Miracle-dz/UniFAR-FTUs huggingface dataset:
Eurecom IPv6 Soft Handover

The growing demand for high-speed wireless access to the Internet is the driving force behind the current trends to design All-IP wireless networks, whose Access Router and mobile stations use Internet Protocol for signaling and /or data transport. That will allow an ubiquitous IPbased access by mobile users, with special emphasis on the ability to use a wide variety of wireless and wired access technologies to access the common information infrastructure. The design of an AllIP wireless network requires an efficient and flexible handover management, independent of layer 2 protocol, which allows efficient mobile stations roaming between Access Routers. In this paper we propose new IPv6-based Soft-handover approach, which guarantee an efficient and flexible mobile handover between heterogeneous wireless networks.
Comparison of the Mismatch Repair System between Primary and Metastatic Colorectal Cancers Using Immunohistochemistry

Background Colorectal cancer (CRC) is one of the most common malignancies worldwide. Approximately 10%–15% of the CRC cases have defective DNA mismatch repair (MMR) genes. Although the high level of microsatellite instability status is a predictor of favorable outcome in primary CRC, little is known about its frequency and importance in secondary CRC. Immunohistochemical staining (IHC) for MMR proteins (e.g., MLH1, MSH2, MSH6, and PMS2) has emerged as a useful technique to complement polymerase chain reaction (PCR) analyses. Methods In this study, comparison between the MMR system of primary CRCs and paired liver and lung metastatic lesions was done using IHC and the correlation with clinical outcomes was also examined. Results Based on IHC, 7/61 primary tumors (11.4%) showed deficient MMR systems, while 13/61 secondary tumors (21.3%) showed deficiencies. In total, 44 cases showed proficient expression in both the primary and metastatic lesions. Three cases showed deficiencies in both the primary and paired metastatic lesions. In 10 cases, proficient expression was found only in the primary lesions, and not in the corresponding metastatic lesions. In four cases, proficient expression was detected in the secondary tumor, but not in the primary tumor. Conclusions Although each IHC result and the likely defective genes were not exactly matched between the primary and the metastatic tumors, identical results for primary and metastatic lesions were obtained in 77% of the cases (47/61). These data are in agreement with the previous microsatellite detection studies that used PCR and IHC.
Component failure estimation with Unknown Input Observer: A physical approach

In the literature Unknown Input Observers for sensor and actuator failure estimation are widely developed. None of them concerns component fault estimation. These analytical approaches are based on state equation description and complex numerical calculation. The present paper deals with physical component failure estimation based on bond graph approach as power full methodology for dynamic modeling of multi-physical systems. The additional energy due to the component failure (clearly displayed in the model) is considered as unknown input. Furthermore, properties of this observer are based on the finite and infinite structures and are synthesized directly from the graphical model, which can be pointed out from physical, structural and causal properties of used graphical representation. The developed methodology is validated in real experimental hydraulic process allowing valve and tank leakage failure estimation.
Which wavelet bases are the best for image denoising?

We use a comprehensive set of non-redundant orthogonal wavelet transforms and apply a denoising method called SUREshrink in each individual wavelet subband to denoise images corrupted by additive Gaussian white noise. We show that, for various images and a wide range of input noise levels, the orthogonal fractional (α, τ)-B-splines give the best peak signal-to-noise ratio (PSNR), as compared to standard wavelet bases (Daubechies wavelets, symlets and coiflets). Moreover, the selection of the best set (α, τ) can be performed on the MSE estimate (SURE) itself, not on the actual MSE (Oracle). Finally, the use of complex-valued fractional B-splines leads to even more significant improvements; they also outperform the complex Daubechies wavelets.
WHAT FACTORS LEAD TO THE USE OF THE COMPUTER AS AN EFFECTIVE TOOL IN THE TEACHING AND LEARNING OF MATHEMATICS ?

“The most important technological event of our time is the ascendancy of the computer. Computers already play a prominent role in many aspects of our lives, from transportation and communication to personal bookkeeping and entertainment. Scarcely oblivious to these trends, many schools now have computers and networking capabilities. To some extent, these technological appurtenances have been absorbed into the life of the school, though often they simply deliver the old lessons in a more convenient and efficient format. In the future, however, education will be organized largely around the computer. Computers will permit a degree of individualizationpersonalized coaching or tutoring-which in the past was available only to the rich. All students may receive a curriculum tailored to their needs, learning style, pace and profile of mastery, and record of success with earlier materials and lessons. Indeed, computer technology permits us to realize, for the first time, progressive educational ideas of "personalization" and "active, hands-on learning" for students all over the world”. ( H. Gardner, 2000)
Mobile object location discovery in unpredictable environments

Emerging mobile and ubiquitous computing environments present hard challenges to software engineering. The use of mobile code has been suggested as a natural fit for simplifing software development for these environments. However, the task of discovering mobile code location becomes a problem in unpredictable environments when using existing strategies, designed with fixed and relatively stable networks in mind. This paper introduces AMOS, a mobile code platform augmented with a structured overlay network. We demonstrate how the location discovery strategy of AMOS has better reliability and scalability properties than existing approaches, with minimal communication overhead. Finally, we demonstrate how AMOS can provide autonomous distribution of effort fairly throughout a network using probabilistic methods that requires no global knowledge of host capabilities.
Sociale activering in Vlaamse OCMW's: een empirische toetsing

This article explores how the three positions in a Flemish Public Centre for Social Work, being the chair(wo)man, the secretary and the social workers, evaluate the implementation of ‘the individualised project for social integration’. This measure aims at the societal integration of young social assistance clients and fits in the current social activation trend. Two main conclusions are drawn. Firstly, social workers responsible for the daily execution of this government measure are more reserved about the contract than the other two positions. Secondly, lack of time, one of the premises of Lipsky’s theory about street-level bureaucrats, doesn’t have an unambiguous influence on the social workers’ perception of the contract.
Procedures For Multiple Input Functions With Dna Molecules

In recent works for high performance computing, computation with DNA molecules, that is, DNA computing, has been receiving considerable attention as an alternative for silicon based computers. In this paper, we propose two procedures for computing multiple input functions. We first propose a simple procedure for computing AND function. The procedure runs in O(1) steps using O(mn) DNA strands for n binary numbers of m bits. The procedure is also applicable to other simple logic functions, such as OR, NAND and NOR. We next propose a procedure for EX-OR function. The procedure runs in O(1) steps using O(mn2) DNA strands, and is also applicable to other functions, such as majority and threshold functions.
New Neural Network Based Approach Helps to Discover Hidden Russian Parliament Voting Patterns

The sparse encoded Hopfield like neural network is modified to provide the Boolean factor analysis. New, more efficient method of sequential factor extraction, based on the characteristics behavior of the Lyapunov function is introduced. Efficiency of this attempt is shown not only on simulated data but on real data from Russian parliament but as well.
Ai*ia 2015 Dc Proceedings of the Doctoral Consortium (dc) Co-located with the 14th Conference of the Italian Association for Artificial In- Telligence (ai*ia 2015) Programme Chairs Programme Committee Contents Tableau Reasoners for Probabilistic Ontologies Exploiting Logic Programming Techniques Lea

The adoption of Description Logics for modeling real world domains within the Semantic Web is exponentially increased in the last years, also due to the availability of a large number of reasoning algorithms. Most of them exploit the tableau algorithm which has to manage non-determinism, a feature that is not easy to handle using procedural languages such as Java or C++. Reasoning on real world domains also requires the capability of managing probabilistic and uncertain information. We thus present TRILL, for “Tableau Reasoner for descrIption Logics in proLog” and TRILL , for “TRILL powered by Pinpointing formulas”, which implement the tableau algorithm and return the probability of queries. TRILL , instead of the set of explanations for a query, computes a Boolean formula representing them, speeding up the computation.
Half-linear discrete oscillation theory

Oscillatory properties of the second order half-linear difference equation ∆(rk|∆yk| α−2∆yk) + pk|yk+1| α−2 yk+1 = 0, where α > 1, are investigated. A particular attention is devoted to the connection with oscillation theory of its continuous counterpart, half–linear differential equation and also with the theory of linear differential and linear difference equations. We present not only the overview of the existing results but we also establish new oscillation and nonoscillation criteria.
The digital silicon photomultiplier — Principle of operation and intrinsic detector performance

We developed a fully digital implementation of the Silicon Photomultiplier. The sensor is based on a single photon avalanche photodiode (SPAD) integrated in a standard CMOS process. Photons are detected directly by sensing the voltage at the SPAD anode using a dedicated cell electronics block next to each diode. This block also contains active quenching and recharge circuits as well as a one bit memory for the selective inhibit of detector cells. A balanced trigger network is used to propagate the trigger signal from all cells to the integrated time-to-digital converter (TDC). Photons are detected and counted as digital signals, thus making the sensor less susceptible to temperature variations and electronic noise. The integration with CMOS logic has the added benefit of low power consumption and possible integration of data post-processing. In this paper, we discuss the sensor architecture and present first measurements of the technology demonstrator test chip.
String and lattice based discriminative training for the corpus of spontaneous Japanese lecture transcription task

Abstract This article aims to provide a comprehensive set of acousticmodel discriminative training results for the Corpus of Spon-taneous Japanese (CSJ) lecture speech transcription task. Dis-criminativetrainingwascarriedoutforthistaskusinga100,000word trigram for several acoustic model topologies, using bothdiagonal and full covariance models, and using both string-based and lattice-based training paradigms. We describe ourimplementation of the proposal by Macherey et al. for numer-ical subtraction of the reference lattice statistics from the com-petitor lattice statistics during lattice-based Minimum Classiﬁ-cation Error (MCE) training. We also present results for lattice-based training that does not use such subtraction, correspond-ing to the well-known Maximum Mutual Information (MMI)approach. Discriminative training yielded relative reductionsin Word Error Rate of up to 13%. Speciﬁc problems encoun-tered in implementing discriminative training for this task arediscussed. 1. Introduction
Diet, Microbiota and Immune System in Type 1 Diabetes Development and Evolution

Type 1 diabetes (T1D) is the second most frequent autoimmune disease in childhood. The long-term micro- and macro-vascular complications of diabetes are associated with the leading causes of disability and even mortality in young adults. Understanding the T1D etiology will allow the design of preventive strategies to avoid or delay the T1D onset and to help to maintain control after developing. T1D development involves genetic and environmental factors, such as birth delivery mode, use of antibiotics, and diet. Gut microbiota could be the link between environmental factors, the development of autoimmunity, and T1D. In this review, we will focus on the dietary factor and its relationship with the gut microbiota in the complex process involved in autoimmunity and T1D. The molecular mechanisms involved will also be addressed, and finally, evidence-based strategies for potential primary and secondary prevention of T1D will be discussed.
A real-time fall detection system based on HMM and RVM

The growing population of seniors leads to the need for an intelligent surveillance system to ensure the safety of the elders at home. Fall is one kind of the most seriously life-threatening emergencies for elderly people. Fall detection system based on video surveillance provides an efficient solution for detecting fall events automatically by analyzing human behaviors. In this paper, we propose a context-based fall detection system by analyzing human motion and posture using hidden Markov model (HMM) and relevance vector machine (RVM) respectively. Additionally, we integrate homography to deal with falls in any direction. The system is validated on an open fall database and our own video dataset. Experimental results demonstrate that our method achieves high robustness and accuracy in detecting different kinds of falls and runs at a real-time speed.
Speaker recognition using supervised probabilistic principal component analysis

In this study, a supervised probabilistic principal component analysis (SPPCA) model is proposed in order to integrate the speaker label information into a factor analysis approach using the well-known probabilistic principal component analysis (PPCA) model under a support vector machine (SVM) framework. The latent factor from the proposed model is believed to be more discriminative than one from the PPCA model. The proposed model, combined with different types of intersession compensation techniques in the back-end, is evaluated using the National Institute of Standards and Technology (NIST) Speaker Recognition Evaluation (SRE) 2008 data corpus, along with a comparison to the PPCA model. Index Terms: speaker recognition, factor analysis, supervised modeling
Performance Evaluation on Uniform Interactive Markov Chains

The models we considered in this paper are uniformed nondeterminism interactive Markov chains (IMCs). To give the performance specification of IMCs, we first give an algorithm to compute the optimize path probability to reach a set of goal states within a given time bound on an IMC MD, which describing until operator. Using an action-based logic called aCSL the properties of interesting over the IMCs models are described. The correctness of our describing method is proved. It is an extension of performance evaluation pure continue-time Markov chains.
Robust and accurate optical flow estimation for weak texture and varying illumination conditions: Application to cystoscopy

Robustness and accuracy of the existing optical flow algorithms can still be improved for noisy, low textured and poorly contrasted images. Most methods are based on the variational approach given by the Horn-Schunck model which can suffer from over-smoothing problems. In this paper, we propose to take into account the significant structural information of the images during optical flow computation. This will allow for discontinuities across the structure boundaries. In the data term, our optical flow model uses a classical brightness constancy assumption complemented by a modified Hessian constancy assumption based on shape structure. A constraint is added along with the regularization term, for handling the over-smoothing problems. Results on the Middlebury benchmark show the competitiveness of this algorithm. Quantitative optical flow results are given for different noise levels. Finally, application to registration of a complicated bladder cystoscopic data gives a qualitative analysis for robustness of this method.
Design and implementation of the UIMA Common Analysis System

The Common Analysis System (CAS) is the subsystem in the Unstructured Information Management Architecture (UIMA) that handles data exchanges between the various UIMA components, such as analysis engines and unstructured information management applications. CAS supports data modeling via a type system independent of programming language, provides data access through a powerful indexing mechanism, and provides support for creating annotations on text data. In this paper we cover the CAS design philosophy, discuss the major design decisions, and describe some of the implementation details.
A Framework for Online Mobility Pattern Discovery from Trajectory Data Streams

Trajectory pattern mining allows characterizing movement behavior, which leverages new applications and services. Most existing approaches analyse the whole object trajectory rather that the current movement. Besides existing approaches for online pattern discovery are restricted to instantaneous positions. Subsequently, they fail to capture the movement behaviour along time. By continuously tracking moving objects sub-trajectories at each time window, rather than just the last position, it becomes feasible to gain insight on the current behaviour, and potentially detect mobility patterns in real time. This demonstration presents a novel framework for online mobility pattern discovery in sub-trajectory data streams. Key innovations include: (i) Online discovery of mobility patterns and pattern evolution by tracking the sub-trajectories of moving objects, (ii) A novel structure, called micro-group, to represent the relationship among moving objects, and (iii) An incremental algorithm to maintain micro-groups and to capture their evolution on highly dynamic sub-trajectory data. We present various demonstration scenarios using a real data set.
Academic excellence indicators, the perspective of posgraduate students

Part of the results of a Bi-Nacional research about the search for indicators and attributes which define the academic excellence of teachers in eleven universities in Mexico and Spain are presented in this article. It is supported by the summons ITSP (Improvement of Teaching Staff Program) 2015 in Mexico, of support to ACADEMIC NETWORKS: NSIEQ (Network for the Study of the Integrity and Educational Quality). The study, has been taken under a mixed approach. It focuses on obtaining objective evidences that could help to identify indicators to assess the quality and excellence of the teaching staff of Mexican and Spanish universities, as well as the determining of the indicators and attributes of influence in the training of postgraduate students. The data offered at this stage of the research are the result of a 4 open exploratory questions survey. The responses got in the survey were analyzed by means of a mixed analysis. They are a representation of the academic excellence indicators and attributes that make a good teacher, from the students’ perspective; the factors which influenced their major studies, as well as the recommendations the students make to teacher, and which could help them indicators to improve their educational practice. The analysis of the results shows the importance of the attributes on the academic excellence indicators in the teaching work. The previous to be considered for the educational programs for the master and doctor degree in education at the Autonomous University of Tlaxcala in Mexico
Frontier exploration of internet thinking and case study

Traditional enterprises should change their marketing strategy, business model, organizational structure and etc. to cope with the new market in the Internet age. Duo to the above requirement, the conception of Internet Thinking (IT) is created in China. Although many scholars have done a lot of research on this field about the company operating at the context of Internet and a few Chinese researchers begin to pay attention to Internet Thinking. The existing research focused on the specific points without a systematic view. This paper try to integrate different points of view and combine with the practice of Chinese market to figure out what is Internet Thinking. The first, some key words of Internet Thinking are extracted from literature review and practice in business world; the second, the components involved are analyzed and the definition is given; at last, the company of Xiaomi will be taken as the case to test the model.
The Social Lives and Work Experiences of Adults with High Functioning Autism: An Indian Scenario

The purpose of this exploratory study is to attempt to understand how ASD affected the social lives and work of individuals at adulthood stage. The ability to attribute mental states to self and others is called Theory of Mind. The lack of theory of mind means that individuals with ASD have difficulty understanding others’ points of view; drawing inferences from what others say, and extracting the thoughts and assumptions of others from their words and actions. The study aims at exploring how ASD affects individuals in their social lives and how it affected them at their workplace in India. Findings and implications are discussed.
Neglected Parasitic Infections in the United States: Toxocariasis

Toxocariasis is a preventable parasitic disease that is caused by the dog and cat roundworms Toxocara cani and T. cati, respectively. Humans become infected when they accidently ingest infectious Toxocara eggs commonly found in contaminated soil; children are most often affected. Clinical manifestations of Toxocara infection in humans include ocular toxocariasis and visceral toxocariasis. Although infection with Toxocara can cause devastating disease, the burden of toxocariasis in the United States population remains unknown. In addition, risk factors for acquiring infection need to be better defined, and research needs to be conducted to better understand the pathophysiology and clinical course of toxocariasis. Development of diagnostic tests would enable clinicians to detect active infection, and determination of optimal drug regiments would ensure patients were appropriately treated. Addressing these public health gaps is necessary to understand and address the impact of toxocariasis in the United States.