University of Twente Publications
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Verifying Class Invariants in Concurrent Programs - Zaharieva-Stojanovski, M.; Huisman, M.
Class invariants are a highly useful feature for the verification of object-oriented programs, because they can be used to capture all valid object states. In a sequential program setting, the validity of class invariants is typically described in terms of a visible state semantics, i.e., invariants only have to hold whenever a method begins or ends execution, and they may be broken inside a method body. However, in a concurrent setting, this restriction is no longer usable, because due to thread interleavings, any program state is potentially a visible state.
In this paper we present a new approach for reasoning about...
Statistical modeling and analysis of interference in wireless networks - Wildemeersch, Matthias
In current wireless networks, interference is the main performance-limiting
factor. The quality of a wireless link depends on the signal and interference
power, which is strongly related to the spatial distribution of the concurrently
transmitting network nodes, shortly denominated as the network geometry.
Motivated by the ongoing revision of wireless network design, this
dissertation aims to describe the relation between geometry and network
Given the exponential growth of wireless devices, it is meaningful to evaluate how network interference affects signal detection. We propose a unified statistical approach based on the characteristic function of the decision variable to describe the detection performance, accounting for single and multiple...
TaSST: Affective Mediated Touch - Darriba Frederiks, A; Heylen, D.K.J.; Huisman, G.
Communication with others occurs through a multitude of signals, such as speech, facial expressions, and body postures. Understudied in this regard is the way we use our sense of touch in social communication. In this paper we present the TaSST (Tactile Sleeve for Social Touch), a hap- tic communication device that enables two people to communicate through touch at a distance.
Multimedia implicit tagging using EEG signals - Soleymani, M.; Pantic, M.
Electroencephalogram (EEG) signals reflect brain activities associated with emotional and cognitive processes. In this paper, we demonstrate how they can be used to find tags for multimedia content without users' direct input. Alternative methods for multimedia tagging is attracting increasing interest from multimedia community. The new portable EEG helmets are paving the way for employing brain waves in human computer interaction. In this paper, we demonstrate the performance of EEG for tagging purposes using two different scenarios on MAHNOB-HCI database. First, an emotional tagging and classification using a reduced set of electrodes is presented. The emotional responses of 24 participants...
Speaker-adaptive multimodal prediction model for listener responses - de Kok, I.A.; Heylen, D.K.J.; Morency, L.-P.
The goal of this paper is to analyze and model the variability in speaking styles in dyadic interactions and build a predictive algorithm for listener responses that is able to adapt to these different styles. The end result of this research will be a virtual human able to automatically respond to a human speaker with proper listener responses (e.g., head nods). Our novel speaker-adaptive prediction model is created from a corpus of dyadic interactions where speaker variability is analyzed to identify a subset of prototypical speaker styles. During a live interaction our prediction model automatically identifies the closest prototypical speaker...
Interpersonal Stance in Conflict Conversation: Police Interviews - Bruijnes, M.
In this work we focus on the dynamics of the conflict that often arises in a police interview between suspects and police officers. Police interviews are a special type of social encounter, primarily because of the authority role of the police interviewer and the often uncooperative stance that the suspect takes: a conflict situation. The skill to resolve or reduce the conflict, to make an uncooperative suspect more cooperative, requires training of the police officer. Leary's interactional circumplex  is used in police interview training as a theoretical framework to understand how suspects take stance during an interview and how...
What Is at Play? Meta-techniques in Serious Games and Their Effects on Social Believability and Learning - Linssen, J.M.; Theune, M.; de Groot, T.F.
We discuss several examples of meta-techniques, used in Live Action Role Play to communicate information outside the story world, and suggest that they may be used to make non-player characters more socially believable by providing players with insight into what is at play in characters’ minds. We discuss how the use of these techniques could influence player immersion and how this may impact the learning effects of serious games.
Recognition of periodic behavioral patterns from streaming mobility data - Baratchi, M.; Meratnia, N.; Havinga, P.J.M.
Ubiquitous location-aware sensing devices have facilitated collection of large volumes of mobility data streams from moving entities such as people and animals, among others. Extraction of various types of periodic behavioral patterns hidden in such large volume of mobility data helps in understanding the dynamics of activities, interactions, and life style of these moving entities. The ever-increasing growth in the volume and dimensionality of such Big Data on the one hand, and the resource constraints of the sensing devices on the other hand, have made not only high pattern recognition accuracy but also low complexity, low resource consumption, and real-timeness...
Alpha current flow betweenness centrality - Avrachenkov, K.; Litvak, N.; Medyanikov, V.; Sokol, M.
A class of centrality measures called betweenness centralities reflects degree of participation of edges or nodes in communication between different parts of the network. The original shortest-path betweenness centrality is based on counting shortest paths which go through a node or an edge. One of shortcomings of the shortest-path betweenness centrality is that it ignores the paths that might be one or two steps longer than the shortest paths, while the edges on such paths can be important for communication processes in the network. To rectify this shortcoming a current flow betweenness centrality has been proposed. Similarly to the shortest...
Correlated-Spaces Regression for Learning Continuous Emotion Dimensions - Nicolaou, M.; Zafeiriou, S.; Pantic, M.
Adopting continuous dimensional annotations for affective analysis has been gaining rising attention by researchers over the past years. Due to the idiosyncratic nature of this problem, many subproblems have been identified, spanning from the fusion of multiple continuous annotations to exploiting output-correlations amongst emotion dimensions. In this paper, we firstly empirically answer several important questions which have found partial or no answer at all so far in related literature. In more detail, we study the correlation of each emotion dimension (i) with respect to other emotion dimensions, (ii) to basic emotions (e.g., happiness, anger). As a measure for comparison, we...
Human behavior sensing for tag relevance assessment - Soleymani, M.; Kaltwang, S.; Pantic, M.
Users react differently to non-relevant and relevant tags associated with content. These spontaneous reactions can be used for labeling large multimedia databases. We present a method to assess tag relevance to images using the non-verbal bodily responses, namely, electroencephalogram (EEG), facial expressions, and eye gaze. We conducted experiments in which 28 images were shown to 28 subjects once with correct and another time with incorrect tags. The goal of our system is to detect the responses to non-relevant tags and consequently filter them out. Therefore, we trained classifiers to detect the tag relevance from bodily responses. We evaluated the performance...
Variational Hidden Conditional Random Fields with Coupled Dirichlet Process Mixtures - Bousmalis, K.; Zafeiriou, S.; Morency, L.P.; Pantic, M.; Ghahramani, Z.
Hidden Conditional Random Fields (HCRFs) are discriminative latent variable models which have been shown to successfully learn the hidden structure of a given classification problem. An infinite HCRF is an HCRF with a countably infinite number of hidden states, which rids us not only of the necessity to specify a priori a fixed number of hidden states available but also of the problem of overfitting. Markov chain Monte Carlo (MCMC) sampling algorithms are often employed for inference in such models. However, convergence of such algorithms is rather difficult to verify, and as the complexity of the task at hand increases,...
Human Activity Recognition Using Hierarchically-Mined Feature Constellations - Oikonomopoulos, A.; Pantic, M.; ,; ,
In this paper we address the problem of human activity modelling and recognition by means of a hierarchical representation of mined dense spatiotemporal features. At each level of the hierarchy, the proposed method selects feature constellations that are increasingly discriminative and characteristic of a specific action category, by taking into account how frequently they occur in that action category versus the rest of the available action categories in the training dataset. Each feature constellation consists of n-tuples of features selected in the previous level of the hierarchy and lying within a small spatiotemporal neighborhood. We use spatiotemporal Local Steering Kernel...
Automatic Pain Intensity Estimation using Heteroscedastic Conditional Ordinal Random Fields - Rudovic, O.; Pavlovic, V.; Pantic, M.; ,
Automatic pain intensity estimation from facial images is challenging mainly because of high variability in subject-specific pain expressiveness. This heterogeneity in the subjects causes their facial appearance to vary significantly when experiencing the same pain level. The standard classification methods (e.g., SVMs) do not provide a principled way of accounting for this heterogeneity. To this end, we propose the heteroscedastic Conditional Ordinal Random Field (CORF) model for automatic estimation of pain intensity. This model generalizes the CORF framework for modeling sequences of ordinal variables, by adapting it for heteroscedasticity. This is attained by allowing the variance in the ordinal probit...
MeTA: Mediated Touch and Affect - Huisman, G.; Bianchi-Berthouze, N.; Heylen, D.K.J.
The main aim of this first workshop on Mediated Touch and Affect (MeTA) is to bring together researchers from diverse communities, such as affective computing, hap tics, augmented reality, communication, design, psychology, human-robot interaction, and telepresence. The goal is to discuss the current state of research on mediated touch and affect and to formulate a research agenda for future directions in research on aspects of the touch-technology-affect triangle.
Affective Touch at a Distance - Huisman, G.; Darriba Frederiks, A; Heylen, D.K.J.; ,
Touch is an important modality for affective communication between individuals. Here we present a system, named the TaSST (Tactile Sleeve for Social Touch), that allows two people to communicate different types of touch at a distance by touching their own forearm. In this paper we will introduce the TaSST and present a demo setup using two TaSST sleeves.
Shared Gaussian Process Latent Variable Model for Multi-view Facial Expression Recognition - Eleftheriadis, S.; Rudovic, O.; Pantic, M.; ,
Facial-expression data often appear in multiple views either due to head-movements or the camera position. Existing methods for multi-view facial expression recognition perform classification of the target expressions either by using classifiers learned separately for each view or by using a single classifier learned for all views. However, these approaches do not explore the fact that multi-view facial expression data are different manifestations of the same facial-expression-related latent content. To this end, we propose a Shared Gaussian Process Latent Variable Model (SGPLVM) for classification of multi-view facial expression data. In this model, we first learn a discriminative manifold shared by...
Robust Canonical Time Warping for the Alignment of Grossly Corrupted Sequences - Panagakis, Y.; Nicolaou, M.; Zafeiriou, S.; Pantic, M.
Temporal alignment of human behaviour from visual data is a very challenging problem due to a numerous reasons, including possible large temporal scale differences, inter/intra subject variability and, more importantly, due to the presence of gross errors and outliers. Gross errors are often in abundance due to incorrect localization and tracking, presence of partial occlusion etc. Furthermore, such errors rarely follow a Gaussian distribution, which is the de-facto assumption in machine learning methods. In this paper, building on recent advances on rank minimization and compressive sensing, a novel, robust to gross errors temporal alignment method is proposed. While previous approaches...
Extended Analysis of DES S-boxes - De Meyer, L.; Bilgin, B.; Preneel, B.
For more than three decades, the Data Encryption Standard (DES) was one the most widely used cryptographic algorithms. It is still the dominating block cipher for banking applications. The DES was designed by IBM, verified by NSA and published by the National Bureau of Standards as a US Federal Information Processing Standard (FIPS) in 1977. The algorithm itself was fully public but the complete design criteria were only revealed by Coppersmith in 1994. He states that the IBM team was aware of differential cryptanalysis; the DES S-boxes are chosen to satisfy eight design criteria in order to resist this powerful...
Bimodal Log-linear Regression for Fusion of Audio and Visual Features - Rudovic, O.; Petridis, S.; Pantic, M.
One of the most commonly used audiovisual fusion approaches is feature-level fusion where the audio and visual features are concatenated. Although this approach has been successfully used in several applications, it does not take into account interactions between the features, which can be a problem when one and/or both modalities have noisy features. In this paper, we investigate whether feature fusion based on explicit modelling of interactions between audio and visual features can enhance the performance of the classifier that performs feature fusion using simple concatenation of the audio-visual features. To this end, we propose a log-linear model, named Bimodal...