Sympathy Begins with a Smile, Intelligence Begins with a Word: Use of Multimodal Features in Spoken Human-Robot Interaction

Recognition of social signals, from human facial expressions or prosody of speech, is a popular research topic in human-robot interaction studies. There is also a long line of research in the spoken dialogue community that investigates user satisfaction in relation to dialogue characteristics. However, very little research relates a combination of multimodal social signals and language features detected during spoken face-to-face human-robot interaction to the resulting user perception of a robot. In this paper we show how different emotional facial expressions of human users, in combination with prosodic characteristics of human speech and features of human-robot dialogue, correlate with users' impressions of the robot after a conversation. We find that happiness in the user's recognised facial expression strongly correlates with likeability of a robot, while dialogue-related features (such as number of human turns or number of sentences per robot utterance) correlate with perceiving a robot as intelligent. In addition, we show that facial expression, emotional features, and prosody are better predictors of human ratings related to perceived robot likeability and anthropomorphism, while linguistic and non-linguistic features more often predict perceived robot intelligence and interpretability. As such, these characteristics may in future be used as an online reward signal for in-situ Reinforcement Learning based adaptive human-robot dialogue systems.Comment: Robo-NLP workshop at ACL 2017. 9 pages, 5 figures, 6 table

Επίδραση ανακυκλοφορίας καυσαερίων σε επιδόσεις και εκπομπές αιθάλης και NOx στροβιλο-υπερπληρωμένου κινητήρα diesel

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Παραγωγή και Διαχείρηση Ενέργειας

Barrage formation is independent from heterokaryon incompatibility in Verticillium dahliae

Barrage formation has been traditionally used for the assessment of mycelial compatibility in many fungi and has often been assumed to represent a non-self recognition phenotype that is directly associated with vegetative incompatibility in these organisms. In this work, the optimal growth conditions for large-scale studies of barrage formation in the asexual fungus Verticillium dahliae were determined, and they were used for the analysis of a diverse collection comprising 69 isolates of V. dahliae and related species. Barrage formation was very frequent on a defined complete agar medium within V. dahliae and between species of the genus. However, it was not correlated with the classification of V. dahliae isolates into Vegetative Compatibility Groups (VCGs) (based on the standard method using complementary nit mutants), as it was recorded at high frequencies both within and between V. dahliae VCGs. The high overall frequency of barrage formation demonstrated the presence of a higher level of mycelial incompatibility in V. dahliae than heterokaryon incompatibility assessed by forcing complementary nit mutants to form heterokaryons under selective conditions. The possible association of barrage formation with morphological characteristics of the fungal colonies was investigated, and a negative correlation of frequency and intensity of barrages with the isolates’ capacity for pigment production was detected. Real-time quantitative PCR VCG discriminatio

High-Throughput Assessment and Genetic Investigation of Vegetative Compatibility in Verticillium dahliae

Classification of isolates into vegetative compatibility groups (VCGs) using nitrate-non-utilizing (nit) mutants has been widely used for the characterization of Verticillium dahliae populations. However, certain methodological limitations prevent its application on a large scale. Furthermore, systematic investigations into the genetics underlying complementation tests between nit mutants of fungal isolates (i.e. heterokaryon formation) are lacking for Verticillium species. In this work, a diverse collection of 27 V. dahliae isolates – including representatives of all VCGs, both mating types, and heterokaryon self-incompatible isolates – was employed for the development and optimization of (i) a protocol for the rapid generation of nit mutants of V. dahliae isolates using UV-irradiation and (ii) a reproducible high-throughput procedure for complementation tests between nit mutants in liquid cultures using 96-well microplates. The genetic analysis of selected heterokaryons demonstrated that the frequently encountered ‘weak’ cross-reactions between VCGs and their subgroups can be actually heterokaryotic, implying the absence of strict genetic barriers between VCGs. In conclusion, we provide in this work an optimized method for the high-throughput VCG assignment of V. dahliae populations and a genetic analysis of heterokaryons that may have serious implications for the interpretation of VCG classification data. These advancements in the available methodology and the genetic background of vegetative compatibility grouping may contribute to a better understanding of the population biology of V. dahliae and possibly other mitosporic fun

Designing coherent and engaging open-domain conversational AI systems

Designing conversational AI systems able to engage in open-domain ‘social’ conversation is extremely challenging and a frontier of current research. Such systems are required to have extensive awareness of the dialogue context and world knowledge, the user intents and interests, requiring more complicated language understanding, dialogue management, and state and topic tracking mechanisms compared to traditional task-oriented dialogue systems. Given the wide coverage of topics in open-domain dialogue, the conversation can span multiple turns where a number of complex linguistic phenomena (e.g. ellipsis and anaphora) are present and should be resolved for the system to be contextually aware. Such systems also need to be engaging, keeping the users’ interest over long conversations. These are only some of the challenges that open-domain dialogue systems face. Therefore this thesis focuses on designing dialogue systems able to hold extensive open-domain conversations in a coherent, engaging, and appropriate manner over multiple turns. First, different types of dialogue systems architecture and design decisions are discussed for social open-domain conversations, along with relevant evaluation metrics. A modular architecture for ensemble-based conversational systems is presented, called Alana, a finalist in the Amazon Alexa Prize Challenge in 2017 and 2018, able to tackle many of the challenges for open-domain social conversation. The system combines different features such as topic tracking, contextual Natural Language understanding, entity linking, user modelling, information retrieval, and response ranking, using a rich representation of dialogue state. The thesis next analyses the performance of the 2017 system and describes the upgrades developed for the 2018 system. This leads to an analysis and comparison of the real-user data collected in both years with different system configurations, allowing assessment of the impact of different design decisions and modules. Finally, Alana was integrated into an embodied robotic platform and enhanced with the ability to also perform tasks. This system was deployed and evaluated in a shopping mall in Finland. Further analysis of the added embodiment is presented and discussed, as well as the challenges of translating open-domain dialogue systems into other languages. Data analysis of the collected real-user data shows the importance of a variety of features developed and decisions made in the design of the Alana system

Towards Human Society-inspired Decentralized DNN Inference

In human societies, individuals make their own decisions and they may select if and who may influence it, by e.g., consulting with people of their acquaintance or experts of a field. At a societal level, the overall knowledge is preserved and enhanced by individual person empowerment, where complicated consensus protocols have been developed over time in the form of societal mechanisms to assess, weight, combine and isolate individual people opinions. In distributed machine learning environments however, individual AI agents are merely part of a system where decisions are made in a centralized and aggregated fashion or require a fixed network topology, a practice prone to security risks and collaboration is nearly absent. For instance, Byzantine Failures may tamper both the training and inference stage of individual AI agents, leading to significantly reduced overall system performance. Inspired by societal practices, we propose a decentralized inference strategy where each individual agent is empowered to make their own decisions, by exchanging and aggregating information with other agents in their network. To this end, a ”Quality of Inference” consensus protocol (QoI) is proposed, forming a single commonly accepted inference rule applied by every individual agent. The overall system knowledge and decisions on specific manners can thereby be stored by all individual agents in a decentralized fashion, employing e.g., blockchain technology. Our experiments in classification tasks indicate that the proposed approach forms a secure decentralized inference framework, that prevents adversaries at tampering the overall process and achieves comparable performance with centralized decision aggregation methods

Effect of cell residence time variance on the performance of an advanced paging algorithm

The use of advanced sequential paging algorithms has been suggested as a means to reduce the signaling cost in future mobile cellular networks. In a proposed algorithm (Koukoutsidis and Theologou, 2003), the system can use the additional information of the last interaction cell combined with a mobility model to predict the short-term location probabilities at the time of an incoming call arrival. The short-term location probabilities reduce the uncertainty in mobile user position and thus greatly improve the search. In this paper, an analytical model is derived that allows for a general distribution of cell residence times. By considering a Gamma distribution, we study the effect of the variance of cell residence times and derive useful results on the performance of the algorithm.Comment: 8 pages, 3 figure