increasingly data-driven illustrating perfectly the Big-Data era. These experiments are usually specified and enacted in the form of workflows that would need to manage (i.e., read, write, store, and retrieve) highly-sensitive data like persons’ medical records. We assume for this work that the operations that constitute a workflow

  • Data-driven exploratory models of an electric distribution network for fault prediction and diagnosis
    Computing (IF 2.063) Pub Date : 2020-01-11
    Daniela Renga, Daniele Apiletti, Danilo Giordano, Matteo Nisi, Tao Huang, Yang Zhang, Marco Mellia, Elena Baralis

    Abstract Data-driven models are becoming of fundamental importance in electric distribution networks to enable predictive maintenance, to perform effective diagnosis and to reduce related expenditures, with the final goal of improving the electric service efficiency and reliability to the benefit of both the citizens and the grid operators themselves. This paper considers a dataset collected over 6

  • MDPCluster: a swarm-based community detection algorithm in large-scale graphs
    Computing (IF 2.063) 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

  • Transient fault aware application partitioning computational offloading algorithm in microservices based mobile cloudlet networks
    Computing (IF 2.063) Pub Date : 2019-06-03
    Abdullah Lakhan, Xiaoping Li

    Abstract Mobile Cloudlet Computing paradigm (MCC) allows execution of resource-intensive mobile applications using computation cloud resources by exploiting computational offloading method for resource-constrained mobile devices. Whereas, computational offloading needs the mobile application to be partitioned during the execution in the MCC so that total execution cost is minimized. In the MCC, at

  • Scheduling real time tasks in an energy-efficient way using VMs with discrete compute capacities
    Computing (IF 2.063) Pub Date : 2019-07-24
    Manojit Ghose, Sawinder Kaur, Aryabartta Sahu

    Cloud computing has emerged to be a promising computing paradigm of the recent time. As the high energy consumption in the cloud system creates several problems, the cloud service providers need to focus on the energy consumption along with providing the required service to their users. Cloud system needs to efficiently execute various real-time applications and designing energy-efficient scheduling

  • A parallel stabilized finite element method based on the lowest equal-order elements for incompressible flows
    Computing (IF 2.063) Pub Date : 2019-05-31
    Yueqiang Shang

    Abstract Based on a fully overlapping domain decomposition technique, a parallel stabilized equal-order finite element method for the steady Stokes equations is presented and studied. In this method, each processor computes a local stabilized finite element solution in its own subdomain by solving a global problem on a global mesh that is locally refined around its subdomain, where the lowest equal-order

  • Bringing SQL databases to key-based NoSQL databases: a canonical approach
    Computing (IF 2.063) Pub Date : 2019-06-29
    Geomar A. Schreiner, Denio Duarte, Ronaldo dos Santos Mello

    Abstract Big Data management has brought several challenges to data-centric applications, like the support to data heterogeneity, rapid data growth and huge data volume. NoSQL databases have been proposed to tackle Big Data challenges by offering horizontal scalability, schemaless data storage and high availability, among others. However, NoSQL databases do not have a standard query language, which

  • Data collection from underwater acoustic sensor networks based on optimization algorithms
    Computing (IF 2.063) Pub Date : 2019-06-21
    Mingzhi Chen, Daqi Zhu

    Due to the unique nature of underwater acoustic communication, data collection from the Underwater Acoustic Sensor Networks (UASNs) is a challenging problem. It has been reported that data collection from the UASNs with the assistance of the autonomous underwater vehicles (AUVs) will be more convenient. The AUV needs to schedule a tour to contact all sensors once, which is a variant of the Traveling

  • Resource-efficient fast prediction in healthcare data analytics: A pruned Random Forest regression approach
    Computing (IF 2.063) Pub Date : 2020-01-09
    Khaled Fawagreh, Mohamed Medhat Gaber

    Abstract In predictive healthcare data analytics, high accuracy is both vital and paramount as low accuracy can lead to misdiagnosis, which is known to cause serious health consequences or death. Fast prediction is also considered an important desideratum particularly for machines and mobile devices with limited memory and processing power. For real-time health care analytics applications, particularly

  • Quantumized approach of load scheduling in fog computing environment for IoT applications
    Computing (IF 2.063) Pub Date : 2020-01-09
    Munish Bhatia, Sandeep K. Sood, Simranpreet Kaur

    Load scheduling has been a major challenge in distributed fog computing environments for meeting the demands of decision-making in real-time. This research proposes an quantumized approach for scheduling heterogeneous tasks in fog computing-based applications. Specifically, a node-specific metric is defined in terms of Node Computing Index for estimating the computational capacity of fog computing

  • Micro-journal mining to understand mood triggers
    Computing (IF 2.063) Pub Date : 2020-01-09
    Liuyan Chen, Lukasz Golab

    In computational linguistics, binary sentiment analysis methods have been proposed to predict whether a document expresses a positive or a negative opinion. In this paper, we study a unique research problem—identifying environmental stimuli that contribute to different moods (mood triggers). Our analysis is enabled by an anonymous micro-journalling dataset, containing over 700,000 short journals from

  • A deep learning based framework for optimizing cloud consumer QoS-based service composition
    Computing (IF 2.063) Pub Date : 2020-01-09
    Samar Haytamy, Fatma Omara

    The service composition problem in Cloud computing is formulated as a multiple criteria decision-making problem. Due to the extensive search space, Cloud service composition is addressed as an NP-hard problem. In addition, it is a long term based and economically driven. Building an accurate economic model for service composition has great attention to interest and importance for the Cloud consumer

  • Comparison of analytical and ML-based models for predicting CPU–GPU data transfer time
    Computing (IF 2.063) Pub Date : 2020-01-08
    Ali Riahi, Abdorreza Savadi, Mahmoud Naghibzadeh

    The overhead of data transfer to the GPU poses a bottleneck for the performance of CUDA programs. The accurate prediction of data transfer time is quite effective in improving the performance of GPU analytical modeling, the prediction accuracy of kernel performance, and the composition of the CPU with the GPU for solving computational problems. For estimating the data transfer time between the CPU

  • Immersing citizens and things into smart cities: a social machine-based and data artifact-driven approach
    Computing (IF 2.063) Pub Date : 2020-01-08
    Emir Ugljanin, Ejub Kajan, Zakaria Maamar, Muhammad Asim, Vanilson Burégio

    Abstract This paper presents an approach for allowing the transparent co-existence of citizens and IoT-compliant things in smart cities. Considering the particularities of each, the approach embraces two concepts known as social machines and data artifacts. On the one hand, social machines act as wrappers over applications (e.g., social media) that allow citizens and things to have an active role in

  • Dynamic multi-swarm global particle swarm optimization
    Computing (IF 2.063) Pub Date : 2020-01-04
    Xuewen Xia, Yichao Tang, Bo Wei, Yinglong Zhang, Ling Gui, Xiong Li

    To satisfy the distinct requirements of different evolutionary stages, a dynamic multi-swarm global particle swarm optimization (DMS-GPSO) is proposed in this paper. In DMS-GPSO, the entire evolutionary process is segmented as an initial stage and a later stage. In the initial stage, the entire population is divided into a global sub-swarm and multiple dynamic multiple sub-swarms. During the evolutionary

  • A family of software product lines in educational technologies
    Computing (IF 2.063) Pub Date : 2020-01-02
    Sridhar Chimalakonda, Kesav V. Nori

    Abstract Rapid advances in education domain demand the design and customization of educational technologies for a large scale and variety of evolving requirements. Here, scale is the number of systems to be developed and variety stems from a diversified range of instructional designs such as varied goals, processes, content, teaching styles, learning styles and, also for eLearning Systems for 22 Indian

  • Application of deep reinforcement learning in stock trading strategies and stock forecasting
    Computing (IF 2.063) Pub Date : 2019-12-23
    Yuming Li, Pin Ni, Victor Chang

    The role of the stock market across the overall financial market is indispensable. The way to acquire practical trading signals in the transaction process to maximize the benefits is a problem that has been studied for a long time. This paper put forward a theory of deep reinforcement learning in the stock trading decisions and stock price prediction, the reliability and availability of the model are

  • On cycling risk and discomfort: urban safety mapping and bike route recommendations
    Computing (IF 2.063) Pub Date : 2019-12-09
    David Castells-Graells, Christopher Salahub, Evangelos Pournaras

    Abstract Bike usage in Smart Cities is paramount for sustainable urban development: cycling promotes healthier lifestyles, lowers energy consumption, lowers carbon emissions, and reduces urban traffic. However, the expansion and increased use of bike infrastructure has been accompanied by a glut of bike accidents, a trend jeopardizing the urban bike movement. This paper leverages data from a diverse

  • Assessing mobile applications performance and energy consumption through experiments and Stochastic models
    Computing (IF 2.063) Pub Date : 2019-01-24
    Júlio Mendonça, Ermeson Andrade, Ricardo Lima

    Abstract Energy consumption, execution time, and availability are common terms in discussions on application development for mobile devices. Mobile applications executing in a mobile cloud computing (MCC) environment must consider several issues, such as Internet connections problems and CPU performance. Misconceptions during the design phase can have a significant impact on costs and time-to-market

  • Efficient techniques of parallel recovery for erasure-coding-based distributed file systems
    Computing (IF 2.063) Pub Date : 2019-03-29
    Dong-Oh Kim, Hong-Yeon Kim, Young-Kyun Kim, Jeong-Joon Kim

    Replication has been widely used to ensure the data availability in a distributed file system. In recent years, erasure coding (EC) has been adopted to overcome the problem of space efficiency in Replication. However, EC has various performance degrading factors such as parity calculation and degraded input/output. In particular, the recovery performance of EC is degraded because of various factors

  • Batch activity: enhancing business process modeling and enactment with batch processing
    Computing (IF 2.063) Pub Date : 2019-04-12
    Luise Pufahl, Mathias Weske

    Organizations strive for efficiency in their business processes by process improvement and automation. Business process management (BPM) supports these efforts by capturing business processes in process models serving as blueprint for a number of process instances. In BPM, process instances are typically considered running independently of each other. However, batch processing–the collectively execution

  • Personalized detection of lane changing behavior using multisensor data fusion
    Computing (IF 2.063) Pub Date : 2019-02-27
    Jun Gao, Yi Lu Murphey, Honghui Zhu

    Abstract Side swipe accidents occur primarily when drivers attempt an improper lane change, drift out of lane, or the vehicle loses lateral traction. In this paper, a fusion approach is introduced that utilizes multiple differing modality data, such as video data, GPS data, wheel odometry data, potentially IMU data collected from data logging device (DL1 MK3) for detecting driver’s behavior of lane

  • Hinode: implementing a vertex-centric modelling approach to maintaining historical graph data
    Computing (IF 2.063) Pub Date : 2019-03-27
    Andreas Kosmatopoulos, Anastasios Gounaris, Kostas Tsichlas

    Abstract Over the past few years, there has been a rapid increase of data originating from evolving networks such as social networks, sensor networks and others. A major challenge that arises when handling such networks and their respective graphs is the ability to issue a historical query on their data, that is, a query that is concerned with the state of the graph at previous time instances. While

  • An efficient and batch verifiable conditional privacy-preserving authentication scheme for VANETs using lattice
    Computing (IF 2.063) Pub Date : 2018-12-13
    Sankar Mukherjee, Daya Sagar Gupta, G. P. Biswas

    With the rapid increase in the internet technologies, Vehicular Ad hoc Networks (VANETs) are identified as a crucial primitive for the vehicular communication in which the moving vehicles are treated as nodes to form a mobile network. To ameliorate the efficiency and traffic security of the communication, a VANET can wirelessly circulate the traffic information and status to the participating vehicles

  • On the performance, availability and energy consumption modelling of clustered IoT systems
    Computing (IF 2.063) Pub Date : 2019-05-02
    Enver Ever, Purav Shah, Leonardo Mostarda, Fredrick Omondi, Orhan Gemikonakli

    Wireless sensor networks (WSNs) form a large part of the ecosystem of the Internet of Things (IoT), hence they have numerous application domains with varying performance and availability requirements. Limited resources that include processing capability, queue capacity, and available energy in addition to frequent node and link failures degrade the performance and availability of these networks. In

  • Homomorphically encrypted k-means on cloud-hosted servers with low client-side load
    Computing (IF 2.063) Pub Date : 2019-02-18
    Georgios Sakellariou, Anastasios Gounaris

    Abstract The significance of data analytics has been acknowledged in many scientific and business domains. However, the required processing power and memory capacity is a prohibiting factor for performing data analytics on proprietary platforms. An obvious solution is the outsourcing of data analytics to cloud storage and cloud computing providers but this entails that privacy and security issues are

  • An accurate and flexible technique for camera calibration
    Computing (IF 2.063) Pub Date : 2019-05-09
    Jun Jiang, Liangcai Zeng, Bin Chen, Yang Lu, Wei Xiong

    Abstract The traditional calibration paradigms fail to give reliable and accurate results in case of low-quality 2D planar calibration plates. In this paper, an active method is proposed by employing an LCD panel for camera calibration. This method automatically generates a sequence of virtual patterns in different views by pre-defined transforms without manually manipulation or other equipment’s help

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全球疫情及响应:BMC Medicine专题征稿