1697432
A case for an interleaving constrained shared-memory multi-processor
Shared-memory multi-threaded programming is inherently more difficult than single-threaded programming. The main source of complexity is that, the threads of an application can interleave in so many different ways. To ensure correctness, a programmer has to test all possible thread interleavings, which, however, is impractical.
 Many rare thread interleavings remain untested in production systems, and they are the root cause for a majority of concurrency bugs. We propose a shared-memory multi-processor design that avoids untested interleavings to improve the correctness of a multi-threaded program. Since untested interleavings tend to occur infrequently at runtime, the performance cost of avoiding them is not high.
 We propose to encode the set of tested correct interleavings in a program's binary executable using Predecessor Set (PSet) constraints. These constraints are efficiently enforced at runtime using processor support, which ensures that the runtime follows a tested interleaving. We analyze several bugs in open source applications such as MySQL, Apache, Mozilla, etc., and show that, by enforcing PSet constraints, we can avoid not only data races and atomicity violations, but also other forms of concurrency bugs.
522561
POLUS: A POwerful Live Updating System
This paper presents POLUS, a software maintenance tool capable of iteratively evolving running software into newer versions. POLUS's primary goal is to increase the dependability of contemporary server software, which is frequently disrupted either by external attacks or by scheduled upgrades. To render POLUS both practical and powerful, we design and implement POLUS aiming to retain backward binary compatibility, support for multithreaded software and recover already tainted state of running software, yet with good usability and very low runtime overhead. To demonstrate the applicability of POLUS, we report our experience in using POLUS to dynamically update three prevalent server applications: vsftpd, sshd and apache HTTP server. Performance measurements show that POLUS incurs negligible runtime overhead: a less than 1% performance degradation (but 5% for one case). The time to apply an update is also minimal.
12847635
Parameterized discriminant analysis for image classification
Linear and nonlinear (i.e., kernel) discriminant analysis have been proposed to address the difficulties of the small sample problem, the curse of dimensionality, and the multi-modality of image data distribution in content-based image retrieval (CBIR). The existing discriminant analysis is implemented either in a regular way, such as MDA (multiple discriminant analysis), or in a biased way, such as biased discriminant analysis (BDA). A rich set of parameterized discriminant analysis is proposed as an alternative to the regular MDA and BDA when taking regularization into account to avoid the singularity of the scatter matrices. Extensive experiments are carried out for performance evaluation and the results show the superior performance of the parameterized discriminant analysis over regular MDA and BDA for both linear and nonlinear settings.
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.
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.
131644
Adaptive Discriminant Projection for Content-based Image Retrieval
Content-based image retrieval (CBIR) is a computer vision application that aims at automatically retrieving images based on their visual content. Linear discriminant analysis and its variants have been widely used in CBIR applications because of their effectiveness in finding a projection that maps the original high-dimensional space to a low-dimensional one and preserves the most discriminant features. Those techniques assume images from certain class(es) are all visually similar and try to cluster them in the projected space. In this paper we show that the human high-level concept of semantic similarity between images may not arise only from the low-level visual similarity and consequently that assumption is inappropriate in many cases. We propose an adaptive discriminant projection (ADP) framework which could model different data distributions based on the clustering of different classes. To learn the best model fitting the real scenario, boosted adaptive discriminant projection is further proposed. Extensive experiments are designed to evaluate our methods and compare them to the state-of-the-art techniques on benchmark data set and real image retrieval applications. The results show the superior performance of our proposed methods
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