An active learning method, called Two-stage Active learning algorithm (TAL), is developed for software defect prediction. Combining the clustering and support vector machine techniques, this method improves the performance of the predictor with less labeling effort. Experiments validate its effectiveness.

Tree partitioning arises in many parallel and distributed computing applications and storage systems. Some operator scheduling problems need to partition a tree into a number of vertex-disjoint subtrees such that some constraints are satisfied and some criteria are optimized. Given a tree T with each vertex or node assigned a nonnegative integer weight, two nonnegative integers l and u (l < u), and a positive integer p, we consider the following tree partitioning problems: partitioning T into minimum number of subtrees or p subtrees, with the condition that the sum of node weights in each subtree is at most u and at least l. To solve the two problems, we provide a fast polynomial-time algorithm, including a pre-processing method and another bottom-up scheme with dynamic programming. With experimental studies, we show that our algorithm outperforms another prior algorithm presented by Ito et al. greatly.

This letter proposes an efficient Location-Aware Social Routing (LASR) scheme for Delay Tolerant Networks (DTNs). LASR makes forwarding decisions based on a new metric which uses location information to reflect the node relations and community structure. Simulation results are presented to support the effectiveness of our scheme.

An asymmetric classifier based on kernel partial least squares is proposed for software defect prediction. This method improves the prediction performance on imbalanced data sets. The experimental results validate its effectiveness.

A kernel based asymmetric learning method is developed for software defect prediction. This method improves the performance of the predictor on class imbalanced data, since it is based on kernel principal component analysis. An experiment validates its effectiveness.

We propose a Junction-Based Traffic Aware Routing (JTAR) protocol for Vehicular Ad Hoc Networks (VANETs) in sparse urban environments. A traffic aware optimum junction selection solution is adopted in packet-forwarding, and a metric named critical-segment is defined in recovery strategy. Simulation results show that JTAR can efficiently increase the packet delivery ratio and reduce the delivery delay.

Establishing trust measurements among peer-to-peer (P2P) networks is fast becoming a de-facto standard, and a fair amount of work has been done in the area of trust aggregation and calculation algorithms. However, the area of developing secure underlying protocols to distribute and access the trust ratings in the overlay network has been relatively unexplored. We propose an elliptic curve-based trust management protocol for P2P systems, which is designed to provide authentication and signature functions to protect the processes of trust value query and rating report. Additionally, instead of using single identities, the protocol generates two verifiable pseudonyms, one is used for transaction, the other is applied when the peer acts as a trust holding peer. A security analysis shows that the proposed protocol is extremely secure in the face of a variety of possible attacks.

The potential of infrastructureless vehicular ad hoc networks (VANETs) for providing multihop applications is quite significant. Although the Epidemic Routing protocol performs well in highly mobile and frequently disconnected VANETs with low vehicle densities or light packet traffic loads, its performance degrades greatly in environments of high vehicle density together with heavy packet traffic loads that create serious bandwidth contention and frequent collisions. We propose a new epidemic routing protocol in urban environments called Greedy Zone Epidemic Routing (GZER), in which the neighbors of a vehicle are divided into different zones according to their physical locations. Each vehicle maintains a summary vector (SV) of packets buffered locally and zone summary vectors (ZSVs) of all packets buffered in each zone. Whether the infection will be transmitted in each zone is decided by the difference between SV and ZSV. Simulation results show that the proposed GZER protocol outperforms the existing solutions significantly, especially in the environments of high vehicle densities together with heavy packet traffic loads.

A Balanced Neighborhood Classifier (BNEC) is proposed for class imbalanced data. This method is not only well positioned to capture the class distribution information, but also has the good merits of high-fitting-performance and simplicity. Experiments on both synthetic and real data sets show its effectiveness.

A new training algorithm for the chaotic Adachi Neural Network (AdNN) is investigated. The classical training algorithm for the AdNN and it's variants is usually a “one-shot” learning, for example, the Outer Product Rule (OPR) is the most used. Although the OPR is effective for conventional neural networks, its effectiveness and adequateness for Chaotic Neural Networks (CNNs) have not been discussed formally. As a complementary and tentative work in this field, we modified the AdNN's weights by enforcing an unsupervised Hebbian rule. Experimental analysis shows that the new weighted AdNN yields even stronger dynamical associative memory and pattern recognition phenomena for different settings than the primitive AdNN.