Miracle-dz/UniFAR-FTUs huggingface dataset:
Vehicle localization in urban environments using feature maps and aerial images

This paper presents two variants of a Bayesian algorithm for vehicle localization which use vehicle motion data, a low-cost GNSS receiver, a gray scale camera, and different digital map data. The key idea of the algorithm is not to extract features like points or lines from the camera image for the Bayes update, but to predict entire images. While the first variant performs this image prediction based on explicit landmark information of a digital map, the second variant predicts camera images directly based on aerial images. In doing so, no conversion step from aerial images to feature maps is necessary. Finally, the paper presents results for both approaches based on extensive test drive data with highly accurate reference data.
Joint Uplink Power Control , Beamforming and Bandwidth Allocation for Optimal QoS Assignment

This paper addresses the problem of joint resource allocation and QoS assignment in multi-cellular uplink of wireless data networks. We show how to jointly optimize three degrees of design freedom: transmit power, bandwidth allocation, and spatial diversity through antenna beamforming, in order to maximize the network-wide utility as a function of attained QoS. Many existing formulations of power control and antenna-beamforming can be viewed as special cases of this joint optimization. Our holistic framework leads to a quantitative understanding of the tradeoff among increasing power, partitioning the spectrum, and installing multiple antennas. The joint optimization is a globally coupled, non-convex problem. We solve it in several steps. First, we fix the bandwidth allocation and optimize transmit power across cells for a given beamforming vector. This extends naturally to the problem of jointly optimizing over receiver beamformer and transmit power, for which we develop a distributed algorithm that is proved to be convergent in general and globally optimal in the two user case, despite the non-convexity of this optimization. Next, the joint power-bandwidth optimization with fixed beamforming is shown to be a convex optimization, and we propose a distributed algorithm that computes the global optimum. Finally, an alternate maximization method is used to combine these two algorithms and shown to be convergent. Simulations show that the algorithm always converges to the jointly and globally optimal allocation, and demonstrate the tradeoff among power, bandwidth, and beamforming resources in maximizing network utility.
Absolute and relative angles nonlinear control of an underactuated 5-link biped with dynamic singularities

The feedback linearisation technique is used in order to control an underactuated 5-link biped model. A dynamic model with impacts is described and used for simulation. The choice of either absolute or relative angles for output function is briefly discussed and configuration space is checked for the occurrence of singularities in the decoupling matrix.
Catastrophes and the Role of Social Networks in Recovery: A Case Study of St. Bernard Parish, LA, Residents After Hurricane Katrina

Part of the Community-Based Research Commons, Demography, Population, and Ecology Commons, Environmental Policy Commons, Geographic Information Sciences Commons, Human Geography Commons, Nature and Society Relations Commons, Other Public Affairs, Public Policy and Public Administration Commons, Place and Environment Commons, Politics and Social Change Commons, Public Affairs Commons, Public Policy Commons, Quantitative, Qualitative, Comparative, and Historical Methodologies Commons, Regional Sociology Commons, Urban Studies Commons, and the Urban Studies and Planning Commons
Parallel Fast Transform-Based Preconditioners for Large-Scale Power Grid Analysis on Graphics Processing Units (GPUs)

Efficient analysis of on-chip power delivery networks is one of the most challenging problems facing the electronic design automation industry today. The fast dc and transient simulation of power grids is necessary to determine the proper operation of the integrated circuits at the design phase, but is made very difficult by the sheer size of modern power grids, reaching quite a few million nodes in nanometer-scale integrated circuits. This paper presents two efficient and highly parallel preconditioning mechanisms for the analysis of large-scale power grids of near-2-D structure (with small via resistances) or 3-D structure (with large via resistances) by iterative solution methods. The proposed preconditioners approximate the matrices of practical power grids well enough to ensure fast convergence of the iterative method, while their application within the core of the method is based on a fast transform solver which makes use of a series of independent fast Fourier transforms. Apart from the near-optimal operation complexity, the main characteristics of a fast transform solver are the large degree of multilevel parallelism and low memory requirements, which enable harnessing the computational resources of massively parallel architectures like graphics processing units (GPUs). Experimental evaluation of the proposed methodology on a set of large-scale industrial benchmarks demonstrates nearly two orders of magnitude speedup and reduction in memory footprint over parallel implementations of state-of-the-art direct and iterative methods, when GPUs are utilized.
The broadening of Fe II lines by neutral hydrogen collisions

Data for the broadening of 24188 Fe II lines by collisions with neutral hydrogen atoms have been computed using the theory of Anstee & O'Mara as extended to singly ionised species and higher orbital angular momentum states by Barklem & O'Mara. Data have been computed for all Fe II lines between observed energy levels in the line lists of Kurucz with log g f > −5 for which the theory is applicable. The variable energy debt parameter Ep used in computing the second order perturbation theory potential is chosen to be consistent with the long range dispersion interaction constant C6 computed using the f -values from Kurucz.
User Characteristics Acquisition from Logs with Semantics

IVIany current web-based information systems need to adapt to individual user characteristics in order to streamline common tasks and enable users to use them efficiently. In this paper we describe an approach to user characteristics acquisition based on automatic analysis of user behavior thus minimizing the amount of necessary user involvement. We stress the importance of appropriate data collection and propose a novel approach to logging, in which we preserve the semantics of logged evelrts. This has a significant inrpact on the successive log analysis, which focuses on the estimation of user characteristics from user navigation.
Research on active learning based computer viruses detection approaches

As traditional computer viruses detection approaches update slowly and have poor ability in detecting unknown viruses, active learning is well-suited to many problems in viruses detect processing, where unlabeled data may be abundant but annotationis slow and expensive. This paper aim to shed light on the application of the active learning theory in computer viruses detection. Moreover, to improve the precision of the virus detection and the efficiency of the active learning process, query function based on the uncertainty based sampling is realized. Experiments' results show that the model has very good detection precision against unknown computer viruses and can greatly shorten the training time and reduce the requirements of the training data and improve the learning efficiency of the system.
Incremental maintenance of schema-restructuring views in SchemaSQL

The integration of data, especially from heterogeneous sources, is a hard and widely studied problem. One particularly challenging issue is the integration of sources that are semantically equivalent but schematically heterogeneous. While two such data sources may represent the same information, one may store the information inside tuples (data) while the other may store it in attribute or relation names (schema). The SchemaSQL query language is a recent solution to this problem powerful enough to restructure such sources into each other without the loss of information. We propose the first incremental view maintenance strategy for such schema-restructuring views. Our strategy, based on an algebraic representation of the view query, correctly transforms a data update or a schema change to a source into sequences of schema and data updates to be applied to the view. We also introduce an optimization of incremental maintenance using batching. We present a proof of correctness of the propagation approach. We also describe the implementation of our SchemaSQL Query Processor and View Maintainer. Last, our experimental results demonstrate that, in many cases, incremental SchemaSQL view maintenance is significantly faster than complete view recomputation.
Intrusion Detection and Prevention System for Cloud Simulation Environment using Hidden Markov Model and MD5

Any activity aimed at disrupting a service or making a resource unavailable or gaining unauthorized access can be termed as an intrusion. Intrusion detection systems (IDSs) play a key role in detecting such malicious activities and enable administrators in securing network systems. The cloud computing platform gives people the opportunity for sharing resources, services and information among the people of the whole world. In private cloud system, information is shared among the persons who are in that cloud. For this, security or personal information hiding process hampers. In this paper we have proposed new security architecture for cloud computing platform. This ensures secure communication system and hiding information from others by authentication using shared secret key MD5 and provides security using Hidden Markov Model. This structure can be easily applied with main cloud computing features, e.g. PaaS, SaaS and IaaS. Our work mainly deals with the security system of the whole cloud computing platform.
Tobacco Product Demand, Cigarette Taxes, and Market Substitution

This paper presents a model of estimated demand for four tobacco products: cigarettes, cigars, chewing tobacco, and smoking tobacco products. Own elasticities and cross-price elasticities are used to obtain insights into the effectiveness and implications of new policy measures. Of particular interest is whether substitution of various tobacco products varies by market outlet. Several variations of the tobacco product outlet-choice model were estimated using iterative SUR. Four separate product-specific models where also estimated and represent consumers’ choice of a retail market outlet to purchase a particular tobacco product. In contrast to the joint product-outlet choice model, the two stage budgeting model rests on the assumption that consumers first choose a tobacco product, and then choose where to purchase it. In addition to estimating overall demand for the four tobacco products, models are estimated for tobacco products from three specific outlets: grocery stores, drug stores, and convenience stores. The paper investigates whether the various outlets substitute or complement each other and briefly deals with the issue of the premium consumers pay when purchasing tobacco products at convenience stores.
Crowdsourcing software evaluation

Crowdsourcing is an emerging online paradigm for problem solving which involves a large number of people often recruited on a voluntary basis and given, as a reward, some tangible or intangible incentives. It harnesses the power of the crowd for minimizing costs and, also, to solve problems which inherently require a large, decentralized and diverse crowd. In this paper, we advocate the potential of crowdsourcing for software evaluation. This is especially true in the case of complex and highly variable software systems, which work in diverse, even unpredictable, contexts. The crowd can enrich and keep the timeliness of the developers' knowledge about software evaluation via their iterative feedback. Although this seems promising, crowdsourcing evaluation introduces a new range of challenges mainly on how to organize the crowd and provide the right platforms to obtain and process their input. We focus on the activity of obtaining evaluation feedback from the crowd and conduct two focus groups to understand the various aspects of such an activity. We finally report on a set of challenges to address and realize correct and efficient crowdsourcing mechanisms for software evaluation.
Suppression of LFM interference in direct sequence spread spectrum communications based on compressive sensing

The existing linearnstryction frequency modulation (LFM) interference suppression algorithms for direct sequence spread spectrum (DSSS) communications are confined to the high sampling rate. In order to solve the problem, compressive sensing (CS) was applied to suppress the LFM interference in DSSS communications. The DSSS signal sparse dictionary and LFM interference waveform matching dictionary was built. The algorithm was designed to suppress the LFM interference in compressed domain by using the orthogonal matching pursuit (OMP) algorithm. The effectiveness of the algorithm was validated by computer simulation. The simulation results show that the algorithm could suppress the LFM interference effectively, interference suppression effect don't change with the interference intensity and interference position. This will provide an effective method for the reconstruction of the compressed DSSS signal in the condition of the LFM interference.
Using Error-Correcting Codes for Text Classification

This paper explores in detail the use of Error Correcting Output Coding (ECOC) for learning text classifiers. We show that the accuracy of a Naive Bayes Classifier over text classification tasks can be significantly improved by taking advantage of the error-correcting properties of the code. We also explore the use of different kinds of codes, namely Error-Correcting Codes, Random Codes, and Domain and Data-specific codes and give experimental results for each of them. The ECOC method scales well to large data sets with a large number of classes. Experiments on a real-world data set show a reduction in classification error by up to 66% over the traditional Naive Bayes Classifier. We also compare our empirical results to semitheoretical results and find that the two closely agree.
Roadmap constrained SLAM in neighborhood environment

Robot localisation in neighbourhood environments situated in equatorial regions presents many different challenges to those found elsewhere, buildings are sparse and the surrounding areas are covered by dense vegetation. In this paper, a Bayesian formulated framework that uses a road network topology in the form of a map to constrain the simultaneous localization and mapping (SLAM) solution is presented. The implementation uses a Rao-Blackwellized particle filter with adaptive particle sampling sets to optimise computation. The rationale is to bind effectively the adaptive sample-based representation of robot localisation estimation using a road network map whilst detecting and mapping distinct features found at the roadsides. This allows the reduction of the number of samples for ease of computation. In addition the particle depletion problem that compromises the robot closing large loops is minimized. The effectiveness of the approach is demonstrated via experimentation on a vehicle test-bed travelling in a university campus road network.
Extraction of drug-disease relations from MEDLINE abstracts

Biological research literature, as in many other domains of human activity, is a rich source of knowledge. MEDLINE is a huge database of biomedical information and life sciences; it provides information in the form of abstracts and documents. However, extracting this information leads to various problems, related to the types of information such as recognition of all terms related to the domain of texts, concepts associated with them, as well as identifying the types of relationships. In this context, we suggest in this paper an approach to extract disease-drug relations: in a first step, we employ Natural Language Processing techniques for the abstracts' preprocessing. In a second step we extract a set of features from the preprocessed abstracts. Finally we extract a disease-drug relation using machine learning classifier.
SUPERPIXEL-BASED UNSUPERVISED CHANGE DETECTION USING MULTI-DIMENSIONAL CHANGE VECTOR ANALYSIS AND SVM-BASED CLASSIFICATION

In this paper, a novel superpixel-based approach is introduced for unsupervised change detection using remote sensing images. The proposed approach contains three steps: 1) Superpixel segmentation. The simple linear iterative cluster (SLIC) algorithm is applied to obtain lattice-like homogenous superpixels. To avoid discordances of the superpixel boundaries obtained from bi-temporal images, the two images are firstly fused using principle component analysis. And then, the SLIC algorithm is applied on the first three principle components, which contain the main information of the two images. 2) For each superpixel, which is considered as the basic unit of the image space, the multi-dimensional change vector is computed from spectral, textural and structural features. 3) The superpixels are classified into two type: changed and unchanged through two progressive classification processes. The superpixels are firstly cataloged into three types: changed, unchanged and undefined by thresholding the change vectors and a voting process. And then the undefined superpixels are further classified into two classes: changed and unchanged, using a SVM-based classifier, which is trained by the derived changed and unchanged superpixels from the former step. The experiment using Indonesia data set has confirmed that the proposed approach is able to detect the changes automatically, by exploiting multiple change features.
C Self-consistent description of nuclear compressional modes

Isoscalar monopole and dipole compressional modes are computed for a variety of closed-shell nuclei in a relativistic random-phase approximation to three different parametrizations of the Walecka model with scalar self-interactions. Particular emphasis is placed on the role of self-consistency which by itself, and with little else, guarantees the decoupling of the spurious isoscalar-dipole strength from the physical response and the conservation of the vector current. A powerful new relation is introduced to quantify the violation of the vector current in terms of various ground-state form-factors. For the isoscalar-dipole mode two distinct regions are clearly identified: (i) a high-energy component that is sensitive to the size of the nucleus and scales with the compressibility of the model and (ii) a low-energy component that is insensitivity to the nuclear compressibility. A fairly good description of both compressional modes is obtained by using a " soft " parametrization having a compression modulus of K = 224 MeV.
Accelerating the Extraction of Representative Behaviors of Programs with Dynamic Binary Translation

Program behavior analysis is the foundation of computer architecture research. Therefore it is vital to be able to extract the representative behaviors of programs in an efficient manner. Representative behaviors of programs are usually extracted through the SimPoint methodology. However, generating BBV (Basic Block Vector) profiles for SimPoint is usually quite slow. This paper evaluates the effectiveness of accelerating BBV profile generation with dynamic binary translation technique. First, A general framework for BBV profile generation using dynamic binary translation is presented. Then several optimization techniques and accuracy enhancements are proposed. Based on the framework and the optimizations, a highly efficient BBV profile generator, QPoint, is presented. The performance, overhead and accuracy of QPoint is evaluated using the SPEC2006 benchmark set. Experimental results show that the optimization method proposed can improve the performance by up to 147%, on average 56%. The speed of the optimized QPoint is up to 40x, and on average 10.5x compared with a functional simulation based BBV profile generator. The overhead incurred by BBV profile gathering is less than 4% which is the lowest among existing tools. The accuracy of QPoint is also validated against a functional simulation based tool. Compared with existing tools, the proposed QPoint tool has two main advantages. First, the performance of QPoint is tremendous, with a speed of up to 292 MIPS, on average 109 MIPS, on an ordinary PC. Second, QPoint supports most architectures, including x86/x86 64, ARM, POWER, SPARC, MIPS et al., and can be used to generate cross-platform BBV profiles.
Molecular Diagnosis of Primary Mediastinal B Cell Lymphoma Identifies a Clinically Favorable Subgroup of Diffuse Large B Cell Lymphoma Related to Hodgkin Lymphoma

Using current diagnostic criteria, primary mediastinal B cell lymphoma (PMBL) cannot be distinguished from other types of diffuse large B cell lymphoma (DLBCL) reliably. We used gene expression profiling to develop a more precise molecular diagnosis of PMBL. PMBL patients were considerably younger than other DLBCL patients, and their lymphomas frequently involved other thoracic structures but not extrathoracic sites typical of other DLBCLs. PMBL patients had a relatively favorable clinical outcome, with a 5-yr survival rate of 64% compared with 46% for other DLBCL patients. Gene expression profiling strongly supported a relationship between PMBL and Hodgkin lymphoma: over one third of the genes that were more highly expressed in PMBL than in other DLBCLs were also characteristically expressed in Hodgkin lymphoma cells. PDL2, which encodes a regulator of T cell activation, was the gene that best discriminated PMBL from other DLBCLs and was also highly expressed in Hodgkin lymphoma cells. The genomic loci for PDL2 and several neighboring genes were amplified in over half of the PMBLs and in Hodgkin lymphoma cell lines. The molecular diagnosis of PMBL should significantly aid in the development of therapies tailored to this clinically and pathogenetically distinctive subgroup of DLBCL.
Public key cryptosystem based security in wireless body area network

The wireless technology of electronics is growing day by day. Using this technology, real time medical data about patients' medical data can be collected by using simple wearable sensors based on a wireless body sensor network. A portable biomedicai system can be build based on the network types available, which can monitor the patient's health in real time. In this paper we developed light weight public key crypto system for security in wireless body sensor network and also the experimental results of proposed scheme on bio sensor nodes which shows the efficiency of our system in practice.