Energy labelling of electric appliances, among other goods and services, matters to both consumers and producers. It aids the consumers in gathering an expectation of how much electricity the good they are acquiring will consume, and it establishes guidelines that producers can follow to create more efficient products and services. Energy labelling is, nevertheless, nearly absent for software due to the heterogeneous hardware execution environments and unequal circumstances. Throughout this article, we provide an equal ground for software energy labelling by providing an intelligent prospective behavioural, hardware and energy-consumption patterns rating algorithm meant for the design phase of the Software-Development Life Cycle, as opposed to the existing approaches whose labelling procedure applies to the implementation phase onward. We expect our categorization method to guide software developers, helping them to decrease their energy consumption by raising their awareness of the energy footprint that their design choices produce; culminating in aware consumers who know what energy usage to expect from the software they consume.

International audience

The rising influence of social media platforms in various domains, including tourism, has highlighted the growing need for efficient and automated Natural Language Processing (NLP) strategies to take advantage of this valuable resource. However, the transformation of multilingual, unstructured, and informal texts into structured knowledge still poses significant challenges, most notably the never-ending requirement for manually annotated data to train deep learning classifiers. In this work, we study different NLP techniques to establish the best ones to obtain competitive performances while keeping the need for training annotated data to a minimum. To do so, we built the first publicly available multilingual dataset (French, English, and Spanish) for the tourism domain, composed of tourism-related tweets. The dataset includes multilayered, manually revised annotations for Named Entity Recognition (NER) for Locations and Fine-grained Thematic Concepts Extraction mapped to the Thesaurus of Tourism and Leisure Activities of the World Tourism Organization, as well as for Sentiment Analysis at the tweet level. Extensive experimentation comparing various few-shot and fine-tuning techniques with modern language models demonstrate that modern few-shot techniques allow us to obtain competitive results for all three tasks with very little annotation data: 5 tweets per label (15 in total) for Sentiment Analysis, 30 tweets for Named Entity Recognition of Locations and 1K tweets annotated with fine-grained thematic concepts, a highly fine-grained sequence labeling task based on an inventory of 315 classes. We believe that our results, grounded in a novel dataset, pave the way for applying NLP to new domain-specific applications, reducing the need for manual annotations and circumventing the complexities of rule-based, ad-hoc solutions.

Distributed systems, spanning pervasive, cloud, and edge computing, require robust scheduling for tasks and data management, facing challenges like hardware diversity, QoS, localization, and energy efficiency. Current solutions often lack a comprehensive approach, failing to achieve balanced load management, QoS, and notably in minimizing application energy use, a key modern concern. We propose a distributed scheduler using multidimensional spaces and data structures aimed at comprehensive load balancing, energy efficiency, and QoS. The scheduler indexes devices within the structure based on diverse criteria such as hardware availability, GPS, and energy features. It then organizes applications into abstract graphs, where nodes are containers and their data items (units of data accessed by the containers), and edges are the transfer rates among them. To migrate containers, the scheduler performs range queries within the structure for optimal devices. To organize data items, it finds the barycenter of the devices executing the containers linked to them. We evaluated our scheduler’s effectiveness using the PISCO simulator, comparing it against other energy-efficient schedulers.

Regardless of the improvements in the efficiency of energy consumption of information and communication technology, energy consumption will forever be a requisite for software execution. Consequently, researchers have promoted the development of green and sustainable software with new development methods and tools. These, however, have been adopted with limited success due to technicalities and specific language/platform requirements. In this paper, we present RADIANCE: a web app for designing greener software with a model-driven approach based on the Behavior-Based Consumption Profiles (BBCP) external Domain-Specific Language (DSL). RADIANCE, in contrast to other tools, embraces users with different levels of knowledge about green software and software architecture. Moreover, RADIANCE assesses and rates, reports, and provides advice on the energy-consuming patterns of the software models created by the user, assisting them in identifying possible design changes that result in greener software designs from the initial stages of software development.

According to many analysts, February 2022 is dated as the exacerbation of the armed conflict between Ukraine and Russia. Of course, this shocking event has an impact on mood of people. In this context, we studied the music listening habits of people in Eastern European countries. We applied timeseries and statistical analysis of mood-relate features (danceability, valence and energy) to a Spotify dataset in order to define new paradigms of emotion-aware systems that can be applied to detect the population’s state of mind. The results show that the trend in the type of music changes notably towards songs with less genre diversity and reflecting less positivity.

Nowadays, many researchers work to identify microservices-based application deployments and scheduling solutions to save energy without decreasing functional QoS. In this work, we present PISCO: A simulator that allows facing this challenge in a simple and efficient way, enabling its users to focus uniquely on microservices deployment/scheduling algorithms and its hardware/software repercussions (load vs. energy consumption) without worrying about low-level network configurations or operating system issues. PISCO is able to deploy and schedule (move, duplicate, start/stop) microservices and their dependencies on various devices with software and hardware heterogeneity (CPU, bandwidth, RAM, Battery, etc.), taking into account various scheduling heuristics algorithms: centralized vs non-centralized. To do this, PISCO allows deploying custom network topologies based on client-server schemes or p2p distributions, where devices can (dis)appear, turn on/off obeying random circumstances or user strategies.Finally, the simulator performs relevant operations such as QoS definition, resource monitoring, calculation of energy saved and consumption tracking (at device and network level). We tested some ideas based on our previous work “Kaligreen” to demonstrate the effectiveness of PISCO.

Abstract In this article, we propose a decentralized agent‐based autonomic semantic service adaptation controller and reconfiguration (ASSACR) to solve the problem of context‐aware service selection and deployment for distributed healthcare mobile applications, where functional and nonfunctional users' needs are expressed using domain‐independent ontology. The main objective of this approach is to provide optimal personalized services as fast as possible. Indeed, a new ontology for semantic description and parallel management of the contextual services is presented. At first, a new dominance operator‐based constraints violation degree is used to reduce the search space. Then, optimal personalized paths that meet user's needs, preferences and devices' availability are discovered and selected in a smaller search space more effectively and efficiently using dynamic services selection algorithm, based on new ASSACR multiagent strategies. To validate the performance and efficiency of the proposed approach, we compared the proposed approach with existing multiagent strategies of the diabetic follow‐up use cases. Experimental results show that the proposed approach effectively addressed both the semantic service representation and agent aspects in terms of performance and quality service selection.

Best Student Paper Award

Smart environments are currently overpowering traditional Human-Computer Interaction (HCI) approaches for people's everyday life applications. Proxemic interaction is an emerging area for improving HCI experiences in such environments, causing the so-called proxemic environments to emerge. Proxemic interaction describes how the five dimensions (i.e., Distance, Identity, Location, Movement, and Orientation - DILMO) can be used to define interactions among people and digital devices. Current studies in this area are focused on developing proxemic applications with specific functionalities, based on a toolkit that allows developers to obtain DILMO information from sensors. However, there exists a notable lack of general approaches able to support the whole implementation process, starting from the modelling of proxemic environments to represent general proxemics behaviours, and finalizing with the development of specific applications. In order to facilitate the integration of proxemic capabilities in HCI, we propose an approach for modelling proxemic environments based on a graphical Domain-Specific Language (DSL). The DSL allows non-specialist designers to express proxemic interactions for modelling proxemic environments and supports the development process. In order to show its suitability and functionality, we have implemented a prototype to design proxemic behaviours in different proxemic environments.

The widespread diffusion of smart and mobile devices continuously connected to the Internet has facilitated the users' contact and interaction with other people, devices, and with their physical surroundings.Proxemic interaction, derived from proxemics theory, focuses on how Human-Computer Interaction (HCI) works with smart devices, using the five proxemic dimensions: Distance, Identity, Location, Movement, and Orientation (DILMO).The current tools for developing proxemic applications require fixed devices that make it difficult to build proxemic mobile apps.In this work, we propose a framework to manage all components in a proxemic environment (i.e., interaction objects and DILMO dimensions that govern the HCI).We demonstrate and evaluate the effectiveness and suitability of our framework, through the development of two proxemic mobile applications, as proof-of-concept.

We present a prototype for the design of applications dedicated to the processing and analysis of digital spatio-temporal data. The platform must be easy to use, ergonomic and adaptable as it targets users such as researchers in geography or decision-makers in local authorities. We assume that tourits' spatiotemporal footprints can contribute to better understanding of touristic cities. We want to provide end-users with methodological means and a software environment to exploit these sources of information in order to assist them in decision-making.

Currently, mobile technologies are present in our daily day in different activities and their use keeps increasing. In this context, proxemic interaction, derived from proxemic theory, is becoming an influential approach to implement Mobile Human-Computer Interaction (MobileHCI) in smart environments, based on five proxemic dimensions: Distance, Identity, Location, Movement, and Orientation (DILMO). The existing tools for implementing proxemic applications require fixed devices that make it difficult to built mobile proxemic apps. This work aims to propose a framework for the development of proxemic applications for smart environments comprised by entities, whose interactions are supported by MobileHCI defined in terms of DILMO dimensions. Our proposed framework allows defining and managing all components in a smart proxemic environment. The framework also provides an API, that allows developers to simplify the process of proxemic information sensing (i.e., detection of DILMO dimensions) from mobile phones and wearable sensors. We demonstrate and evaluate the effectiveness and suitability of our framework, through the proof-of-concept which describes the implementation of two proxemic mobile applications built in a context-based infrastructure for smart proxemic environments based on mobile devices.

Nowadays, many researchers work to identify microservices-based application deployments and scheduling solutions to save energy without decreasing functional QoS. In this work, we present DRACeo: A simulator that allows facing this challenge in a simple and efficient way, enabling its users to focus uniquely on microservices deployment/scheduling algorithms and its hardware/software repercussions (load vs. energy consumption) without worrying about low-level network configurations or operating system issues. DRACeo is able to deploy and schedule (move, duplicate, start/stop) microservices and their dependencies on various devices with software and hardware heterogeneity (CPU, bandwidth, RAM, Battery, etc.), taking into account various scheduling heuristics algorithms: centralized vs non-centralized. To do this, DRACeo allows deploying custom network topologies based on client-server schemes or p2p distributions, where devices can (dis)appear, turn on/off obeying random circumstances or user strategies. Finally, the simulator performs relevant operations such as QoS definition, resource monitoring, calculation of energy saved and consumption tracking (at device and network level). We tested some ideas based on our previous work “Kaligreen” to demonstrate the effectiveness of DRACeo.

Currently, people's daily lives are affected by the pandemic produced by the COVID-19. One of the main problem is the quickly and easy spreading of the virus. Healthcare workers are affected by nosocomial infections (also called as hospital-acquired infections) that exist in workplaces and more specifically, from health care equipment. In practice, the use of technology is quite common in health care settings. However, due to the touchability of mobile digital devices, their use contributes to nosocomial infections, according to several studies. Some applications based on tracking people have been implemented in order to facilitate Human-Computer Interaction (HCI) and preventing contamination of surfaces by people's hands. Notwithstanding, their use still presents limitations related to implementing applications to be used in some hospital environments, such as care rooms, laboratories, clinical workrooms. To overcome these limitations, we propose the use of interpersonal distances and proxemic dimensions (i.e., Distance, Identity, Location, Movement, and orientation- DILMO) for implementing HCI with mobile devices that reduces their touchability. The aim is to facilitate the development of mobile apps with proxemic HCI, supported in a proposed architecture, to stop spreading of nosocomial infection of COVID-19 and others. To show the usability and suitability of our proposal, we present two prototypes of apps for mobile devices as proof-of concept, using several combination of proxemic DILMO dimensions to model proxemic HCI that allow flexibility in interpersonal and devices people interactions.

In this paper, we propose a framework to study the AGGIR (Autonomy Gerontology Iso-Resources Groups) grid model, in order to evaluate the level of independency of elderly people, according to their capabilities of performing activities and interact with their environments over the time. To model the Activities of Daily Living (ADL), we also extend a previously proposed Domain Specific Language (DSL), in order to employ operators to deal with constraints related to time and location of activities, and event recognition. Our framework aims at providing an analysis tool regarding the performance of elderly/handicapped people within a home environment by means of data recovered from sensors using the iCASA simulator. To evaluate our approach, we pick three of the AGGIR variables (i.e., dressing, toileting, and transfers) and evaluate their testability in many scenarios, by means of records representing the occurrence of activities of the elderly. Results demonstrate the accuracy of our framework to manage the obtained records correctly and thus generate the appropriate event information.

In the field of smart environments, many devices and multimedia oriented connected objects has gained a significant attention in the last years. In such a domain, there is a large number of heterogeneous specific profiles (user devices, connected objects, smart homes, health sensors...). These profiles are highly dynamic, according to different contexts (user profile, user environment, monitoring, social activities...). There is a critical need to offer to users a flexible and efficient services selection among a large set of candidates based on their surrounding environments, user's current needs and situations. In this paper, we propose a two layered architecture including a local server (Fog computing), a central Cloud providing an efficient situation management and good scalability. We particularly focus on context-aware e-health mobile applications for achieving the efficient and quality diagnosis of complex situations and for providing all distributed multimedia services that help users to access/broadcast multimedia documents.

In this paper, which describes a work in progress, we wish to propose context aware service delivery, and we focus on distributed human computer interface, in order to exploit the variety of interaction mode/capabilities according to current situation, users, and context. Our aim is providing new kind of more dynamic interaction modes. We particularly focus on the domain of smart-home (including digital health home preoccupations). One objective is the use of distributed interfaces associated to interactive services in order to help in maintaining old people at home (Digital Health Smart Homes).

Currently, everybody wish to access to applications from a wide variety of devices (PC, Tablet, Smartphone, Set-top-box, etc.) in situations including various interactions and modalities (mouse, tactile screen, voice, gesture detection, etc.). At home, users interact with many devices and get access to many multimedia oriented documents (hosted on local drives, on cloud storage, on line streaming, etc.) in various conditions and with multiple (and sometimes at the same time) devices. This paper presents an efficient approach to context aware content delivery. We particularly focus on cloud ontology model, as a way to inference user's preferences according to different sensitive situations. Thus, we can determine the necessary adaptations that the user has full exploitation of multimedia documents and their use with interactive services in order to help in achieving user's preferences under multiples devices constraints with multiple interacting modalities. Our simulation study shows the efficiency and effectiveness of our approach.

Currently, everybody wish to access to applications from a wide variety of devices (PC, Tablet, Smartphone, Set-top-box, etc.) in situations including various interactions and modalities (mouse, tactile screen, voice, gesture detection, etc.). At home, users interact with many devices and get access to many multimedia oriented documents (hosted on local drives, on cloud storage, online streaming, etc.) in various situations with multiple (and sometimes at the same time) devices. The diversity and heterogeneity of users profiles and service sources can be a barrier to discover the available services sources that can come from anywhere from the home or the city. The objective of this paper is to suggest a meta-level architecture for increasing the high level of context concepts abstracting for heterogeneous profiles and service sources via a top-level ontology. We particularly focus on context-aware mHealth applications and propose an ontologies-based architecture, OntoSmart (a top-ONTOlogy SMART), which provides adapted services that help users to broadcast of multimedia documents and their use with interactive services in order to help in maintaining old people at home and achieving their preferences. In order to validate our proposal, we have used Semantic Web, Cloud and Middlewares by specifying and matching OWL profiles and experiment their usage on several platforms.

This paper presents some features of our semantic social adaptation tool that facilitates user's access to exchange multimedia contents over heterogeneous mobile environments. The main idea is to provide a tool for satisfying the needs of mobiles users sharing a common interest related to a same situation. The adoption of advanced semantic generic profile for modeling the different users needs and their relationships is essential and will provide immediate benefit to overcome the limitations of social networks. The proposed approach has been validated through a prototype for the authors of oral conference presentation.

Modern mobile devices spreading introduce a new way of using and designing software applications. These pervasive systems are now part of our lives. More and more connected objects provide us more and more software capabilities. In this environment we have to rethink the way we traditionally consider our interactions with this software. These interactions are now potentially spread over multiple devices with different interacting modalities. We propose, in this paper, a software container component based architecture to design such interactions and a software platform to manage their dynamic life-cycle.

In this work, we propose a framework for automatic dependency evaluation that can notify the changes of the elderly dependency, hence providing persons with required services in time. In order to validate our approach, we target the French dependent population. Especially, we focus our proposal on the AGGIR dependency model used in the French medical field. Our objective is to provide a flexible architecture and an extensible model linked to opened data that can refer to a wide variety of services (hardware sensors and software; simple or composed services).

In this paper we propose a meta-model for architectures with heterogeneous multimedia components. Currently, a generic solution does not exist to automatically deploy a distributed architecture based on multimedia components. The description of the incompatibilities between components is a need in such approaches. Indeed, software architectures validate the functional aspects, which are not sufficient to guarantee a realistic assembly. For instance, the problem of heterogeneity related to the exchanged data flows. In order to highlight these incompatibilities and to find solutions, a model-based approach called MMSA (Meta-model Multimedia Software Architecture) is proposed. It enables the description of the software architectures expressing a software system as a collection of components which handle various types and formats of data, and interacts between them via connectors including the adaptation connectors.

One of the current challenges of Information Systems is to ensure semi-structured data transmission, such as multimedia data, in a distributed and pervasive environment. Information Sytems must then guarantee users a quality of service ensuring data accessibility whatever the hardware and network conditions may be. They must also guarantee information coherence and particularly intelligibility that imposes a personalization of the service. Within this framework, we propose a design method based on original models of multimedia applications and quality of service. We also define a supervision platform Kalinahia using a user centered heuristic allowing us to define at any moment which configuration of software components constitutes the best answers to users’ wishes in terms of service.

Handling the inter-flow synchronization (e.g. images and sound of a video) in distributed multimedia applications seems to be fundamental. In this paper, we propose a new component model, named OSAGAIA , in order to tackle synchronization between multimedia flows from the source (e.g. media capture) to the destination (e.g. media player). This model is made of two entities. The first one is called "elementary processor" and is used as a runtime environment (container) for multimedia components. The second one is called "conduit" and is used to transport synchronous multimedia flows within the application. To ensure the synchronization within both entities, we sample multimedia flows in order to constitute temporal units and synchronous slices. We test this mechanism with a distributed prototype implemented with Java language using JMF (Java media framework) API and TCP/IP as the network protocol. This prototype shows that synchronization is kept between a non-processed and a processed image flow.

The lack of information for COTS evaluation may results on a bad evaluation and so has important consequences into the final product. The results are mostly traduced by a strong increasing of integration costs. That's why we focus on this crucial point of getting/producing information before and for evaluation of COTS.

Previously, we developed a method and a distributed platform for the re-engineering of applications by adding cooperation. The goal was to supply a way of communication based on the exchange of events, messages and shared data. Now, we propose to adapt this approach (method and platform) to distributed multimedia applications. These applications present the characteristic to be organized around the communication. So, we can consider them as composed of distributed components which have to collaborate. Since it is heavily interactive, such an application needs to adapt itself in real-time to the user and to the environment in which it runs. Our approach consists of breaking down the application into two levels: the first one reflects the users' point of view in terms of functionalities; and the second one reflects the way to achieve these functionalities in terms of quality of service. We propose to organize components into workgroups and subworkgroups corresponding to this two levels. The platform manages these groups and makes them evolve in real time. It also ensures the interoperability of components which cooperate in these groups.

There is an increasing demand from firms to evolve their applications to be more co-operative. The problem is to realise this migration and to maintain the quality of existing applications which are reliable due to previously encountered difficulties We will propose a method based on an organisation of dynamic workgroups (their composition can evolve with time) composed of components. The method is entirely derivable (after a formal verification) into rules integrated into a cooperative architecture allowing the cooperation. In order to propose an operational method, the last stage will propose to obtain rules derived from a specification language associated to the method Our approach allows the provision of cooperation thanks to the application re-engineering without modifying any existing component.