Abstract One of the main features of the physical environment in which humans live is spatial dimensionality. When we think about 3D, we usually refer to 3D video, even if it is not the only existing channel of natural interaction. On the contrary, related work in the field of 3D audio mainly refers to the investigation of acoustic effects by adding reverberation in the context of virtual... One of the main features of the physical environment in which humans live is spatial dimensionality. When we think about 3D, we usually refer to 3D video, even if it is not the only existing channel of natural interaction. On the contrary, related work in the field of 3D audio mainly refers to the investigation of acoustic effects by adding reverberation in the context of virtual 3D environments. In this paper, we present an interaction system based on spatialized sounds. We developed an innovative cloud application in the cultural heritage context; a personal guide, in 3D sound, attracting the tourists attention toward monuments or buildings, offering sound scapes of augmented reality. The designed system interacts with smart headphones that remotely takes the orientation of the listener's head and properly generates an audio output, which also takes into account the listener's position and orientation in the environment. Thus, such innovative headphones using an inertial measurement unit for determining the orientation of user's head have been designed and developed in open-ear mode, in order to locate the user in the real context. Read More Read Less

Abstract Differential evolution (DE) is a simple and powerful evolutionary algorithm, which has been successfully used in various scientific and engineering fields. Generally, the base and difference vectors of the mutation operator in most of DE are randomly selected from the current population. Additionally, the population information is not fully exploited in the design of DE. In order to alleviate these drawbacks and enhance the performance of DE, this... Differential evolution (DE) is a simple and powerful evolutionary algorithm, which has been successfully used in various scientific and engineering fields. Generally, the base and difference vectors of the mutation operator in most of DE are randomly selected from the current population. Additionally, the population information is not fully exploited in the design of DE. In order to alleviate these drawbacks and enhance the performance of DE, this study presents a DE framework with Composite Population Information based mutation operator (DE-CPI) for global numerical optimization. In DE-CPI, the ring topology is employed to define a neighborhood for each individual and then the direction information with the neighbors is introduced into the mutation operator of DE. By this way, the composite population information, i.e., neighborhood and direction information, can be fully and simultaneously utilized in DE-CPI to guide the search of DE. In order to evaluate the effectiveness of the proposed method, DE-CPI is incorporated into the original DE algorithms, as well as several advanced DE variants. Experimental results clearly show that DE-CPI is able to enhance the performance of most of the DE algorithms studied. Read More Read Less

Abstract A number of reputation mechanisms are introduced in recent years to alleviate the blindness during peer selection in distributed P2P environment where malicious peers coexist with honest ones. They indeed provide incentives for peers to contribute more resources to the system, and thus, promote the whole system performance. However, little attention has been paid on how to identify the malicious peers in this situation. In this paper, a... A number of reputation mechanisms are introduced in recent years to alleviate the blindness during peer selection in distributed P2P environment where malicious peers coexist with honest ones. They indeed provide incentives for peers to contribute more resources to the system, and thus, promote the whole system performance. However, little attention has been paid on how to identify the malicious peers in this situation. In this paper, a general framework is presented for detecting malicious peers in Reputation-based P2P systems. Firstly, the malicious peers are divided into various categories and the problem is formulated. Secondly, the general framework is put forward which mainly contains four steps, i.e. data collection, data processing, malicious peers detection and malicious peers clustering. Thirdly, an algorithm implementation of this general framework named IDEA is put forward based on Principal Direction Divisive Partitioning and K-means clustering. Finally, a series of simulation experiments are conducted to evaluate the effectiveness of IDEA. Simulation results have shown that IDEA can precisely distinguish and cluster the malicious peers. Read More Read Less

Abstract Due to the recent popularization of IP multicasts, the continuous broadcasting of audio or video media data is attracting great attention. Although servers can concurrently deliver data to many clients, they have to wait until their data are broadcast. In division-based broadcasting, many researches have proposed scheduling methods to reduce waiting time. A division-based broadcasting system has also been proposed to evaluate these schemes. However, this system does... Due to the recent popularization of IP multicasts, the continuous broadcasting of audio or video media data is attracting great attention. Although servers can concurrently deliver data to many clients, they have to wait until their data are broadcast. In division-based broadcasting, many researches have proposed scheduling methods to reduce waiting time. A division-based broadcasting system has also been proposed to evaluate these schemes. However, this system does not consider several problems that are assumed in actual networks. In this paper, we implement a division-based broadcasting system for webcasts. In our proposed system, we evaluate the effectiveness of reducing both the waiting and interruption times using conventional methods. Read More Read Less

Abstract The huge number of sensors envisioned to be deployed within Internet of Things applications will result in large amounts of likely confidential data that will be required to traverse the open Internet. This confidential data could range from the current security status of a smart house to the current health status of an elderly person connected to smart monitors. A secure and... The huge number of sensors envisioned to be deployed within Internet of Things applications will result in large amounts of likely confidential data that will be required to traverse the open Internet. This confidential data could range from the current security status of a smart house to the current health status of an elderly person connected to smart monitors. A secure and able connection will be required to connect either a cloud based or an in-house server to the embedded sensors. The ability to ensure that the data generated by the sensor networks is securely streamed to the central server will allow for real time access to the data. The use of the less favoured Datagram Transport Layer Security protocol is compared with the widely used Transport Layer Security protocol. Additionally, the use of Internet Protocol Security is investigated as an option. A comparison of the functioning and capabilities of Internet Protocol Security is also completed. The research shows that Datagram Transport Layer Security is a capable alternative to Transport Layer Security when it comes to streaming data across the Internet from connected Wireless Sensor Networks. Read More Read Less

Abstract Mobile traffic is significantly growing, thanks to the increased access capacity provided by 3G and 4G technologies and to the rising computing power of the latest smart devices. Due to the widespread diffusion of mobile applications that require and process sensitive customers data, mobile traffic is more and more subject to security attacks. Recently, traffic analysis techniques are being successfully adopted to characterize, from a security point of... Mobile traffic is significantly growing, thanks to the increased access capacity provided by 3G and 4G technologies and to the rising computing power of the latest smart devices. Due to the widespread diffusion of mobile applications that require and process sensitive customers data, mobile traffic is more and more subject to security attacks. Recently, traffic analysis techniques are being successfully adopted to characterize, from a security point of view, applications and networks behaviour in order to detect and avoid intrusion attempts, malware injections and data theft. When applied to the mobile domain, such techniques have to cope on the one hand with the performance constraints posed by the limited devices resources and, on the other hand, with the need for accurate and up-to-date traffic models, resulting from a continuous processing of meaningful traffic data and threat-related information. To face these issues, we propose a two-tier service-based traffic analysis infrastructure: at the mobile network layer, mobile devices run a high-accuracy hardware traffic analyser, which allows for the processing of large data sets in an energy-efficient way; at the service layer, a high-scale traffic analyser takes advantage of the correlation of traffic data involving heterogeneous and geographically distributed sources, in order to produce enhanced traffic models, which can be distributed to the devices in an on-demand fashion, according to the as-a- Service approach. To show the feasibility of our proposal, we provide a case study based on the implementation of a decision treebased traffic analyser on a Xilinx Zynq 7000 architecture,and present an overview of the service layer, by referring to a cloud infrastructure for its implementation. Read More Read Less

Abstract Cloud computing is fundamental for wireless sensor network. Firstly massive data is gathered by Wireless sensor. Then those data is sent to platform of cloud computing. In the platform data is shared and transferred. The whole System is controlled and managed. In the process reliable and integrity of data depends on performance of wireless sensor Networks Topology control is one of core key factors which can influence... Cloud computing is fundamental for wireless sensor network. Firstly massive data is gathered by Wireless sensor. Then those data is sent to platform of cloud computing. In the platform data is shared and transferred. The whole System is controlled and managed. In the process reliable and integrity of data depends on performance of wireless sensor Networks Topology control is one of core key factors which can influence performance of wireless-sensor networks (WSN). So how to design topology structure with high reliable and survivability is becoming the important subject on wireless-sensor networks. For the issue, a topology evolution strategy based on scale-free network with energy efficient is proposed. First by clustering algorithm cluster nodes are distributed evenly. Then those nodes start evolving in accordance with random walk processes on scale-free network. Since some key factors such as residual energy of node and node degree are taken into account in evolving procedure, network topology obtains quality features such as load and energy balance. So the network is quite practical. Finally the simulation shows that network topology from the evolution algorithm have good robustness regardless of deliberate and random attack. Consequently robustness feature of wireless-sensor network under severe environment is satisfied. Read More Read Less

Abstract Chaos, a kind of noise like signals, generated by deterministic nonlinear Dynamical system, has good auto correlation performance. Previous researches indicate that broad bandwidth chaos can be easily obtained by semiconductor laser subjecting optical feedback, optoelectronic feedback, or external optical injection under an appropriate conditions. Break detection for WDM-PON needed to solve the multi-branch; node density problem for the realization of high-precision positioning of each branch proposed a breakpoint detection... Chaos, a kind of noise like signals, generated by deterministic nonlinear Dynamical system, has good auto correlation performance. Previous researches indicate that broad bandwidth chaos can be easily obtained by semiconductor laser subjecting optical feedback, optoelectronic feedback, or external optical injection under an appropriate conditions. Break detection for WDM-PON needed to solve the multi-branch; node density problem for the realization of high-precision positioning of each branch proposed a breakpoint detection method based broadband chaotic light lasers, with the help of powerful and elastic computation ability and storage capacity, put proposes a framework of service platform with a collaborative service model of hybrid cloud in the cloud computing environment. On the basis of analysing the characteristics of broadband chaotic sources on a preliminary simulation results show that this method can be highly accurate positioning WDM-PON branch break point location, spatial resolution of 7cm and independent of the detection distance. Read More Read Less

Abstract This article explicitly stated the concept of national value chain and chain mode innovation from the two perspectives: first is to organize capable enterprises embedded in the value chain of global industry, and urge them to stretch on both ends of the smiling curve to reach a higher level of cooperation, and then catch the leading position of the value chain; second is to construct technology innovation... This article explicitly stated the concept of national value chain and chain mode innovation from the two perspectives: first is to organize capable enterprises embedded in the value chain of global industry, and urge them to stretch on both ends of the smiling curve to reach a higher level of cooperation, and then catch the leading position of the value chain; second is to construct technology innovation system that matches the development of related industries, cultivate the national prominent value chain of domestic companies, evolve the global value chain to a higher level through the continuous optimization and upgrading of the industrial structure; next is to explain function mechanism of chain mode innovation and framework of industrial policy mix, design and simulate the evolution process under the action of the policy, then evaluate the effect of the policy mix. Finally it is concluded that policy mix of chain mode innovation has the best effect. Read More Read Less

Abstract At present, knowledge management (KM) is critical to the success of a firm in digital information era. KM effectiveness and competitive advantages of a firm can be increased and sustained by effectively encouraging employees to propagate useful tacit knowledge across the organization. Tacit knowledge propagates through direct contact among individuals in an organization. Therefore, we consider employees and their contacts as an undirected network, which is also considered as... At present, knowledge management (KM) is critical to the success of a firm in digital information era. KM effectiveness and competitive advantages of a firm can be increased and sustained by effectively encouraging employees to propagate useful tacit knowledge across the organization. Tacit knowledge propagates through direct contact among individuals in an organization. Therefore, we consider employees and their contacts as an undirected network, which is also considered as a knowledge exchange network. We model the peculiarities of a tacit knowledge spreading in knowledge network with consideration of intention mechanism, and this model is compared with Susceptible- Infected-Susceptible (SIS) model. We provide simulation and numerical results to support our analyses and results. Results show that the degree distribution of knowledge exchange network and intention mechanism have great influence on tacit knowledge spreading. Our results show that a threshold that governs whether a tacit knowledge can spread across the organization exists. Analysis results can guide KM and digital information management in view of the theoretical and practical aspects. Read More Read Less

Abstract Biomedical event extraction from literature is a new research topic in the field of biomedical text mining. Trigger detection is a crucial and challenging subtask in this complex task. A method based on the conditional random field (CRF) model combined with support vector machine (SVM) is developed in this study to detect biomedical event triggers. Rich features, including lexical and structural, are used in model training. Experiment results... Biomedical event extraction from literature is a new research topic in the field of biomedical text mining. Trigger detection is a crucial and challenging subtask in this complex task. A method based on the conditional random field (CRF) model combined with support vector machine (SVM) is developed in this study to detect biomedical event triggers. Rich features, including lexical and structural, are used in model training. Experiment results show the promising perfor-mance of the method, which achieved an F-score of 72.07 on the BioNLP2013-ST corpus. Different from the other similar systems, the valid proteins that participate in the event are identified which is helpful to detect the triggers. We believe this work is a positive contribution to the biomedical text mining community by easing biomedical event extraction. Read More Read Less