1039556
Indexing Noncrashing Failures: A Dynamic Program Slicing-Based Approach
Recent software systems usually feature an automated failure reporting component, with which a huge number of failures are collected from software end-users. With a proper support of failure indexing, which identifies failures due to the same fault, the collected failure data can help developers prioritize failure diagnosis, among other utilities of the failure data. Since crashing failures can be effectively indexed by program crashing venues, current practice has seen great success in prioritizing crashing failures. A recent study of bug characteristics indicates that as excellent memory checking tools are widely adopted, semantic bugs and the resulting noncrashing failures have become dominant. Unfortunately, the problem of how to index non-crashing failures has not been seriously studied before. In previous study, two techniques have been proposed to index noncrashing failures, and they are T-Proximity and R-Proximity. However, as T-Proximity indexes failures by the profile of the entire execution, it is generally not effective because most information in the profile is fault-irrelevant. On the other hand, although R-Proximity is more effective than T-Proximity, it relies on a sufficient number of correct executions that may not be available in practice. In this paper, we propose a dynamic slicing-based approach, which does not require any correct executions, and is comparably effective as R-Proximity. A detailed case study with gzip is reported, which clearly strates the advantages of the proposed approach.
15016600
Addressing Collaborative Attacks and Defense in Ad Hoc Wireless Networks
Since ad hoc networks rely on nodes cooperation to establish communication, malicious nodes can compromise the entire network. If they collaborate the devastation is even worse. Collaborative attacks may cause more devastating impacts on wireless environments than single and uncoordinated groups of attacks, as they combine efforts of more than one attacker against the target victim. In this paper, we present the most important forms of attacks,discuss possible collaborations among attackers, show how machine learning techniques and signal processing techniques can be used to detect and defend against collaborative attacks in such environments, and discuss implementation issues.
6059000
Preserving User Location Privacy in Mobile Data Management Infrastructures
Location-based services, such as finding the nearest gas station, require users to supply their location information. However, a user's location can be tracked without her consent or knowledge. Lowering the spatial and temporal resolution of location data sent to the server has been proposed as a solution. Although this technique is effective in protecting privacy, it may be overkill and the quality of desired services can be severely affected. In this paper, we suggest a framework where uncertainty can be controlled to provide high quality and privacy-preserving services, and investigate how such a framework can be realized in the GPS and cellular network systems. Based on this framework, we suggest a data model to augment uncertainty to location data, and propose imprecise queries that hide the location of the query issuer and yields probabilistic results. We investigate the evaluation and quality aspects for a range query. We also provide novel methods to protect our solutions against trajectory-tracing. Experiments are conducted to examine the effectiveness of our approaches.
13946371
Self-Learning Disk Scheduling
Performance of disk I/O schedulers is affected by many factors, such as workloads, file systems, and disk systems. Disk scheduling performance can be improved by tuning scheduler parameters, such as the length of read timers. Scheduler performance tuning is mostly done manually. To automate this process, we propose four self-learning disk scheduling schemes: change-sensing Round-Robin, feedback learning, per-request learning, and two-layer learning. experiments show that the novel two-layer learning scheme performs best. It integrates the workload-level and request-level learning algorithms. It employs feedback learning techniques to analyze workloads, change scheduling policy, and tune scheduling parameters automatically. We discuss schemes to choose features for workload learning, divide and recognize workloads, generate training data, and integrate machine learning algorithms into the two-layer learning scheme. We conducted experiments to compare the accuracy, performance, and overhead of five machine learning algorithms: decision tree, logistic regression, naive Bayes, neural network, and support vector machine algorithms. Experiments with real-world and synthetic workloads show that self-learning disk scheduling can adapt to a wide variety of workloads, file systems, disk systems, and user preferences. It outperforms existing disk schedulers by as much as 15.8% while consuming less than 3%-5% of CPU time.
43302296
A Novel Immune Based Approach for Detection of Windows PE Virus
Generic computer virus detection is the absolute need of the hour as most commercial antivirus products fail to detect unknown and new Windows PE viruses. Motivated by the success of immune-based techniques in intrusion detection systems, recent research in detecting computer viruses is directed towards devising efficient non-signature-based techniques. We observe that each Windows PE virus whether or not it is encrypted must have a relocation module to relocate its variables or constants in the infected programs. Due to its unique characteristic, the virus relocation module can be extracted as an antibody in the immune systems to detect the specific antigens. In this paper, we presented a novel Windows PE virus detection approach that draws inspiration from artificial immune system and the structure of the relocation module of the virus. The structure of Windows PE virus is sufficiently analyzed. The dynamic evolution of self and nonself, the presentation of the antigen, and the generation of the antibody are proposed. The experiment is conducted and its results indicate that this approach not only has relatively higher detection rate of unknown Windows PE virus than the earlier known methods, but also has better capability of self-adaptive and self-learning.
31437218
Choosing Business Collaborators Using Computing Intelligence Methods
Inter-firm collaboration has become a common feature of the developing international economy. Firms as well as the nations have more relationships with each other. Even relatively closed economies or industries are becoming more open, Australia and China are examples of this case. The benefits generated from collaboration and the motivations to form collaboration are investigated by some researchers. However, the widely studied relationships between collaboration and profits are based on tangible assets and turnovers whereas most intangible assets and benefits are neglected during the economic analysis. In the present paper, two methods, naive Bayes and neural network, from computing intelligence are used to study the benefits acquired from collaboration. These two methods are used to learn the relationship and make prediction for a specified collaboration. The proposed method has been applied to a practical case of WEMOSOFT, an independent development department under MOBOT. The predication accuracies are 87.18% and 92.31%, for neural network and naive Bayes, respectively. Experimental result demonstrates that the proposed method is an effective and efficient way to prediction the benefit of collaboration and choose the appropriate collaborator.
18823520
A Pursuit-Evasion Algorithm Based on Hierarchical Reinforcement Learning
This paper proposed a pursuit-evasion algorithm based on the Option method from hierarchical reinforcement learning and applied it into multi-robot pursuit-evasion game in 2D-Dynamic environment. The algorithm efficiency is studied by comparing it with Q-learning. We decompose the complex task with option method, and divide the learning process into two parts: High-level learning and Low-level learning, then design a new mechanism in order to make the learning process perform parallel. The simulation result shows the Option algorithm can efficiently reduce the complexity of pursuit-evasion task, avoid traditional reinforcement learning curse of dimensionality, and improve the learning result.
11913920
Content-Based Trust Mechanism for E-commerce Systems
Previous trust mechanisms are mainly focused on reputational models based on explicit trust ratings. However, the large amount of system-provided and user-generated content published on Web is often ignored. These information is very important for providing more accurate, fine-grained and efficient trust management. In this paper, we propose a content-based trust mechanism and introduce trust reasoning functionalities which can infer a user's interests and expertise. We present a new method to calculate user similarity based on their feedbacks on reviews to reduce data sparseness of User Similarity matrix. We pinpoint several characteristics of trust which have been overlooked previously: trust is field-dependant, similarity-sensitive, personalized and etc. we differ various trust levels between pairs of users to support more accurate and efficient trust calculation. Although we choose Epinions as our targeted application, the mechanism proposed in this paper are also applicable to other e-commerce systems.
815666
An Efficient Markerless Method for Resynthesizing Facial Animation on an Anatomy-Based Model
This paper describes a markerless method for automatically estimating animation parameters of an anatomy-based face model from a video sequence. The method is based upon an efficient tracking procedure which employs a linear subspace model of facial appearance. During the training phase of the tracking algorithm, a set of templates is computed from the subspace basis to alleviate the online computation. The efficient tracking algorithm establishes temporal correspondence of the face region by simultaneously determining both motion and appearance parameters. Using a set of key expressions of the actor together with corresponding animation parameters of the face model, we learn a linear relationship between facial animation parameters and appearance parameters. It allows us to efficiently estimate the animation parameters from appearance parameters provided by the tracker. We present experimental results performed on synthetic and live video sequences to demonstrate the effectiveness and efficiency of our method.