Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximate reasoning and Semantic Web Services

The initiative of representing Web Services in a machine-understandable way creates a new era for software agent interoperability. A recent introduction of a concept of the Semantic Web makes it possible to automatically locate, discover, composite, and execute the services. A user agent works on behalf of its owner, and knows user's personal preferences. As there might be many service providers on the web, finding the best one which matches user's needs is critical for a suitable performance of the agent. In the paper, fuzziness and approximate reasoning methodology are applied within the Semantic Web environment. The proposed approach aims at providing capability to mimic human behavior in the case of a multi-criteria decision making process. Ontology with fuzziness is used to represent human needs and preferences. This ontology contains also information about different acceptance levels which user may have depending on responses obtained from different service providers. A prototype Semantic Web Service representing hotel reservation is built using the approach proposed.
On Accurate Automatic Verification of Publish-Subscribe Architectures

The paper presents a novel approach based on Bogor for the accurate verification of applications based on Publish- Subscribe infrastructures. Previous efforts adopted standard model checking techniques to verify the application behavior, but they introduce strong simplifications on the underlying infrastructure to cope with the state space explosion problem and make automatic verification feasible. Instead of building on top of existing model checkers, our proposal embeds the asynchronous communication mechanisms of Publish-Subscribe infrastructures within Bogor. This way, Publish-Subscribe primitives become part of the specification language as additional, domain-specific, constructs. Accurate models become feasible without incurring in state space explosion problems, thus enabling the automated verification of applications on top of realistic communication infrastructures.
Cervicovaginal HIV-1 Shedding in Women Taking Antiretroviral Therapy in Burkina Faso: A Longitudinal Study

Background:Antiretroviral therapy (ART) reduces transmission of HIV-1. However, genital HIV-1 can be detected in patients on ART. We analyzed factors associated with genital HIV-1 shedding among high-risk women on ART in Burkina Faso. Methods:Plasma viral load (PVL) and enriched cervicovaginal lavage HIV-1 RNA were measured every 3–6 months for up to 8 years. Random-effects logistic and linear regression models were used to analyze associations of frequency and quantity of genital HIV-1 RNA with behavioral and biological factors, adjusting for within-woman correlation. The lower limit of detection of HIV-1 RNA in plasma and eCVL samples was 300 copies per milliliter. Results:One hundred and eighty-eight participants initiated ART from 2004 to 2011. PVL was detectable in 16% (171/1050) of visits, in 52% (90/174) of women. Cervicovaginal HIV-1 RNA was detectable in 16% (128/798) of visits with undetectable plasma HIV-1 RNA in 45% (77/170) of women. After adjusting for PVL, detectable cervicovaginal HIV-1 RNA was independently associated with abnormal vaginal discharge and use of nevirapine or zidovudine vs. efavirenz and stavudine, respectively; longer time on ART and hormonal contraception were not associated with increased shedding. The presence of bacterial vaginosis, herpes simplex virus-2 DNA, and the use of nevirapine vs efavirenz were independently associated with an increased quantity of cervicovaginal HIV-1 RNA. Conclusions:Certain ART regimens, abnormal vaginal discharge, bacterial vaginosis, and genital herpes simplex virus-2 are associated with HIV-1 cervicovaginal shedding or quantity in women on ART after adjusting for PVL. This may reduce the effectiveness of ART as prevention in high-risk populations.
Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World

In this paper, we focus on online representation learning in non-stationary environments which may require continuous adaptation of model architecture. We propose a novel online dictionary-learning (sparse-coding) framework which incorporates the addition and deletion of hidden units (dictionary elements), and is inspired by the adult neurogenesis phenomenon in the dentate gyrus of the hippocampus, known to be associated with improved cognitive function and adaptation to new environments. In the online learning setting, where new input instances arrive sequentially in batches, the neuronal-birth is implemented by adding new units with random initial weights (random dictionary elements); the number of new units is determined by the current performance (representation error) of the dictionary, higher error causing an increase in the birth rate. Neuronal-death is implemented by imposing l1/l2-regularization (group sparsity) on the dictionary within the block-coordinate descent optimization at each iteration of our online alternating minimization scheme, which iterates between the code and dictionary updates. Finally, hidden unit connectivity adaptation is facilitated by introducing sparsity in dictionary elements. Our empirical evaluation on several real-life datasets (images and language) as well as on synthetic data demonstrates that the proposed approach can considerably outperform the state-of-art fixed-size (nonadaptive) online sparse coding of Mairal et al. (2009) in the presence of nonstationary data. Moreover, we identify certain properties of the data (e.g., sparse inputs with nearly non-overlapping supports) and of the model (e.g., dictionary sparsity) associated with such improvements.
Structural Studies of N-Glycans of Filarial Parasites

N-Type glycans containing phosphorylcholine (PC-glycans), unusual structures found in the important human pathogens filarial nematodes, represent a novel target for chemotherapy. Previous work in our laboratories produced compositional information on the PC-glycan of ES-62, a secreted protein of the rodent parasite Acanthocheilonema viteae. In particular, we established using fast atom bombardment mass spectrometry (MS) analysis that PC was attached to a glycan with a trimannosyl core, with and without core fucosylation, carrying between one and four additional N-acetylglucosamine residues. In the present study, we demonstrate that this structure is conserved among filarial nematodes, including the parasite of humans,Onchocerca volvulus, for which new drugs are most urgently sought. Furthermore, by employing a variety of procedures, including collision-activated dissociation MS-MS analysis and matrix-assisted laser desorption MS analysis, we reveal that surprisingly, filarial nematodes also contain N-linked glycans, the antennae of which are composed of chito-oligomers. To our knowledge, this is the first report of such structures in a eukaryotic glycoprotein.
Indentation plastic displacement field: Part I. The case of soft films on hard substrates

The plastic deformation behavior of Knoop indentations made in a soft, porous titanium/aluminum multilayered thin film on a hard silicon substrate is studied through use of the focused-ion-beam milling and imaging technique. Pileup is observed for indentations with depths larger than 30% of the total film thickness. Analysis of the indentation cross sections shows that plastic deformation around the indentation is partly accommodated by the closing of the pores within the multilayers. This densification process reduces the amount of pileup formed below that predicted by finite element simulations. Experimental results show that the pileup is formed by an increase of the titanium layer thickness near the edges of the indentation. The thickness increase is largest near the film/substrate interface and decreases toward the surface of the multilayered film. The amount of normal compression near the center of the indenter is characterized, and it is demonstrated that the deformation becomes more nonuniform with increasing indentation depth.
Pediatric rhabdoid meningioma: a morphological, immunohistochemical, ultrastructural and molecular case study

Rhabdoid meningioma is an uncommon meningioma variant categorized as WHO grade III. The majority of cases occur in adulthood. Herein, we describe a right fronto‐temporal rhabdoid meningioma affecting a 3‐year‐old boy. The lesion measured approximately 4 cm in diameter and incorporated the ipsilateral middle cerebral artery. Sub‐total surgical excision of the mass was performed. Histologically, the tumor was mainly composed of globoid plump cells with inclusion‐like eosinophilic cytoplasm, peripheral nuclei, prominent nucleoli and occasional intra‐nuclear cytoplasmic pseudo‐inclusion. The cells appeared in many areas loosely arranged and focally disclosed a papillary architecture. At immunohistochemistry, the tumor cells were EMA, vimentin, HHF35, PgR, INI‐1 and p53 positive. The proliferative index (Mib‐1) was 15% in the most positive areas. Ultrastructurally, tumoral cells showed an abundant cytoplasm, which was filled with numerous intermediate filaments. Desmosomal junctions were seen. RT‐PCR revealed the presence of NF2 gene expression. Molecular study did not indicate alterations of the INI‐1 gene, whereas it showed the presence of Pro72Arg in exon 4 at heterozygous state in the TP53 gene. Morphologic features along with immunohistochemical, ultrastructural and molecular results were consistent with the diagnosis of rhabdoid meningioma. The patient was treated with chemotherapy. The lesion remained stable after 33 months of follow‐up. Rhabdoid meningiomas rarely occur in children. Owing to its rarity, each new case should be recorded to produce a better clinical, pathological, molecular, prognostic and therapeutic characterization of this lesion.
Seeing Eye to Eye : Perspective Adjustment for Videoconferencing

The quality of communication in videoconferencing is compromised by the fact that participants cannot maintain eye contact. We propose an original solution to this problem. Using color and depth information from a Kinect camera, we will use a Conditional Random Field to model each participant’s eye position in 3D space, which will allow us to reorient the image to create the illusion of looking through a window. Finally, we will use a Fields of Experts model to fill in unobserved parts of the transformed image.
The future of complexity engineering

Complexity Engineering encompasses a set of approaches to engineering systems which are typically composed of various interacting entities often exhibiting self-* behaviours and emergence. The engineer or designer uses methods that benefit from the findings of complexity science and often considerably differ from the classical engineering approach of “divide and conquer”. This article provides an overview on some very interdisciplinary and innovative research areas and projects in the field of Complexity Engineering, including synthetic biology, chemistry, artificial life, self-healing materials and others. It then classifies the presented work according to five types of nature-inspired technology, namely: (1) using technology to understand nature, (2) nature-inspiration for technology, (3) using technology on natural systems, (4) using biotechnology methods in software engineering, and (5) using technology to model nature. Finally, future trends in Complexity Engineering are indicated and related risks are discussed.
Resilience within Ultrascale Computing System: Challenges and Opportunities from Nesus Project

Although resilience is already an established field in system science and many methodologies and approaches are available to deal with it, the unprecedented scales of computing, of the massive data to be managed, new network technologies, and drastically new forms of massive scale applications bring new challenges that need to be addressed. This paper reviews the challenges and approaches of resilience in ultrascale computing systems from multiple perspectives involving and addressing the resilience aspects of hardware-software co-design for ultrascale systems, resilience against security attacks, new approaches and methodologies to resilience in ultrascale systems, applications and case studies.
Conflict-Driven Clause Learning SAT Solvers

One of the most important paradigm shifts in the use of SAT solvers for solving industrial problems has been the introduction of clause learning. Clause learning entails adding a new clause for each conflict during backtrack search. This new clause prevents the same conflict from occurring again during the search process. Moreover, sophisticated techniques such as the identification of unique implication points in a graph of implications, allow creating clauses that more precisely identify the assignments responsible for conflicts. Learned clauses often have a large number of literals. As a result, another paradigm shift has been the development of new data structures, namely lazy data structures, which are particularly effective at handling large clauses. These data structures are called lazy due to being in general unable to provide the actual status of a clause. Efficiency concerns and the use of lazy data structures motivated the introduction of dynamic heuristics that do not require knowing the precise status of clauses. This chapter describes the ingredients of conflict-driven clause learning SAT solvers, namely conflict analysis, lazy data structures, search restarts, conflict-driven heuristics and clause deletion strategies.
Exploring visual prominence of multi-channel highlighting in visualizations

Visualizations make rich use of multiple visual channels so that there are few resources left to make selected focus elements visually distinct from their surrounding context. A large variety of highlighting techniques for visualizations has been presented in the past, but there has been little systematic evaluation of the design space of highlighting. We explore highlighting from the perspective of visual marks and channels - the basic building blocks of visualizations that are directly controlled by visualization designers. We present the results from two experiments, exploring the visual prominence of highlighted marks in scatterplots: First, using luminance as a single highlight channel, we found that visual prominence is mainly determined by the luminance difference between the focus mark and the brightest context mark. The brightness differences between context marks and the overall brightness level have negligible influence. Second, multi-channel highlighting using luminance and blur leads to a good trade-off between highlight effectiveness and aesthetics. From the results, we derive a simple highlight model to balance highlighting across multiple visual channels and focus and context marks, respectively.
A Hardware Approach to Detect, Expose and Tolerate High Level Data Races

Concurrent programs are more complex and error prone than their sequential peers, and are much harder to debug as well. High level data races (HLDR) are one of the concurrency bugs most difficult to debug. They are a class of concurrency errors that are not commonly addressed by the testing and debugging techniques and tools. HLDR result from the misdefinition of the scope of an atomic block, which should be unique but was wrongly split into two or more independent atomic blocks. Interleavings involving these misdefined atomic blocks may violate the program correctness invariants and cause the concurrent program to fail. In this work we propose a hardware module to detect, expose and tolerate HLDR in concurrent programs, with applications in both the software testing and debugging and the software deployment phases. In the detecting mode, our proposal detects HLDR with few false positives and without the overhead and intrusion of other dynamic software approaches. In the exposing mode, it "stimulates" the program to expose existing latent HLDR and trigger hidden HLDRs. Finally, in the tolerating mode, it may act as a software healing technique by inhibiting certain buggy interleavings. The results shows a reasonable performance overhead and few false positives in all modes.
Amniotic Fluid Embolism Pathophysiology Suggests the New Diagnostic Armamentarium: β-Tryptase and Complement Fractions C3-C4 Are the Indispensable Working Tools

Amniotic fluid embolism (AFE) is an uncommon obstetric condition involving pregnant women during labor or in the initial stages after delivery. Its incidence is estimated to be around 5.5 cases per 100,000 deliveries. Therefore, this paper investigated the pathophysiological mechanism, which underlies AFE, in order to evaluate the role of immune response in the development of this still enigmatic clinical entity. The following databases (from 1956 to September 2014) Medline, Cochrane Central, Scopus, Web of Science and Science Direct were used, searching the following key words: AFE, pathophysiology, immune/inflammatory response, complement and anaphylaxis. The main key word “AFE” was searched singularly and associated individually to each of the other keywords. Of the 146 sources found, only 19 were considered appropriate for the purpose of this paper. The clinical course is characterized by a rapid onset of symptoms, which include: acute hypotension and/or cardiac arrest, acute hypoxia (with dyspnoea, cyanosis and/or respiratory arrest), coagulopathies (disseminated intravascular coagulation and/or severe hemorrhage), coma and seizures. The pathology still determines a significant morbidity and mortality and potential permanent neurological sequelae for surviving patients. At this moment, numerous aspects involving the pathophysiology and clinical development are still not understood and several hypotheses have been formulated, in particular the possible role of anaphylaxis and complement. Moreover, the detection of serum tryptase and complement components and the evaluation of fetal antigens can explain several aspects of immune response.
Steepest Descent Adaptation of Min-Max Fuzzy If-Then Rules 1

A new technique for adaptation of fuzzy membership functions in a fuzzy inference system is proposed. The technique relies upon the isolation of the specific membership function that contributed to the final decision, followed by the updating of this function’s parameters using steepest descent. The error measure used is thus back propagated from output to input, through the min and max operators used during the inference stage. This is feasible because the operations of min and max are continuous differentiable functions and therefore can be placed in a chain of partial derivatives for steepest descent backpropagation adaptation. More interestingly, the partials of min and max (or any other order statistic, for that matter) act as ‘pointers’ with the result that only the function that gave rise to the min or max is adapted; the others are not. To illustrate, let α max β1 β2 βN . Then ∂α ∂βn 1w henβn is the maximum and is otherwise zero. We apply this property to the fine tuning of membership functions of fuzzy min-max decision processes and illustrate with an estimation example.
Toward a Reference Process for Developing Wireless Internet Services

Wireless Internet services such as mobile Web applications promise an enormous market potential. The field is characterized by extreme time-to-market pressure and insufficient knowledge about development procedures and technical constraints. This results in insufficient guidance for project managers and software developers on selecting appropriate development processes, techniques, methods, and tools. In addition, there is an enormous lack of knowledge about the effects (such as effort consumption, defect injection) of such technologies that hinders the transfer of innovative technologies into practice. This article describes an initial reference process by summarizing essential technologies for the development of wireless Internet services and experience with these technologies on the levels of life cycle processes, engineering processes, and managerial processes. The reference process is based on a comprehensive literature survey and the execution of development projects for wireless Internet services. The goal of the article is to provide domain-specific guidance for project managers and software developers with accompanying lessons learned from the past.
A Robust Face Detection Method Based on Skin Color and Edges

In this paper we propose a method to detect human faces in color images. Many existing systems use a window-based classifier that scans the entire image for the presence of the human face and such systems suffers from scale variation, pose variation, illumination changes, etc. Here, we propose a lighting insensitive face detection method based upon the edge and skin tone information of the input color image. First, image enhancement is performed, especially if the image is acquired from an unconstrained illumination condition. Next, skin segmentation in YCbCr and RGB space is conducted. The result of skin segmentation is refined using the skin tone percentage index method. The edges of the input image are combined with the skin tone image to separate all non- face regions from candidate faces. Candidate verification using primitive shape features of the face is applied to decide which of the candidate regions corresponds to a face. The advantage of the proposed method is that it can detect faces that are of different sizes, in different poses, and that are making different expressions under unconstrained illumination conditions
Energy-aware resource allocation with energy harvesting in heterogeneous wireless network

Energy harvesting is becoming a promising technology for future wireless communication network for its potential of saving nonrenewable energy and making the communication environmentally friendly. However, due to the unpredictable and intermittent nature of energy source, the road to the communication system powered solely by harvested energy is plagued with potholes. In this paper, we consider a heterogeneous network consisting of one macro base station (BS) powered with power grid and one small BS powered by both harvested and grid energy over a given time period (e.g. 24 hours), using statistical information of harvested energy. Then we study the resource allocation problem to minimize the power grid input while satisfying users' quality of service (outage probability) requirements, where dynamic programming (DP) algorithm is proposed to analyze the outage probability and solve the problem. The simulation results show that the DP algorithm can greatly reduce the grid power consumption compared with current heuristic methods. There is also an interesting observation of our results that the amount of harvested energy have no impact on the optimum resource allocation strategies.