Miracle-dz/UniFAR-FTUs huggingface dataset:
Emotional Virtual Agent to Improve Ageing in Place with Technology

Designing useful, usable and acceptable technology for elders to help them to age well is a challenging process. An interactive calendar helps elders to stay longer at home and organize their daily life. Moreover, emotional virtual agent can help to enhance the interaction between elders and computer, through verbal and non-verbal communication. Based on a participatory design procedure, we involve the user in designing the ideal calendar including an emotional virtual agent. Preliminary results showed a strong desire for acquiring a such technology. Emotional virtual agent could improve the acceptability and the use of technology.
A novel hybrid biogeography-based optimization with differential mutation

Biogeography-based optimization (BBO) is a new biogeography inspired algorithm. As a novel evolutionary computing technique, BBO is simple and effective, which is paid wide attention in both academic and industry fields and achieves many successful applications. Meanwhile, Differential Evolution (DE) is a fast and robust evolutionary algorithm for global optimization. It has been widely used in many areas. In this paper, we propose a hybrid BBO with DE, namely Differential BBO (DBBO), which employs the mutation operator of DE. DBBO combines the exploration of DE with the exploitation of BBO effectively, and hence it can generate the promising candidate solutions. To verify the performance of our proposed DBBO, 10 benchmark functions with 30 dimensions are employed. Experimental results indicate that DBBO is most effective when compared with BBO and DE.
A framework for effective retrieval

The aim of an effective retrieval system is to yield high recall and precision (retrieval effectiveness). The nonbinary independence model, which takes into consideration the number of occurrences of terms in documents, is introduced. It is shown to be optimal under the assumption that terms are independent. It is verified by experiments to yield significant improvement over the binary independence model. The nonbinary model is extended to normalized vectors and is applicable to more general queries. Various ways to alleviate the consequences of the term independence assumption are discussed. Estimation of parameters required for the nonbinary independence model is provided, taking into consideration that a term may have different meanings.
Noise Condition-Dependent Training Based on Noise Classification and SNR Estimation

Condition-dependent training strategy divides a training database into a number of clusters, each corresponding to a noise condition and subsequently trains a hidden Markov model (HMM) set for each cluster. This paper investigates and compares a number of condition-dependent training strategies in order to achieve a better understanding of the effects on automatic speech recogntion (ASR) performance as caused by a splitting of the training databases. Also, the relationship between mismatches in signal-to-noise ratio (SNR) is analyzed. The results show that a splitting of the training material in terms of both noise type and SNR value is advantageous compared to previously used methods, and that training of only a limited number of HMM sets is sufficient for each noise type for robustly handling of SNR mismatches. This leads to the introduction of an SNR and noise classification-based training strategy (SNT-SNC). Better ASR performance is obtained on test material containing data from known noise types as compared to either multicondition training or noise-type dependent training strategies. The computational complexity of the SNT-SNC framework is kept low by choosing only one HMM set for recognition. The HMM set is chosen on the basis of results from noise classification and SNR value estimations. However, compared to other strategies, the SNT-SNC framework shows lower performance for unknown noise types. This problem is partly overcome by introducing a number of model and feature domain techniques. Experiments using both artificially corrupted and real-world noisy speech databases are conducted and demonstrate the effectiveness of these methods.
Faults classification in analog electronic circuits with use of the SVM algorithm

In the paper the application of the SVM (Support Vector Machine) algorithm has been used for diagnosis and test of analog electronic circuits in time domain. Procedure designed belongs to simulation before test technique, where the fault models should be assumed at the before test stage (prototype stage). Both, parametric and catastrophic faults based on standard fault models are considered. Selected features from transient domain are classified at the after test stage with use of decision function, which has been designed by SVM algorithm. An exemplary benchmark has been tested and the results prove high classification ability of the SVM application.
UWB network recognition based on impulsiveness of energy profiles

Two important functionalities in cognitive networking are network detection and recognition. Previous investigations showed that MAC sub-layer technology-specific features may offer a simple and direct way of performing such tasks; in particular, they allow to by-pass complex physical layer feature extraction based on a simple energy detection scheme, capable of producing a time-varying profile reflecting the presence vs. absence of packets over the air interface. Beyond summarizing previous experimental evidence that confirmed the validity of the approach for technologies in the ISM band, the purpose of this work is to investigate the possibility of extending the network recognition concept to underlay networks such as Ultra Wide Band. Preliminary results of experiments on UWB signals indicate that short-term energy profiles may highlight the peculiar impulsive characteristic of IEEE 802.15. 4a-like signals. Continuous vs. impulsive signals may be correctly classified based on a simple but relevant feature such as short-term energy statistics. Moreover, short-term energy statistical features, as a function of increased window duration, seem to highlight a multi-static vs. continuous behavior for impulse vs. continuous-wave radio transmissions.
Self Adaptive Body Biasing Scheme for Leakage Power Reduction under 32nm CMOS Regime

This paper presents techniques to determine the optimal reverse body bias (RBB) voltage to minimize leakage currents in modern nano-scale CMOS technology. The proposed self-adaptive RBB system finds the optimum reverse body bias voltage for minimal leakage power adaptively by comparing subthreshold leakage current (I SUBTH ), gate tunneling leakage (I GATE ), and band-to-band tunneling leakage currents (I BTBT ) in standby mode. The proposed circuit has been designed and tested using 32nm bulk CMOS technology at 25oC under a supply voltage of less than 1V. The optimal RBB was achieved at -0.38V with 15% error in the test case of the paper, and the simulation result shows that it is possible to reduce the total leakage current significantly as much as 69% of the total leakage using the proposed circuit techniques.
The Moderating Effect of Attitude toward Violence in a Video Game setting: Initial Explorations.

The discussion around the violence exhibited in video games has been ongoing for quite some time, however very little has been focused on the attitudes and understandings of violence as exhibited by video game players. This study examined the interrelation between self concept, presence and player views of violence. Initial results indicate that experiencing presence via interaction and emotional connection has an impact on the participants views of violence, whereas experiencing presence vial spatial understanding and orientation did not.
Preoperative Arrhythmias Such as Atrial Fibrillation: Cardiovascular Surgery Risk Factor

Atrial fibrillation is still the most common arrhythmia that occurs in heart surgery. However, there is few literature data on the manner in which preoperative atrial fibrillation may influence the postoperative outcome of various heart surgery procedures. The purpose of our research is to assess the effects of preoperative atrial fibrillation on patients having undergone different heart surgery procedures. The results of our research are a review of clinical data which were collected prospectively, over a 10-year period, from all the patients who had undergone heart surgery in our Institute. The study group included 1119 heart surgery patients, who were divided as follows: the preoperative AFib group (n = 226, 20.19%) and the sinus rhythm group (n = 893, 79.80%). Major postoperative complications and hospital mortality rates were analyzed. According to our statistical analysis, preoperative atrial fibrillation significantly increased the mortality risk (P = 0.001), the patients' mechanical ventilation needs (P = 0.022), the rate of occurrence of infectious complications (P < 0.5), the rate of occurrence of complications such as acute kidney failure (P = 0.012), and the time spent by the patients in the intensive care ward (P < 0.01). In conclusion, preoperative atrial fibrillation in heart surgery patients increases the mortality and major complication risk further to heart surgery.
Contributions of Tutor and Bird’s Own Song Experience to Neural Selectivity in the Songbird Anterior Forebrain

Auditory neurons of the anterior forebrain (AF) of zebra finches become selective for song during song learning. In adults, these neurons respond more to the bird’s own song (BOS) than to the songs of other zebra finches (conspecifics) or BOS played in reverse. In contrast, AF neurons from young birds (30 d) respond equally well to all song stimuli. AF selectivity develops rapidly during song learning, appearing in 60-d-old birds. At this age, many neurons also respond equally well to BOS and tutor song. These similar neural responses to BOS and tutor song might reflect contributions from both song experiences to selectivity, because auditory experiences of both BOS and tutor song are essential for normal song learning. Alternatively, they may simply result from acoustic similarities between BOS and tutor song. Understanding which experience shapes selectivity could elucidate the function of song-selective AF neurons. To minimize acoustic similarity between BOS and tutor song, we induced juvenile birds to produce abnormal song by denervating the syrinx, the avian vocal organ, before song onset. We recorded single neurons extracellularly in the AF at 60 d, after birds had had substantial experience of both the abnormal BOS (tsBOS) and tutor song. Some neurons preferred the unique tsBOS over the tutor song, clearly indicating a role for BOS experience in shaping neural selectivity. In addition, a sizable proportion of neurons responded equally well to tsBOS and tutor song, despite their acoustic dissimilarity. These neurons were not simply immature, because they were selective for tsBOS and tutor song relative to conspecific and reverse song. Furthermore, their similar responses to tsBOS and tutor song could not be attributed to residual acoustic similarities between the two stimuli, as measured by several song analyses. The neural sensitivity to two very different songs suggests that single AF neurons may be shaped by both BOS and tutor song experience.
Weighted Graph Laplace Operator under Topological Noise

Recently, various applications have motivated the study of spectral structures (eigenvalues and eigenfunctions) of the so-called Laplace-Beltrami operator of a manifold and their discrete versions. A popular choice for the discrete version is the so-called Gaussian weighted graph Laplacian which can be applied to point cloud data that samples a manifold. Naturally, the question of stability of the spectrum of this discrete Laplacian under the perturbation of the sampled manifold becomes important for its practical usage. Previous results showed that the spectra of both the manifold Laplacian and discrete Laplacian are stable when the perturbation is "nice" in the sense that it is restricted to a diffeomorphism with minor area distortion. However, this forbids, for example, small topological changes. We study the stability of the spectrum of the weighted graph Laplacian under more general perturbations. In particular, we allow arbitrary, including topological, changes to the hidden manifold as long as they are localized in the ambient space and the area distortion is small. Manifold Laplacians may change dramatically in this case. Nevertheless, we show that the weighted graph Laplacians computed from two sets of points, uniformly randomly sampled from a manifold and a perturbed version of it, have similar spectra. The distance between the two spectra can be bounded in terms of the size of the perturbation and some intrinsic properties of the original manifold.
Outcome in Hodgkin's Lymphoma Can Be Predicted from the Presence of Accompanying Cytotoxic and Regulatory T Cells

Purpose: Recent studies of Hodgkin's lymphoma (HL) have suggested that the presence of regulatory T cells in the reactive background may explain the inhibition of the antitumoral host immune response observed in these patients. This study aimed to assess the relevance of regulatory T cells and CTLs present in the background of HL samples in the prognosis of a series of classic HL (cHL) patients. Experimental Design: Expression of granzyme B and TIA-1 (markers for CTL) and FOXP3 (a marker for regulatory T cells) were evaluated independently by immunohistochemistry in tissue microarrays of 257 cHL patients and correlated with patient outcome. Results: The combined influence of the presence of FOXP3+ and TIA-1+ cells distinguished three risk groups of patients with 5-year overall survival of 100%, 88%, and 73%. The presence of a small number of FOXP3+ cells and a high proportion of TIA-1+ cells in the infiltrate represent an independent prognostic factor that negatively influenced event-free survival and disease-free survival in cHL. Compared with the features at diagnosis, relapsed samples tended to have more TIA-1+ cells and a lower proportion of FOXP3+ cells in the reactive background. Conclusions: These data suggest that low infiltration of FOXP3+ cells in conjunction with high infiltration of TIA-1+ cells in cHL may represent biological markers predicting an unfavorable outcome. Moreover, the variation of these markers over the course of the disease implies a possible role for them in the progression of HL cases.
On the Global Stability of Optimal Unbounded Growth Models

This study proves various global stability results for unbounded optimal growth models. The main theorem states that any optimal path converges into the neighborhood of a balanced growth path if future utility is sufficiently weakly discounted. The assumptions allow for non-smooth technologies, joint production, and production in independent sectors. Hence, the results form the integration of new growth and turnpike theory sought by McKenzie (1998) in his Ely lecture. The applicability of the results is exemplified by means of a number of cases from growth theory and other areas of economics.
Highly localized sensitivity to climate forcing drives endemic cholera in a megacity

The population dynamics of endemic cholera in urban environments—in particular interannual variation in the size and distribution of seasonal outbreaks—remain poorly understood and highly unpredictable. In part, this situation is due to the considerable demographic, socioeconomic, and environmental heterogeneity of large and growing urban centers. Despite this heterogeneity, the influence of climate variability on the population dynamics of infectious diseases is considered a large-scale, regional, phenomenon, and as such has been previously addressed for cholera only with temporal models that do not incorporate spatial structure. Here we show that a probabilistic spatial model can explain cholera dynamics in the megacity of Dhaka, Bangladesh, and afford a basis for cholera forecasts at lead times of 11 mo. Critically, we find that the action of climate variability (El Niño southern oscillation and flooding) is quite localized: There is a climate-sensitive urban core that acts to propagate risk to the rest of the city. The modeling framework presented here should be applicable to cholera in other cities, as well as to other infectious diseases in urban settings and other biological systems with spatiotemporal interactions.
Study of core vector regression and particle swarm optimization for rapid electric load forecasting

Load forecasting is very essential to the operations of electric companies. This paper presents a rapid electric load forecasting algorithm based on Particle Swarm Optimization (PSO) and Core Vector Regression (CVR), called PSO-CVR algorithm. PSO is applied to determine the parameters of CVR, then CVR manages the issues of forecasting and training. In order to compare the results among different size of data sets, 4 training sets of different size are created based on a standard data set for global electric load forecasting competition. Experiment results indicate that the PSO-CVR algorithm is comparable with Support Vector Regression (SVR) and can achieve faster training and forecasting speed.
Dispersal in cactophilic Drosophila

Three species of Drosophila each breed in necrotic tissue of specific columnar cacti endemic to the Sonoran Desert. Drosophila pachea breeds in senita (Lophocereus schottii), D. nigrospiracula breeds in saguaro (Carnegiea gigantea) or cardon (Pachycereus pringlei), and D. mojavensis uses organ pipe (Stenocereus thurberi) in Sonora, Mexico and southern Arizona. Patches of these three host cacti have very different spatial distributions, with those of senita being quite frequent and close together, while those of the other hosts are much father apart. Testing all three species simultaneously, we used capture-mark-release-recapture methods to ask if dispersal differed in these species and if differences were those predicted by the spatial availability of the host patches. D. pachea dispersed the shortest distance in all experiments. Furthermore, D. pachea was the only species showing sex-biased dispersal, with male flies exhibiting the greater propensity to disperse. The observations suggest that across similar spatial scales, D. pachea should show greater population genetic structure than the other two species, and that mitochondrial DNA, because of its maternal inheritance, might show greater evidence of structure than nuclear markers.
Literature-Based Discovery of IFN-γ and Vaccine-Mediated Gene Interaction Networks

Interferon-gamma (IFN-γ) regulates various immune responses that are often critical for vaccine-induced protection. In order to annotate the IFN-γ-related gene interaction network from a large amount of IFN-γ research reported in the literature, a literature-based discovery approach was applied with a combination of natural language processing (NLP) and network centrality analysis. The interaction network of human IFN-γ (Gene symbol: IFNG) and its vaccine-specific subnetwork were automatically extracted using abstracts from all articles in PubMed. Four network centrality metrics were further calculated to rank the genes in the constructed networks. The resulting generic IFNG network contains 1060 genes and 26313 interactions among these genes. The vaccine-specific subnetwork contains 102 genes and 154 interactions. Fifty six genes such as TNF, NFKB1, IL2, IL6, and MAPK8 were ranked among the top 25 by at least one of the centrality methods in one or both networks. Gene enrichment analysis indicated that these genes were classified in various immune mechanisms such as response to extracellular stimulus, lymphocyte activation, and regulation of apoptosis. Literature evidence was manually curated for the IFN-γ relatedness of 56 genes and vaccine development relatedness for 52 genes. This study also generated many new hypotheses worth further experimental studies.
Detecting parabolas in ultrasound B-scan images with Genetic-Based Inverse Voting Hough Transform

In this paper we propose a Genetic-Based Inverse Voting Hough Transform (GBIVHT) method for detecting parabolic shapes iii B-scan images obtained by the ultrasonic Time Of Flight Diffraction inspection technique. These parabolic shapes are characteristics of the presence of crack defects in the engineering structure under inspection. In our method, the local peak detection problem in the parameters space of conventional Hough Transform is converted into a parameter optimization problem that operates directly on the ultrasound B-scan image. The optimization is done using the well-known Genetic Algorithms. Our main goals are an accurate detection of the parabolas while circumventing the computational complexity and huge storage problem tied to conventional Hough Transform.
Evidence for Correlated Titanium and Deuterium Depletion in the Galactic ISM

Current measurements indicate that the deuterium abundance in diffuse interstellar gas varies spatially by a factor of ∼4 among sightlines extending beyond the Local Bubble. One plausible explanation for the scatter is the variable depletion of D onto dust grains. To test this scenario, we have obtained high signal-to-noise, high resolution profiles of the refractory ion Ti II along seven Galactic sightlines with D/H ranging from 0.65 to 2.1 × 10. These measurements, acquired with the recently upgraded Keck/HIRES spectrometer, indicate a correlation between Ti/H and D/H at the > 95% c.l. Therefore, our observations support the interpretation that D/H scatter is associated with differential depletion. We note, however, that Ti/H values taken from the literature do not uniformly show the correlation. Finally, we identify significant component-to-component variations in the depletion levels among individual sightlines and discuss complications arising from this behavior. Subject headings: cosmology: observations, ISM: evolution, ISM: abundances, galaxy: abundances
Neuropsychologie et cognition sociale

La notion de cognition sociale fait reference a l’ensemble des aptitudes et experiences emotionnelles et sociales regulant les relations entre les individus et permettant d’expliquer les comportements humains individuels ou en groupe. Elle implique des connaissances sociales, la perception et le traitement de signaux sociaux, ainsi que la representation des etats mentaux. La notion de cognition sociale renvoie donc a de multiples aptitudes. Ce chapitre s'arrete sur plusieurs d'entre-elles, a savoir la theorie de l'esprit, l'empathie, le raisonnement et le jugement moral. Nous proposons une approche conceptuelle de chacune de ces aptitudes en nous appuyant, pour une large part, sur les travaux developpes dans le champ de la neuropsychologie humaine. Nous nous arretons egalement sur les soubassements neuro-anatomiques de chacune de ces aptitudes.