Miracle-dz/UniFAR-FTUs huggingface dataset:
Soluble antigens from group B streptococci induce cytokine production in human blood cultures

Group B streptococcal antigens stimulated tumor necrosis factor alpha (TNF-alpha), interleukin-1 (IL-1), and IL-6 production in human blood cultures in a concentration- and time-dependent fashion. The minimal concentrations of type-specific polysaccharides, lipoteichoic acid, and group-specific polysaccharide required to produce these effects were, respectively, 0.01, 1, and 10 microg/ml. Cell separation experiments indicated that monocytes were the cell type mainly responsible for cytokine production. Time course studies indicated that TNF-alpha was released before the other cytokines. TNF-alpha, however, did not appear to directly induce IL-1beta, as shown by blockade experiments with anti-TNF-alpha antibodies. IL-6 levels were moderately but significantly decreased by anti-TNF-alpha. These data indicate that several products from group B streptococci are able to directly stimulate human monocytes to release TNF-alpha, IL-1beta, and IL-6. These findings may be clinically relevant, since proinflammatory cytokines can mediate pathophysiologic changes during sepsis.
A Secured Adaptive Mobile Video Streaming and Efficient Social Video Sharing in the Clouds

--The video traffic over mobile networks have been increasing enormously but the wireless link capacity cannot keep up with the traffic. This gap results in poor service quality of video streaming over mobile networks. Inculcating cloud computing technology into mobile networks, a new framework is introduced called Secured AMES-Cloud containing two parts: AMoV (Adaptive Mobile Video streaming) and ESoV (Efficient Social Video sharing) . For each user,AMov constructs a private agent to adjust streaming flow based on link quality using scalable video coding technique. ESoV allows social network interactions among users and private agents’ prefetch user requested videos in advance. Here, security is provided to each user so that their videos cannot be seen by others unless the user requires and cannot be seen by cloud providers using Homomorphic and Incremental encryption.
THE REGULATION OF SLEEP AND WAKEFULNESS BY THE HYPOTHALAMIC NEUROPEPTIDE OREXIN/HYPOCRETIN

ABSTRACT Orexins, also known as hypocretins, are neuropeptides that are exclusively expressed by neurons in the lateral hypothalamic area. Although originally recognized as regulators of feeding behavior, orexins are now mainly regarded as key modulators of the sleep/wakefulness cycle. In addition, anatomical studies of neural networks and analyses of transgenic mice have revealed integrated roles for orexin neurons in the coordination of emotion, energy homeostasis, and the reward system. A functional link between the limbic system and orexin neurons may be important for increasing vigilance in response to emotional stimuli. These findings suggest that orexin neurons relay information about an organism’s environment to maintain the proper amount of sleep and wakefulness in animals.
Accessibility and ergonomics study with virtual reality , a state of the art

The development and the use of DMU (Digital Mock-Up) and virtual reality in industry enhance the design of new tools for testing and validating virtual prototypes. This paper deals with the problem of mountability and maintenability analysis of parts in encumbered environments by virtual human operators (namely, manikins). This analysis is performed in relation with some ergonomic constraints from the operator point of view. There exist two main methods for performing such task. The first one consists in immersing the designer in the virtual world of the mechanical system; the second one takes advantage of a semi-automatic approach where the manikins are controlled through a predefined kinematical and ergonomic behaviour. We propose a comparison of these two methods.  2002 Éditions scientifiques et médicales Elsevier SAS. All rights reserved. Mots-clés : Accessibilité ; Réalité virtuelle ; Ergonomie ; Immersion ; Collaboration
HIV Risk Perception, Sexual Behavior, and HIV Prevalence among Men-Who-Have-Sex-with-Men at a Community-Based Voluntary Counseling and Testing Center in Kuala Lumpur, Malaysia

We describe the HIV risk perception, sexual behavior, and HIV prevalence among 423 men-who-have-sex-with-men (MSM) clients who received voluntary counseling and testing (VCT) services at a community-based center in Kuala Lumpur, Malaysia. The mean age was 29 years old. One hundred one (23.9%) clients rated themselves as low risk, 118 (27.9%) as medium risk, 36 (8.5%) as high risk, and 168 (39.7%) were unsure of their risk. Twenty-four (9.4%) clients tested HIV positive (4 (4%) low risk, 9 (7.6%) medium risk, 11 (30.6%) high risk, and 13 (7.7%) unsure risk). We found a positive correlation between risk perception and HIV infection in this study. Clients with high HIV risk perception have 17x the odds of testing HIV positive compared to low risk clients. High HIV risk perception was significantly associated with multiple sex partners, multiple types of sex partners, alcohol use before intercourse, unprotected sex beyond 6 months, and inconsistent condom use during anal sex compared to low risk clients. There were no statistically significant differences between medium risk and unsure risk clients compared to low risk clients. Strategies should be targeted towards change in sexual practices among those who are perceived to be at high risk.
An Adaptive Problem-Solving Solution to Large-Scale Scheduling Problems

Although the general class of most scheduling problems is NP-hard in worst-case complexity, in practice, domain-specific techniques frequently solve problems in much better than exponential time. Unfortunately, constructing special-purpose systems is a knowledge–intensive and time-consuming process that requires a deep understanding of the domain and problem-solving architecture. The goal of our work is to develop techniques to allow for automated learning of an effective domain-specific search strategy given a general problem solver with a flexible control architecture. In this approach, a learning system explores a space of possible heuristic methods a strategy well-suited to the regularities of the given domain and problem distribution. We discuss an application of our approach to scheduling satellite communications. Using problem distributions based on actual mission requirements, our approach identifies strategies that not only decrease the amount of CPU time required to produce schedules, but also increase the percentage of problems that are solvable within computational resource limitations.
Detection of Double-Compression in MPEG-2 Videos

In recent years, with the easy availability of digital video cameras and associated software to manipulate video, ascertaining the integrity and authenticity of digital videos has become an urgent and critical issue. In this paper, we propose a novel approach to detect double MPEG-2 compression which often occurs in digital video tampering. Specifically, we show how a doubly compressed MPEG-2 video sequence introduces convex pattern in the distribution of quantized DCT coefficients whose presence can be used as an evidence of tampering. Some commercial digital video cameras and multimedia editing softwares are introduced to simulate the process of double MPEG-2 compression, and a series of experiments show that the proposed scheme can effectively detect double MPEG-2 compression at different output bit-rates. Excellent test results verify that our proposed algorithm is of great practical value.
Simulating and benchmarking the shallow-water fluid dynamical equations on multiple graphical processing units

The shallow-water model equations provide a simple yet realistic benchmark problem in computational fluid dynamics (CFD) that can be implemented on a variety of computational platforms. Graphical Processing Units can be used to accelerate such problems either singly using a data parallel decompositional scheme or with multiple devices using a domain decompositional approach. We implement the SW equations on a range of modern GPUs with both parallel schemes and report on the typical performance. We compare integer optimised GPUs and very modern floating-point intensive GPU devices such as NVidia's Kepler K20X, and also investigate different m-GPU communication methods for geometric decompositions. We give detailed performance results and a summary of the main parallelisation issues.
Communication of cognition? A minimal computational model of evolutionary interactions between individual and social learning

Animals including humans adapt to the environment by 2 different mechanisms working on 2 levels, evolution and learning, a population level mechanism and an individual level mechanism, respectively. Learning can further be classified into individual learning and social learning (IL and SL, hereafter). Our purpose is to understand how complex systems, specifically language emerged through the evolutionary interaction between IL and SL by using an agent-based model. Language is a communication tool but also a cognitive tool while most evolutionary scenarios see it as the former (Reboul, 2015). Indeed, in the brain, utilizing language-related circuits, some form of linguistic knowledge is linked to the external world by producing/perceiving sounds and gestures, and at the same time, is connected to the inner mental world composed of concepts, intentions and reasoning (Berwick et. al., 2013), regardless whether there is a shared computational core (e.g. Strong Minimalist Thesis (Chomsky, 2000)). We assume the fitness function represents the 2 components of selection. One is the directional component (DC, hereafter) that drives the evolution of fundamental traits underlying the whole linguistic activities. For DC, we do not use a simple unimodal function but a minimal fitness function representing a multimodal fitness landscape with linguistic levels. A linguistic level represents a fitness value expected when communication succeeds, corresponding to the number of traits involved in the trait interaction. The basic idea is as follows. In general, language ability regardless of cognitive or communicative is based on multiple subordinate traits, and its mutual interactions are nonlinear. We can further assume that the more adaptive the trait set is, the more inter-trait 165
Geodesic histogram based 3D deformable shape correspondence

Geodesic distance has been widely used in building correspondence between two 3D shapes. This paper extends previous work and proposes a correspondence method by geodesic histogram. Geodesic histogram owns two distinct characteristics for correspondence. Firstly, it captures not only the length of two feature points, but also local geometrical features along with the geodesic path. Secondly, it can support partial correspondence since it is locally stable for incomplete 3D shape. Experimental results demonstrate that our algorithm is better able to produce a correct correspondence in the case of pose variation, deformation and even missing parts. Furthermore, statistical analysis shows the proposed method obtains an obvious improvement over the similar method in the literature.
Optimizing Area Under Roc Curve with SVMs

For many years now, there is a growing interest around ROC curve for characterizing machine learning performances. This is particularly due to the fact that in real-world problems misclassifi- cation costs are not known and thus, ROC curve and related metrics such as the Area Under ROC curve (AUC) can be a more mean- ingful performance measures. In this paper, we propose a quadratic programming based algorithm for AUC maximization and show that under certain conditions 2-norm soft margin Support Vector Ma- chines can also maximize AUC. We present experiments that com- pare SVMs performances to those of other AUC maximization based algorithms and provide empirical analysis of SVMs behavior with regards to ROC- based metrics. Our main conclusion is that SVMs can maximize both AUC and accuracy compared to other algorithms like RankBoost that optimize only AUC 2 .
Restructuring multimodal interaction data for browsing and search

This paper presents recent work on techniques for indexing and structuring recordings of interactive activities, such as collaborative editing, computer-mediated and computer-assisted meetings, and presentations. A unifying architecture is presented which encompasses modality translation (e.g. speech recognition and image analysis) as well as the underlying recording under a single linked time-based model. This model is illustrated through case studies and implemented prototypes, which support remote multiparty collaboration and co-located recorded meetings. Requirements and the issues they pose for the design of this kind of systems are discussed. Consider a remote collaboration between two people who were given the task of organising a final-year weekend trip for staff and students. The trip planners communicated through a dedicated audio conferencing system which included 3 4 Semantic Multimedia Analysis and Processing a real-time audio channel, a text chat channel, and a shared editor on which the participants could write as well as highlight and point to objects such as sentences and paragraphs while talking [41]. During the meeting, the participants used the shared surface as a common note book where they could jot down their thoughts and options considered (or to be considered), record decisions and " action points " , share references to external data (such as URLs containing useful resources), and so on. The excerpt in Table 1.1 is an example of a typical text that remained on the shared editor once the meeting was concluded. Traces of textual interaction on a shared (synchronous) text editor.
Therapeutic Advances in Drug Safety Lesley Wise Risks and benefits of ( pharmaco ) epidemiology

Pharmacoepidemiology is a relatively young scientific discipline that has seen tremendous growth in interest over the past two decades. This is partly due to changes in the approval of new drugs, which often now require proactive studies to monitor and extend the knowledge of safety in the postauthorization period. Pharmacoepidemiology studies are observational in nature, in contrast to the randomized controlled trial which is the mainstay of drug development. This means that such studies need careful design and execution in order to maximize the benefits of proactive research in the ‘real-life’ clinical environment and minimize the risks of false results.
You are who you know: user authentication by face recognition

In this paper, a novel method of authentication based on user-performed identification of known and unknown faces is described. The target group of the method is the elderly users for which the use of traditional techniques, such as passwords, personal identification numbers (PIN), or biometrics is not without problems. The performance of this method and authentication by PIN has been compared in a two-pass usability study. Our method performed significantly better than PIN. The method is suitable for low-security applications in hospitals and senior houses.
Analysis of 3D Corpus Callosum Images in the Brains of Autistic Individuals

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is characterized by abnormalities in behavior and higher cognitive functions. The corpus callosum (CC) is the largest fiber bundle that connects the left and the right cerebral hemispheres of the human brain. Several studies have revealed an abnormal anatomy of the CC in the brains of autistic individuals that associates this neurodevelopmental condition with impaired communication between the hemispheres. In this chapter, we develop a framework to analyze the CC of autistic individuals in order to provide a diagnostic tool for autism. The key advantage of this approach is the development of a cylindrical mapping that offers simplified coordinates for comparing the brains of autistic individuals and neurotypicals. Experimental results showed significant differences (at the 95% confidence level) between 17 normal and 17 autistic subjects in four anatomical divisions, i.e. splenium, rostrum, genu, and body of their CCs. Moreover, the initial centerline-based shape analysis of the CC documented a promising supplement to the current techniques for diagnosing autism.
A Confidence-Based Hierarchical Feature Clustering Algorithm for Text Classification

In this paper, we propose a novel feature reduction ap- proach to group words hierarchically into clusters which can then be used as new features for document classifica- tion. Initially, each word constitutes a cluster. We calculate the mutual confidence between any two different words. The pair of clusters containing the two words with the highest mutual confidence are combined into a new cluster. This process of merging is iterated until all the mutual confi- dences between the un-processed pair of words are smaller than a predefined threshold or only one cluster exists. In this way, a hierarchy of word clusters is obtained. The user can decide the clusters, from a certain level, to be used as new features for document classification. Experimental re- sults have shown that our method can perform better than other methods.
Optimization of mapping modes for heterodyne instruments

Context. Astronomic line mapping with single-pixel heterodyne instruments is usually performed in an on-the-fly (OTF) or a raste rmapping mode depending on the capabilities of the telescope and the instrument. The observing effi ciency can be increased by combining several source-point integrations with a common reference measurement. This is implemented at many telescopes, but a thorough investigation of the optimum calibration of the modes and the best way of performing these observations is still lacking. Aims. We derive optimum mapping strategies and the corresponding calibration schemes based on the known instrumental performance in terms of system stability and slew times. Methods. We use knowledge of the instrumental stability obtained by an Allan variance measurement to derive a mathematical formalism for optimizing the setup of mapping observations. Special attention has to be paid to minimizing of the impact of correlated noise introduced by the common OFF integrations and to the correction of instrumental drifts. Both aspects can be covere d using a calibration scheme that interpolates between two OFF measurements and an appropriate OFF integration time. Results. The total uncertainty of the calibrated data consisting of r adiometric noise and drift noise can be minimized by adjusting the source integration time and the number of data points observed between two OFF measurements. It turns out that OTF observations are very robust. They provide a low relative noise, even if their setup deviates considerably from the optimum. Fast data readouts are often essential to minimize the drift contributions. In particular, continuum measurements may be easily spoiled by instrumental drifts. The main drawback of the described mapping modes is the limited use of the measured data at different spatial or spectroscopic resolutions obtained by additional rebinning.
Integrating Line Segment Based Maps in Multi-Robots Exploration

Exploring unknown environment by Multi-robots brings the problem of partial maps integrating. In traditional methods, global map building is almost based on an estimate of robot pose (i.e., its position and orientation). In this paper we propose an improved approach without robots’ pose information to integrate maps into a unified coordinate system. Line segment based geometric features in common part of partial maps are used for map matching. Local maps are integrated into a global map based on geometric relationships of line segments. Moreover, we reduce the computation of map matching and improve the accuracy rate of integrating in similar landform. Simulation results show the effectiveness of our approach in structured environment.
A Qualitative Analysis of Early Career Women’s Adjustment to Work in Professional Psychology: Practitioners’ Reflections

The experiences of early career psychologists (ECPs) have become a focal point within the profession of psychology, with trends reflecting increasing financial burdens and career struggles. Despite these trends, few attempts to directly explore themes among the experiences of ECP women’s career adjustment in clinical settings have been published. In this study, consensual qualitative research (CQR) methodology was used to extract themes among a sample of recently licensed ECPs (N = 13) identifying as women in clinical positions who were licensed between 1 and 3 years since graduating. Specific themes related to struggles (e.g., the licensing process, financial burdens, and lack of mentorship) as well as strengths (e.g., family, peer, and workplace support) are discussed. Implications for career development research, theory, and practice are suggested for stakeholders interested in the career development of ECP women in clinical settings.
Syntactic-prosodic labeling of large spontaneous speech data-bases

In automatic speech understanding, the division of continuously running speech into syntactic chunks is a great problem. Syntactic boundaries are often marked by prosodic means. For the training of statistic models for prosodic boundaries large databases are necessary. For the German VERBMOBIL project (automatic speech-to-speech translation), we developed a syntactic-prosodic labeling scheme where two main types of boundaries (major syntactic boundaries and syntactically ambiguous boundaries) and some other special boundaries are labeled for a large VERBMOBIL spontaneous speech corpus. We compare the results of classifiers (multilayer perceptrons and language models) trained on these syntactic-prosodic boundary labels with classifiers trained on perceptual-prosodic and pure syntactic labels. The main advantage of the rough syntactic-prosodic labels presented in this paper is that large amounts of data could be labeled within a short time. Therefore, the classifiers trained with these labels turned out to be superior (recognition rates of up to 96%).
TUGAS: Exploiting unlabelled data for Twitter sentiment analysis

This paper describes our participation in the message polarity classification task of SemEval 2014. We focused on exploiting unlabeled data to improve accuracy, combining features leveraging word representations with other, more common features, based on word tokens or lexicons. We analyse the contribution of the different features, concluding that unlabeled data yields significant improvements.