Orofacial cutaneous function in speech motor control and learning

International audienceSomatosensory signals from facial skin can provide a rich source of sensory input. However, it is unknown yet how cutaneous input works on speech motor control and learning. This chapter introduces a kinesthetic role of orofacial cutaneous afferents in speech processing. We argue for specificity of the orofacial somatosensory system from anatomical and physiological perspectives. The contribution of cutaneous afferents to speech production is evident in neurophysiological and psychophysical findings. Somatosensory modulation associated with facial skin deformation induces a reflex for articulatory motion adjustment in speech production and also an adaptive motion change in speech motor learning. In addition, cutaneous mechanoreceptors are narrowly tuned at the skin lateral to the oral angle. An intriguing function of somatosensory inputs associated with facial skin deformation is to interact with the processing of speech perception. Taken together, orofacial cutaneous afferents play an important role in both speech production and perception

Towards Agent-based Large-scale Decision Support System: The Effect of Facilitator

Good discussions are essential for group decisions, especially when the number of people in thea group has many people is large. So, it is important to Pprovidinge good support is critical for having establishing and maintaining coherent discussions that avoid some of thesuch anti-social bad behaviors, like as flaming, that have, which has been observed in some large discussion groups. We have developed a large-scale online decision support system that has facilitator support functions, and deployed it in case studies for several real-world online discussion supports as case studies. In this paper, weWe propose a facilitator-mediated online discussion model in order to lead discussions to in a better direction for ato reach decisions. Our extreme ultimate goal is an to realize automated facilitator agent that can adequately leadhelp participants to achieve reach reasonable decisions. In reality, online discussion is often fails plagued byinto flaming, , which is the act of posting or sending offensive messages during a discussion. Such flaming phenomena have been focused on as anti-social bad behavior of in online discussion forums. After several cases studies, we learned several lessons. Critically, The most important achievement is that in any all of our social experiments, no flaming has not been observed in our facilitator-mediated decision support system. Also, we obtained Our some insights also suggest in whichthat the social presence of a facilitator would have largegreatly aeffect for participants’ behavior

Self-Agreement: A Framework for Fine-tuning Language Models to Find Agreement among Diverse Opinions

Finding an agreement among diverse opinions is a challenging topic in multiagent systems. Recently, large language models (LLMs) have shown great potential in addressing this challenge due to their remarkable capabilities in comprehending human opinions and generating human-like text. However, they typically rely on extensive human-annotated data. In this paper, we propose Self-Agreement, a novel framework for fine-tuning LLMs to autonomously find agreement using data generated by LLM itself. Specifically, our approach employs the generative pre-trained transformer-3 (GPT-3) to generate multiple opinions for each question in a question dataset and create several agreement candidates among these opinions. Then, a bidirectional encoder representations from transformers (BERT)-based model evaluates the agreement score of each agreement candidate and selects the one with the highest agreement score. This process yields a dataset of question-opinion-agreements, which we use to fine-tune a pre-trained LLM for discovering agreements among diverse opinions. Remarkably, a pre-trained LLM fine-tuned by our Self-Agreement framework achieves comparable performance to GPT-3 with only 1/25 of its parameters, showcasing its ability to identify agreement among various opinions without the need for human-annotated data

Instantiating the Contingent Bids Model of Truthful Interdependent Value Auctions

We consider the problem of auction design with agents that have interdependent values, i.e. values that depend on each others' private signals. We adopt the contingent bids model of Dasgupta and Maskin [3], and allow agents to submit bids of the form "if player 1 bids $x for good A then I will bid$y." Our main contribution is to identify a specific linear valuation model for which there exists an efficient auction for a single item, and then extend this to provide an approximately efficient combinatorial auction with single-minded bidders. In both auction, winners and payments are computed from the fixed point of the valuation mapping defined by contingent bids. We also adopt search in order to construct a variation on the single-item auction with improved revenue. In closing, we discuss the (many) challenges in moving to more general models of interdependent valuations.Engineering and Applied Science

Ontology-Based Architecture to Improve Driving Performance Using Sensor Information for Intelligent Transportation Systems

Intelligent transportation systems are advanced applications with aim to provide innovative services relating to road transport management and enable the users to be better informed and make safer and coordinated use of transport networks. A crucial element for the success of these systems is that vehicles can exchange information not only among themselves but with other elements in the road infrastructure through different applications. One of the most important information sources in this kind of systems is sensors. Sensors can be located into vehicles or as part of an infrastructure element, such as bridges or traffic signs. The sensor can provide information related to the weather conditions and the traffic situation, which is useful to improve the driving process. In this paper a multiagent system using ontologies to improve the driving environment is proposed. The system performs different tasks in automatic way to increase the driver safety and comfort using sensor information