Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating surface reflectance properties from images under unknown illumination

Physical surfaces such as metal, plastic, and paper possess different optical qualities that lead to different characteristics in images. We have found that humans can effectively estimate certain surface reflectance properties from a single image without knowledge of illumination. We develop a machine vision system to perform similar reflectance estimation tasks automatically. The problem of estimating reflectance form single images under unknown, complex illumination proves highly under-constrained due to the variety of potential reflectances and illuminations. Our solution relies on statistical regularities in the spatial structure of real-world illumination. These regularities translate into predictable relationships between surface reflectance and certain statistical features of the image. We determine these relationships using machine learning techniques. Our algorithms do not depend on color or polarization; they apply even to monochromatic imagery. An ability to estimate reflectance under uncontrolled illumination will further efforts to recognize materials and surface properties, tp capture computer graphics models from photographs, and to generalize classical motion and stereo algorithms such that they can handle non-Lambertian surfaces.
An efficient approach to the simultaneous localisation and mapping problem

This paper presents a novel approach to the simultaneous localisation and mapping algorithm that exploits the manner in which observations are fused into the global map of the environment to manage the computational complexity of the algorithm and improve the data association process. Rather than incorporating every observation directly into the global map of the environment, the constrained local submap filter relies on creating an independent, local submap of the features in the immediate vicinity of the vehicle. This local submap is then periodically fused into the global map of the environment using appropriately formulated constraints between the common feature estimates. This approach is shown to be effective in reducing the computational complexity of maintaining the global map estimates as well as improving the data association process.
Nearest Neighbourhood Classifiers in Biometric Fusion

Introduction This paper presents fusion decision technique comparisons based on nearest- neighborhood (NN) classifiers family for a bimodal biometric verification system that makes use of face images and speech utterances. The system is essentially constructed by a face expert, a speech expert and a fusion decision module. Each individual expert has been optimized to operate in automatic mode and designed for security access application. Fusion decision schemes considered are ordinary, modified k-NN and evidence theoretic k-NN classifier based on Dampster-Safer theory. The aim is to obtain the optimum fusion module from amongst these three techniques best suited to the target application.
Reasoning about rational agents

One goal of modern computer science is to engineer computer programs that can act as autonomous, rational agents; software that can independently make good decisions about what actions to perform on our behalf and execute those actions. Applications range from small programs that intelligently search the Web buying and selling goods via electronic commerce, to autonomous space probes. This book focuses on the belief-desire-intention (BDI) model of rational agents, which recognizes the primacy of beliefs, desires, and intentions in rational action. The BDI model has three distinct strengths: an underlying philosophy based on practical reasoning in humans, a software architecture that is implementable in real systems, and a family of logics that support a formal theory of rational agency.The book introduces a BDI logic called LORA (Logic of Rational Agents). In addition to the BDI component, LORA contains a temporal component, which allows one to represent the dynamics of how agents and their environments change over time, and an action component, which allows one to represent the actions that agents perform and the effects of the actions. The book shows how LORA can be used to capture many components of a theory of rational agency, including such notions as communication and cooperation.
Radial velocities, dynamics of stars and nebulosities with GAIA and VLT-GIRAFFE

GEPI, Observatoire de Paris, CNRS, Universite´ Paris Diderot; place Jules Janssen 92195 Meudon Cedex, FranceThe dates of receipt and acceptance should be inserted laterKey words Magellanic Clouds – Galaxy: kinematics and dynamics – ISM: b ubbles – stars: kinematics – methods: dataanalysisThis document is divided in two parts. The ﬁrst part deals wit h the radial velocities (RV) distributions for B-type starsand nebulosities observed with the VLT-GIRAFFE in the Largeand Small Magellanic Clouds towards the open clustersNGC2004 and NGC330. Thanks to the resolution of GIRAFFE spectra, we found that the RV distribution for the nebu-losities in the LMC is bi-modal. This bi-modality can be interpreted, in term of dynamics, by the expansion of the LMC4superbubble. The second part deals with the GAIA space mission and the determination of the radial velocities by us-ing Radial Velocity Spectrometer (RVS) spectra. The methods to determine the radial velocities are presented as well aspreliminary results on simulated RVS spectra.
SRATS: Software reliability assessment tool on spreadsheet (Experience report)

This paper presents a software reliability assessment tool which is built as a Microsoft Excel AddIn. The tool handles 11 types of software reliability growth models (SRGMs) and easily provides the maximum likelihood estimates (MLEs) from input data on spreadsheets. The tool consists of two components: the user interface written by Visual Basic for Application (VBA) and the dynamic link library (DLL) written by C language. In the DLL, the state-of-the-art parameter estimation algorithms for SRGMs are implemented, which are developed from the EM (expectation-maximization) algorithm. These algorithms enable us to derive the MLEs for any patterns of data.
Combining EPI and motion correction for fMRI human brain images with big motion

Motion correction is an important component in fMRI brain image analysis. Linear registration technique is mostly used in the process based on the assumption that there is not any shape changes of human brain during imaging process. Echo planar imaging (EPI) technique has been widely adapted in fMRI imaging to shorten encoding duration and increase temporal resolution. However, due to the magnetic field inhomogeneity caused by tissues, shape distortion and signal intensity lose are brought into fMRI images by the technique. On the other hand, subject's pose in scanner has a effect on magnetic field inhomogeneity, so the EPI distortions are subject to head movement, especially when the movement is big. As a result, most current motion correction techniques, which are based on rigid registration, cannot handle the problem. In this paper, a technique that combines EPI distortion correction and motion correction to handle the above-mentioned problem is proposed. Since it is almost impossible to obtain ground truth at present, a task-related fMRI BOLD time course image with big motion is selected as experimental material to test its performance. The image is pre-processed with the proposed EPI-motion correction scheme then analyzed by FSL feat tool [1]. Compared with another process with only motion correction and FSL feat analysis, the experimental result using the proposed method has no false activation detection. It is suggested the proposed EPI-motion correction scheme has the ability to handle the fMRI human brain images with big motion.
How to Display Group Information on Node-Link Diagrams: An Evaluation

We present the results of evaluating four techniques for displaying group or cluster information overlaid on node-link diagrams: node coloring, GMap, BubbleSets, and LineSets. The contributions of the paper are three fold. First, we present quantitative results and statistical analyses of data from an online study in which approximately 800 subjects performed 10 types of group and network tasks in the four evaluated visualizations. Specifically, we show that BubbleSets is the best alternative for tasks involving group membership assessment; that visually encoding group information over basic node-link diagrams incurs an accuracy penalty of about 25 percent in solving network tasks; and that GMap's use of prominent group labels improves memorability. We also show that GMap's visual metaphor can be slightly altered to outperform BubbleSets in group membership assessment. Second, we discuss visual characteristics that can explain the observed quantitative differences in the four visualizations and suggest design recommendations. This discussion is supported by a small scale eye-tracking study and previous results from the visualization literature. Third, we present an easily extensible user study methodology.
RECLAMO: Virtual and Collaborative Honeynets Based on Trust Management and Autonomous Systems Applied to Intrusion Management

Security intrusions in large systems is a problem due to its lack of scalability with the current IDS-based approaches. This paper describes the RECLAMO project, where an architecture for an Automated Intrusion Response System (AIRS) is being proposed. This system will infer the most appropriate response for a given attack, taking into account the attack type, context information, and the trust and reputation of the reporting IDSs. RECLAMO is proposing a novel approach: diverting the attack to a specific honey net that has been dynamically built based on the attack information. Among all components forming the RECLAMO's architecture, this paper is mainly focused on defining a trust and reputation management model, essential to recognize if IDSs are exposing an honest behavior in order to accept their alerts as true. Experimental results confirm that our model helps to encourage or discourage the launch of the automatic reaction process.
RASH: RAdon soft hash algorithm

In this paper, we present a high compression and collision resistant algorithm for images either suitable to extract an indexing pattern of the image and to detect deformations applied to original image. Some transforms are extracting characteristics invariant against geometrical deformations (rotation and scalling). Among them, the Radon transform, largely used in magnetic resonance imaging, is also robust against image processing basic attacks (like compression, filtering, blurring, etc.) and strong attacks (Stirmark). This transformation allows to caracterize easily features of geometrical transforms. It permits also an easy extraction of an indexing vector of the image.
La théorie des jeux pour l’allocation de ressources dans les réseaux à petites cellules

Cette these consiste a developper des mecanismes distribues pour la gestion de ressources dans les reseaux cellulaires futurs. Dans la premiere partie de cette these, les defis techniques et economiques pour la mise en œuvre des politiques de stockage distribuees dans les reseaux a petites cellules sont traites. En particulier, un mecanisme de stockage proactif est propose permettant aux stations de base d'exploiter les informations extraites des reseaux sociaux afin d'estimer la popularite locale des fichiers avant de le stocker. Une autre approche de stockage optimisee est proposee pour les reseaux cellulaires ultra-denses tout en prenant en compte les variations instantanees de l'etat des unites de stockage. Pour faciliter le deploiement de ces solutions de stockage, de nouveaux mecanismes economiques sont developpes pour motiver les fournisseurs de contenu a cooperer avec les operateurs reseaux et stocker leurs fichiers au sein des petites stations de base. Dans la deuxieme partie de cette these, le probleme de gestion du spectre est etudie dans des reseaux contenant des stations de base munies de capacites de stockage ainsi que dans les systemes LTE-U. En particulier, une approche de gestion de backhaul distribuee est proposee pour des reseaux cellulaire ayant des capacites de stockage et des liens de backhaul heterogenes. D'autre part, un modele multi-jeux est propose comme un nouvel outil de la theorie des jeux pour faire face aux nouveaux problemes d'allocation de ressources qui emergent avec l'introduction de la technologie LTE-U dans les reseaux sans fil. A cet egard, un multi-jeux compose de deux sous-jeux de types differents est formule pour optimiser la coexistence des stations de base LTE-U et utilisateurs WiFi sur les bandes non-licencies, tout en empechant les stations de base LTE-U de degrader la performance du reseau WiFi.
Automatic content extraction and visualization of Thai websites for improved information representation

This paper presents an integrated approach to automatically provide an overview of content on Thai websites based on tag cloud. This approach is intended to address the information overload issue by presenting the overview to users in order that they could assess whether the information meets their needs. The approach has incorporated Web content extraction, Thai word segmentation, and information presentation to generate a tag cloud in Thai language as an overview of the key content in the webpage. From the experimental study, the generated Thai Tag clouds are able to provide an overview of the tags which frequently appear in the title and body of the content. Moreover, the first few lines in the tag cloud offer an improved readability.
Prevalence of Soil-Transmitted Helminths and Molecular Clarification of Hookworm Species in Ethnic Ede Primary Schoolchildren in Dak Lak Province, Southern Vietnam

To know the infection status of helminths in primary schoolchildren of southern parts of Vietnam, we performed an epidemiological study in Krong Pac district, Dak Lak Province, Vietnam. A total of 1,206 stool specimens were collected from ethnic Ede schoolchildren in 4 primary schools in 2015 and examined by the Kato-Katz technique. In addition, stool cultures were done by the Harada-Mori method to obtain hookworm larvae and then to clarify the species of hookworms infected. The results showed that the helminth infection rate was 25.0%, including 2.0% Ascaris lumbricoides, 0.33% Trichuris trichiura, and 22.8% hookworm infections. The average intensity of infection was 102.0 eggs per gram of feces (EPG) for Ascaris, 36.0 EPG for Trichuris, and 218.0 EPG for hookworms. ITS1 gene sequences of the hookworm larvae were identical with those of Necator americanus (100% homology) reported in GenBank. It has been confirmed in this study that the hookworm, N. americanus, is a dominant helminth species infected in primary schoolchildren of a southern part of Vietnam. Public health attention is needed for control of hookworm infections among schoolchildren in surveyed areas of Vietnam.
Analysis of robustness in trust-based recommender systems

Much research has recently been carried out on the incorporation of trust models into recommender systems. It is generally understood that trust-based recommender systems can help to improve the accuracy of predictions. Moreover they provide greater robustness against profile injection attacks by malicious users. In this paper we analyze these contentions in the context of two trust-based algorithms. We note that one of the characteristics of trust-based algorithms is that ratings are often exposed in the user population in order for users to develop opinions on the trustworthiness of their peers. We will argue that exposing ratings presents a robustness vulnerability in these systems and we will show how this vulnerability can be exploited in the development of profile injection attacks. We conclude that the improved accuracy obtained in trust-based systems may well come at a cost of decreased robustness. In the end, trust models should be selected very carefully when building trust-based collaborative filtering (CF) systems.
Learning Relation Networks for Relational Retrieval

Text corpora with rich metadata can be represented as labeled directed graphs. This graph representation enables a number of retrieval tasks such as ad hoc retrieval, personalized recommendation, or collaborative filtering to be formulated as typed proximity queries in the graph. However, existing retrieval models are not designed to fully leverage the rich metadata. We propose a thesis work on effectively learning complex retrieval strategies which can leverage rich structural information presented as heterogeneous graphs. More specifically, we generalize the popularly used random walk with restart models so that the retrieval strategies are represented as relation paths or relation networks on the graph. A major challenge of such structural learning tasks on relational data is that the space of candidate strategy grows prohibitively fast as the model’s order of Markov dependency grows. Therefore, we develop algorithms that can efficiently discover and execute these complex strategies. We propose to test these models on recommendation tasks in biology domain (possibly extending to other domains like computer science or chemistry), and other information extraction/integration tasks.
Bandit processes and dynamic allocation indices

The paper aims to give a unified account of the central concepts in recent work on bandit processes and dynamic allocation indices; to show how these reduce some previously intractable problems to the problem of calculating such indices; and to describe how these calculations may be carried out. Applications to stochastic scheduling, sequential clinical trials and a class of search problems are discussed.
Exoskeleton to Rehabilitate Paralyzed Arm Based on Patient Healthy Arm Guidance

In this paper we discuss the stroke rehabilitation by robotic device and a novel exoskeleton, which is based on the patient's self-guided control will be presented. It is realized by moving the patient's own healthy arm in order to provide the movement trajectories for the exoskeleton. The main advantage of the exoskeleton is its flexibility and adaptivity for individual patient's rehabilitation training. A new model predictive control (MPC) algorithm for handling coupling and nonlinearity is also proposed and a microprocessor-based drive system for the exoskeleton will be adopted. In addition, to measure the angular positions corresponding to the joint angles of arm high resolution potentiometers are used. Stroke is a medical emergency which can cause permanent neurological damage, complications, and lead to death. It is a rapid development leading to the loss of brain functions due to disturbance in the blood supply to the brain. As a result, the affected area of the brain is unable to function, which might result in an inability to move one or more limbs on one side of body. The World Health Organization (WHO) estimates that about 15 million individuals suffer from stroke every year around the world. At least one-third of them will require rehabilitation from disabilities after a stroke while another five million of these people will die. It is the third leading cause of death and the leading cause of serious long term disability in the world. Stroke rehabilitation is the process by which patients with disabling strokes undergo treatment to help them return to normal life as much as possible. This process is usually supported by one rehabilitation team which is multidisciplinary as it involves staff with different skills working together to assist patients. Obviously, this traditional means of rehabilitation is extremely resource-limited and time-consuming, resulting in inadequate therapeutic practice received by patients. In the field of Bio-Engineering, one of most important goals is to develop technology and systems for patients with motor impairment. Rehabilitation robotics research is dedicated to the understanding and augmenting rehabilitation
Classification of High-throughput Data Using Correlation-shared Gene Clusters

Molecular predictor is a new tool for disease diagnosis, which uses gene expression to classify the diagnostic category of a patient. The statistical challenge for constructing such a predictor is that there are thousands of genes to predict disease category, but only a small number of samples are available. We explored a correlation-sharing based method to integrate ‘essential’ correlation structure among genes into the predictor in order that the cluster structure of genes, which is related to diagnostic classes we look for, can have potential biological interpretation. We evaluated performance of the method with other methods using three real examples. Our results show that the approach has the advantage of computational simplicity and efficiency with lower classification error rates than the compared methods.
Robust Testing for Fractional Integration Using the Bootstrap

Asymptotic tests for fractional integration are usually badly sized in small samples, even for normally distributed processes. Furthermore, tests that are well-sized under normality may be severely distorted by non-normalities and ARCH errors. This paper demonstrates how the bootstrap can be implemented to correct for such size distortions. It is shown that a well-designed bootstrap test based on the MRR and GPH tests is exact, and a procedure based on the REG test is nearly exact.
Acquiring Knowledge for Affective State Recognition in Social Media

Over the last decade, social media has emerged as a revolutionary platform for informal communication and social interactions among people. Publicly expressing thoughts, opinions, and feelings is one of the key characteristics of social media. In this dissertation, I present research on automatically acquiring knowledge from social media that can be used to recognize people’s affective state (i.e., what someone feels at a given time) in text. This research addresses two types of affective knowledge: 1) hashtag indicators of emotion consisting of emotion hashtags and emotion hashtag patterns, and 2) affective understanding of similes (a form of figurative comparison). My research introduces a bootstrapped learning algorithm for learning hashtag indicators of emotions from tweets with respect to five emotion categories: Affection, Anger/Rage, Fear/Anxiety, Joy, and Sadness/Disappointment. With a few seed emotion hashtags per emotion category, the bootstrapping algorithm iteratively learns new hashtags and more generalized hashtag patterns by analyzing emotion in tweets that contain these indicators. Emotion phrases are also harvested from the learned indicators to train additional classifiers that use the surrounding word context of the phrases as features. This is the first work to learn hashtag indicators of emotions. My research also presents a supervised classification method for classifying affective polarity of similes in Twitter. Using lexical, semantic, and sentiment properties of different simile components as features, supervised classifiers are trained to classify a simile into a positive or negative affective polarity class. The property of comparison is also fundamental to the affective understanding of similes. My research introduces a novel framework for inferring implicit properties that 1) uses syntactic constructions, statistical association, dictionary definitions and word embedding vector similarity to generate and rank candidate properties, 2) re-ranks the top properties using influence from multiple simile components, and 3) aggregates the ranks of each property from different methods to create a final ranked list of properties. The inferred properties are used to derive additional features for the supervised classifiers to further improve affective polarity recognition. Experimental results show substantial improvements in affective understanding of similes over the use of existing sentiment resources. To my loving wife, Snigdha