An Energy-aware Routing Mechanism for Latency-sensitive Traffics

With the rapid development of Internet technology and enhanced QoS requirements, network energy consumption has attracted more and more attentions due to the overprovision of network resources. Generally, energy saving can be achieved by sacrificed some performance. However, many popular applications require real-time or soft real-time QoS performance for attracting potential users, and existing technologies can hardly obtain satisfying tradeoffs between energy consumption and performance. In this paper, a novel energy-aware routing mechanism is presented with aiming at reducing the network energy consumption and maintaining satisfying QoS performance for these latency-sensitive applications. The proposed routing mechanism applies stochastic service model to calculate the latency-guarantee for any given network links. Based on such a quantitative latencyguarantee, we further propose a technique to decide whether a link should be powered down and how long it should be kept in power saving mode. Extensive experiments are conducted to evaluate the effectiveness of the proposed mechanism, and the results indicate that it can provide better QoS performance for those latency-sensitive traffics with improved energyefficiency

Identification of Opinion Spammers using Reviewer Reputation and Clustering Analysis

Online reviews have increasingly become a very important resource before making a purchasing decisions. Unfortunately, malicious sellers try to game the system by hiring a person or team (which is called spammers) to fabricate fake reviews to improve their reputation.Existing methods mainly take the problem as a general binary classification or focus on some heuristic rules. However, supervised learning methods relies heavily on a large number of labeled examples of deceptive and truthful opinions by domain experts, and most of features mentioned in the heuristic strategy ignore the characteristic of the group organization among spammers. In this paper, an effective method of identifying opinion spammers is proposed. Firstly, suspected spammers are detected by means of unsupervised learning based on reviewer’s reputation. We believe that the reviewer’s reputation has a direct relation with the quality of reviews. Generally, review written by user with lower reputation, shows lower quality and higher possibility to be fake. Therefore, the model assigns reputation score to each reviewer wherein the content based factors and activeness of reviewers are employed efficiently. On basis of all suspected spammers, k-center clustering algorithm is performed to further spot the spammers based on the observation of burst of review release time. Experimental results on Amazon’s dataset are encouraging and indicate that our approach poses high accuracy and recall, and good performance is achieved

Topological optimization of an offshore-wind-farm power collection system based on a hybrid optimization methodology

This paper proposes a hybrid optimization method to optimize the topological structure of an offshore-wind-farm power collection system, in which the cable connection, cable selection and substation location are optimally designed. Firstly, the optimization model was formulated, which integrates cable investment, energy loss and line construction. Then, the Prim algorithm was used to initialize the population. A novel hybrid optimization, named PSAO, based on the merits of the particle swarm optimization (PSO) and aquila optimization (AO) algorithms, was presented for topological structure optimization, in which the searching characteristics between PSO and AO are exploited to intensify the searching capability. Lastly, the proposed PSAO method was validated with a real case. The results showed that compared with GA, AO and PSO algorithms, the PSAO algorithm reduced the total cost by 4.8%, 3.3% and 2.6%, respectively, while achieving better optimization efficiency.Web of Science112art. no. 27

A New Systemic Safety Detecting Software

Because it is hard to find and to clear cockhorse and virus developed by root kit technology, antivirus soft at present is hard to clear virus in the system, which make the system in dangers status of hazard. So, designing a speedy clear Trojan and virus makes by root kit is very important. The article.is based on SDK, adopting the technology.of kernel to design the Clairvoyant systemic safety detecting software. It major function is monitors.the service of the system and the operation. Monitor the register changer. Search the file, process, system module hided by the virus. It can also end protected processes and delete protected files forcibly. Through the port.mapping of processes, it can find.port messages opened.by system, processes opening ports and.cockhorse effectively. http://dx.doi.org/10.11591/telkomnika.v12i10.5345

An Adaptive Redundant Reservation Strategy in Distributed Highperformance Computing Environments

In distributed high-performance computing environments, resource reservation mechanism is an effective approach to provide desirable quality of service for large-scale applications. However, conventional reservation service might result in lower resource utilization and higher rejection rate if it is excessively applied. Furthermore, redundant reservation policy has been widely applied in many practical systems with aiming to improve the reliability of application execution at runtime. In this paper, we proposed an adaptive redundant reservation strategy, which uses overlapping technique to implement reservation admission and enable resource providers dynamically determine the redundant degree at runtime. By overlapping a new reservation with an existing one, a request whose reservation requirements can not be satisfied in traditional way might be accepted. Also, by dynamically determining the redundant degree, our strategy can obtain optimal tradeoff between performance and reliability for distributed high-performance computing systems. Experimental results show that the strategy can bring about remarkably higher resource utilization and lower rejection rate when using redundant reservation service at the price of a slightly increasing of reservation violations

Influence Maximization based on Threshold Model in Hypergraph

Node centrality problem has been actively and deeply explored in recent years thanks to the application of the node influence maximization problem in product recommendation, public opinion dissemination, disease propagation, and other aspects. This paper mainly studies the problem of node influence maximization based on threshold model in hypergraph. For the first time, we extend Message Passing method from ordinary graph to hypergraph to describe the process of information passing in hypergraph. We determine the index of high-order collective inflence(HCI) value to assess the influence of the node and propose the high-order collective influence based on threshlod model(HCI-TM) algorithm through the study of the self-satisfying equation. In comparison to other algorithms, the HCI-TM algorithm may maximize both the node activation scale and the hyperedge activation scale when a particular percentage of seed nodes is chosen. Finally, numerical simulation on generated networks and real networks are used to demonstrate the superiority of the HCI-TM algorith.Comment: 14 pages, 6 figure

Adaptive Neural Network Approach for a Class of Uncertain Non-affine Nonlinear Systems

The paper proposes a new output feedback adaptive tracking control scheme using neural network for a class of uncertain non-affine nonlinear systems that only the system output variables can be measured. The scheme adopts low-pass filter to transform non-affine nonlinear systems into affine in the pseudo-input dynamics. No state observer is employed and few adapting parameters to be tuned and Lipschiz assumption, SPR condition are not required. Only the output error is used in control laws and weights update laws which make the system construct simple. Boundedness for the output tracking error and all states in the closed-loop system are guaranteed, and simulation results have verified the effectiveness of the proposed approach

Video Object Matching Based on SIFT and Rotation Invariant LBP

Object detection and tracking is an essential preliminary task in event analysis systems (e.g. Visual surveillance).Typically objects are extracted and tagged, forming representative tracks of their activity. Tagging is usually performed by probabilistic data association. However, as data may have been collected at different times or in different locations, it is often impossible to establish such associations in systems capturing disjoint areas. In this case, appearance matching is a valuable aid. This paper proposes a object matching method for multi-camera by combining HOG and block LBP, and computes accuracy rate by SVM. Using independent tracks of 30 different persons, we show that the proposed representation effectively discriminates visual object and that it presents high resilience to incorrect object segmentation and illumination. Experimental results show that the average accuracy DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.341