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Although time-sharing CPUs has been an essential technique to virtualize CPUs for threads and virtual machines, most of the commercial operating systems and hyper visors maintain relatively coarse-grained time slices to mitigate the costs of context switching. However, the proliferation of system virtualization poses a new challenge for the coarse-grained time sharing techniques, since operating systems...
In this paper we present a new way of mapping data-parallel applications on coarse-grained reconfigurable architectures (CGRAs) to increase their performance. Traditional mapping approaches aim to map an application to a minimum number of contexts. In this work we gave up this idea. We propose to use the temporal domain with multiple contexts, as the preferred mapping domain. The benefit of this approach...
As an important branch of biomedical information extraction, Protein-Protein Interaction extraction (PPIe) from biomedical literatures has been widely researched, and machine learning methods have achieved great success for this task. However, the word feature generally adopted in the existing methods suffers badly from vocabulary gap and data sparseness, weakening the classification performance....
Pipelining is an important technique in high-level synthesis, which overlaps the execution of successive loop iterations or threads to achieve high throughput for loop/function kernels. Since existing pipelining techniques typically enforce in-order thread execution, a variable-latency operation in one thread would block all subsequent threads, resulting in considerable performance degradation. In...
In this paper we describe a probabilistic framework that models the interaction between multiple objects in a scene. We present a spatio-temporal feature encoding pairwise interactions between each object in the scene. By the use of a kernel representation we embed object interactions in a vector space which allows us to define a metric comparing interactions of different temporal extent. Using this...
Multi-concept image query is a multi-label classification challenge. Traditional query methods focus on single concept query, and only use image visual data without considering the associated textual tag data. In this work, we address the problem of bimodal multi-concept image query, namely retrieving bimodal images with multiple target concepts from the image set. We propose a novel Bimodal Learning...
The desire for both industry and U.S. Department of Defense (DoD) to converge on an all-IP infrastructure, fueled by the increased usage of mobile applications on smart phones and VoIP applications have driven research in maximizing bandwidth efficiency amidst a shrinking allocation of radio frequency (RF) spectrum. One method of providing increased bandwidth efficiency (especially with the desire...
Stream processing has emerged as an important model of computation in the context of multimedia and communication sub-systems of embedded System-on-Chip (SoC) architectures. The dataflow nature of streaming applications allows them to be most naturally expressed as a set of kernels iteratively operating on continuous streams of data. The kernels are computationally intensive and exhibit large amounts...
Kernel preemption is an important feature among mainstream OS kernels. It allows that a kernel thread can be preempted by another kernel thread by performing context switch. A kernel thread can access shared data by disabling kernel preemption temporarily without turning off interruption. Kernel preemption and preemption control reduce kernel response time, but at the same time make kernels more difficult...
While most microkernel-based systems implement non-essential software components as user space tasks and strictly separate those tasks during runtime, they often rely on a static configuration and composition of their software components to ensure safety and security. In this paper, we extend a microkernel-based system architecture with a Trusted Platform Module (TPM) and propose a verification mechanism...
Digital medical images are becoming even more widespread and used in a large variety of applications. Such images are often stored in local repositories and need to be transmitted/ received over the network. Therefore, data compression is an essential mechanism to adopt for improving the transmission time, as well as to optimize the required storage space. However, due to the clinical relevance of...
In this work we consider a security model for resource constrained embedded systems, where memory is sometimes measured in a handful of kilobytes. Such systems normally do not include any security or safety mechanisms. The minority that do provide such functions often do so in an inflexible and ad hoc manner. The simple separation kernel demonstrated in this work enables a dynamic security model with...
Customer reviews are important sources of opinions of customers. The reviews contain some sentence pattern and some sort of patterns will be useful to extract the opinions of the customers easily. We propose a method to classify customer reviews using estimation expression patterns by support vector machine. We confirm an effectiveness of the proposed method by experimental results.
Point of interest (POI) categorization is the task of finding of categories of POIs within a document. Because the documents that possess POIs have clue words for identifying POI categories, the task can be solved as document classification. However, this approach misses two crucial factors for identifying the category of a POI. First, the approach pays no attention to onomastic information, even...
It is commonly known that the success of support vector machines in image classification and annotation it highly dependent on the relevance of the chosen kernels. The latter, defined as symmetric positive semi-definite functions, take high values when images share similar visual content and vice-versa. However, usual kernels relying only on the visual content are not appropriate in order to capture...
In this work we consider a machine learning setting where data are represented as graphs. First, we derive a kernel function which evaluates the similarity between graphs, while capturing pair-wise constraints between graph nodes. Second, we apply it to the problem of classifying collective activities: on this respect we first represent groups of people located in a spatial neighborhood as graphs,...
A number of NUMA-aware synchronization algorithms have been proposed lately to stress the scalability inefficiencies of existing locks. However their presupposed local lock granularity, a physical processor, is often not the optimum configuration for various workloads. This paper further explores the design space by taking into consideration the physical affinity between the cores within a single...
High Performance Computing systems expect applications to leverage the most of their processing power. This need is even more present for applications such as Monte Carlo simulations that require noteworthy CPU time and memory footprint. Optimizing applications is one approach to reduce the consumption of these resources. Before optimizing, it is mandatory to profile the application in order to pinpoint...
Existing VMI techniques have high overhead, and require customized introspection programs/tools for different guest OS versions - lack of generality. In this paper, we present Shadow Context, a system for close-to-real time manual-effort-free VMI. Shadow Context can meet several important real-world VMI needs which existing VMI techniques cannot. Compared to other automatic introspection tool generation...
This paper presents the architecture for a self-stabilizing hypervisor able to recover itself in the presence of Byzantine faults regardless of the state it is currently in. Our architecture is applicable to wide variety of underlying hardware and software and does not require augmenting computers with special hardware. The actions representing defense and recovery strategies can be specified by a...
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