10430483
Knowledge contribution in problem solving virtual communities: the mediating role of individual motivations
Increasing knowledge contribution in problem solving virtual communities (PSVCs) is a long-pursued question in the knowledge management arena for both researchers and practitioners. In view of various mechanisms developed in hopes of encouraging more active participations, we proposed a motivational model for PSVC contributions. Drawn on functional motivation theory and expectancy-value theory, we highlight the mediating role of individual motivations in the relationships between characteristics of PSVCs and knowledge contribution intention. By taking both self-interest and public-good perspectives, we identify eight individual motivations to knowledge contribution in the context of PSVCs. They are active learning, self-enhancement, reciprocity, reputation, enjoyment of helping others, self-protection, moral obligation and advancement of virtual community motive. Besides, we also identify four major mechanisms in PSVCs that are associated with members' motivations: codification, identity management, virtual community norms and communication methods.
1807096
The effects of web site aesthetics and shopping task on consumer online purchasing behavior
This research-in-progress investigates the effects of website aesthetics and shopping task type on consumer attitude towards electronic shopping sites. The conceptualization of website aesthetics is based on the two-dimensional structure, namely, classical and expressive aesthetics, proposed by Lavie and Tractinsky (2004). The online shopping tasks are categorized as hedonic versus utilitarian shopping tasks. A pre-test was conducted for selection of utilitarian and hedonic products. Based on the results of the pre-test, a laboratory experiment was designed to capture the effects of website aesthetics on online consumer purchasing behavior across different shopping tasks.
13918438
Multi-cue Based Visual Tracking in Clutter Scenes with Occlusions
Object tracking is important for video analysis applications. However, tracking through occlusions is a difficult task due to significant appearance changes of the objects. Approaches based on either global features or one kind of local features can not solve the problem completely. In this paper, a multi-cue based tracking approach is introduced. It combines a corner tracking with a color and a shape model to resolve the object tracking problem through occlusions for most scenes (indoor and outdoor).To obtain an objective evaluation of the proposed method, a set of detection and tracking measures are used to perform a quantitative analysis based on a large sequence dataset with ground-truth annotation. The experimental results show that the proposed approach works robustly under varying conditions.
45747
A new analysis of the value of unlabeled data in semi-supervised learning for image retrieval
There has been an increasing interest in using unlabeled data in semi-supervised learning for various classification problems. Previous work shows that unlabeled data can improve or degrade the classification performance depending on whether the model assumption matches the ground-truth data distribution, and also on the complexity of the classifier compared with the size of the labeled training set. In this paper, we provide a new analysis on the value of unlabeled data by considering different distributions of the labeled and unlabeled data and showing the migrating effect for semi-supervised learning. Extensive experiments have been performed in the context of image retrieval applications. Our approach evaluates the value of unlabeled data from a new aspect and is aimed to provide a guideline on how unlabeled data should be used
10879292
Rate-limited EAFRP-a new improved model for high-speed network traffic
The Extended Alternating Fractal Renewal Process (EAFRP) model has recently been proposed for modeling the self-similar and impulsive traffic of high-speed networks. For mathematical simplicity, it assumes that the available transmission bandwidth in the network is infinite. In reality, the network has a limit R on the total traffic rate through it, and in addition, the ith user's traffic rate is often limited to special value L/sub i/, which is assigned by the bandwidth sharing protocol. We propose a model for single-user traffic, which, by taking into account the aforementioned rate limit L/sub i/ and R, and in the absence of congestion, provides insight on the distinctive two slope behavior of the loglog survival function of multiuser traffic. For small to medium number of users, such as in local area networks (LANs), the model results in non-Gaussian traffic, whereas as the number of users increases, the resulting traffic is Gaussian, both of which are consistent with real network measurements. We discuss model parameter estimation, provide queuing analysis of the multiple-user traffic model, and, based on real data, show that it achieves a closer approximation of the observed reality than existing models.
16398028
A Detection and Offense Mechanism to Defend Against Application Layer DDoS Attacks
Application layer DDoS attacks, which are legitimate in packets and protocols, gradually become a pressing problem for commerce, politics and military. We build an attack model and characterize layer-7 attacks into three classes: session flooding attacks, request flooding attacks and asymmetric attacks. We proposed a mechanism named as DOW (defense and offense wall), which defends against layer-7 attacks using combination of detection technology and currency technology. An anomaly dete-ction method based on K-means clustering is introduced to detect and filter request flooding attacks and asymmetric attacks. To defend against session-flooding attacks, we propose an encouragement model that uses client's session rate as currency. Detection model drops suspicious sessions, while currency model encourages more legitimate sessions. By collaboration of these two models, normal clients could gain higher service rate and lower delay of response time.
782382
A Lightweight Mechanism to Mitigate Application Layer DDoS Attacks
Application layer DDoS attacks, to which network layer solutions is not applicable as attackers are indistinguishable based on packets or protocols, prevent legitimate users from accessing services. In this paper, we propose Trust Management Helmet (TMH) as a partial solution to this problem, which is a lightweight mitigation mechanism that uses trust to differentiate legitimate users and attackers. Its key insight is that a server should give priority to protecting the connectivity of good users during application layer DDoS attacks, instead of identifying all the attack requests. The trust to clients is evaluated based on their visiting history, and used to schedule the service to their requests. We introduce license, for user identification (even beyond NATs) and storing the trust information at clients. The license is cryptographically secured against forgery or replay attacks. We realize this mitigation mechanism and implement it as a Java package and use it for simulation. Through simulation, we show that TMH is effective in mitigating session flooding attack: even with 20 times number of attackers, more than 99% of the sessions from legitimate users are accepted with TMH; whereas less than 18% are accepted without it.