This demo presents an educational e-learning module that teaches emotion regulation to medical students in a 3D virtual environment. The scenario focuses on the pressure and stress experienced by students when studying for an exam. The survey results based on 50 university students have shown that the immersive tool is effective to elicit stress and anxiety emotions. Although the majority of students were able to regulate their emotions, half of them used inappropriate strategies to manage their negative emotions.

The practitioner-patient relationship (PPR) is essential to the improvement of health care services for patients suffering from cognitive impairments. However, teaching medical students how to develop a healthy PR using conventional approaches is difficult, due to the lack of real context. To address this issue, TeachMod presents a new educational game-based course that offers an interactive and immersive learning through two different scenarios of psychiatric consultations. The course was tested and evaluated with 64 undergraduate students who expressed a positive attitude to the game design and its pedagogical content. The survey results indicate that the course is sufficiently intuitive to foster learning the fundamentals of an ethical and appropriate PPR.

Apathy is characterized by symptoms such as reduced emotional response, lack of motivation, and limited social interaction. Current methods for apathy diagnosis require the patient’s presence in a clinic and time consuming clinical interviews, which are costly and inconvenient for both, patients and clinical staff, hindering among other large-scale diagnostics. In this work, we propose a novel spatio-temporal framework for apathy classification, which is streamlined to analyze facial dynamics and emotion in videos. Specifically, we divide the videos into smaller clips, and proceed to extract associated facial dynamics and emotion-based features. Statistical representations/descriptors based on each feature and clip serve as input of the proposed Gated Recurrent Unit (GRU)-architecture. Temporal representations of individual features at the lower level of the proposed architecture are combined at deeper layers of the proposed GRU architecture, in order to obtain the final feature-set for apathy classification. Based on extensive experiments, we show that fusion of characteristics such as emotion and facial dynamics in proposed deep-bi-directional GRU obtains an accuracy of 95.34% in apathy classification.

The goal of Mixed Reality (MR) is to achieve a seamless and realistic blending between real and virtual worlds. This requires the estimation of reflectance properties and lighting characteristics of the real scene. One of the main challenges within this task consists in recovering such properties using a single RGB-D camera. In this article, we introduce a novel framework to recover both the position and color of multiple light sources as well as the specular reflectance of real scene surfaces. This is achieved by detecting and incorporating information from both specular reflections and cast shadows. Our approach is capable of handling any textured surface and considers both static and dynamic light sources. Its effectiveness is demonstrated through a range of applications including visually-consistent mixed reality scenarios (e.g., correct real specularity removal, coherent shadows in terms of shape and intensity) and retexturing where the texture of the scene is altered whereas the incident lighting is preserved.

Apathy is characterized by symptoms such as reduced emotional response, lack of motivation, and limited social interaction. Current methods for apathy diagnosis require the patient's presence in a clinic and time consuming clinical interviews, which are costly and inconvenient for both patients and clinical staff, hindering among others large-scale diagnostics. In this work we propose a multi-task learning (MTL) framework for apathy classification based on facial analysis, entailing both emotion and facial movements. In addition, it leverages information from other auxiliary tasks (i.e., clinical scores), which might be closely or distantly related to the main task of apathy classification. Our proposed MTL approach (termed MTL+) improves apathy classification by jointly learning model weights and the relatedness of the auxiliary tasks to the main task in an iterative manner. Our results on 90 video sequences acquired from 45 subjects obtained an apathy classification accuracy of up to 80%, using the concatenated emotion and motion features. Our results further demonstrate the improved performance of MTL+ over MTL.

Reduced emotional response, lack of motivation, and limited social interaction comprise the major symptoms of apathy. Current methods for apathy diagnosis require the patient's presence in a clinic, and time consuming clinical interviews and questionnaires involving medical personnel, which are costly and logistically inconvenient for patients and clinical staff, hindering among other large scale diagnostics. In this paper we introduce a novel machine learning framework to classify apathetic and non-apathetic patients based on analysis of facial dynamics, entailing both emotion and facial movement. Our approach caters to the challenging setting of current apathy assessment interviews, which include short video clips with wide face pose variations, very low-intensity expressions, and insignificant inter-class variations. We test our algorithm on a dataset consisting of 90 video sequences acquired from 45 subjects and obtained an accuracy of 84% in apathy classification. Based on extensive experiments, we show that the fusion of emotion and facial local motion produces the best feature set for apathy classification. In addition, we train regression models to predict the clinical scores related to the mental state examination (MMSE) and the neuropsychiatric apathy inventory (NPI) using the motion and emotion features. Our results suggest that the performance can be further improved by appending the predicted clinical scores to the video-based feature representation.

Interpretable modeling of heterogeneous data channels is essential in medical applications, for example when jointly analyzing clinical scores and medical images. Variational Autoencoders (VAE) are powerful generative models that learn representations of complex data. The flexibility of VAE may come at the expense of lack of interpretability in describing the joint relationship between heterogeneous data. To tackle this problem, in this work we extend the variational framework of VAE to bring parsimony and interpretability when jointly account for latent relationships across multiple channels. In the latent space, this is achieved by constraining the variational distribution of each channel to a common target prior. Parsimonious latent representations are enforced by variational dropout. Experiments on synthetic data show that our model correctly identifies the prescribed latent dimensions and data relationships across multiple testing scenarios. When applied to imaging and clinical data, our method allows to identify the joint effect of age and pathology in describing clinical condition in a large scale clinical cohort.

In this paper, we consider the problem of estimating the 3D position and intensity of multiple light sources without using any light probe or user interaction. The proposed approach is twofold and relies on RGB-D data acquired with a low cost 3D sensor. First, we separate albedo/texture and illumination using lightness ratios between pairs of points with the same reflectance property but subject to different lighting conditions. Our selection algorithm is robust in presence of challenging textured surfaces. Then, estimated illumination ratios are integrated, at each frame, within an iterative process to recover position and intensity of light sources responsible of cast shadows. Estimated lighting characteristics are finally used to achieve realistic Augmented Reality (AR).

We consider in this paper an important Quality of Experience (QoE) indicator in mobile networks that is reneging of users due to impatience. We specifically consider a cell under heavy load conditions and compute the reneging probability by using a fluid limit analysis. By solving the fixed point equation, we obtain a new QoE perturbation metric quantifying the impact of reneging on the performance of the system. This metric is then used to devise a new pricing scheme accounting of reneging. We specifically propose several flavors of this pricing around the idea of having a flat rate for accessing the network and an elastic price related to the level of QoE perturbation induced by communications.

Distributed storage systems have to ensure data availability and durability despite the occurrence of failures. To do so, many of them rely on replication mechanisms. We show that the layout of the data block copies on the nodes, chiefly the way the copies are scattered, has a major impact on the reparation speed and thus on the data loss ratio. In this paper, we propose an approach that provides the ability: (i) to finely tune the proportion of common content stored by the nodes, and (ii) to control the storage load distribution while creating new data block copies. We propose a simulation model that allows us to present a long-term study of the impact of the data block copies layout and the system's storage load on the data loss ratio.

We analyze the problem of how to correctly construct dense point trajectories from optical flow fields. First, we show that simple Euler integration is unavoidably inaccurate, no matter how good is the optical flow estimator. Then, an inverse integration scheme is analyzed which is more robust to bias and input noise and shows better stability properties. Our contribution is threefold: 1) a theoretical analysis that demonstrates why and in what sense inverse integration is more accurate; 2) a rich experimental validation both on synthetic and real (image) data; and 3) an algorithm for approximate online inverse integration. This new technique is precious whether one is trying to propagate information densely available on a reference frame to the other frames in the sequence or, conversely, to assign information densely over each frame by pulling it from the reference.

We present a novel VR solution for Reminiscence Therapy (RT), developed jointly by a group of memory clinicians and computer scientists. RT involves the discussion of past activities, events or experiences with others, often with the aid of tangible props which are familiar items from the past; it is a popular intervention in dementia care. We introduce an immersive VR system designed for RT, which allows easy presentation of familiar environments. In particular, our system supports highly-realistic Image-Based Rendering in an immersive setting. To evaluate the effectiveness and utility of our system for RT, we perform a study with healthy elderly participants to test if our VR system can help with the generation of autobiographical memories. We adapt a verbal Autobiographical Fluency protocol to our VR context, in which elderly participants are asked to generate memories based on images they are shown. We compare the use of our image-based system for an unknown and a familiar environment. The results of our study show that the number of memories generated for a familiar environment is higher than that for an unknown environment using our system. This indicates that IBR can convey familiarity of a given scene, which is an essential requirement for the use of VR in RT. Our results also show that our system is as effective as traditional RT protocols, while acceptability and motivation scores demonstrate that our system is well tolerated by elderly participants.

Accurate estimation of dense point correspondences between two distant frames of a video sequence is a challenging task. To address this problem, we present a combinatorial multistep integration procedure which allows one to obtain a large set of candidate motion fields between the two distant frames by considering multiple motion paths across the video sequence. Given this large candidate set, we propose to perform the optimal motion vector selection by combining a global optimization stage with a new statistical processing. Instead of considering a selection only based on intrinsic motion field quality and spatial regularization, the statistical processing exploits the spatial distribution of candidates and introduces an intra-candidate quality based on forward-backward consistency. Experiments evaluate the effectiveness of our method for distant motion estimation in the context of video editing.

International audience

We propose in this paper an on-line algorithm based on Bloom filters to detect port scan attacks in IP traffic. Only relevant information about destination IP addresses and destination ports are stored in two steps in a two-dimensional Bloom filter. This algorithm can be indefinitely performed on a real traffic stream thanks to a new adaptive refreshing scheme that closely follows traffic variations. It is a scalable algorithm able to deal with IP traffic at a very high bit rate thanks to the use of hashing functions over a sliding window. Moreover it does not need any a priori knowledge about traffic characteristics. When tested against real IP traffic, the proposed on-line algorithm performs well in the sense that it detects all the port scan attacks within a very short response time of only 10 seconds without any false positive.

Dense point matching and tracking in image sequences is an open issue with implications in several domains, from content analysis to video editing. We observe that for long term dense point matching, some regions of the image are better matched by concatenation of consecutive motion vectors, while for others a direct long term matching is preferred. We propose a method to optimally estimate the correspondence of a point w.r.t. a reference image from a set of input motion estimations over different temporal intervals. Results on texture insertion by point tracking in the context of video editing are presented and compared with a state-of-the-art approach.

Passive optical networks are increasingly used for access to the Internet and it is important to understand the performance of future long-reach, multi-channel variants. In this paper we discuss requirements on upstream dynamic wavelength and bandwidth allocation (DWBA) and propose a simple novel algorithm that is considerably more efficient than classical approaches. We demonstrate that the algorithm emulates a multi-server polling system and deduce capacity formulas that are valid for general traffic processes.

Dynamic Spectrum Access systems exploit temporarily available spectrum ('white spaces') and can spread transmissions over a number of non-contiguous sub-channels. Such methods are highly beneficial in terms of spectrum utilization. However, excessive fragmentation degrades performance and hence off-sets the benefits. Thus, there is a need to study these processes so as to determine how to ensure acceptable levels of fragmentation. Hence, we present experimental and analytical results derived from a mathematical model. We model a system operating at capacity serving requests for bandwidth by assigning a collection of gaps (sub-channels) with no limitations on the fragment size. Our main theoretical result shows that even if fragments can be arbitrarily small, the system does not degrade with time. Namely, the average total number of fragments remains bounded. Within the very difficult class of dynamic fragmentation models (including models of storage fragmentation), this result appears to be the first of its kind. Extensive experimental results describe behavior, at times unexpected, of fragmentation under different algorithms. Our model also applies to dynamic linked-list storage allocation, and provides a novel analysis in that domain. We prove that, interestingly, the 50% rule of the classical (non-fragmented) allocation model carries over to our model. Overall, the paper provides insights into the potential behavior of practical fragmentation algorithms.

A new time-sequential approach for motion layer extraction is presented. We assume that the scene can be described by a set of layers associated to affine motion models. In one or more key frames, the segmentation is obtained using a semi-automatic method. At a subsequent instant, the first step of the proposed algorithm is the prediction of the segmentation from one image to the next one, using motion models estimated for each layer. The second step is the refinement of the predicted motion boundaries by graph cut. Only the appearing areas and a strip around the predicted boundaries are questioned. A new rigidity constraint improves the temporal consistency of foreground rigid objects. Experimental results show that our sequential approach is at least as effective as more complex simultaneous approaches while being less computationally demanding.

We investigate in this paper the performance of a simple file sharing principle. For this purpose, we consider a system composed of N peers becoming active at exponential random times; the system is initiated with only one server offering the desired file and the other peers after becoming active try to download it. Once the file has been downloaded by a peer, this one immediately becomes a server. To investigate the transient behavior of this file sharing system, we study the instant when the system shifts from a congested state where all servers available are saturated by incoming demands to a state where a growing number of servers are idle. In spite of its apparent simplicity, this queueing model (with a random number of servers) turns out to be quite difficult to analyze. A formulation in terms of an urn and ball model is proposed and corresponding scaling results are derived. These asymptotic results are then compared against simulations.

In this paper, it is shown that the coexistence of a variety of different traffics in third generation cellular networks may lead to a very undesirable behavior of the whole network: a metastability property. When this property holds, the state of the network fluctuates on a very long time scale between different set of states. These long oscillations of the network make impossible to predict the average performances of some of the key characteristics of the connections, such as the handoff blocking rate or the probability of call blocking. As a consequence, the quality of service provided by such a network can be guaranteed only by, sometimes poor, lower bounds. Experiments of a UMTS network with this behavior are presented and the analysis of a corresponding simplified mathematical model is developed. The practical implications in the design of radio resource management for CDMA cellular networks are discussed.

On the basis of a reference model for ADSL traffic on an IP backbone link, established in an earlier study, we show that it is possible to infer the characteristics of long flows by performing a deterministic 1/N packet sampling. By using the fact that the number of active long flows can be represented by means of the number of customers in an M/G//spl infin/ queue with Weibullian service times, we derive some probabilistic properties of the sampled data. These properties are then used to infer the characteristics of the original flows. The method is illustrated by considering an actual traffic trace captured in the France Telecom IP backbone network. Experimental data show that the method proves quite efficient.

In this paper we analyze the M/G/1 processor sharing queue with heavy tailed services and with impatient customers. It is assumed that impatience depends on the value of the service required. We prove that a reduced service rate (RSR) approximation holds for estimating the sojourn time of a customer in the system, when the queue capacity is finite or infinite. This allows us to evaluate the reneging probability of customers with very large service times. We then use these results to investigate the impact of admission control on a link of a packet network. Admission control simply consists of limiting the number of simultaneous connections. It turns out that there is a real benefit for the efficiency of the system to perform admission control: It globally increases the fraction of customers, who complete their service (i.e. without being impatient). Finally, we investigate the fairness of the system and propose a criterion to assess the capacity of the system so as to allow the completion of very large service times.

The paper discusses the integration of augmented reality techniques into interactive television (ITV) and presents a new method for ensuring photometric realism. ITV services known today are email and World Wide Web (WWW) access. This article focuses on a future option for interactive television: the integration of games and virtual 3D scenes. For example, while watching football, you may be invited by a joint WWW commercial to have a look at a virtual 3D model of a sporting shoe. Displaying the sporting shoe on the screen in front of the football scene is in fact a case of augmented reality. The paper proposes a fast and simple method to extract a number of light sources from the football video that ensure a dynamic illumination of the virtual object depending on the content of the TV screen. The method works for all kinds of video content since only the light source intensities are estimated from the video signal while the light source positions are set by a fixed rule.

We study in this paper the throughput of a TCP connection performing ideal congestion avoidance. With respect to existing studies published on the same topic, we determine upper and lower bounds for the throughput of a TCP connection experiencing an arbitrary constant loss. We then derive exact convergence results when the loss probability becomes arbitrarily small. A remarkable property of the results obtained in this paper is that they justify a posteriori constants, which appear in the approximation of the throughput and which has been observed earlier via simulation or experiments. The analysis is performed by assuming a finite and infinite maximum congestion window size.

This paper proposes a simple but effective admission control algorithm for integrated services packet networks. The admission control scheme, based on stochastic control, aims at ensuring user discrimination, by enforcing different call blocking probabilities. The queueing behavior of reservations is analytically characterized in the absence of admission control. From this, call blocking probabilities and analytical estimates of the performance of admission control are derived, allowing proper tuning of the algorithm. Simulations illustrate the effectiveness of the algorithm.

Messages arrive randomly at one end of a slotted communication channel. They are assigned to (packed in) packets of fixed duration which queue up for transmission in first-in-first-out order; the packets are sent one per time slot. In a stochastic setting, where message durations are also random, we analyze a model which yields statistics on message delays and the number of waiting messages, assuming that the assignment protocol is the well-known next-fit rule of one-dimensional bin packing. A stability condition is obtained as a function of general discrete message-length distributions. As a by-product, we contribute a new result to the literature on the probabilistic analysis of the static next-fit bin-packing rule, viz. the limiting expected bin occupancy for general discrete distributions. Specializations of the results to constant message lengths and to uniform message-length distributions are worked out in detail.>