Natural Language based Context Modeling and Reasoning with LLMs: A Tutorial

Large language models (LLMs) have become phenomenally surging, since 2018--two decades after introducing context-awareness into computing systems. Through taking into account the situations of ubiquitous devices, users and the societies, context-aware computing has enabled a wide spectrum of innovative applications, such as assisted living, location-based social network services and so on. To recognize contexts and make decisions for actions accordingly, various artificial intelligence technologies, such as Ontology and OWL, have been adopted as representations for context modeling and reasoning. Recently, with the rise of LLMs and their improved natural language understanding and reasoning capabilities, it has become feasible to model contexts using natural language and perform context reasoning by interacting with LLMs such as ChatGPT and GPT-4. In this tutorial, we demonstrate the use of texts, prompts, and autonomous agents (AutoAgents) that enable LLMs to perform context modeling and reasoning without requiring fine-tuning of the model. We organize and introduce works in the related field, and name this computing paradigm as the LLM-driven Context-aware Computing (LCaC). In the LCaC paradigm, users' requests, sensors reading data, and the command to actuators are supposed to be represented as texts. Given the text of users' request and sensor data, the AutoAgent models the context by prompting and sends to the LLM for context reasoning. LLM generates a plan of actions and responds to the AutoAgent, which later follows the action plan to foster context-awareness. To prove the concepts, we use two showcases--(1) operating a mobile z-arm in an apartment for assisted living, and (2) planning a trip and scheduling the itinerary in a context-aware and personalized manner.Comment: Under revie

Control of Data Flows Based on Drag-and-Drop between Appliances Using a Camera Phone

修士論

A Framework for Designing Fair Ubiquitous Computing Systems

Over the past few decades, ubiquitous sensors and systems have been an integral part of humans' everyday life. They augment human capabilities and provide personalized experiences across diverse contexts such as healthcare, education, and transportation. However, the widespread adoption of ubiquitous computing has also brought forth concerns regarding fairness and equitable treatment. As these systems can make automated decisions that impact individuals, it is essential to ensure that they do not perpetuate biases or discriminate against specific groups. While fairness in ubiquitous computing has been an acknowledged concern since the 1990s, it remains understudied within the field. To bridge this gap, we propose a framework that incorporates fairness considerations into system design, including prioritizing stakeholder perspectives, inclusive data collection, fairness-aware algorithms, appropriate evaluation criteria, enhancing human engagement while addressing privacy concerns, and interactive improvement and regular monitoring. Our framework aims to guide the development of fair and unbiased ubiquitous computing systems, ensuring equal treatment and positive societal impact.Comment: 8 pages, 1 figure, published in 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computin

Context Mining with Machine Learning Approach: Understanding, Sensing, Categorizing, and Analyzing Context Parameters

Context is a vital concept in various fields, such as linguistics, psychology, and computer science. It refers to the background, environment, or situation in which an event, action, or idea occurs or exists. Categorization of context involves grouping contexts into different types or classes based on shared characteristics. Physical context, social context, cultural context, temporal context, and cognitive context are a few categories under which context can be divided. Each type of context plays a significant role in shaping our understanding and interpretation of events or actions. Understanding and categorizing context is essential for many applications, such as natural language processing, human-computer interaction, and communication studies, as it provides valuable information for interpretation, prediction, and decision-making. In this paper, we will provide an overview of the concept of context and its categorization, highlighting the importance of context in various fields and applications. We will discuss each type of context and provide examples of how they are used in different fields. Finally, we will conclude by emphasizing the significance of understanding and categorizing context for interpretation, prediction, and decision-making

An ontology-based P2P infrastructure to support context discovery in pervasive computing

Master'sMASTER OF ENGINEERIN

Concevoir des interfaces tangibles et spatiales pour les déficients visuels : pourquoi et comment ?

International audienceMaking geographical maps and graphical representations accessible to visually impaired users is essential. To do so, a variety of approaches exist, whether traditional or technological. Among them, tangible interfaces, although very promising, have not been thoroughly investigated. In this article, we analyze five research prototypes in order to identify the aspects that have to be taken into account when designing spatial tangible interfaces for visually impaired users, and we present the various solutions that have been proposed to address the most commonly encountered problems. In this way we hope to facilitate and support the development and evaluation of spatial tangible interfaces.Rendre les cartes géographiques et les représentations graphiques accessibles aux personnes déficientes visuelles est primordial. Pour cela, de nombreuses solutions existent, qu’elles soient artisanales ou technologiques. Parmi ces dernières, les interfaces tangibles, bien que très prometteuses, ont été très peu étudiées. Dans cet article, nous analysons cinq prototypes issus de la littérature afin d’identifier les facteurs à prendre en compte lors de la conception d’interfaces tangibles et spatiales pour les déficients visuels, et nous présentons les solutions proposées pour répondre aux difficultés techniques fréquemment rencontrées. Nous souhaitons ainsi faciliter et encourager le développement et l’évaluation d’interfaces tangibles et spatiales

Une approche de gestion de contextes métiers pour l'accès à l'information

International audienceLa prise en compte du contexte améliore la pertinence des informations fournies par les systèmes pour les utilisateurs. Nous introduisons dans ce papier un gestionnaire de situations contextuelles métier basé sur une nouvelle définition générique du contexte. Ce gestionnaire prend en compte diverses dimensions contextuelles et agit comme un intermédiaire entre les systèmes d’accès à l’information (SAI) et les informations contextuelles. Notre approche repose sur un processus original qui gère les différentes dimensions contextuelles afin de créer une situation unique à un instant t. Pour cela, le processus de Mise En Situation (MES) utilise la base de règles qui représente la connaissance contextuelle du gestionnaire. Les situations seront utilisées par les SAI à des fins d’adaptation de processus informationnel. Par ailleurs, un processus d’extraction est proposé pour améliorer la fiabilité du gestionnaire de contexte au fil du temps en faisant évoluer sa base de connaissances. Le gestionnaire a été mis en œuvre à travers un prototype qui a été utilisé pour l’expérimentation afin de mesurer l’impact de nos propositions dans le domaine de la maintenance aéronautique

Mobile Sensing Systems

[EN] Rich-sensor smart phones have made possible the recent birth of the mobile sensing research area as part of ubiquitous sensing which integrates other areas such as wireless sensor networks and web sensing. There are several types of mobile sensing: individual, participatory, opportunistic, crowd, social, etc. The object of sensing can be people-centered or environment-centered. The sensing domain can be home, urban, vehicular Currently there are barriers that limit the social acceptance of mobile sensing systems. Examples of social barriers are privacy concerns, restrictive laws in some countries and the absence of economic incentives that might encourage people to participate in a sensing campaign. Several technical barriers are phone energy savings and the variety of sensors and software for their management. Some existing surveys partially tackle the topic of mobile sensing systems. Published papers theoretically or partially solve the above barriers. We complete the above surveys with new works, review the barriers of mobile sensing systems and propose some ideas for efficiently implementing sensing, fusion, learning, security, privacy and energy saving for any type of mobile sensing system, and propose several realistic research challenges. The main objective is to reduce the learning curve in mobile sensing systems where the complexity is very high.This work has been partially supported by the "Ministerio de Ciencia e Innovacion", through the "Plan Nacional de I+D+i 2008-2011" in the "Subprograma de Proyectos de Investigacion Fundamental", project TEC2011-27516, and by the Polytechnic University of Valencia, through the PAID-05-12 multidisciplinary projects.Macias Lopez, EM.; Suarez Sarmiento, A.; Lloret, J. (2013). Mobile Sensing Systems. Sensors. 13(12):17292-17321. https://doi.org/10.3390/s131217292S1729217321131