740678
Taint-Enhanced Policy Enforcement: A Practical Approach to Defeat a Wide Range of Attacks
Policy-based confinement, employed in SELinux and specification-based intrusion detection systems, is a popular approach for defending against exploitation of vulnerabilities in benign software. Conventional access control policies employed in these approaches are effective in detecting privilege escalation attacks. However, they are unable to detect attacks that "hijack" legitimate access privileges granted to a program, e.g., an attack that subverts an FTP server to download the password file. (Note that an FTP server would normally need to access the password file for performing user authentication.) Some of the common attack types reported today, such as SQL injection and cross-site scripting, involve such subversion of legitimate access privileges. In this paper, we present a new approach to strengthen policy enforcement by augmenting security policies with information about the trustworthiness of data used in securitysensitive operations. We evaluated this technique using 9 available exploits involving several popular software packages containing the above types of vulnerabilities. Our technique sucessfully defeated these exploits.
156293
An efficient and backwards-compatible transformation to ensure memory safety of C programs
Memory-related errors, such as buffer overflows and dangling pointers, remain one of the principal reasons for failures of C programs. As a result, a number of recent research efforts have focused on the problem of dynamic detection of memory errors in C programs. However, existing approaches suffer from one or more of the following problems: inability to detect all memory errors (e.g., Purify), requiring non-trivial modifications to existing C programs (e.g., Cyclone), changing the memory management model of C to use garbage collection (e.g., CCured), and excessive performance overheads. In this paper, we present a new approach that addresses these problems. Our approach operates via source code transformation and combines efficient data-structures with simple, localized optimizations to obtain good performance.
7170011
Accelerating regularized iterative ct reconstruction on commodity graphics hardware (GPU)
Iterative reconstruction algorithms augmented with regularization can produce high-quality reconstructions from few views and even in the presence of significant noise. In this paper we focus on the particularities associated with the GPU acceleration of these. First, we introduce the idea of using exhaustive benchmark tests to determine the optimal settings of various parameters in iterative algorithm, here OS-SIRT, which proofs decisive for obtaining optimal GPU performance. Then we introduce bilateral filtering as a viable and cost-effective means for regularization, and we show that GPU-acceleration reduces its overhead to very moderate levels.
2663170
A Framework for Building Privacy-Conscious Composite Web Services
The rapid growth of Web applications has prompted increasing interest in the area of composite Web services that involve several service providers. The potential for such composite Web services can be realized only if consumer privacy concerns are satisfactorily addressed. In this paper, we propose a framework that addresses consumer privacy concerns in the context of highly customizable composite Web services. Our approach involves service producers exchanging their terms-of-use with consumers in the form of "models". Our framework provides automated techniques for checking these models at the consumer site for compliance of consumer privacy policies. In the event of a policy violation, our framework supports automatic generation of "obligations" that the consumer generates for the composite service. These obligations are automatically enforced through a dynamic program analysis approach on the Web service composition code. We illustrate our approach with the implementation of two example services
1723065
An approach for realizing privacy-preserving web-based services
The past decade has witnessed a phenomenal growth in the number of users who routinely use the web to obtain information, conduct research, or carry out financial transactions. While the ability of the web to provide customized information and financial services has boosted personal and business productivity, it has raised significant concerns regarding an individual’s privacy. The privacy concerns are exacerbated further with the advent of more complex services that involve multiple service providers, as it raises the potential of personal information being shared across these providers in ways that weren’t intended by the owner of the information. To illustrate the increased privacy concerns raised by composite web-based services, consider a web-based service that allows an individual to research and order over-the-counter medication (or health supplements) at a low cost, while ensuring that there are no adverse interactions based on the individuals current prescription. Such a service will require access to the individual’s personal medical history, and will also require interaction with other entities such as web-based medical references, discount pharmacies and creditcard companies. Users may not want their personal health histories to be shared with discount pharmacies or credit card companies. Similarly, they may not want their credit card information to be shared with providers of free on-line information. Web-based service providers have long recognized the importance of addressing user privacy concerns, and taken steps to state their policies regarding the storage and use of personal user information. Unfortunately, since these policies are stated in English, they are imprecise and ambiguous, posing problems in accountability and enforceability, as well as in checking compatibility between consumer and provider policies. Indeed, the verbose nature of these policies discourage most users from even reading them! To address the above problem, we present a new approach where the consumers as well as providers can express their privacy concerns in a formal way. Specifically, consumers express their requirements in the form of policies, while providers specify their use of consumer data using models. Our approach automates compatibility checking between policies and models. If there is an incompatibility, the consumer is informed how she can refine her policies in order to use the service. If she does not want to change her policies in any way, the approach passes on additional privacy requirements to the provider. Service access can continue in case
517875
Detecting Unsafe Driving Patterns using Discriminative Learning
We propose a discriminative learning approach for fusing multichannel sequential data with application to detect unsafe driving patterns from multi-channel driving recording data. The fusion is performed using a discriminatively trained graphical model -conditional random field (CRF). The proposed approach offers several advantage over existing information fusing approaches. First, it derives its classification power by directly modelling and maximizing the conditional probability. Second, it represents the variable dependency in an undirected graph, which is very efficient in inference. Third, it does not require to label all the training data and utilizes both labelled and unlabelled data efficiently by semi-supervised learning algorithms. The proposed approach is evaluated on driving recording data collected from driving simulator -STISIM. Experiments show it outperforms the simple discriminative classifier (SVM) and generative model (HMM).
120158207
Thermoelastic plastic metals with mass diffusion
Abstract. In this paper we develop new governing equations for contact stresses in metals. They include the coupled effects of thermal elastic-plastic theory, including balance and constitutive laws. While the results are quite general, this work had it origins in describing the metal cutting process. As one example, the resulting equations can be applied to precision and ultraprecision cutting.

47184510
Fast exact maximum likelihood estimation for mixture of language model
Language modeling is an effective and theoretically attractive probabilistic framework for text information retrieval. The basic idea of this approach is to estimate a language model of a given document (or document set), and then do retrieval or classification based on this model. A common language modeling approach assumes the data D is generated from a mixture of several language models. The core problem is to find the maximum likelihood estimation of one language model mixture, given the fixed mixture weights and the other language model mixture. The EM algorithm is usually used to find the solution. In this paper, we proof that an exact maximum likelihood estimation of the unknown mixture component exists and can be calculated using the new algorithm we proposed. We further improve the algorithm and provide an efficient algorithm of O(k) complexity to find the exact solution, where k is the number of words occurring at least once in data D. Furthermore, we proof the probabilities of many words are exactly zeros, and the MLE estimation is implemented as a feature selection technique explicitly.
13976369
Design of a nanosensor array architecture
This paper describes a nanowire sensor array architecture for high-speed, high-accuracy sensor systems. The chip has very simple processing elements (PEs) in a massively parallel architecture, in which each PE is directly connected to seven sensors. A sampling rate of 100 ns is enough to realized high-speed sensing feedback for electronic nose. We aim to create a very simple architecture, because a compact design is required ton integrate as many PEs as possible on a single chip. A widely used, easy to implement estimator-minimum distance classifier is introduced to realize the pattern recognition. A sample design is implemented in VHDL and has been simulated and synthesized using TSMC 0.25 standard cell library and a commercial 0.16 standard cell library.
45935923
Responses of strongly non-linear oscillator parametrically excited by random narrow-band noise
The principal response of a strongly Van der Pol-Duffing oscillator subjected to parametric random narrow-band excitation is investigated. The technique of modified Lindstedt Poincare (MLP) method is used to transform the strongly non-linear system to a weak one by introducing a new expansion parameter, and then the multiple scales method is applied to determine the modulation equations for amplitude and phase of the response of the system. The effect of damping, detuning, and bandwidth on the dynamic behaviors such as stability, bifurcation are examined by computing the maximum Lyapunov exponent analytically. Also the numerical simulation is carried out to verify the analytical results, and random jump phenomenon may be observed in the region of the parameters of the system. The excellent agreement between theoretical results and numerical ones can be found immediately, and so the present method in this paper is applicable to solve strongly non-linear problems.