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Question Answering Systems (QAS) are rising solutions providing exact and precise answers to natural questions. Duplicate Question Detection (DQD), which aims to reuse previous answers, has shown its ability to improve user experience and reduce significantly the response time. However, few Arabic QAS integrate solutions able to detect duplicate questions in their workflow. In this paper, we build a DQD method based on contextual word representation, question classification and forward/backward structured self attention. First, we extract contextual word representation Embeddings from Language Models (ELMo) to map questions into a vector space. Next, we train two models to classify question embedding according to two taxonomies: Hamza et al. and Li & Roth. Then, we introduce a class label matching step to filter out questions that have different class labels. Finally, we propose a Bidirectional Attention Bidirectional LSTM (BiAttention BiLSTM) model that focuses only on keywords to predict whether a question pair is a duplicate or not. We also apply a data augmentation strategy based on symmetry, reflexivity, and transitivity relations to improve the generalization of our model. Various experimentations are performed to evaluate the impact of question classification and pre-processing step on DQD model. The obtained results show that our model achieves good performances as compared to the baseline results.

The unability of search engines to retrieve precise answer for a given question leads research teams to build question answering systems (QAS). These systems provide exact answers of questions formulated in natural languages. Question classification is a crucial task for QAS since finding the correct answer type increases the performance of this latter. The questions taxonomy plays an important role in question classification. A broad range of taxonomies are proposed; most of these are not designed for Arabic questions. The contribution of the paper is twofold. First, we build a taxonomy for open domain Arabic questions. Second, we propose an efficient method for classifying Arabic questions. The basic idea consists of two stages: first, we compute representation of questions according to continuous distributed representation of words which allows to capture syntactic and semantic relations between words. Then, we apply a machine learning approach to classify questions into seven types or categories. We carried out several experiments and compared the proposed method with different state of arts Arabic question classification methods. Experimental results show that the proposed method achieves 90% in terms of accuracy.

The quaternary orthogonal moments have been widely used as color image descriptors owe to their remarkable color and shape information encapsulation capability. Their computation, however, depends on finding the optimal value of a unit pure quaternion parameter, which is done empirically and with no warranty of optimality. We propose a 2D color object recognition method that relies on the quaternion-valued parameter-free disc-harmonic moment invariants (QHMs) fed into the quaternion extreme learning machine (QELM). The role of this latter is to maintain the correlation between the four parts, real and imaginary, of the quaternary descriptor coefficients. Several datasets are used for recognition experiments. We draw the conclusion that: (1) our quaternion-valued QHMs invariants outperform other quaternary moments, (2) the quaternion-valued moment invariants give results better than the modulus-based moment invariants and (3) the QELM yields results better than the state-of-the-art classifiers.

Extractive multi-document summarization (MDS) is the process of automatically summarizing a collection of documents by ranking sentences according to their importance and informativeness. Text representation is a fundamental process that affects the effectiveness of many text summarization methods. Word embedding representations have shown to be effective for several Natural Language Processing (NLP) tasks including Automatic Text Summarization (ATS). However, most of these representations do not consider the order and the semantic relationships between words in a sentence. This does not fully allow grasping the sentence semantics and the syntactic relationships between sentences constituents. In this paper, to overcome this problem, we propose an unsupervised method for generic extractive multi-document summarization based on the sentence embedding representations and the centroid approach. The proposed method selects relevant sentences according to the final score obtained by combining three scores: sentence content relevance, sentence novelty, and sentence position scores. The sentence content relevance score is computed as the cosine similarity between the centroid embedding vector of the cluster of documents and the sentence embedding vectors. The sentence novelty metric is explicitly adopted to deal with redundancy. The sentence position metric assumes that the first sentences of a document are more relevant to the summary, and it assigns high scores to these sentences. Moreover, this paper provides a comparative analysis of nine sentence embedding models used to represent sentences as dense vectors in a low dimensional vector space in the context of extractive multi-document summarization. Experiments are performed on the standard DUC’2002–2004 benchmark datasets and the Multi-News dataset. The overall obtained results have shown that our method outperforms several state-of-the-art methods and achieves promising results compared to the best performing methods including supervised deep learning based methods.

International audience; Text representation is a fundamental cornerstone that impacts the effectiveness of several text summarization methods. Transfer learning using pre-trained word embedding models has shown promising results. However, most of these representations do not consider the order and the semantic relationships between words in a sentence, and thus they do not carry the meaning of a full sentence. To overcome this issue, the current study proposes an unsupervised method for extractive multi-document summarization based on transfer learning from BERT sentence embedding model. Moreover, to improve sentence representation learning, we fine-tune BERT model on supervised intermediate tasks from GLUE benchmark datasets using single-task and multi-task fine-tuning methods. Experiments are performed on the standard DUC’2002–2004 datasets. The obtained results show that our method has significantly outperformed several baseline methods and achieves a comparable and sometimes better performance than the recent state-of-the-art deep learning–based methods. Furthermore, the results show that fine-tuning BERT using multi-task learning has considerably improved the performance.

Identifying the category of a question is an essential task in Question Answering Systems. Contextual continuous word representation proved to be effective in various natural language processing tasks. However, these models have not been considered in the field of Arabic question classification. In this paper, we investigate the use of a contextual word representation named Embeddings from Language Models (ELMo) on Arabic question classification. We study the behaviour of this representation by building numerous neural network architectures trained to classify questions. The dataset contains 3173 questions annotated with two taxonomies including Arabic taxonomy and an updated Li & Roth taxonomy. By comparing to enriched word2vec with subword information technique, which is a context-free representation, we show that ELMo representation achieves better performance at the cost of reduced sized word vector. The best classifier achieves up to 94% in terms of accuracy, macro F 1 score, and weighted F 1 score.

Question Answering Systems (QASs) aim to provide a precise answer to questions written in natural language. Passages extraction is a challenging task that affects directly the performance of any QAS. In this paper, we propose a passages extraction method for Arabic Question Answering Systems. It consists of two steps: (1) formulating the query from the Arabic questions user and (2) extracting candidate passages that contain most probably, the correct answers. First, we describe the querys formulation by using stemmed words and performing a Pos-tagging process. Then, we identify relevant passages from Arabic Wikipedia based on two levels of Information Retrieval (IR). In the first level, we extract relevant documents from Arabic Wikipedia based on both documents titles and Named Entities (NEs) contained in the formulated query. The second IR level extracts candidate passages from the pages extracted in the first level based on the similarity with the query. This allows to reduce the number of extracted passages and keep the N top-ranked ones. The obtained primary results are promising as they show a high level of similarity between a given question and the candidate passages.

Questions classification which consists of assigning a category to each question is a crucial process in question answering systems. Deep neural networks have appeared to be effective in several text mining applications such as text categorization, information retrieval, etc. However, these models have not gained much attention in the field of Arabic question classification. In this paper, we propose an efficient Arabic question classification method based on continuous distributed word representation and deep neural networks. First, we opt for continuous distributed representation to capture syntactic and semantic relations between words. Then, we apply Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to classify Arabic questions into seven categories (or classes) according to Li and Roth [3] taxonomy. We investigate several variations of CNN and RNN where the output layer depends on two activation functions, the softmax and the sigmoid functions. We carry out several experiments and compare different architectures of CNN and RNN trained on our dataset containing 3173 Arabic labeled questions. The obtained results demonstrate that both deep neural network models are promising as they achieve up to 92% in terms of micro average F1 measure.

The selective removal of undesired or damaged mitochondria by autophagy, known as mitophagy, is crucial for cellular homoeostasis, and prevents tumour diffusion, neurodegeneration and ageing. The pro-autophagic molecule AMBRA1 (autophagy/beclin-1 regulator-1) has been defined as a novel regulator of mitophagy in both PINK1/PARKIN-dependent and -independent systems. Here, we identified the E3 ubiquitin ligase HUWE1 as a key inducing factor in AMBRA1-mediated mitophagy, a process that takes place independently of the main mitophagy receptors. Furthermore, we show that mitophagy function of AMBRA1 is post-translationally controlled, upon HUWE1 activity, by a positive phosphorylation on its serine 1014. This modification is mediated by the IKKα kinase and induces structural changes in AMBRA1, thus promoting its interaction with LC3/GABARAP (mATG8) proteins and its mitophagic activity. Altogether, these results demonstrate that AMBRA1 regulates mitophagy through a novel pathway, in which HUWE1 and IKKα are key factors, shedding new lights on the regulation of mitochondrial quality control and homoeostasis in mammalian cells.

Vehicular Ad-hoc Networks (VANETs) have been suggested as an active and powerful field of research to mitigate environmental problems and challenges. The main challenge in a VANET is to ensure routing with a good Quality of Service (QoS). The Greedy Perimeter Stateless Routing (GPSR) protocol is one of the most promising position-based routing mechanisms used to overcome this challenge. Its effectiveness depends entirely on the information on a node's mobility and the precision of this information. By broadcasting periodic beaconing within transmission boundary ranges, GPSR can manage neighbors' mobility information and maintain up-to-date lists of neighbours. Nevertheless, information on the position of a neighboring vehicle quickly becomes outdated, which negatively influences the efficiency of the routing. In order to monitor information mobility and to increase the QoS in this challenging area, position estimation needs to be considered. Thus, in this study, we examine the position estimation problem, and propose an improvement to the GPSR protocol, named KF-GPSR, where each vehicle estimates in real time the position of its neighbors using the Kalman filter algorithm. Indeed, by employing this strong estimation technique, it is possible to reduce considerably the frequency of exchanged beacon packets, while maintaining high position accuracy. For greater reliability, we also propose an extension to KF-GPSR, called BOD-KF-GPSR, that uses the “beacon-on-demand” process only if a node needs to rediscover its neighborhood. Simulation experiments using the network simulator NS-2 are presented to demonstrate the ability and usefulness of our two proposals. Here, we compare the proposed protocols against diverse common protocols: GPSR, AODV, DSR, and ZRP. The results show that BOD-KF-GPSR achieves a significant enhancement in terms of its packet delivery ratio, routing cost, normalized routing load, end-to-end delay, and throughput.

International audience; Term mismatch is a common limitation of traditional information retrieval (IR) models where relevance scores are estimated based on exact matching of documents and queries. Typically, good IR model should consider distinct but semantically similar words in the matching process. In this paper, we propose a method to incorporate word embedding (WE) semantic similarities into existing probabilistic IR models for Arabic in order to deal with term mismatch. Experiments are performed on the standard Arabic TREC collection using three neural word embedding models. The results show that extending the existing IR models improves significantly baseline bag-of-words models. Although the proposed extensions significantly outperform their baseline bag-of-words, the difference between the evaluated neural word embedding models is not statistically significant. Moreover, the overall comparison results show that our extensions significantly improve the Arabic WordNet based semantic indexing approach and three recent WE-based IR language models.

Vehicular ad-hoc networks (VANETs) are still more challenging to overcome even if they have been widely studied during the last decades. The routing mechanism is the essentially relevant issue in this field. Indeed, it must strictly to be adapted to specific and unique characteristics such as the high mobility of the vehicles, the dynamic nature of network topology as well as the high link breakage probability. In this paper, our objective is to improve the greedy perimeter stateless routing protocol (GPSR) as being the most promising position-based mechanism. However, according to the impact of position information on routing decision, our proposed approach defined by GPSR+PRedict protocol ensures that each vehicle estimates its own position for the near future. Afterwards, through extensive experiments, we show the ability of the GPSR+PRedict to overcome the observed problems and to enhance the overall performance of the traditional GPSR approach. The simulations are carried out on both highway and urban scenarios by using NS-2 and VanetMobiSim simulators.

Development of therapeutic antibodies in oncology has attracted much interest in the past decades. More than 30 of them have been approved and are being used to treat patients suffering from cancer. Despite encouraging results, and albeit most clinical trials aiming at evaluating monoclonal antibodies directed against TRAIL agonist receptors have been discontinued, DR4 or DR5 remain interesting targets, since these receptors are overexpressed by tumour cells and are able to trigger their death. In an effort to develop novel and specific anti-DR4 and anti-DR5 antibodies with improved properties, we used genetic immunization to express native proteins in vivo. Injection of DR4 and DR5 cDNA into the tail veins of mice elicited significant humoral anti-DR4 and anti-DR5 responses and fusions of the corresponding spleens resulted in numerous hybridomas secreting antibodies that could specifically recognize DR4 or DR5 in their native forms. All antibodies bound specifically to their targets with a very high affinity, from picomolar to nanomolar range. Among the 21 anti-DR4 and anti-DR5 monoclonal antibodies that we have produced and purified, two displayed proapoptotic properties alone, five induced apoptosis after cross-linking, four were found to potentiate TRAIL-induced apoptosis and three displayed antiapoptotic potential. The most potent anti-DR4 antibody, C#16, was assessed in vivo and was found, alone, to inhibit tumour growth in animal models. This is the first demonstration that DNA-based immunization method can be used to generate novel monoclonal antibodies targeting receptors of the TNF superfamily that may constitute new therapeutic agents.

In this paper, we present a color texture characterization by studying novel information of complex wavelet coefficients which we call extended relative phase (ERP) information. By definition, this latter permits to consider relation between color subbands by using straightforward univariate models, and thus avoiding cumbersomeness of multivariate fitting. The ERP information is modeled by three well known circular distributions namely, VonMises (VM), Wrapped Cauchy (WC) and Vonn. All the three models present, advantages of simple feature extraction (using maximum likelihood estimation) and similarity measurement (known forms of Kullback-leibler divergence) steps, which extremely helps for the runtime issue. Experimental results on the Vistex database show that considering ERP inter color subbands improves retrieval performances over the use of conventional relative phase intra grayscale subbands.

The neurodegenerative disease multiple sclerosis (MS) is pathologically characterized by the massive influx of immune cells into the central nervous system. This contributes to demyelination and axonal damage which causes symptoms such as motor and cognitive dysfunctions. The migration of leukocytes from the blood vessel is orchestrated by a multitude of factors whose determination is essential in reducing cellular influx in MS patients and the experimental autoimmune encephalomyelitis (EAE) animal model. The here studied enzyme tissue Transglutaminase (TG2) is present intracellularly, on the cell surface and extracellularly. There it contributes to cellular adhesion and migration via its transamidation activity and possibly by facilitating cellular interaction with the extracellular matrix. Previous data from our group showed reduced motor symptoms and cellular infiltration after using a pharmacological TG2 transamidation activity inhibitor in a rat EAE model. However, it remained elusive if the cross-linking activity of the enzyme resulted in the observed effects. To follow-up, we now characterized two new small molecule TG2 activity inhibitors, BJJF078 and ERW1041E. Both compounds are potent inhibitor of recombinant human and mouse Transglutaminase enzyme activity, mainly TG2 and the close related enzyme TG1. In addition they did not affect the binding of TG2 to the extracellular matrix substrate fibronectin, a process via which TG2 promotes cellular adhesion and migration. We found, that ERW1041E but not BJJF078 resulted in reduced EAE disease motor-symptoms while neither caused apparent changes in pathology (cellular influx), Transglutaminase activity or expression of inflammation related markers in the spinal cord, compared to vehicle treated controls. Although we cannot exclude issues on bioavailability and in vivo efficacy of the used compounds, we hypothesize that extracellular TG1/TG2 activity is of greater importance than (intra-)cellular activity in mouse EAE pathology.

SummaryPotent-selective peptidomimetic inhibitors of tissue transglutaminase (TG2) were developed through a combination of protein-ligand docking and molecular dynamic techniques. Derivatives of these inhibitors were made with the aim of specific TG2 targeting to the intra- and extracellular space. A cell-permeable fluorescently labeled derivative enabled detection of in situ cellular TG2 activity in human umbilical cord endothelial cells and TG2-transduced NIH3T3 cells, which could be enhanced by treatment of cells with ionomycin. Reaction of TG2 with this fluorescent inhibitor in NIH3T3 cells resulted in loss of binding of TG2 to cell surface syndecan-4 and inhibition of translocation of the enzyme into the extracellular matrix, with a parallel reduction in fibronectin deposition. In human umbilical cord endothelial cells, this same fluorescent inhibitor also demonstrated a reduction in fibronectin deposition, cell motility, and cord formation in Matrigel. Use of the same inhibitor in a mouse model of hypertensive nephrosclerosis showed over a 40% reduction in collagen deposition.

Question Classification is a very important component in question answering system. In this paper, we present a rule-based method to classify Arabic questions. We have used two taxonomies: Arabic taxonomy and Li & Roth taxonomy. We have conducted several experiments using TREC and CLEF questions. The obtained results show the importance of our method.

Arabic Multiword Terms (AMWTs) are relevant strings of words in text documents. Once they are automatically extracted, they can be used to increase the performance of any Arabic Text Mining applications such as Categorization, Clustering, Information Retrieval System, Machine Translation, and Summarization, etc. Mainly the proposed methods for AMWTs extraction can be categorized in three approaches: Linguistic-based, Statistic-based, and hybrid-based approach. These methods present some drawbacks that limit their use. In fact they can only deal with bi-grams terms and their yield not good accuracies. In this paper, to overcome these drawbacks, we propose a new and efficient method for AMWTs Extraction based on a hybrid approach. This latter is composed by two main filtering steps: the Linguistic filter and the Statistical one. The Linguistic Filter uses our proposed Part Of Speech (POS) Tagger and the Sequence identifier as patterns in order to extract candidate AMWTs. While the Statistical filter incorporate the contextual information, and a new proposed association measure based on Termhood and Unithood Estimation named NTC-Value. To evaluate and illustrate the efficiency of our proposed method for AMWTs extraction, a comparative study has been conducted based on Kalimat Corpus and using nine experiment schemes: In the linguistic filter, we used three POS Taggers such as Taani’s method based Rule-approach, HMM method based Statistical-approach, and our recently proposed Tagger based Hybrid –approach. While in the Statistical filter, we used three statistical measures such as C-Value, NC-Value, and our proposed NTC-Value. The obtained results demonstrate the efficiency of our proposed method for AMWTs extraction: it outperforms the other ones and can deal correctly with the tri-grams terms.

Part of speech tagging (POS tagging) has a crucial role in different fields of natural language processing (NLP) including Speech Recognition, Natural Language Parsing, Information Retrieval and Multi Words Term Extraction. This paper proposes an efficient and accurate POS Tagging technique for Arabic language using hybrid approach. Due to the ambiguity issue, Arabic Rule-Based method suffers from misclassified and unanalyzed words. To overcome these two problems, we propose a Hidden Markov Model (HMM) integrated with Arabic Rule-Based method. Our POS tagger generates a set of three POS tags: Noun, Verb, and Particle. The proposed technique uses the different contextual information of the words with a variety of the features which are helpful to predict the various POS classes. To evaluate its accuracy, the proposed method has been trained and tested with two corpora: the Holy Quran Corpus and Kalimat Corpus for undiacritized Classical Arabic language. The experiment results demonstrate the efficiency of our method for Arabic POS Tagging. In fact, the obtained accuracies rates are 97.6%, 96.8% and 94.4% for respectively our Hybrid Tagger, HMM Tagger and for the Rule-Based Tagger with Holy Quran Corpus. And for Kalimat Corpus we obtained 94.60%, 97.40% and 98% for respectively Rule-Based Tagger, HMM Tagger and our Hybrid Tagger.

Arabic Documents Clustering is an important task for obtaining good results with the traditional Information Retrieval (IR) systems especially with the rapid growth of the number of online documents present in Arabic language. Documents clustering aim to automatically group similar documents in one cluster using different similarity/distance measures. This task is often affected by the documents length, useful information on the documents is often accompanied by a large amount of noise, and therefore it is necessary to eliminate this noise while keeping useful information to boost the performance of Documents clustering. In this paper, we propose to evaluate the impact of text summarization using the Latent Semantic Analysis Model on Arabic Documents Clustering in order to solve problems cited above, using five similarity/distance measures: Euclidean Distance, Cosine Similarity, Jaccard Coefficient, Pearson Correlation Coefficient and Averaged Kullback-Leibler Divergence, for two times: without and with stemming. Our experimental results indicate that our proposed approach effectively solves the problems of noisy information and documents length, and thus significantly improve the clustering performance.

O1 IL-15 primes an mTOR-regulated gene-expression program to prolong anti-tumor capacity of human natural killer cells #### Andreas Lundqvist1, Vincent van Hoef1, Xiaonan Zhang1, Erik Wennerberg2, Julie Lorent1, Kristina Witt1, Laia Masvidal Sanz1, Shuo Liang1, Shannon Murray3, Ola Larsson1

Unlike French and English, the richness and ambiguity of written Arabic texts cause a great deal of errors. The purpose of this article is to resolve issues of tolerance of some errors in Arabic texts and to develop an automatic detection system as well as a correction system of those errors. This work represents a combination of the Levenshtein Distance (LD) and bi-context language models based on a large training corpus. The method involves the automatic detection and correction of no-word1 errors and the real words2 errors. This method consists of two parts. The first part is to extract the context information from the training corpus, which means to create the vocabulary and the database of the bi-context language models. The second part implements the automatic detection and correction of the incorrect words. The experimental results show a successful extraction of the context information from the corpus. Furthermore, they also show that the use of this system can reduce the rate of errors in Arabic texts.

In vehicular ad-hoc network (VANET), routing was and will remain an active field for research as long as the researchers can improve it. In fact, VANET encounters many confrontations due to the high speed and mobility pattern. Thus, the most dialectical and debatable issue is how to ensure the routing in a brief delay and with a minimum lost packets. To this end, various geographical routing protocols have been proposed to confront many challenges. One of these protocols is the greedy perimeter stateless routing (GPSR) that represents the most promising position-based routing protocol in wireless networks. The nodes use the current location information of the neighbours within their transmission boundary range and the location of the destination. These information are important to choose the adequate next hop for forwarding data. However, there are some cases when this strategy of GPSR is not suitable. The paper presents an enhancement of this famous protocol identified as GPRS+PRedict. This improvement is defined by estimating the future position of all participating nodes. Our experimental results show the ability of the GPSR+PRedict protocol to overcome the observed problem and to improve the overall performance of the traditional GPSR. The simulation is carried out on highway scenario by using NS-2 and VanetMobiSim simulators.

Stemming is a process of reducing inflected words to their stem or root from a generally written word form. This process is used in many text mining application as a feature selection technique. Moreover, Arabic text summarization has increasingly become an important task in natural language processing area (NLP). Therefore, the aim of this paper is to evaluate the impact of three different Arabic stemmers (i.e. Khoja, Larekey and Alkhalil's stemmer) on the text summarization performance for Arabic language. The evaluation of the proposed system, with the three different stemmers and without stemming, on the dataset used shows that the best performance was achieved by Khoja stemmer in term of recall, precision and F1-measure. The evaluation also shows that the performances of the proposed system are significantly improved by applying the stemming process in the pre-processing stage.

Representation of semantic information contained in the words is needed for any Arabic Text Mining applications. More precisely, the purpose is to better take into account the semantic dependencies between words expressed by the co-occurrence frequencies of these words. There have been many proposals to compute similarities between words based on their distributions in contexts. In this paper, we compare and contrast the effect of two preprocessing techniques applied to Arabic corpus: Rootbased (Stemming), and Stem-based (Light Stemming) approaches for measuring the similarity between Arabic words with the well known abstractive model -Latent Semantic Analysis (LSA)- with a wide variety of distance functions and similarity measures, such as the Euclidean Distance, Cosine Similarity, Jaccard Coefficient, and the Pearson Correlation Coefficient. The obtained results show that, on the one hand, the variety of the corpus produces more accurate results; on the other hand, the Stem-based approach outperformed the Root-based one because this latter affects the words meanings.

In order to describe color images, the use of the algebra of quaternions in combination with existing image orthogonal moments, meant for binary and grayscale images, has been widely investigated. This is because of their advantages in (1) gathering the three-channel color information in a single feature vector while preserving the correlation between them and in (2) eliminating shape information redundancy. However, the computation of these quaternary orthogonal moments depends on a unit pure quaternion parameter. The optimal value of this latter can be fixed only with the help of experiments and it is application-dependent. We propose a parameter-free formulation of the quaternary orthogonal moments. The general formula for the computation of the proposed moments, whose rotation invariance is achieved by retaining the modulus, is provided. Furthermore, experiments are conducted to evaluate the performance of the proposed modulus-based moment invariants for color image retrieval and recognition.

“Advance procedure for resource reservation QoSNSLP Signaling based” By TAAM Abdelkarim, HJIAJ Hassane, Said El Alaoui Oua Stiikg,n OalUinMg SbIaSs eMdo”h amed, RAOUYANE Brahim Journal of Networks Volume 10, Number 5, May 2015, Page(s): 281-286 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, the above paper has been found to be in violation of Academy Publisher’s Publication Principles. The first author (TAAM Abdelkarim) has taken full responsibility and this violation was done without the knowledge of the rest authors (HJIAJ Hassane, Said El Alaoui Oua Stiikg,n OalUinMg SbIaSs eMdo”h amed, RAOUYANE Brahim). This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original authors and/or paper title) and without permission: Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article: Badr Benmammar, Francine Krief, “MQoS NSLP: a mobility profile management based approach for advance resource reservation in a mobile environment,” 7th IFIP IEEE International Conference on Mobile and Wireless Communications Networks (MWCN 2005) , Marrakech, Morocco, 2005.

OLSR (Optimized Link State Routing Protocol) is a routing protocol designed especially for MANETs (Mobile Ad hoc Networks) and is currently the most widely deployed for this type of networks. OLSR uses multipoint relay (MPR: MultiPoint Relay) flooding mechanism to optimize topology control messages broadcasting throughout the network. Each node of the network selects a subset of nodes (MPR) among its neighbors to disseminate control messages. Reducing topology control packets (TC) is the key functionality of OLSR. In the Ad hoc network, the number of TC depends on the choice of MPR. In this paper, we propose a new cooperative MPR selection algorithm with the aim to reduce TC packets. We implemented this algorithm in NS2 and three other algorithms are known to be performant in the domain. Simulation results show that our algorithm reduces TC packets relative to other algorithms. This reduction is reflected positively on other performance parameters

We introduce a hybrid fingerprint recognition method built from minutiae and quaternion orthogonal moments. The proposed algorithm includes four steps: extraction of the minutiae triplets (m-triplets), first pass of triplets minutiae matching, validation step of these triplets by characterizing their neighboring gray-level image information through feature vectors of quaternion radial moments, and an adequate similarity measure. By boosting the local minutiae matching step, we avoid consolidation and global matching. To show the added-value of our method, several algorithms for extracting and matching m-triplets are considered and an experimental comparison is established. Experiments are carried out using all four parts of the FVC2004 dataset. Results indicate that the combination of the geometrical features and the quaternion radial moments of the m-triplets leads to an improvement in the overall fingerprint matching performance and demonstrate the expected gain of integrating a validation step in an m-triplets based fingerprint matching algorithm.

Robustness to photometric transformations and invariance under geometric transformations are two crucial criteria that a shape descriptor must satisfy. In this direction, orthogonal moments have proved their performance since the image can be reconstructed from its descriptor. However, these conventional moments deal only with binary and gray-level images. Recently, the algebra of quaternions have been widely used in combination with these moments in order to describe color images. In this paper, we introduce the quaternion Disc- Harmonic moments (QDHMs) as an extension of the conventional Disc-Harmonic moments (DHMs) for describing 2D color shapes. The conventional DHMs were inspired by the spherical harmonics which use orthogonal basis functions and are known for their rotation-invariance property. Experiments on images extracted from the COIL-100 database were conducted in order to evaluate the performance of our descriptor. First, we have fixed some parameters that are the maximal order, the measure of similarity and the color space. Second, tests on photometric transformations robustness are provided. Finally, the discriminative power of the QDHMs based on recall-precision criterion is compared to the conventional disc-harmonic moments and the existing orthogonal quaternion-based moments.

Recognizing Biomedical Named Entities (BioNEs) such as genes, proteins, cells, drugs, diseases, etc. play a vital role in many Biomedical Text Mining applications. BioNER fall into five approaches: Dictionary-Based, Rule-Based, Machine-Learning-Based, Statistical-Based, and Hybrid-Based. Methods Based Machine Learning approach, are more effective than those of other approaches, and therefore have been widely used for learning to recognize BioNEs. In this paper, we present a comparative theoretical and experimental study between seven Machine Learning methods, by summarizing their advantages and weaknesses, and comparing their performance on two standard biomedical Corpora (GENIA and JNLPBA). The obtained results show that CRF outperforms all the other Machine-Learning methods on both corpora. That method (CRF) will be integrated in our future works.

Arabic Multiword Term are relevant strings of words in text documents. Once they are automatically extracted, they can be used to increase the performance of any text mining applications such as Categorisation, Clustering, Information Retrieval System, Machine Translation, and Summarization, etc. This paper introduces our proposed Multiword term extraction system based on the contextual information. In fact, we propose a new method based a hybrid approach for Arabic Multiword term extraction. Like other method based on hybrid approach, our method is composed by two main steps: the Linguistic approach and the Statistical one. In the first step, the Linguistic approach uses Part Of Speech (POS) Tagger (Taani’s Tagger) and the Sequence Identifier as patterns in order to extract the candidate AMTWs. While in the second one which includes our main contribution, the Statistical approach incorporates the contextual information by using a new proposed association measure based on Termhood and Unithood for AMWTs extraction. To evaluate the efficiency of our proposed method for AMWTs extraction, this later has been tested and compared using three different association measures: the proposed one named NTC-Value, NC-Value, and C-Value. The experimental results using Arabic Texts taken from the environment domain, show that our hybrid method outperforms the other ones in term of precision, in addition, it can deal correctly with tri-gram Arabic Multiword terms.

Transglutaminase (TGase) is an enzyme that catalyzes both isopeptide cross-linking and incorporation of primary amines into proteins. Eight TGases have been identified in humans, and each of these TGases has a unique tissue distribution and physiological significance. Although several assays for TGase enzymatic activity have been reported, it has been difficult to establish an assay for discriminating each of these different TGase activities. Using a random peptide library, we recently identified the preferred substrate sequences for three major TGases: TGase 1, TGase 2, and factor XIII. In this study, we use these substrates in specific tests for measuring the activities of TGase 1 and factor XIII.