Miracle-dz/UniFAR-FTUs huggingface dataset:
Opportunistic spectrum access for mobile cognitive radios

Cognitive radios (CRs) can mitigate the impending spectrum scarcity problem by utilizing their capability of accessing licensed spectrum bands opportunistically. While most existing work focuses on enabling such opportunistic spectrum access for stationary CRs, mobility is an important concern to secondary users (SUs) because future mobile devices are expected to incorporate CR functionality. In this paper, we identify and address three fundamental challenges encountered specifically by mobile SUs. First, we model channel availability experienced by a mobile SU as a two-state continuous-time Markov chain (CTMC) and verify its accuracy via in-depth simulation. Then, to protect primary/incumbent communications from SU interference, we introduce guard distance in the space domain and derive the optimal guard distance that maximizes the spatio-temporal spectrum opportunities available to mobile CRs. To facilitate efficient spectrum sharing, we formulate the problem of maximizing secondary network throughput within a convex optimization framework, and derive an optimal, distributed channel selection strategy. Our simulation results show that the proposed spectrum sensing and distributed channel access schemes improve network throughput and fairness significantly, and reduce SU energy consumption for spectrum sensing by up to 74%.
Feasible and Optimal Path Planning in Strong Current Fields

This paper addresses the problem of path planning in strong current fields. In such situations, existing approaches are subject to incorrectness and incompleteness issues. That is, they may return physically infeasible paths or no path at all, even if a feasible path exists. That is why we propose here a new approach called the sliding wavefront expansion. This algorithm, which combine an appropriate cost function and continuous optimization techniques, guarantees the existence of a path with an arbitrary precision. The validity and the global optimality of the path are theoretically proven. Simulation results on realistic environments, which is based on actual wind charts, are also provided.
Features of distributed representations for tree-structures: A study of RAAM

This paper presents an in-depth analysis of properties of patterns, generated by the Recursive Auto-Associative Memory, based on the idea that representational features can be detected by a classification network. The intension of this analysis is to examine the actual reasons for the success of connectionist processes acting on super-positional activity vectors generated in the fashion described. We show that the structure supplied during training is maintained and is extractable from the generated pattern. Further, we show that the influence of the actual constituents in the structures supplied during training is not necessarily available, in the generated patterns, for holistic processing. The outlook for holistic processing is therefore limited unless new forms can be found which take into account, what Sharkey and Jackson call, `whole net' representations.
Fruits Classification Using MPEG-7 Descriptors from Image Patches

In a supermarket checkout, employees deal with a great number of different fruits and vegetables. Sometimes, the correct pricing of those items is a difficult task, due to the short available time or even inexperience. In this paper we present a complete method for the automatic classification of fruits, extendable for vegetables. We use patches extracted from the images and represented by means of MPEG-7 color and texture descriptors. To obtain the patches, a simple and robust technique is used. This is an important characteristic of the method, since dispenses the use of strong segmentation, which is usually difficult to be achieved, especially in real-world conditions. Two classification methods are evaluated: a multi-layer perceptron network and a support vector machine. An image dataset with 320 images of eight different fruits was constructed and used in the assessments. The proposed method presents excellent results, with accuracy values higher than 90 %. Keywords— Fruit classification, fruits image dataset, MPEG-7 descriptors, neural network, support vector machine.
Cloud QoS Scaling by Fuzzy Logic

One of the biggest advantages of cloud infrastructures is the elasticity. Cloud services are monitored and based on the resource utilization and performance load, they get scaled up or down, by provision or de-provision of cloud resources. The goal is to guarantee the customers an acceptable performance with a minimum of resources. Such Quality of Service (QoS) characteristics are stated in a contract, called Service Level Agreement (SLA) negotiated between customer and provider. The approach of this paper shows that with additional imprecise information (e.g. expected daytime/week- time performance) modeled with fuzzy logic and used in a behavior, load and performance prediction model, the up and down scaling mechanism of a cloud service can be optimized. Evaluation results confirm, that using this approach, SLA violation can be minimized.
Query Brokers for Distributed and Flexible Query Evaluation

This paper presents our work on supporting flexible query evaluation over large distributed, heterogeneous, and autonomous sources. Flexibility means that the query evaluation process can be configured according to application contextspecific, resources constraints and also can interact with its execution environment. Our query evaluation is based on Query Brokers as basic units which allow the query processing to interact with its environment. Queries are evaluated under query brokers contexts defining constraints of the evaluation task. Query Brokers have dynamic behaviors towards their execution environment. This paper focuses on the definition and the role of query brokers in the query evaluation task in large-scale mediation systems. We show also how query brokers ensure the flexibility of this task.
Subunit‐specific gating controls rat NR1/NR2A and NR1/NR2B NMDA channel kinetics and synaptic signalling profiles

NR2A and NR2B are the predominant NR2 NMDA receptor subunits expressed in cortex and hippocampus. The relative expression level of NR2A and NR2B is regulated developmentally and these two subunits have been suggested to play distinct roles in long‐term synaptic plasticity. We have used patch‐clamp recording of recombinant NMDA receptors expressed in HEK293 cells to characterize the activation properties of both NR1/NR2A and NR1/NR2B receptors. Recordings from outside‐out patches that contain a single active channel show that NR2A‐containing receptors have a higher probability of opening at least once in response to a brief synaptic‐like pulse of glutamate than NR2B‐containing receptors (NR2A, 0.80; NR2B, 0.56), a higher peak open probability (NR2A, 0.50; NR2B, 0.12), and a higher open probability within an activation (NR2A, 0.67; NR2B, 0.37). Analysis of the sequence of single‐channel open and closed intervals shows that both NR2A‐ and NR2B‐containing receptors undergo multiple conformational changes prior to opening of the channel, with at least one of these steps being faster for NR2A than NR2B. These distinct properties produce profoundly different temporal signalling profiles for NR2A‐ and NR2B‐containing receptors. Simulations of synaptic responses demonstrate that at low frequencies typically used to induce long‐term depression (LTD; 1 Hz), NR1/NR2B makes a larger contribution to total charge transfer and therefore calcium influx than NR1/NR2A. However, under high‐frequency tetanic stimulation (100 Hz; > 100 ms) typically used to induce long‐term potentiation (LTP), the charge transfer mediated by NR1/NR2A considerably exceeds that of NR1/NR2B.
A Comparative Study: Syntactic versus Semantic Matching Systems

Although there have been many matching systems evaluations, the need for more evaluations remains one of the most important issues in this field, because without evaluation no progress. In this paper, we propose to compare two matching systems according to the matching approaches classification which distinguishes between syntactic and semantic systems using three owl ontologies describing the same domain: the American University Organization.
Men’s Collective Action Willingness: Testing Different Theoretical Models of Protesting Gender Inequality for Women and Men

The present study compares models of collective action to reduce gender inequality. The dual pathway model argues that identification with the disadvantaged group along with anger and group efficacy predict collective action. Social dominance theory argues that general support for intergroup equality and rejecting sexist beliefs in particular motivate collective action. Structural equation modeling revealed that both models were supported but were moderated by gender. Men’s support for equality increased opposition to sexism, which was associated with collective action willingness, and women’s ingroup identification predicted anger and group efficacy, which predicted collective action willingness. In all, the present study demonstrates the utility of theoretical pluralism and has implications for programs and interventions that seek to encourage women and men to engage in collective action.
Quality Criteria for Design and Science

This paper discusses the quality criteria that are used in design and science to evaluate the value of the produced knowledge and artifacts. The scientific criteria discussed are: generizability, falsifiability, truth, novelty, parsimony, precision, accuracy and efficiency. Their design counterparts are also discussed. This comparison may help to guide the design method into a more scientific direction. If design is to become a useful research method, then its resulting knowledge must achieve the same or better quality than the traditional scientific method. Author Keywords
An information-theoretic quality assessment approach for X-ray images

In this work, Shannon's formulation of the information capacity of a communication channel is considered as a framework for the quantification of image quality. We describe a method to apply Shannon's noisy channel coding theorem on radiographic images, which exhibit signal-dependent noise. Results of a number of experiments on real X-ray images are presented, showing the correlation of the proposed method with classical image quality metrics from Linear Systems Theory.
Embedded real-time bilingual ALPR

Automatic License Plate Recognition (ALPR) systems are useful for various surveillance and security purposes. While ALPR is a mature technology, customization for individual countries plates is ongoing. The utility of such systems may be increased if they provide real-time information and if they can be deployed easily using low-cost embedded hardware. In this paper we describe a FPGA-based real-time ALPR system which may be embedded and which is geared towards plates with either Roman or Arabic characters. We believe it is the first system with this combination of features. We report a modest 84% success rate for our OCR algorithm in field tests and a corresponding hardware response time of 1.3ms reflecting a 200x improvement over software only techniques.
Heat Kernel Signature

Due to the increasing computational power and new low cost depth cameras the analysis of 3D shapes has become more popular during the last view years. A special challenge is provided by the comparison of non-rigid shapes. The Heat Kernel Signature is an approach, which is well suited for this kind of problem. 1 Intoduction 3D shape matching is a common field in modern geometry processing. It is based on computational understanding geometry. A lot of methods has been proposed by various scientist to solve this problem, but there is no default choice algorithm yet, like the SIFT algorithm[1] in the 2D Image processing. The Heat Kernel Signature (HKS)[2] is one of the concepts that face this challenge. The HKS has received a lot of attention in the last years. In 2010 the SHREC benchmark proposed the heat kernel based algorithms as the methods with the best performance[3]. 1.1 Point Signatures A point signature is a data set that represents specific features of a given point. They can be used to define some feature points on a manifold, detect similarities of two different manifolds or to describe the geometry. In 1996 one of the first point signatures was proposed by Chua and Jarvis[4] for effictient feature point mapping. The idea was to directly map two feature point of different 3D shape, instead of the two step process, first detecting all feature points and afterwards map them by a matching algorithm like the RANSAC[5], known from image matching. This point signature describes for each point x a one dimensional function f : [0, 2π] → R using the intersection of the manifold and a sphere with constant radius. Unfortunately this representation has some disadvantages. It is not stable against perturbations and it does not describe the shape or the point of the shape in a unique manner. Another approach is the global point signature given by Rustamov[6]. By using the eigenvalues λi and eigenfunctions φi of the Laplace Beltrami operator ∆φi = λiφi. (1) Given the ordering of the eigenvalues 0 = λ0 < λ1 < λ2 < . (2) the Global Point Signature GPS is defined as GPS(p) = ( 1 √ λ1 φ1(p), 1 √ λ2 φ2(p), 1 √ λ3 φ3(p), . ). (3) The GPS has some useful properties like invariance under isometric mappings and uniqueness of the signature for every point in the manifold. But it is not stable under perturbations. This can result in a change of the sign for some eigenfunctions as shown on Figure 1. The last point signature that is mentioned here is the Wave Kernel Signature[7] that has a lot of similarities the the HKS, but is based on Schrödingers equation ∂ψ ∂t (x, t) = i∆ψ(x, t). (4) The Wave Kernel Signature is defined as WKS(E, x) = lim T→∞ 1 T ∫ T 0 |ψE(x, t)| (5)
Working paper 4-2000 ON THE RELATIONSHIP BETWEEN THE DANISH STOCK AND BOND MARKET IN THE MEDIUM AND LONG TERM

This short paper studies the empirical relationship between realized stock returns and bond yields at the 5and 10-year investment horizons, respectively. Using annual Danish data since 1927, we find that stock returns and bond yields are closely linked in the medium and long term, as we estimate strong cointegrating relations at both horizons. Hence, at the 5and 10year investment horizons a high bond yield tends to go hand in hand with a high stock return, and vice versa. Results show that stock returns tend to respond less than one-to-one to changes in the bond yield.
Compressive video microscope via structured illumination

A compressive video microscope based on structured illumination is built. The source-side illumination coding scheme allows the emission photons being collected by the full aperture of the microscope objective, and thus is suitable for the fluorescence readout mode. A block-wise total variation algorithm has been proposed to address the mismatch between the illumination pattern size and the detector pixel size. Image sequences with a temporal compression ratio of 4:1 are demonstrated.
Evaluation and Deployment of Models for Activity Recognition

There is a growing need to monitor humans and animals in order to observe their behaviour. To understand animals in their natural environment, and to monitor children and the elderly, scientists rely on remote assessments. Automatic monitoring systems are a possibility to obtain direct observation of a subject. An automatic monitoring system can support biological and medical studies providing identification of the activity, without the intrusiveness of a human observer. AR is an emerging field but will soon be providing innovative solutions to many problems. In literature many models have been presented as a core part of an automatic monitoring system to recognise the activity of a subject. During my PhD, Machine Learning models were developed to detect physical activities of subjects using Activity Recognition (AR) techniques. Machine Learning models were programmed to autonomously identify activity patterns in accelerometer data. The versatility of Machine Learning models make them useful when managing monitoring activity where direct observation would not otherwise be possible.
Measuring Test Measurement Error

Test-based accountability as well as value-added asessments and much experimental and quasi-experimental research in education rely on achievement tests to measure student skills and knowledge. Yet, we know little regarding fundamental properties of these tests, an important example being the extent of measurement error and its implications for educational policy and practice. While test vendors provide estimates of split-test reliability, these measures do not account for potentially important day-to-day differences in student performance. In this article, we demonstrate a credible, low-cost approach for estimating the overall extent of measurement error that can be applied when students take three or more tests in the subject of interest (e.g., state assessments in consecutive grades). Our method generalizes the test–retest framework by allowing for (a) growth or decay in knowledge and skills between tests, (b) tests being neither parallel nor vertically scaled, and (c) the degree of measurement error varying across tests. The approach maintains relatively unrestrictive, testable assumptions regarding the structure of student achievement growth. Estimation only requires descriptive statistics (e.g., test-score correlations). With student-level data, the extent and pattern of measurement-error heteroscedasticity also can be estimated. In turn, one can compute Bayesian posterior means of achievement and achievement gains given observed scores—estimators having statistical properties superior to those for the observed score (score gain). We employ math and English language arts test-score data from New York City to demonstrate these methods and estimate the overall extent of test measurement error is at least twice as large as that reported by the test vendor.
Understanding Student Attitudes of Mobile Phone Applications and Tools: A study using conjoint, cluster and SEM analyses

Mobile phone use has grown dramatically throughout the world. While researchers have explored various facets of use and perceptions across age groups, genders and nationalities, segmentation by utility of mobile phone attributes has received little attention in academic research. Further, understanding how antecedent of attitudes differ by utility-based consumer segments has been underexplored. This research helps to fill these gaps by presenting a holistic view of mobile phone user preferences and perceptions among university students by applying methodologies from the marketing and information systems domains. Conjoint analysis provides insights into how students value various mobile phone applications and tools. Cluster analysis extracts salient and homogenous consumer segments from the conjoint analysis output. Structural equation modelling (SEM) then explores how antecedents to attitude may differ by the elicited consumer segments found through the cluster analysis. Implications of this work for theory and practice are presented.
C-Transformers: a framework to write C program transformations

Program transformation techniques have reached a maturity level that allows processing high-level language sources in new ways. Not only do they revolutionize the implementation of compilers and interpreters, but with modularity as a design philosophy, they also permit the seamless extension of the syntax and semantics of existing programming languages. The C-Transformers project provides a transformation environment for C, a language that proves to be hard to transform. We demonstrate the effectiveness of C-Transformers by extending C’s instructions and control flow to support Design by Contract. C-Transformers is developed by members of the LRDE: EPITA undergraduate students.
Automatic 3D Indoor Scene Updating with RGBD Cameras

Since indoor scenes are frequently changed in daily life, such as re‐layout of furniture, the 3D reconstructions for them should be flexible and easy to update. We present an automatic 3D scene update algorithm to indoor scenes by capturing scene variation with RGBD cameras. We assume an initial scene has been reconstructed in advance in manual or other semi‐automatic way before the change, and automatically update the reconstruction according to the newly captured RGBD images of the real scene update. It starts with an automatic segmentation process without manual interaction, which benefits from accurate labeling training from the initial 3D scene. After the segmentation, objects captured by RGBD camera are extracted to form a local updated scene. We formulate an optimization problem to compare to the initial scene to locate moved objects. The moved objects are then integrated with static objects in the initial scene to generate a new 3D scene. We demonstrate the efficiency and robustness of our approach by updating the 3D scene of several real‐world scenes.
Generalized consistent robust Capon beamforming for arbitrarily large arrays

In this paper, the consistency of sample robust Capon beamforming (RCB) solutions that are constructed under signature-mismatch constraints from a set of received array observations is revised and improved. Particular emphasis is given to the class of robust filters heuristically modeling the adverse effects of practical finite sample-size conditions as due to an imperfect knowledge of the effective spatial signature. In contrast, and as in practice, a small sample-size relative to the array dimension is identified in this paper as the actual source of filter estimation errors under unknown second-order statistics. Accordingly, a new alternative random matrix theory based approach to RCB is proposed in this work that explicitly addresses both the signature-mismatch problem and the limitations due to a finite sample-size. An improved performance is demonstrated via numerical simulations in the context of source power estimation in sensor array signal processing.
Electromagnetic torque calculation of 12/10 outer rotor type switched reluctance motor using finite element method

This paper addresses the electromagnetic torque calculation of 12/10 outer rotor type switched reluctance motor (SRM). It is demonstrated in this paper by the finite element method (FEM) based simulation results that the presented SRM with short path of the magnetic flux produces enough electromagnetic torque for this class of applications. This paper provides some interesting and informative insight into flux distribution, inductance and torque characteristics of SRM.
Security Management System for Sensor Network

Wireless sensor network which has more weak security than general network is not considering management principles although it is used in a various part. This paper is concerned with the security management system for sensor network including security function. We design the sensor network management functions and implement the system to manage the security information for detecting an attack from the illegal sensor node.
Facial expression recognition with local Gabor filters

This paper investigates the local Gabor filters for facial expression recognition. The local Gabor filters can not only reduce the memory requirement but also lower the time of extracting Gabor features. By contracting among the recognition rates using different local Gabor filters, we can get that not each of the Gabor filters which choose different scales can provide the same power for facial expression recognition. The local Gabor filters choosing lower scales always provide higher power than the ones choosing higher scales does. The quantitative results clearly suggest that the proposed approach produces encouraging results and opens a promising direction for speeding facial expression recognition.