23,829 research outputs found

    Introduction: Advances in E-Business Engineering

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    (First Paragraph) E-business is one of the most exciting and challenging research areas. Today, not only large companies, but also medium or small-sized companies are learning that e-business is a required component of doing business. E-business has rapidly evolved in the last decade and this trend will continue. In this rapid process, a variety of e-business engineering methods and techniques have been developed. There are many research issues needed to be addressed. As a result, there is a growing demand for insights into challenges, issues, and solutions related to the design, implementation, and management of e-business systems

    Compressed sensing signal and data acquisition in wireless sensor networks and internet of things

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    The emerging compressed sensing (CS) theory can significantly reduce the number of sampling points that directly corresponds to the volume of data collected, which means that part of the redundant data is never acquired. It makes it possible to create standalone and net-centric applications with fewer resources required in Internet of Things (IoT). CS-based signal and information acquisition/compression paradigm combines the nonlinearreconstruction algorithm and random sampling on a sparsebasis that provides a promising approach to compress signal and data in information systems. This paper investigates how CS can provide new insights into data sampling and acquisition in wireless sensor networks and IoT. First, we briefly introduce the CS theory with respect to the sampling and transmission coordination during the network lifetime through providing a compressed sampling process with low computation costs. Then, a CS-based framework is proposed for IoT, in which the end nodes measure, transmit, and store the sampled data in the framework. Then, an efficient cluster-sparse reconstruction algorithm is proposed for in-network compression aiming at more accurate data reconstruction and lower energy efficiency. Performance is evaluated with respect to network size using datasets acquired by a real-life deployment

    Face hallucination based on nonparametric Bayesian learning

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    © 2015 IEEE. In this paper, we propose a novel example-based face hallucination method through nonparametric Bayesian learning based on the assumption that human faces have similar local pixel structure. We cluster the low resolution (LR) face image patches by nonparametric method distance dependent Chinese Restaurant process (ddCRP) and calculate the centres of the clusters (i.e., subspaces). Then, we learn the mapping coefficients from the LR patches to high resolution (HR) patches in each subspace. Finally, the HR patches of input low resolution face image can be efficiently generated by a simple linear regression. The spatial distance constraint is employed to aid the learning of subspace centers so that every subspace will better reflect the detailed information of image patches. Experimental results show our method is efficient and promising for face hallucination

    Computational intelligence-enabled cybersecurity for the Internet of Things

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    The computational intelligence (CI) based technologies play key roles in campaigning cybersecurity challenges in complex systems such as the Internet of Things (IoT), cyber-physical-systems (CPS), etc. The current IoT is facing increasingly security issues, such as vulnerabilities of IoT systems, malware detection, data security concerns, personal and public physical safety risk, privacy issues, data storage management following the exponential growth of IoT devices. This work aims at investigating the applicability of computational intelligence techniques in cybersecurity for IoT, including CI-enabled cybersecurity and privacy solutions, cyber defense technologies, intrusion detection techniques, and data security in IoT. This paper also attempts to provide new research directions and trends for the increasingly IoT security issues using computational intelligence technologies

    A stream processing framework based on linked data for information collaborating of regional energy networks

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    © 2005-2012 IEEE. Coordinating of energy networks to form a city-level multidimensional integrated energy system becomes a new trend in Energy Internet (EI). The collaborating in the information layer is a core issue to achieve smart integration. However, the heterogeneity of multiagent data, the volatility of components, and the real-time analysis requirement in EI bring significant challenges. To solve these problems, in this article we propose a stream processing framework based on linked data for information collaboration among multiple energy networks. The framework provides a universal data representation based on linked data and semantic relation discovery approach to model and semantically fuse heterogeneous data. Semantics-based information transmission contracts and channels are automatically generated to adapt to structural changes in EI. A multimodel-based dynamic adjusting stream processing is implemented using data semantics. A real-world case study is implemented to demonstrate the adaptability, feasibility, and flexibility of the proposed framework

    Electronic Markets in Emerging Markets

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    (First paragraph) Electronic markets and networked business is one of most challenging areas for industry and research communities. Electronic markets have evolved from the classic article on the Electronic market hypothesis published in Communications of the ACM in 1987 to the highly integrated and collaborative e-business such as Alibaba (Malone et al. 1987;Wangetal. 2008). This evolution has reshaped the ways of doing business and supply chain networks (Peruzzini and Stjepandić 2018). In last two decades, we have seen how novel and dynamic electronic markets applications have been bringing about a variety of new developments, new organizational forms and shapes in respective industries. In the last decade, one of the most fundamental trends is the emergence of markets such as China, India, Brazil, and Russia as drivers of global economic growth (Li 2013). The emerging markets account for more andmoreworld exports. Emerging markets including the emerging market country classifications have been defined in the literature (Atilgan et al. 2016). We believe that there are significant opportunities for improving our understanding of electronic markets in emerging economies, in ways that also advance theories of electronic markets and their impact on both developed and developing (emerging) economies

    QoS Recommendation in Cloud Services

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    As cloud computing becomes increasingly popular, cloud providers compete to offer the same or similar services over the Internet. Quality of service (QoS), which describes how well a service is performed, is an important differentiator among functionally equivalent services. It can help a firm to satisfy and win its customers. As a result, how to assist cloud providers to promote their services and cloud consumers to identify services that meet their QoS requirements becomes an important problem. In this paper, we argue for QoS-based cloud service recommendation, and propose a collaborative filtering approach using the Spearman coefficient to recommend cloud services. The approach is used to predict both QoS ratings and rankings for cloud services. To evaluate the effectiveness of the approach, we conduct extensive simulations. Results show that the approach can achieve more reliable rankings, yet less accurate ratings, than a collaborative filtering approach using the Pearson coefficient