Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining Slanted-Frame Classifiers for Improved HMM-Based Arabic Handwriting Recognition

The problem addressed in this study is the offline recognition of handwritten Arabic city names. The names are assumed to belong to a fixed lexicon of about 1,000 entries. A state-of-the-art classical right-left hidden Markov model (HMM)-based recognizer (reference system) using the sliding window approach is developed. The feature set includes both baseline-independent and baseline-dependent features. The analysis of the errors made by the recognizer shows that the inclination, overlap, and shifted positions of diacritical marks are major sources of errors. In this paper, we propose coping with these problems. Our approach relies on the combination of three homogeneous HMM-based classifiers. All classifiers have the same topology as the reference system and differ only in the orientation of the sliding window. We compare three combination schemes of these classifiers at the decision level. Our reported results on the benchmark IFN/ENIT database of Arabic Tunisian city names give a recognition rate higher than 90 percent accuracy and demonstrate the superiority of the neural network-based combination. Our results also show that the combination of classifiers performs better than a single classifier dealing with slant-corrected images and that the approach is robust for a wide range of orientation angles.
Developing a smart camera for road traffic surveillance

Smart camera system design and implementation is a challenging task due to the constant need to perform computationally demanding image processing tasks with the limited resource constraints of embedded systems. This paper presents the hardware and software co-design and implementation of the first stage of TraffiCam, an FPGA based smart camera prototype for traffic surveillance at intersections, consisting of a CMOS image sensor capture device and FPGA main video processor. In particular, creative solutions for balancing gate array utilization, memory and computation time are presented for the initial stage of Harris keypoint detection with discussions on the algorithm implementation conversions between PC-based to FPGA based platforms. Preliminary results show satisfactory real-time tracking and estimation performance.
Motivated Agents with Career Concerns: Signalling skills and organizational involvement

The paper studies the interaction between two kinds of incentives: career concerns and intrinsic motivation emerging from agent’s alignment with organization’s objectives or another source of organizational involve- ment. The information on both skills and involvement can be asymmetric and is updated over time, as in standard career concerns model. It is shown that career concerns is weakened for the involved agent. The agent with low involvement can be more aggressive in career and reputation building at the earlier stages of career, but will be outperformed by the more involved agent in the long-run. The results of the analysis are applied to a number of contexts.
Standard Cosmology Through Similarity

In this paper we apply dimensional analysis (D.A.) to two cosmological models: Einstein-de Sitter and one Friedmann-Robertson-Walker (FRW) with radiation predominance. We believe that this method leads to the simplest form of solution the diﬀerential equations that arise in both models and would be useful as a base for the solution of more complex models. The aim of the paper is therefore rather pedagogical since it tries to show diﬀerent dimensional techniques.
Active Learning with Transfer Learning

In sentiment classification, unlabeled user reviews are often free to collect for new products, while sentiment labels are rare. In this case, active learning is often applied to build a high-quality classifier with as small amount of labeled instances as possible. However, when the labeled instances are insufficient, the performance of active learning is limited. In this paper, we aim at enhancing active learning by employing the labeled reviews from a different but related (source) domain. We propose a framework Active Vector Rotation (AVR), which adaptively utilizes the source domain data in the active learning procedure. Thus, AVR gets benefits from source domain when it is helpful, and avoids the negative affects when it is harmful. Extensive experiments on toy data and review texts show our success, compared with other state-of-the-art active learning approaches, as well as approaches with domain adaptation.
Electroweak pinch technique to all orders

The generalization of the pinch technique to all orders in the electroweak sector of the Standard Model within the class of the renormalizable 't Hooft gauges, is presented. In particular, both the all-order PT gauge-boson– and scalar–fermions vertices, as well as the diagonal and mixed gauge-boson and scalar self-energies are explicitly constructed. This is achieved through the generalization to the Standard Model of the procedure recently applied to the QCD case, which consist of two steps: (i) the identification of special Green's functions, which serve as a common kernel to all self-energy and vertex diagrams, and (ii) the study of the (on-shell) Slavnov-Taylor identities they satisfy. It is then shown that the ghost, scalar and scalar–gauge-boson Green's functions appearing in these identities capture precisely the result of the pinching action at arbitrary order. It turns out that the aforementioned Green's functions play a crucial role, their net effect being the non-trivial modification of the ghost, scalar and scalar–gauge-boson diagrams of the gauge-boson– or scalar–fermions vertex we have started from, in such a way as to dynamically generate the characteristic ghost and scalar sector of the background field method. The pinch technique gauge-boson and scalar self-energies are also explicitly constructed by resorting to the method of the background-quantum identities.
The use of belief theory to assess driver’s vigilance

Human error has been implicated as a causative factor in 85% of drivers’ and operators’ crashes, and lack of vigilance has been identified as the single most important factor in incidents involving human error. Driver vigilance could decline with sleepiness, fatigue or monotony. In Queensland, inattention and fatigue respectively contribute to 27% and 5% of reported crashes. Vigilance decline is characterised by an increased or absence of response to critical events. The current technology to assess and prevent vigilance decline is based on the isolate use of a particular device such as eye tracker or steering wheel movements. The reliability of these devices is debatable as the value of the readings could be highly inaccurate, uncertain, partial, conflictual or unreliable. Furthermore, there has been very little research examining the use of multiple devices to diagnose vigilance decline. The aim of this paper is to use belief theory to assess driver’s vigilance. Belief theory is a formal tool suitable for representing the inaccuracy, uncertainty and asynchnocity of knowledge. Our approach consists of merging a set of measurements, related to the environment, driver, and vehicle, gathered from different Advanced Driver Assistance Systems (ADAS). This paper presents the theoretical basis leading to the development of an advanced in-vehicle system capable of assessing vigilance decline. The development of such a tool has a potential to be a major contributor to reducing death and injury rates due hypovigilance related driver’s errors.
Energy-Efficient Transceiver for Wireless NoC

Network-on-Chip (NoC) with wireless interconnects is one of the potential solutions to overcome limitations of conventional NoC architectures over far-apart communications in multicore systems. Detailed investigations of Wireless NoC (WNoC) highlight their many benefits. But, idle-state power consumption associated with WI interfaces and routers is significantly high. To reduce the idle-state power consumption, a power gating technique can be incorporated with WNoC architectures. However, power gating can lead to adverse effects like IR drop, short-term sleep/wake up that increase the transient energy consumption specially for burst traffic, and also increases the cumulative settling time to get exact output response from the power gated components. To address these problems, we propose an energy-efficient transceiver for WNoC architecture using power gating. In this paper, we also present the details of techniques that minimizes the impact of power gating on performance. Proposed architecture saves up to 62.50% of idle-state power of WI as compared with traditional WNoC with minimum impacts of power gating method. This saves the overall packet energy on average by 49% compared to regular WNoC. Design considerations for augmenting power gating in WNoC and corresponding overheads are also presented.
CCM: A Text Classification Model by Clustering

In this paper, a new Cluster based Classification Model (CCM) for suspicious email detection and other text classification tasks, is presented. Comparative experiments of the proposed model against traditional classification models and the boosting algorithm are also discussed. Experimental results show that the CCM outperforms traditional classification models as well as the boosting algorithm for the task of suspicious email detection on terrorism domain email dataset and topic categorization on the Reuters-21578 and 20 Newsgroups datasets. The overall finding is that applying a cluster based approach to text classification tasks simplifies the model and at the same time increases the accuracy.
A Systematic Review of Controlled Trials Evaluating Interventions Following Non-Marital Relationship Separation

The effect of a relationship separation on wellbeing is substantial. However, without divorce parameters, individuals in dating or cohabiting relationships may struggle to access support mechanisms. A systematic review was conducted to identify controlled trials of interventions targeting individuals who have experienced a non-marital relationship separation, to supplement the divorce literature. The aim of the review was to assess the impact of these interventions on mental health. Five articles were identified through PsycINFO, PsycARTICLES and Medline databases. Overall, two of the trials reported a significant improvement in specific mental health outcomes at post-test and/or follow-up. Of the two trials demonstrating efficacy in mental health outcomes, one used a weekly, forgiveness-based group intervention and the other was a writing-based, self-initiated intervention. A lack of trials testing theory-driven interventions for relationship separation is of particular concern. Limitations of the existing literature and corresponding directions for future research are discussed.
A QoS-Aware Web Service Replica Selection Framework for an Extranet

Business partners in an extranet expect Web services to be offered in a timely fashion. Replicating Web services may reduce the end-to-end Web service response time and add fault tolerance to the system. In this paper, we propose a Web service replica selection framework that can be deployed in an extranet. The selection procedure is based on dynamic factors such as communication delays between the Web service provider and the Web service requester and the workload of the Web service provider. To analyze the performance of the framework, two Web service replica selection policies are simulated: minU and random. Simulation results show that the minU policy has better response time especially under high load conditions
Combining POS taggers in master-slaves technique for highly inflected languages as Arabic

Part Of Speech tagging (POS) is the basic process for almost all natural language processing (NLP) applications. The typical methods for combining different taggers, the program doing POS tagging, are voting or stacking techniques. We propose here a Master-Slaves Technique, which can combine Hidden Markov Model (HMM) tagger as master and any number of other taggers of any type as slaves. We describe the construction, whose main idea is that, prior to tagging an input sentence by the master tagger, its probabilities are modified to privilege the tags that slave taggers used in tagging the same sentence. Then we report about tests we did with this technique, using maximum match (MM) and Brill taggers as slaves. Additionally we report a similar, custom method of integrating HMM and MM, with a higher degree of integration of the component taggers. Despite its simplicity, this method offers an increase in accuracy in several tests on Arabic and English languages. We used a well-known data set (Brown corpus) for English language and private Arabic corpus consists of 45k word.
Intracerebroventricular Administration of Nerve Growth Factor Induces Gliogenesis in Sensory Ganglia, Dorsal Root, and within the Dorsal Root Entry Zone

Previous studies indicated that intracerebroventricular administration of nerve growth factor (NGF) leads to massive Schwann cell hyperplasia surrounding the medulla oblongata and spinal cord. This study was designed to characterize the proliferation of peripheral glial cells, that is, Schwann and satellite cells, in the trigeminal ganglia and dorsal root ganglia (DRG) of adult rats during two weeks of NGF infusion using bromodeoxyuridine (BrdU) to label dividing cells. The trigeminal ganglia as well as the cervical and lumbar DRG were analyzed. Along the entire neuraxis a small number of dividing cells were observed within these regions under physiological condition. NGF infusion has dramatically increased the generation of new cells in the neuronal soma and axonal compartments of sensory ganglia and along the dorsal root and the dorsal root entry zone. Quantification of BrdU positive cells within sensory ganglia revealed a 2.3- to 3-fold increase in glial cells compared to controls with a similar response to NGF for the different peripheral ganglia examined. Immunofluorescent labeling with S100β revealed that Schwann and satellite cells underwent mitosis after NGF administration. These data indicate that intracerebroventricular NGF infusion significantly induces gliogenesis in trigeminal ganglia and the spinal sensory ganglia and along the dorsal root entry zone as well as the dorsal root.
Distribution of the Propagation System

The goal of this thesis work is to find a distributed solution for the propagation manager of SIES, to improve the throughput and availability of the whole system. SIES is the abbreviation for “Stuttgart Information and Exploration System”. It is an instance of Enterprise Application Integration (EAI) in data level in the context of heterogeneous industrial IT infrastructures. Based on the concept of data change propagation, SIES propagates the data changes from source systems to dependent target systems, according to the predefined “dependencies”. This approach has highly saved the development expenses comparing with other EAI approaches. However, as a single-node solution, the throughput of the system is still limited. In previous works, one of the authors demonstrated that the complex propagation process, in which many XML objects processing are involved, is the main reason of low performance. Optimization on the program reduced the average processing time, but did not help a lot to improve the system throughput. To meet the requirements from the industry, an extended, scalable and reliable solution is therefore required for future. This thesis explores possible parallel/distributed approaches for extending SIES, especially Propagation Manager. After comparing alternative solutions, a central load manager based solution is selected, in which congenetic propagation, m-to-n propagation and load balancing are well managed. Based on this, the concept of Propagation Service Unit (PSU) is proposed as a scalable solution for further extension. Evaluation shows that load dispatching algorithms in Load Manager are efficient.
Estimation of Parameters of Gravitational Wave Signals fromCoalescing

With the on going construction of several large and medium scale laser in-terferometric gravitational wave antennas around the globe, the problem of the detection of gravitational waves has acquired great impetus. Since grav-itational wave signals from astrophysical sources are expected to be weak, despite the state of the art technology being employed, the development of optimal signal extraction techniques and the consequent accurate determination of the parameters of the signal is of major importance. Coalescing binary systems are one of the most promising sources of gravitational waves. One reason is that such sources are easier to model and thus one can design detection strategies tuned to such signals. A lot of attention has been devoted in the literature studying such techniques and most of the work has revolved around matched ltering and maximum likelihood estimation. In a previous work, Monte Carlo simulations were carried out of the detection process using matched ltering for the initial LIGO/VIRGO conng-uration for the rst post-Newtonian corrected coalescing binary waveform. We had compared the results of our simulations with available estimates, obtained from covariance matrix considerations, of the errors in the determination of the parameters. Our results showed that the covariance matrix 1 underestimates, by over a factor of two, the actual errors in the estimation of parameters even when the signal-to-noise ratio (SNR) is as high as 10. Sources having SNR higher than 10 are expected to be few and hence this issue is of major concern. In this paper we probe the question as to why the Monte Carlo simulations give such high errors as opposed to those obtained via the covariance matrix. We present, a computationally viable statistical model of the distribution , of the maximum likelihood estimates (MLE), of the parameters. This model reproduces the essential features of the Monte Carlo simulations, thereby explaining the large root mean square errors in the estimates, obtained in numerical experiments. The chief reason for the large errors seems to be the fact that the probability distribution of the estimated parameters is multimodal. Though only the central peak (corresponding to the actual values of the parameters) is dominant, the subsidary peaks occur`far' away thereby contributing to large variances. We suggest that the variance or the standard deviation of an estimated parameter may not provide the best measure of the error, for the kind of situation we encounter here. We therefore propose another criterion by which …
Silicon-Based Dynamic Synapse With Depressing Response

A compact implementation of a dynamic charge transfer synapse cell, capable of implementing synaptic depression, is presented. The cell is combined with a simple current mirror summing node to produce biologically plausible postsynaptic potentials (PSPs). A single charge packet is effectively transferred from the synapse to the summing node, whenever a presynaptic pulse is applied to one of its terminals. The charge packet is “weighted” by a voltage applied to the second terminal of the synapse. A voltage applied to the third terminal determines the charge recovery time in the synapse, which can be adjusted over several orders of magnitude. This voltage determines the paired pulse ratio for the synapse. The fall time of the PSP is also adjustable and is set by the gate voltage of a metal-oxide-semiconductor field-effect transistor operating in subthreshold. Results extracted from chips fabricated in a 0.35-μm complementary metal-oxide-semiconductor process, alongside theoretical and simulation results, confirm the ability of the cell to produce PSPs that are characteristic of real synapses. The concept addresses a key requirement for scalable hardware neural networks.
Design and Implementation of a Novel User Interface for Embedded System

This paper presents an implementation of self-designed user interface for embedded systems by making use of flash which is now widely used on website. Traditional graphic systems basing on components lack of flexibility and are complicated to be customized, so we propose a new architecture of the user interface with an in-depth description of the inter-module communication methods. Aiming at implementation on embedded system, several optimizations are made to improve the performance. This scheme has achieved good performance on two Set-top boxes with Linux operating system.
Reperfusion Does Not Improve Impaired Rapid-Onset Cortical Plasticity in Patients with Severe Stenosis of the Internal Carotid Artery

Background Severe stenosis of the internal carotid artery (ICA) has been associated with impaired cognition in patients, but its effect on rapid-onset cortical plasticity is not known. Carotid endarterectomy (CEA) in patients with severe ICA stenosis reduces stroke risk, but the impact on cognition or physiology of the respective hemisphere remains controversial. Methods/Results 16 patients with severe stenosis of the ICA and 16 age and sex matched controls were included. Rapid-onset cortical plasticity was assessed using the paired-associative stimulation (PAS) protocol. PAS models long-term synaptic potentiation in human motor cortex, combining repetitive stimulation of the peripheral ulnar nerve with transcranial magnetic stimulation of the contralateral motor cortex. Cognitive status was assessed with a neuropsychological test battery. In patients, verbal learning and rapid-onset cortical plasticity were significantly reduced as compared to controls. Identical follow-up tests in 9 of the 16 patients six months after CEA revealed no improvement of cognitive parameters or cortical plasticity. Conclusions Decreased rapid-onset cortical plasticity in patients with severe stenosis of the ICA was not improved by reperfusion. Thus, other strategies known to increase plasticity should be tested for their potential to improve cortical plasticity and subsequently cognition in these patients.
Intestinal adenomas can develop with a stable karyotype and stable microsatellites

Loss of function of the adenomatous polyposis coli (APC)/Apc tumor suppressor gene occurs early in the etiology of intestinal cancer in mammals. In human colonic tumors, genomic instability is proposed to be associated with tumor initiation by inducing loss of APC function. We have used a mouse model of inherited intestinal cancer (ApcMin/+, Min/+) to analyze the earliest stages of tumorigenesis in this organ. We find that tumors from C57BL/6 Min/+ mice have a stable karyotype and stable microsatellites. In contrast to previous claims, we find that homozygosity for the Min allele of Apc in tumors can proceed by homologous somatic recombination. Further, our analysis of early, benign human colorectal adenomas failed to reveal any evidence for generalized chromosomal or microsatellite instability. These results cast doubt on the hypothesis that either of these forms of genomic instability is necessary for the initial development of colorectal adenomas. We contrast our analysis of autochthonous primary tumors to other studies involving xenografts or cultured cells.