A Proximity-Aware BitTorrent System via Tracker-Side Biased Neighbor Selection

To address cross-ISP traffic problem caused by BitTorrent,we present our design and evaluation of a proximity-aware BitTorrent system. In our approach,clients generate global proximity-aware information by using landmark clustering;the tracker uses this proximity to maintain all peers in an orderly way and hands back a biased subset consisting of the peers who are physically closest to the requestor. Our approach requires no co-operation between P2P users and their Internet infra structures,such as ISPs or CDNs,no constantly path monitoring or probing their neighbors. The simulation results show that our approach can not only reduce unnecessary cross-ISP traffic,but also allow downloadsing fast.

Adaptive Neighboring Selection Algorithm Based on Curvature Prediction in Manifold Learning

Recently manifold learning algorithm for dimensionality reduction attracts more and more interests, and various linear and nonlinear,global and local algorithms are proposed. The key step of manifold learning algorithm is the neighboring region selection. However,so far for the references we know,few of which propose a generally accepted algorithm to well select the neighboring region. So in this paper,we propose an adaptive neighboring selection algorithm,which successfully applies the LLE and ISOMAP algorithms in the test. It is an algorithm that can find the optimal K nearest neighbors of the data points on the manifold. And the theoretical basis of the algorithm is the approximated curvature of the data point on the manifold. Based on Riemann Geometry,Jacob matrix is a proper mathematical concept to predict the approximated curvature. By verifying the proposed algorithm on embedding Swiss roll from R3 to R2 based on LLE and ISOMAP algorithm,the simulation results show that the proposed adaptive neighboring selection algorithm is feasible and able to find the optimal value of K,making the residual variance relatively small and better visualization of the results. By quantitative analysis,the embedding quality measured by residual variance is increased 45. 45% after using the proposed algorithm in LLE.

Multi-type ant system algorithm for the time dependent vehicle routing problem with time windows

The time dependent vehicle routing problem with time windows（TDVRPTW） is considered. A multi-type ant system（MTAS） algorithm hybridized with the ant colony system（ACS）and the max-min ant system（MMAS） algorithms is proposed. This combination absorbs the merits of the two algorithms in solutions construction and optimization separately. In order to improve the efficiency of the insertion procedure, a nearest neighbor selection（NNS） mechanism, an insertion local search procedure and a local optimization procedure are specified in detail. And in order to find a balance between good scouting performance and fast convergence rate, an adaptive pheromone updating strategy is proposed in the MTAS. Computational results confirm the MTAS algorithm＇s good performance with all these strategies on classic vehicle routing problem with time windows（VRPTW） benchmark instances and the TDVRPTW instances, and some better results especially for the number of vehicles and travel times of the best solutions are obtained in comparison with the previous research.

EA-RDSP: Energy Aware Rapidly Deployable Wireless Ad hoc System for Post Disaster Management

In post disaster scenarios such as war zones floods and earthquakes,the cellular communication infrastructure can be lost or severely damaged.In such emergency situations,remaining in contact with other rescue response teams in order to provide inputs for both headquarters and disaster survivors becomes very necessary.Therefore,in this research work,a design,implementation and evaluation of energy aware rapidly deployable system named EA-RDSP is proposed.The proposed research work assists the early rescue workers and victims to transmit their location information towards the remotely located servers.In EA-RDSP,two algorithms are proposed i.e.,Hop count Assignment(HCA)algorithm and Maximum Neighbor Selection(MNS)algorithm.The EA-RDSP contains three types of nodes;the client node sends information about casualty in the disaster area to the server,the relay nodes transmit this information from client node to server nodes via multi-hop transmission,the server node receives messages sent by client node to alert rescue teams.The EAM-RDSP contains three types of nodes;the client node sends information about casualty in the disaster area to the server,the relay nodes transmit this information from client node to server nodes via multi-hop transmission,the server node receives messages sent by client node to alert rescue teams.The proposed EA-RDSP scheme is simulated using NS2 simulator and its performance is compared with existing scheme in terms of end-to-end delay,message delivery ratio,network overhead and energy consumption.

Exponential Fuzzy C-Means for Collaborative Filtering

Collaborative filtering （CF） is one of the most popular techniques behind the success of recommendation system. It predicts the interest of users by collecting information from past users who have the same opinions. The most popular approaches used in CF research area are Matrix factorization methods such as SVD. However, many well- known recommendation systems do not use this method but still stick with Neighborhood models because of simplicity and explainability. There are some concerns that limit neighborhood models to achieve higher prediction accuracy. To address these concerns, we propose a new exponential fuzzy clustering （XFCM） algorithm by reformulating the clustering＇s objective function with an exponential equation in order to improve the method for membership assignment. The proposed method assigns data to the clusters by aggressively excluding irrelevant data, which is better than other fuzzy C-means （FCM） variants. The experiments show that XFCM-based CF improved 6.9% over item-based method and 3.0% over SVD in terms of mean absolute error for 100 K and 1 M MovieLens dataset.

Unsupervised Nonlinear Adaptive Manifold Learning for Global and Local Information

In this paper,we propose an Unsupervised Nonlinear Adaptive Manifold Learning method(UNAML)that considers both global and local information.In this approach,we apply unlabeled training samples to study nonlinear manifold features,while considering global pairwise distances and maintaining local topology structure.Our method aims at minimizing global pairwise data distance errors as well as local structural errors.In order to enable our UNAML to be more efficient and to extract manifold features from the external source of new data,we add a feature approximate error that can be used to learn a linear extractor.Also,we add a feature approximate error that can be used to learn a linear extractor.In addition,we use a method of adaptive neighbor selection to calculate local structural errors.This paper uses the kernel matrix method to optimize the original algorithm.Our algorithm proves to be more effective when compared with the experimental results of other feature extraction methods on real face-data sets and object data sets.