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Wireless Sensor Networks enable flexibility, low operational and maintenance costs, as well as scalability in a variety of scenarios. However, in the context of industrial monitoring scenarios the use of Wireless Sensor Networks can compromise the system's performance due to several factors, being one of them the presence of outliers in raw data. In order to improve the overall system's resilience,...
The Spatial Pyramid Matching approach has become very popular to model images as sets of local bag-of-words. The image comparison is then done region-by-region with an intersection kernel. Despite its success, this model presents some limitations: the grid partitioning is predefined and identical for all images and the matching is sensitive to intra- and inter-class variations. In this paper, we propose...
The rise of big data, which need computationally demanding manipulation has posed unprecedented challenges in the machine learning community. In this context, a variety of dimensionality reduction methods has been introduced in order to deal with the large-scale aspect of the data. However, their employment in very large scales often becomes impractical due to memory and computation limitations. In...
Contexts like Defense and Aerospace are steadily focused on high assurance solutions designed to protect their data. One of the most relevant and unresolved issue is the Multilevel Security problem. Information and Communications Technology systems have to exchange data, to be regularly patched and updated hence, they require a link to the Internet. Through this connection and through a compromised...
Several studies on autism spectrum disorder (ASD) show that there exists significant heterogeneity in phenotype of the disorder. Additionally, many published findings also suggested that ASD is defined by atypical local/global processing. In this paper, we designed a puzzled-based intervention to examine the sensitiveness to the information of local /global processing on individuals with ASD. Additionally,...
Wide vector units in Intel's Xeon Phi accelerator cards can significantly boost application performance when used effectively. However, there is a lack of performance tools that provide programmers accurate information about the level of vectorization in their codes. This paper presents VecMeter, an easy-to-use tool to measure vectorization on the Xeon Phi. VecMeter utilizes binary instrumentation...
Migration to multicore is inevitable. To harness the potential of this technology, embedded system designers need to have available operating systems (OSes) with built-in capabilities for multicore hardware. When designed to meet real-time requirements, multicore SMP (Symmetric Multiprocessing) OSes not only face the inherent problem of concurrent access to shared kernel resources, but still suffer...
OpenCL is a portable interface that can be used to program cluster nodes with heterogeneous compute devices. The OpenCL specification tightly binds its workflow abstraction, or "command queue," to a specific device for the entire program. For best performance, the user has to find the ideal queue -- device mapping at command queue creation time, an effort that requires a thorough understanding...
The paper considers the challenge of deductively verifying Linux kernel code written in C programming language with extensive use of low-level memory operations and interactions with the highly concurrent environment. The paper presents an initial approach to specification and verification of concurrent code working with shared data by proving the code's compliance with specified synchronization discipline...
Genome-wide association (GWA) studies form an important category of research studies in personalized medicine which discuss on associations between single-nucleotide polymorphisms (SNPs) and phenotypic traits. Considering the fast growing rate of GWA studies, automatic extraction of SNP-Traits associations from text is a highly demanding task. In this research, first an SNP-Trait association corpus...
Debugging is an important work in software development. Along with the increasing complexity of the software debugging process also becomes as simple as the original. This paper will discuss the generic pattern of debugging system. Vary approaches and algorithms that have been proposed to build a good debugger system and easily used by system developers. The bug can be the caused by internal or external...
The paper develops recursive least square algorithms for nonlinear filtering of multivariate or functional data streams. The framework relies on kernel Hilbert spaces of operators. The results generalize to this framework the kernel recursive least squares developed in the scalar case. We particularly propose two possible extensions of the notion of approximate linear dependence of the regressors,...
For the functioning of American democracy, the Lobbying Disclosure Act (LDA), for the very first time, provides data to empirically research interest groups behaviors and their influence on congressional policymaking. One of the main research challenges is to automatically find the topic(s), by short & sparse text classification, in a large corpus of unorganized, semi-structured, and poorly...
This paper summarizes a process of operating system adaptation to an Intel Atom processor. The main objectives of this project was to adapt a simple micro kernel embedded operating system to a more complicated processor family, without destroying the original modules of system or changing their functionality. Our motivation was the lack of information or techniques regarding operating system migration...
As the core density of future processors keeps increasing, MPI+Threads is becoming a promising programming model for large scale SMP clusters. Generally speaking, hybrid MPI+Threads runtime can largely improve intra-node parallelism and data sharing on shared-memory architectures. However, it does not help much on inter-node communication due to the inefficient integration of existing communication...
The first step of understanding the structure of a music piece is to segment it into formative parts. A recently successful method for inding segment boundaries employs a Convolutional Neural Network (CNN) trained on spectrogram excerpts. While setting a new state of the art, it often misses boundaries deined by non-local musical cues, such as segment repetitions. To account for this, we propose a...
This paper presents machine learning-based measurement models with state-augmenting contexts as a paradigm of dynamic data-driven application systems (DDDAS). In order to formulate well-posed statistical inference problems in realistic scenarios, one needs to identify and take into account all environmental factors and ambient conditions, called contexts, which affect sensor measurements. A kernel-based...
Most of the current variability resolution tools are based on feature model. However, due to the lack of binding representation in feature model, most of them rely on other build systems for variability resolution to produce product implementations. The disconnection incurs redundant efforts by requiring developers to make or manage two separate artifacts, a variability model and an instantiation...
The recent advancement of social media has given users a platform to socially engage and interact with a global population. With millions of images being uploaded onto social media platforms, there is an increasing interest in inferring the emotion and mood display of a group of people in images. Automatic affect analysis research has come a long way but has traditionally focussed on a single subject...
The main aim of this paper is to study some boundedness inequalities of certain semi-discrete operators. These operators allow to unify some inequalities for both the Shannon sampling operators and Kantorovich-type operators.
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