Author：Al-Garadi, MA;Varathan, KD;Ravana, SD;Ahmed, E;Chang, V

Abstract:Online social networks (OSNs) portray a multi-layer of interactions through which users become a friend, information is propagated, ideas are shared, and interaction is constructed within an OSN. Identifying the most influential spreaders in a network is a significant step towards improving the use of existing resources to speed up the spread of information for application such as viral marketing or hindering the spread of information for application like virus blocking and rumor restraint. Users communications facilitated by OSNs could confront the temporal and spatial limitations of traditional communications in an exceptional way, thereby presenting new layers of social interactions, which coincides and collaborates with current interaction layers to redefine the multiplex OSN. In this paper, the effects of different topological network structure on influential spreaders identification are investigated. The results analysis concluded that improving the accuracy of influential spreaders identification in OSNs is not only by improving identification algorithms but also by developing a network topology that represents the information diffusion well. Moreover, in this paper a topological representation for an OSN is proposed which takes into accounts both multilayers interactions as well as overlaying links as weight. The measurement results are found to be more reliable when the identification algorithms are applied to proposed topological representation compared when these algorithms are applied to single layer representations.

Author：Li, Yuming ; Ni, Pin ; Chang, Victor

Abstract:The stock market plays a major role in the entire financial market. How to obtain effective trading signals in the stock market is a topic that stock market has long been discussing. This paper first reviews the Deep Reinforcement Learning theory and model, validates the validity of the model through empirical data, and compares the benefits of the three classical Deep Reinforcement Learning models. From the perspective of the automated stock market investment transaction decision-making mechanism, Deep Reinforcement Learning model has made a useful reference for the construction of investor automation investment model, the construction of stock market investment strategy, the application of artificial intelligence in the field of financial investment and the improvement of investor strategy yield. © 2019 International Conference on Complexity, Future Information Systems and Risk.

Author：Fischer, T;De Biswas, K;Ham, JJ;Naka, R;Huang, WX

Author：Vijayakumar, P;Chang, V;Deborah, LJ;Kshatriya, BSR

Abstract:With the computing systems becoming more and more pervasive and ubiquitous due to the invention of cloud computing and mobile phone based applications, secure data transmission is the pressing need for a real time perspective of the technologies. Examples of the need for secure key management and distribution environments include secure transmission of health related SMS, telecare medicine provisioning for critical applications such as heart disorders, secure agriculture monitoring, data transmission in surveillance scenarios, secure military networks, etc. In the context of key exchange for secure group communication, the computational complexities need to be addressed in particular due to the advent of resource constrained mobile phones, sensors and other embedded devices. This special issue introduces some of the novel approaches for enabling secure group communication in the contexts related to cloud and mobile computing. (C) 2018 Published by Elsevier B.V.

Author：Cao,Xuanwei;Zabe-Brechtel,Christoph

Abstract:Purpose: The purpose of this paper is to elucidate the dynamic interactions and co-evolution of institutions with the technology and organization fields in emerging industry development. Insights and inspirations from comparison of the triangle relationship among government, market and local community in different institutional contexts could contribute to possible institutional innovation in the context of large-scale institutional transition. In this way, this paper is expected to offer insights to the development of emerging industries in China. Design/methodology/approach: The paper reviews the focal literature focusing on institutional change and the co-evolution of institution, industry and technology. A multi-level conceptual framework is put forward to explain the mechanism for the co-evolution of technology, organization and institution. A multi-case comparison method was applied to compare and disclose the process of co-evolution of institutions, and the technology and organizational fields, as well as varied paths of industry development in different institutional contexts. Findings: Emerging industry development in China is still presenting the character of path dependence to a great extent under traditional institutional arrangement, while the power and possible contribution from broader actors in the local community have been ignored. Driving force for a more innovative institutional transition towards emerging industry development should consider decentralized institutional arrangement and actions at local community instead of "command and control" from central planning. Practical implications: First, the comparison of wind energy industry development in three countries creates possibilities for further analysis and reference for China's emerging industry. Second, the illustration of the triangle relationship among government, market and local community in different countries helps policy makers in China reconsider and redesign an effective institutional framework for balancing the powers among indigenous community, local government and market. Institutional alignment should be listed as an important consideration during the process of the policy design of such an effective institutional framework. Originality/value: The paper presents a model to understand the dynamic co-evolution of the institution, technology and organizational fields. It confirms the role of institution in promoting emerging industry development. Particularly, it offers inspirations for the development of emerging industries in nations facing large-scale institutional transition. © Emerald Group Publishing Limited.

Author：Freire, T

Abstract:In 1978, Singapore became the first country to introduce legislation allowing foreign domestic workers to work in the country under special visas. Although Singapore is often cited in the literature as a success story, no studies have quantified the impact of this legislation. In this paper, we use data derived from the Singapore Yearbook of Manpower Statistics between 1974 and 1985 to determine the influence of the 1978 legislation on the labor supply of Singaporean women. We find that the labor supply of women affected by this policy increased by between 3.1%% and 6.2%%.

Author：Yao, XZ;Izzeldin, M;Li, ZX

Abstract:This paper proposes a cluster HAR-type model that adopts the hierarchical clustering technique to form the cascade of heterogeneous volatility components. In contrast to the conventional HAR-type models, the proposed cluster models are based on the relevant lagged volatilities selected by the cluster group Lasso. Our simulation evidence suggests that the cluster group Lasso dominates other alternatives in terms of variable screening and that the cluster HAR serves as the top performer in forecasting the future realized volatility. The forecasting superiority of the cluster models are also demonstrated in an empirical application where the highest forecasting accuracy tends to be achieved by separating the jumps from the continuous sample path volatility process. (C) 2019 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

Author：Lv, ZH;Iqbal, R;Chang, V

Abstract:Sustainability is a paradigm for thinking about the future in which environmental, societal and economic considerations are equitable in the pursuit of an improved lifestyle. Most of the economies are developing with breakneck velocities and are becoming epicenters of unsustainable global growth. Immense utilization of natural resources, waste generation and ecological irresponsibility are the reasons for such a dire situation. Big data analytics is clearly on a penetrative path across all arenas that rely on technology. (C) 2018 Published by Elsevier B.V.

Author：Hallman, RA;Li, SJ;Chang, V

Author：Sun, J;Huang, GH;Sun, G;Yu, HF;Sangaiah, AK;Chang, V

Abstract:As the size and service requirements of today's networks gradually increase, large numbers of proprietary devices are deployed, which leads to network complexity, information security crises and makes network service and service provider management increasingly difficult. Network function virtualization (NFV) technology is one solution to this problem. NFV separates network functions from hardware and deploys them as software on a common server. NFV can be used to improve service flexibility and isolate the services provided for each user, thus guaranteeing the security of user data. Therefore, the use of NFV technology includes many problems worth studying. For example, when there is a free choice of network path, one problem is how to choose a service function chain (SFC) that both meets the requirements and offers the service provider maximum profit. Most existing solutions are heuristic algorithms with high time efficiency, or integer linear programming (ILP) algorithms with high accuracy. It's necessary to design an algorithm that symmetrically considers both time efficiency and accuracy. In this paper, we propose the Q-learning Framework Hybrid Module algorithm (QLFHM), which includes reinforcement learning to solve this SFC deployment problem in dynamic networks. The reinforcement learning module in QLFHM is responsible for the output of alternative paths, while the load balancing module in QLFHM is responsible for picking the optimal solution from them. The results of a comparison simulation experiment on a dynamic network topology show that the proposed algorithm can output the approximate optimal solution in a relatively short time while also considering the network load balance. Thus, it achieves the goal of maximizing the benefit to the service provider.

Author：Yousafzai, Abdullah ; Chang, Victor ; Gani, Abdullah ; Noor, Rafidah Md

Abstract:The advancement in mobile technology and the introduction of cloud computing systems enable the use of educational materials on mobile devices for a location- and time-agnostic learning process. These educational materials are delivered in the form of data and compute-intensive multimedia-enabled learning objects. Given these constraints, the desired objective of mobile learning (m-learning) may not be achieved. Accordingly, a number of m-learning systems are being developed by the industry and academia to transform society into a pervasive educational institute. However, no guideline on the technical issues concerning the m-learning environment is available. In this study, we present a taxonomy of such technical issues that can impede the life cycle of multimedia-enabled m-learning applications. The taxonomy is devised based on the issues related to mobile device heterogeneity, network performance, content heterogeneity, content delivery, and user expectation. These issues are discussed, along with their causes and measures, to achieve solutions. Furthermore, we identify several trending areas through which the adaptability and acceptability of multimedia-enabled m-learning platforms can be increased. Finally, we discuss open challenges, such as low complexity encoding, data dependency, measurement and modeling, interoperability, and security as future research directions. © 2016 Elsevier Ltd. All rights reserved.

Author：Zhou, SS;Zhou, AJ;Feng, JZ;Jiang, SS

Abstract:How firms' dynamic capabilities lead to their competitive advantage and improved firm performance has been a core issue and full of debates. In this research, we theorize that dynamic capabilities, which could be defined by three distinct dimensions (sensing capability, integration capability, and reconfiguration capability), facilitate different types of innovation that in turn improve firm performance. Based on a sample of 204 Chinese firms, results from partial least squares structural equation modeling analyses generally support our arguments despite some nuanced differences existing among different dimensions of dynamic capabilities. This study contributes to dynamic capabilities literature by reducing the scarcity of empirical research and by uncovering the mechanisms through which dynamic capabilities influence firm performance.

Author：Chang, V

Abstract:This paper describes our latest research in data analytics and visualization for bioinformatics and healthcare. Each year many patients have suffered cancers. Analytics and visualization can help to simulate the development of malignant tumors and help identify weak spots of tumor for treatment, inspect malignant tumors in general and inspect whether genes have cancerous cells. Related literature, technologies, simulation results with explanation, performance evaluation and comparisons with other work have been discussed in details. We can process training data with a low completion time to achieve simulations of malignant tumors and genes to inspect their status, as well as the querying the output data within seconds. Our malignant tumor and gene simulation can achieve 360 degrees for an inspection of cancerous presence. We conclude that data analytics and visualization can provide effective and efficient healthcare research and also other type of interdisciplinary research.

Author：李平;杨政银

Abstract:大时代呼唤大战略,大格局孕育大战略.伫立于实现民族复兴的历史关口,置身于“一带一路”倡议的恢弘实践,中国企业迎来了全新的历史发展机遇,世界舞台正拭目以待中国企业的全新表现,尤其是中国企业如何通过国际化战略实现转型升级.毋庸讳言,中国企业迎来了跨国全球发展的春天.然而,“春天”到来之际也可能出现“倒春寒”,跨国经营的战略转型充满挑战与陷阱,一着不慎就可能万劫不复.中国企业如何顺利“春种”,获得“秋收”?本文为中国企业提供了一个在“一带一路”背景下跨国经营战略转型的管理思想及模式——“第二故乡”战略.希望这一战略模式有助于中国企业全球化经营,有助于“一带一路”倡议从宏伟畅想变成累累果实.

Author：Anwar, S;Inayat, Z;Zolkipli, MF;Zain, JM;Gani, A;Anuar, NB;Khan, MK;Chang, V

Abstract:The state-of-the-art Cloud Computing (CC) has been commercially popular for shared resources of third party applications. A cloud platform enables to share resources among mutually distrusting CC clients and offers cost-effective, on-demand scaling. With the exponential growth of CC environment, vulnerabilities and their corresponding exploitation of the prevailing cloud resources may potentially increase. Although CC provides numerous benefits to the cloud computing tenant. However, features namely resource sharing and Virtual Machine (VM) physical co-residency raising the potential for sensitive information leakages such as Side Channel (SC) attacks. In particular, the physical co-residency feature allows attackers to communicate with another VM on the same physical machine and leak the confidential information due to inadequate logical isolation. Unlike encryption, which protects information from being decoded by unauthorized persons, SC attacks aim to exploit the encryption systems and to hide the occurrence of communication. SC attacks were initially identified as the main threat on multi-level secure systems i.e. OS, database, and networks. More recently, the focus of the researchers has shifted toward SC attacks in CC. Since the last level cache (L2 or L3) is always shared between VM, is the most targeting device for these attacks. Therefore, the aim of this article is to explore cross-VM SC attacks involving the CPU cache and their countermeasures in CC and to compare with the traditional SC attacks and countermeasures. We categorized the SC attacks according to the hardware medium they target and exploit, the ways they access the module and the method they use to extract confidential information. We identified that traditional prevention mechanisms for SC attacks are not appropriate for prevention of cross-VM cache-based SC attacks. We also proposed countermeasures for the prevention of these attacks in order to improve security in CC.

Author：Hashem, IAT;Chang, V;Anuar, NB;Adewole, K;Yaqoob, I;Gani, A;Ahmed, E;Chiroma, H

Abstract:The expansion of big data and the evolution of Internet of Things (IoT) technologies have played an important role in the feasibility of smart city initiatives. Big data offer the potential for cities to obtain valuable insights from a large amount of data collected through various sources, and the IoT allows the integration of sensors, radio-frequency identification, and Bluetooth in the real-world environment using highly networked services. The combination of the IoT and big data is an unexplored research area that has brought new and interesting challenges for achieving the goal of future smart cities. These new challenges focus primarily on problems related to business and technology that enable cities to actualize the vision, principles, and requirements of the applications of smart cities by realizing the main smart environment characteristics. In this paper, we describe the state-of-the-art communication technologies and smart based applications used within the context of smart cities. The visions of big data analytics to support smart cities are discussed by focusing on how big data can fundamentally change urban populations at different levels. Moreover, a future business model of big data for smart cities is proposed, and the business and technological research challenges are identified. This study can serve as a benchmark for researchers and industries for the future progress and development of smart cities in the context of big data. (C) 2016 Elsevier Ltd. All rights reserved.

Author：Siddiqa, A;Karim, A;Chang, V

Abstract:Numerous applications are continuously generating massive amount of data and it has become critical to extract useful information while maintaining acceptable computing performance. The objective of this work is to design an indexing framework which minimizes indexing overhead and improves query execution and data search performance with optimum aggregation of computing performance. We propose SmallClient, an indexing framework to speed up query execution. SmallClient has three modules: block creation, index creation and query execution. Block creation module supports improving data retrieval performance with minimum data uploading overhead. Index creation module allows maximum indexes on a dataset to increase index hit ratio with minimized indexing overhead. Finally, query execution module offers incoming queries to utilize these indexes. The evaluation shows that SmallClient outperforms Hadoop full scan with more than 90%% search performance. Meanwhile, indexing overhead of SmallClient is reduced to approximately 50 and 80%% for index size and indexing time respectively.

Author：Pak, DHA;Li, RYM

Abstract:Transaction costs requires reductions and via knowledge practices and the Web 2.0. This can be significantly curtailed, the study looks at the literature and the success of the social network site, Facebook and draws conclusions thereof.

Author：Baghaei, N;Naslund, JA;Hach, S;Liang, HN

Author：Rodrigues, Vinicius Facco ; Da Rosa Righi, Rodrigo ; Da Costa, Cristiano André ; Singh, Dhananjay ; Munoz, Victor Mendez ; Chang, Victor

Abstract:The elasticity feature of cloud computing has been proved as pertinent for parallel applications, since users do not need to take care about the best choice for the number of processes/resources beforehand. To accomplish this, the most common approaches use threshold-based reactive elasticity or time-consuming proactive elasticity. However, both present at least one problem related to the need of a previous user experience, lack on handling load peaks, completion of parameters or design for a specific infrastructure and workload setting. In this regard, we developed a hybrid elasticity service for parallel applications named SelfElastic. As parameterless model, SelfElastic presents a closed control loop elasticity architecture that adapts at runtime the values of lower and upper thresholds. Besides presenting SelfElastic, our purpose is to provide a comparison with our previous work on reactive elasticity called AutoElastic. The results present the SelfElastic’s lightweight feature, besides highlighting its performance competitiveness in terms of application time and cost metrics. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved