Miracle-dz/UniFAR-FTUs huggingface dataset:
Text detection and recognition in natural scene images

Text detection and recognition in natural scene images plays an important role in content analysis of images. In this paper, based on the characteristics of scene text, we propose a robust text detection and recognition method using Maximally Stable Extremal Regions (MSER) and Support Vector Machine (SVM). Different from the end to end text recognition, we split the recognition problem into detection and recognition procedure. Firstly, in the detection stage, in order to extract potential text as much as possible, we use MSER and color clustering to extract connected component. Then, for the obtained candidate connected component, we use visual saliency and some prior information to filter non-text regions. Finally, we can obtain word image by text line generation. In the recognition stage, we use vertical projection to segment word images, then recognize character in SVM based framework. The experiment results evaluated on standard dataset show that with a small amount of prior information and simple segment strategy, the proposed method has a better performance compared to conventional text detection and recognition method.
The Taylor principle in a New Keynesian model with capital accumulation , government debt dynamics and non-Ricardian consumers

This paper develops a small, analytically tractable New Keynesian model with capital accumulation and government debt dynamics. We show that, in the absence of Ricardian equivalence, the latter channel is of conceptual importance for the design of monetary and Þscal policy rules consistent with determinate equilibrium dynamics. Our model can be used to see how deviations from Ricardian equivalence lead to potentially signiÞcant modiÞcations in the design of such rules, compared with a New Keynesian benchmark economy in which Þscal policy typically plays no prominent role. Using a Blanchard-Yaari structure, non-Ricardian features of our economy result from intertemporally disconnected decisions of consumers. This friction is policy-invariant, which distinguishes our approach from recent contributions by Woodford.
Wages and Gender Composition: Why do Women's Jobs Pay Less?

Occupational sex segregation and its relationship with wages during 1973-93 are examined. Wage level and wage change models are estimated using Current Population Survey data matched with measures of occupational skills and job disamenities. Standard analysis confirms that wage levels are substantially lower in predominantly female occupations. Gender composition effects are reduced by about a quarter for women and by over one-half for men following control for skill-related occupational characteristics. Longitudinal analysis indicates that two-thirds or more of the standard gender composition effect is accounted for by occupational characteristics and unmeasured worker skill or taste differences.
On likelihood-ratios , mutual information and estimation error for the additive Gaussian channel

This paper considers the model of an arbitrary distributed signal x observed in additive Gaussian noise w, y = x + w. New relations between the minimal mean square error of the non-causal estimator and the likelihood ratio between y and w are derived. These results are applied to prove an extended version of a recently derived relation between the mutual information I(x, y) and the minimal mean square error. The derivation of the presented results is based on the Malliavin calculus.
Contrasting potential fields and constraints in a shared control task

Shared control between a human supervisor and an autonomous discrete event control system is the focus of this paper. Two task must be considered simultaneously: 1) restricting the human input; and 2) determining the autonomous control commands. For these purposes, methods utilising either state dependent constraints or potential fields are considered. Each of these methods has advantages over the other in providing the desired control function. This paper contrasts the methods as well as presenting experimental results demonstrating the effectiveness of each of the methods in a variety of tasks.
Deletion of Mitochondrial Anchoring Protects Dysmyelinating Shiverer: Implications for Progressive MS

The demyelinating disease multiple sclerosis (MS) has an early inflammatory phase followed by an incurable progressive phase with subdued inflammation and poorly understood neurodegenerative mechanism. In this study, we identified various parallelisms between progressive MS and the dysmyelinating mouse model Shiverer and then genetically deleted a major neuron-specific mitochondrial anchoring protein Syntaphilin (SNPH) from the mouse. Prevailing evidence suggests that deletion of SNPH is harmful in demyelination. Surprisingly, SNPH deletion produces striking benefits in the Shiverer by prolonging survival, reducing cerebellar damage, suppressing oxidative stress, and improving mitochondrial health. In contrast, SNPH deletion does not benefit clinical symptoms in experimental autoimmune encephalomyelitis (EAE), a model for early-phase MS. We propose that deleting mitochondrial anchoring is a novel, specific treatment for progressive MS.
TUB-IRML at MediaEval 2014 Visual Privacy Task: Privacy Filtering through Blurring and Color Remapping

This paper describes the participation of the TUB-IRML group to the MediaEval 2014 Visual Privacy task. We present a method for privacy protection of individuals in surveillance videos. In order to achieve this, our method obscures both shape and appearance of identity-related regions through blurring and color remapping. The intelligibility is preserved by displaying edges and anomalous events are hinted at by special colors. The experimental results obtained on surveillance videos show that our method considerably outperforms other participating teams in terms of privacy score. However, the drawback is that the results in terms of intelligibility are below average.
A Feature-Enriched Tree Kernel for Relation Extraction

Tree kernel is an effective technique for relation extraction. However, the traditional syntactic tree representation is often too coarse or ambiguous to accurately capture the semantic relation information between two entities. In this paper, we propose a new tree kernel, called feature-enriched tree kernel (FTK), which can enhance the traditional tree kernel by: 1) refining the syntactic tree representation by annotating each tree node with a set of discriminant features; and 2) proposing a new tree kernel which can better measure the syntactic tree similarity by taking all features into consideration. Experimental results show that our method can achieve a 5.4% F-measure improvement over the traditional convolution tree kernel.
Sliding mode observer for fault tolerant control of induction motor

In this paper we describe a fault tolerant control (FTC) based on sliding mode observer for stator winding fault of induction motor . we show how the proposed approach can be successfully adopted in order to offset the effect of all possible faults which can occur. To achieve this goal, we develop an algorithm that allows the passage from nominal controllers designed for healthy condition, to robust controllers designed for faulty condition. This algorithm serves as fault indicator as well. The validity of the proposed scheme is verified by experimental results.
An Improved Facial Orthopedic Surgery Planning System with Pre-processing FEM Modeling

Finite Element Method(FEM), which is based upon continuum mechanics and the physics of materials, is one of the most widely used deformation method in digital biomedical area. Unlike other light-weighted deformation models (Mass-Spring, Free Form, Chainmail, BEM, etc)using in other areas, FEM gets the most accurate deforming results in the cost of large scale of computation. In this paper, we present an improved Facial Orthopedic Surgery Planning system using pre-processing FEM modeling. This planning system calculates and simulates the bone-reshaped surgical result of facial soft issue deformation more quickly. According to the specific demands of the facial surgery planning system, we designed and implemented FEM modeling in our own way and improved the computational efficiency. Finally we validate and represent the deformation results, compare our method and results with the traditional one.
A 9.1-to-11.5-GHz Four-Band PLL for X-Band Satellite & Optical Communication Applications

In this paper, a 9.1 to 11.5 GHz four-band PLL is presented. In the proposed PLL, both an improved multi-modulus (MM) divider and an adaptive four-band LC VCO are depicted. The MM divider provides division ratios of 6 to 455 depending on the division mode of the six-mode prescaler. The LC VCO generates four bands of oscillation frequencies covering 9.1-11.6 GHz, including an adaptive cross-coupled MOS array. The cross-coupled MOS array was devised to reduce the area of the capacitor array and compensate for the oscillation power. The PLL achieves phase noises of -98 dBc/Hz from 11.4 GHz and -102 dBc/Hz from 9.61 GHz, at an offset of 980 kHz. The PLL consumes 32 mA at 1.2 V. It was fabricated using 130 nm CMOS process technology.
N ° 2005-31 Onthe-job Search , Productivity Shocks , and the Individual Earnings Process

Individual labor earnings observed in worker panel data have complex, highly persistent dynamics. We investigate the capacity of a structural job search model with i.i. d. productivity shocks to replicate salient properties of these dynamics, such as the covariance structure of earnings, the evolution of individual earnings mean and variance with the duration of uninterrupted employment, or the distribution of year-to-year earnings changes. Specifically, we show within an otherwise standard job search model how the combined assumptions of on-the-job search and wage renegotiation by mutual consent act as a quantitatively plausible “internal propagation mechanism” of i.i. d. productivity shocks into persistent wage shocks. The model suggests that wage dynamics should be thought of as the outcome of a specific acceptance/rejection scheme of i.i. d. productivity shocks. This offers an alternative to the conventional linear ARMA-type approach to modelling earnings dynamics. Structural estimation of our model on a 10-year panel of highly educated British workers shows that our simple framework produces a dynamic earnings structure which is remarkably consistent with the data.
Robomotion: Scalable and Physically Stable Locomotion for Self-Reconﬁgurable Modular Robotics ∗

Self-reconﬁgurable modular robots have an intriguingly ﬂexible design, composing a single robot with many small modules that can autonomously move to transform the robot’s shape and structure. Each module may only have limited processing and memory resources. This makes scaling to a large number of modules quite challenging. This paper introduces a novel distributed locomotion algo-rithm for lattice-style self-reconﬁgurable robots which uses constant memory per module with constant computation and constant communication for each attempted module movement. Our major contributions in this paper is motion planning for self-reconﬁgurable modular robotics that is (1) scalable, (2) speedy, and most importantly, (3) stable. Our algorithm guarantees physical stability in the presence of gravity. By utilizing some robot modules to create a static support structure, other modules are able to move freely through the interior of this structure with minimal path planning and without causing instabilities or losing connectivity. This approach also permits the robot’s locomotion speed to remain nearly constant even as the number of modules in the robot grows very large. Our algorithm makes use of a distributed approach through coordination within small groups of modules. These groups are dynamically formed whenever they are needed, and disassembled when no longer needed. Additionally, we have developed methods to overcome dropped messages between modules or delays in module computation or movement. Empirical results from our simulation are also presented to demonstrate the scalability and locomotion speed advantages of this approach.
Regulation of ectodermal Wnt6 expression by the neural tube is transduced by dermomyotomal Wnt11: a mechanism of dermomyotomal lip sustainment

Ectodermal Wnt6 plays an important role during development of the somites and the lateral plate mesoderm. In the course of development, Wnt6 expression shows a dynamic pattern. At the level of the segmental plate and the epithelial somites, Wnt6 is expressed in the entire ectoderm overlying the neural tube, the paraxial mesoderm and the lateral plate mesoderm. With somite maturation, expression becomes restricted to the lateral ectoderm covering the ventrolateral lip of the dermomyotome and the lateral plate mesoderm. To study the regulation of Wnt6 expression, we have interfered with neighboring signaling pathways. We show that Wnt1 and Wnt3a signaling from the neural tube inhibit Wnt6 expression in the medial surface ectoderm via dermomyotomal Wnt11. We demonstrate that Wnt11 is an epithelialization factor acting on the medial dermomyotome, and present a model suggesting Wnt11 and Wnt6 as factors maintaining the epithelial nature of the dorsomedial and ventrolateral lips of the dermomyotome, respectively, during dermomyotomal growth.
Prospects and Challenges of Deep Understanding Social Users and Urban Services - A Position Paper

With the boom of social media, it is a very popular trend for people to share their consumption experiences and rate the items on the review site. Users can share their experiences, reviews, ratings, photos, check-ins, moods, and so on. The information they shared is valuable for new users to judge whether the items have high-quality services. Nowadays, many researchers focus on personalized recommendation and rating prediction. They miss the significance of service objective evaluation. We can find the evaluations of services from large users by their ratings and comments. The more user ratings, the more easily we obtain the objective evaluation. But how does it work for new items? It is lack of objectivity if there are few users have rated the item, such as there are just two ratings. In this paper, we discuss the prospects and challenges of deep understanding social users and urban services, and propose some key problems for research by making full using of the big urban data generated by social users, including user rating behavior study, user sentiment study, spatial-temporal features study, and user social circle study. We focus on exploring user ratings confidence, which we propose to denote the trustworthiness of user ratings for service objective evaluation by deep understanding social users and urban services. We conduct some preliminary statistical analysis to demonstrate that these studies are necessary and potential for urban service objective evaluation.
Mining Web User Behaviors to Detect Application Layer DDoS Attacks

Distributed Denial of Service (DDoS) attacks have caused continuous critical threats to the Internet services. DDoS attacks are generally conducted at the network layer. Many DDoS attack detection methods are focused on the IP and TCP layers. However, they are not suitable for detecting the application layer DDoS attacks. In this paper, we propose a scheme based on web user browsing behaviors to detect the application layer DDoS attacks (app-DDoS). A clustering method is applied to extract the access features of the web objects. Based on the access features, an extended hidden semi-Markov model is proposed to describe the browsing behaviors of web user. The deviation from the entropy of the training data set fitting to the hidden semi-Markov model can be considered as the abnormality of the observed data set. Finally experiments are conducted to demonstrate the effectiveness of our model and algorithm.
Information literacy assessment: A review of objective and interpretive measures

Information literacy has been recognized as a critical skill by professional associations and regional accrediting bodies. Consequently, institutions are increasingly integrating information literacy instruction into the academic curriculum, in turn creating the need to assess instructional impact. However, information literacy is a relatively new concept and credible assessment tools are only now forthcoming. This paper summarizes several information literacy assessment tools recent to the market, including three instruments that measure cognitive knowledge of information literacy skills at the general education level, and a test that measures knowledge of information sources and structures pertinent to the field of education. Information literacy has roots in library instruction, and two techniques derived from bibliometrics, a library and information science research method, are also presented.
Fine-tuning a tabu search algorithm with statistical tests

Abstract Tabu Search is a metaheuristic that has proven to be very effective for solving various types ofcombinatorial optimization problems. To achieve the best results with a tabu search algorithm, significant benefits can sometimes be gained by determining preferred values for certain search parameters such as tabu tenures, move selection probabilities, the timing and structure of elite solution recovery for intensification, etc. In this paper, we present and implement some new ideas for fine-tuning a tabu search algorithm using statistical tests. Although the focus of this work is to improve a particular tabu search algorithm developed for solving a telecommunications network design problem, the implications are quite general. The same ideas and procedures can easily be adapted and applied to other tabu search algorithms as well.
The viability of an ecologically valid chronic sleep restriction and circadian timing protocol: An examination of sample attrition, compliance, and effectiveness at impacting sleepiness and mood

Chronic sleep restriction (SR) increases sleepiness, negatively impacts mood, and impairs a variety of cognitive performance measures. The vast majority of work establishing these effects are tightly controlled in-lab experimental studies. Examining commonly-experienced levels of SR in naturalistic settings is more difficult and generally involves observational methods, rather than active manipulations of sleep. The same is true for analyzing behavioral and cognitive outcomes at circadian unfavorable times. The current study tested the ability of an at-home protocol to manipulate sleep schedules (i.e., impose SR), as well as create a mismatch between a subject’s circadian preference and time of testing. Viability of the protocol was assessed via completion, compliance with the SR, and success at manipulating sleepiness and mood. An online survey was completed by 3630 individuals to assess initial eligibility, 256 agreed via email response to participate in the 3-week study, 221 showed for the initial in-person session, and 184 completed the protocol (175 with complete data). The protocol consisted of 1 week at-home SR (5-6 hours in bed/night), 1 week wash-out, and 1 week well-rested (WR: 8-9 hours in bed/night). Sleep was monitored with actigraphy, diary, and call-ins. Risk management strategies were implemented for subject safety. At the end of each experimental week, subjects reported sleepiness and mood ratings. Protocol completion was 83%, with lower depression scores, higher anxiety scores, and morning session assignment predicting completion. Compliance with the sleep schedule was also very good. Subjects spent approximately 2 hours less time in bed/night and obtained an average of 1.5 hours less nightly sleep during SR, relative to WR, with 82% of subjects obtaining at least 60 minutes less average nightly sleep. Sleepiness and mood were impacted as expected by SR. These findings show the viability of studying experimental chronic sleep restriction outside the laboratory, assuming appropriate safety precautions are taken, thus allowing investigators to significantly increase ecological validity over strictly controlled in-lab studies.
Books or bullion? Printing, mining and financial integration in Central Europe from the 1460s

This paper examines the role of the advent of printing and the mining boom in explaining financial integration in Central Europe from the 1460s. It finds that changes in liquidity were not a major determinant of financial integration, but the mining boom fostered financial links between the mining districts and the rest of the region. Printing promoted financial integration mainly because it triggered a fall in the costs of transmitting information rather than because it facilitated human capital formation or institutional change. The financial significance of the advent of printing was comparable to that of the mining boom.
Using Pseudo Approximation Algorithms to Approximate Shortest Paths Above a Terrain

In this project, we address a general technique for constructing an ε-approximation algorithm out of a so-called pseudo approximation algorithm. We then show how this technique can be applied to the problem of finding the Lp-shortest path above a polyhedral terrain. Our results includes a new approximation algorithm that for any p ≥ 1, computes a (2(1− 1 p ) + ε)-factor approximation of the Lp-shortest path above a polyhedral terrain in O(nε lg n lg N ε ) time and O(n) space, where n is the number of vertices of the input terrain, and N is the maximum bit-length of the integers appearing in the input coordinates.
Implementation of Digital Signal Processing Algorithm inGeneral Purpose Graphics Processing Unit (GPGPU)

In this paper, we have proposed sequential and parallel matrix and matrix-vector multiplication in compute unified device architecture (CUDA) libraries. We show the process of a class of algorithms parallelization which are used in digital signal processing. We present this approach on the instance of the Linear Convolution, Circular Convolution, and Least Mean Square (LMS) algorithm. We propose an approach which uses a general purpose graphics processor unit (GPGPU) technology. The accelerated version on GPU computed faster because it took less time compare to the MATLAB and sequential implementation.
Habilitation À Diriger Des Recherches Cédric Du Mouza Indexing Very Large Datasets Sara and a Special Thanks to Zeinab and Ryadh for Supporting Such a Despotic Supervisor ;-)

In memory of Michel Scholl Many thanks to. Michel, for the course you gave me during my postgraduate that made me decide to start research in the database area, for the advice you gave me all along during my thesis and after. Thanks you for having worked with me, for all the discussions we had about everything, for all the beers we drank together, and so much more. We all miss you;. Philippe. It is such a pleasure to work and discuss with you. I learned a lot from you. and I am still learning! Thanks for everything! ISID Team: Jacky for its humor, its team management, and the support, which facilitate my work; Isabelle for being always available despite her responsibilities; Elisabeth for the chocolates and humor; Nadira and Samira for all the discussions we have during lunches, for your friendship; all the other team members,
A Shunting Inhibitory Convolutional Neural Network for Gender Classification

Demographic features, such as gender, are very important for human recognition and can be used to enhance social and biometric applications. In this paper, we propose to use a class of convolutional neural networks for gender classification. These networks are built upon the concepts of local receptive field processing and weight sharing, which makes them more tolerant to distortions and variations in two dimensional shapes. Tested on two separate data sets, the proposed networks achieve better classification accuracy than the conventional feedforward multilayer perceptron networks. On the Feret benchmark dataset, the proposed convolutional neural networks achieve a classification rate of 97.1%