ABSTRACT Artificial intelligence (AI) is increasingly adopted in animal‐related domains such as health monitoring, behaviour analysis and welfare assessment. However, concerns about the transparency and interoperability of AI outputs are rising. This scoping review investigates how AI and explainable AI (XAI) are applied in animal‐related systems and examines the role of semantic technologies in enhancing their interoperability. In this review, we followed PRISMA‐ScR guidelines and conducted five structured searches across ScienceDirect, Springer, Scopus, IEEE Xplore and Web of Science. The searches targeted AI applications (S 1 ), XAI applications (S 2 ) and the use of semantic knowledge in AI (S 3 ) and XAI (S 4 ). Studies were screened, assessed using the QualSyst tool and selected based on Q1/Q2 SJR or CORE C–A* classification. A total of 21 review papers were selected for AI applications and 8 for XAI. No eligible papers were found regarding the use of semantics in AI or XAI. While explainability is gaining attention, it remains mostly limited to visual or statistical tools, lacking domain‐specific contextualization. We observed that AI is now pervasive in animal‐related research, yet XAI practices remain underdeveloped and lack semantic grounding. This gap calls for ontology‐based explainability to enhance trust, relevance and usability for both experts and non‐specialists.

Over the last decade, the generation and streaming of images by data providers have grown rapidly. Facial images, in particular, have gained notable interest because of their extensive applications in social media. Nevertheless, the large-scale sharing of images across these platforms presents considerable challenges and necessitates substantial computational resources for efficient processing. Additionally, images are prone to multiple types of distortions arising from processing, transmission, sharing, or other real-world conditions. Therefore, assessing image quality is crucial to ensure the reliable delivery of content, especially in cases where the original, undistorted version is unavailable. In this paper, we propose a novel, lightweight, and reference-free image quality assessment framework that integrates deep convolutional neural networks for feature extraction with an ensemble-based Random Forest model for quality prediction. Unlike existing approaches, the proposed method incorporates a compact, face-aware representation through a novel face alignment metric, enabling more accurate quality estimation in face-centric streaming scenarios while maintaining low computational complexity. For experimental validation, we employed the artificially distorted LIVE and TID2013 benchmark datasets. The results demonstrate that our method outperforms existing state-of-the-art techniques, achieving a Pearson Correlation Coefficient (PCC) of approximately 0.942 and a Spearman Rank Order Correlation Coefficient (SROCC) of approximately 0.931 when compared with human subjective scores. Moreover, the proposed method achieves a processing time reduction from 4.8 ms to 1.8 ms, enhancing overall computational efficiency.

ABSTRACT Context‐awareness is increasingly recognised as a fundamental principle in the development of ubiquitous computing and ambient intelligence. By leveraging contextual data about users and their environments, systems can gain a deeper understanding of the evolving user situation. This empowers them to dynamically adapt their operations, leading to optimised resource utilisation, enhanced decision‐making, and ultimately, greater user satisfaction. However, a critical challenge lies in effectively representing user situations with a high degree of expressiveness. While ontology‐based data models have emerged as a promising approach due to their ability to handle the inherent heterogeneity of context information, existing ontologies have limitations in terms of information coverage, data heterogeneity and uncertainties consideration, and reusability across various application domains. This paper addresses these limitations by proposing uCSN, an ontology that builds upon and extends the Data Privacy Vocabulary (DPV), Semantic Sensor Network (SSN) and W3C Uncertainty ontologies, to provide a rich and expressive vocabulary for representing diverse user situations. We evaluate uCSN based on its consistency, accuracy, clarity and performance.

The Internet of Things (IoT) has connected the physical and digital worlds, resulting in a pervasive and self-organized network. Yet, due to the configurable model's behavioral constraints, ambiguity emerges during the design stage while selecting the most relevant composition plan. The constraints placed on the composition plan model restrict its behavior, leading to a single desired composition plan while eliminating the undesired ones. In this paper, we offer an entropy-based uncertainty metric to assess the predictability and efficiency of IoT object composition plans. Our method assesses the degree of uncertainty associated with a specific composition plan and provides insights into optimizing the performance of the IoT system.

Abstract Artificial Intelligence encompasses a range of technologies that replicate human-like cognitive abilities through computer systems, enabling the execution of tasks associated with intelligent beings. A prominent way to achieve this is machine learning (ML), which optimizes system performance by employing learning algorithms to create models based on data and its inherent patterns. Today, a multitude of ML models exist having diverse characteristics, including the algorithm type, training dataset, and resultant performance. Such diversity complicates the selection of an appropriate model for a specific use case, answering user demands. This paper presents an approach for ML models retrieval based on the matching between user inputs and ML models criteria, all described in a semantic ML ontology named SML model (Semantic Machine Learning model), which facilitates the process of ML models selection. Our approach is based on similarities measures that we tested and experimented to score the ML models and retrieve the ones matching, at best, user inputs.

The Web of Things (WoT) connects physical devices to the web using standard protocols. However, uncertainty in WoT service compositions may lead to critical problems in real- world applications. For example, in a smart hotel system, a user wishing to control the room temperature based on data from multiple sensors may get incorrect service compositions when using inaccurate or incomplete data, which can be dangerous in safety-critical situations. To address this issue, we propose a probabilistic approach that represents uncertain WoT services using Thing Description (TD) by including Quality of Thing (QoT) properties, interactions, and behaviors. Our approach computes the uncertainty of each node used in the composition process and proposes, using a WoT probabilistic algebra, an HTTP GET method that takes into account uncertain input and calculates the confidence degree of outputs when invoking WoT services. Our approach can improve the reliability of WoT services in uncertain environments and can be applied in various domains to create more robust and safer applications.

Data corpus tend to be heterogeneous presenting a significant challenge in extracting meaningful knowledge from, especially with the rapid growth of digital data. Traditional approaches lack when it comes to handling enormous volumes of unstructured data existing across various sources. Knowledge Graphs, becoming a more and more trendy topic, offer advanced modelization that can help cover such gap. Knowingly, data labeled graph and RDF triplestores are data management approaches that are built on modeling, storing, and querying graph-like data. Despite this fundamental idea, each have unique characteristics that hamper database interoperability. While some methods exist to convert databases to RDF graph or to property graphs and vice versa, they still lack consistency and solid formal foundation. This paper describes Novel Approach for Extracting Summarized RDF Graph from Heterogeneous Corpus.

The Internet’s vast amount of content has led to the creation of recommendation systems that help users find what they need. Among these systems, there is a growing field known as expert recommendation systems. These systems aim to identify highly knowledgeable individuals in specific topics by analyzing their activities and the content associated with them. When a user enters a topic or query, the system generates a ranked list of people who are experts in that area.In this context, we present in this paper a new approach for automatically experts recommendation based on a hybrid machine learning approach.

The continuous disclosure of location information in the context of Mobile CrowdSourcing (MCS) raises privacy concerns due to the potential correlation among different location reports. Existing approaches primarily focus on protecting the privacy of individual location data points without considering the risks posed by tracking attacks resulting from the ongoing disclosure of workers’ locations to MCS service providers. Furthermore, these approaches fail to address the potential threats arising from continuously disclosing location information during the execution of various sensing tasks. To address these issues, it becomes essential to maintain a comprehensive historical record of workers who have performed diverse sensing tasks and leverage this historical information to accurately quantify location-privacy disclosure. In this paper, we propose a novel approach that integrates blockchain technology to enhance privacy protection in MCS. We investigate how an attacker could infer the true location by exploiting noisy location data shared on the blockchain, along with their basic knowledge of users and frequently visited places. By employing prediction techniques, we aim to mitigate the potential risks associated with continuous location disclosure. Our approach offers improved privacy management, enhances MCS efficiency, and maintains a balance between privacy preservation and task fulfillment.

Abstract In this article, we describe an imbalanced regression method for making predictions over imbalanced data streams. We present MORSTS (Multiple Output Regression for Streaming Time Series), an online ensemble regressors devoted to non‐stationary and imbalanced data streams. MORSTS relies on several multiple output regressor submodels, adopts a cost sensitive weighting technique for dealing with imbalanced datasets, and handles overfitting by means of the K‐fold cross validation. For assessment purposes, experiments have been conducted on known real datasets and compared with known base regression techniques.

The rise of the energy impact of software systems requires the need to optimize and reduce their energy consumption. One area often neglected is the important role played by users to drive energy reductions. In this paper, we aim to reduce the energy impact of software by pushing end users to change their software usage behavior, through raising awareness and providing software green feedback. We present a comprehensive and detailed field study of the impact of green feedback on software usage by end users, and the efficiency of green feedback on software behavioral change, using a distributed architecture aimed at providing accurate green feedback in real time. We find that green feedback helps in raising awareness about software energy, and on the willingness of users to apply energy-efficient changes. However, we also find that users lack the knowledge and tools to properly adopt lasting and energy-effective behavioral changes.

The Web of Things (WoT) describes a set of standards by the World Wide Web Consortium (W3C) for the interoperability of different Internet of Things (IoT) platforms, and different application domains. Thus, it guarantees not only device-to-device interactions, but also, application-to-application communications, despite their platform heterogeneity. To identify and use provided services (also called resources) that are exposed by either the devices or the applications connected to a Web environment, describing them using an open, shared and dynamic knowledge representation is required, allowing them to interoperate on both syntactic and semantic levels. In this paper, we propose WoR, a Web of Resources ontology that provides a modular and a common vocabulary to describe Web resources. WoR can: (1) ease the discovery, the selection, and the composition of different kind of resources (exposed by connected Web devices or Web applications), (2) provide reasoning means to discover new information, and (3) allow future extensibility and adaptation to new domains needs. Experiments were made to evaluate our proposed WoR ontology, showing promising results on the effectiveness and the performance levels.

Smart connected environments as well as digital contents are more and more present in our daily life. The former monitors various data produced by sensors, while the latter contains valuable additional information (e.g., technical data sheets, maintenance reports, employee register). When an event occurs, users generally want to figure out why this event happened. Unfortunately, most information systems in connected environments do not combine sensor network data with document corpora. Consequently, users have to look for an event explanation by querying both complementary sources with different systems, which is indeed very tedious, time consuming and requires a huge compilation effort. In this paper, we present ISEEapp1, a prototype for event explanation in smart connected environments. The functionalities of ISEEapp are illustrated and the results of a user interface evaluation are presented.

Summary Recently, several healthcare organizations use decision‐making systems based on electronic health record (EHR) data in order to guarantee patient's safety and improve the quality of healthcare. In essence, the evolutions of Internet of Things (IoT) technologies have been of great help for implementing an integrated and interoperable decision‐making system based on EHR and medical devices (MDs). Those IoT‐based systems allow Clinicians collecting real‐time health data and provide accurate patient's monitoring. Nevertheless, several studies have shown that it is hard to improve the quality of healthcare using the current EHR IoT‐based systems since they do not allow to easily express clinician needs. Interactive visualization tools have been proposed to improve the efficacy and utility of these EHR based systems. However, there is no framework that provides a visual summary of patient data to clinician for planning specific clinical tasks, subsequently evaluating clinician responses, visually exploring EHR data and MDs data, gaining insights, supporting dynamic coordination processes care, and forming and validating hypotheses and risks. This article addresses this problem and introduces SIMCard, an aggregation‐based connected EHR visualization framework for patient monitoring, interpreting and predicting with MDs. The proposed framework aims to synthesize patient's clinical data into a single aggregating model for both EHR and MD conforming to health standard and terminologies. It also allows to link the aggregating model to the relevant medical knowledge in order to provide a connected and dynamic care and preventive plan. Last but not least, it provides an aggregated visualization model capable of displaying graphically a patient's personal data from databases, healthcare devices and sensors to reduce cognitive barriers related to the complexity of medical information and interpretation of health data. To demonstrate the refinement and design of our system and to observe user's actual practice of visualizing and analyzing real‐world dataset, we evaluated our system and compare to existing ones.

Summary To allow better communications between computers and people, ontologies have been adopted in several application domains (web, medicine, industry, etc.). Ontology building exhibits a structural and logical complexity. To the end of making high quality domain ontologies, effective and usable methodologies are needed to facilitate their building process. In this article, we propose to extend the classical methods of ontology construction to design semantically richer ontologies. The objective of this article is to study the relevance of the latent Dirichlet allocation model that generates probabilistic topic models for each enrichment proposal by adopting a domain independent core ontology model. The fitted model can be used to estimate the similarity between documents as well as between a set of specified words/terms using an additional layer of latent variables which are referred to as topics. Experiments were conducted to measure the quality of our proposal against other solutions. Obtained results discussed here are satisfactory.

Connected environments are infrastructures providing valuable data and services for applications in various domains, e.g., energy, transportation, environmental domains, to cite a few. Indeed, the resources of the connected environment and their sensed observations should be available upon requests by authorised users or machines from any location at any time. To this end, efficient indexing and discovery mechanisms are required to swiftly locate and retrieve relevant resources/observations that fulfil the query requirements. In this respect, we introduce here a new scalable indexing and discovery scheme in large scale connected environments. One of the main thrusts of the proposed scheme is its fully decentralised architecture that enables to obtain a higher scalability. In addition, a formal model is provided for describing the connected environment resources, their observations as well as their sensing zones. Interestingly enough, our scheme relies on geospatial indexing techniques allowing to reduce the search space and then to swiftly locate the target resource/observations. Furthermore, a resource ranking mechanism is provided to consider the environment constraints and the resources capabilities. The performed experiments show the effectiveness and the efficiency of the introduced scheme compared to a baseline approach from the literature.

Ontology Learning is a process of (semi)automatically creating, maintaining, and transferring various forms of information into an ontology with minimum human intervention to guarantee a better knowledge representation and sharing. In recent years, the research on automating financial data modeling has become a hot topic among researchers because of the exponential increase of the number of financial documents and the heterogeneous of financial data [19, 20]. So, we highlight the emergence of new computational tools and methods to deal with the automatic modeling and exploration of large financial corpus. That's why, we propose here a solution named Norms2Onto which is a semi-automatic ontology construction method based on machine learning algorithms to facilitate the reading and ease updating of financial data and to guarantee their understanding. Experiments have been conducted to measure the effectiveness of our solution compared to a manual classification made by an domain expert. The results show the superiority of our approach.

In recent years, the research on Ontology Learning has become a hot topic among researchers because of the exponential increase of the number of documents and textual data not only on the web but also in digital libraries. This has participated to the emergence of new computational tools and methods to deal with the automatic organization, representation, retrieval and exploration of large corpus in order to have a good way of organizing and managing huge volumes of data. LDA-based approaches have proven to provide the best result [18][16] [4]. However, they suffers to several limitations related to concept and relation extraction, as well as coping with the corpus evolution. In order to cope with these problems, we propose here a new solution named Learn2Construct which is an automatic ontology construction method based on topic modeling. Experiments have been conducted to measure the effectiveness of our solution and compare it to existing ones. The results obtained are more than satisfactory.

Many efforts have been deployed by the IR community to extend free-text query processing toward semi-structured XML search. Most methods rely on the concept of Lowest Comment Ancestor (LCA) between two or multiple structural nodes to identify the most specific XML elements containing query keywords posted by the user. Yet, few of the existing approaches consider XML semantics, and the methods that process semantics generally rely on computationally expensive word sense disambiguation (WSD) techniques, or apply semantic analysis in one stage only: performing query relaxation/refinement over the bag of words retrieval model, to reduce processing time. In this paper, we describe the building blocks of a new approach for XML keyword search aiming to solve the limitations mentioned above. Our solution first transforms the XML document collection (offline) and the keyword query (on-the-fly) into meaningful semantic representations using context-based and global disambiguation methods, specially designed to allow almost linear computation efficiency. Consequently, the semantically augmented XML data tree is processed for structural node clustering, based on semantic query concepts (i.e., key-concepts), in order to identify and rank candidate answer sub-trees containing related occurrences of query key-concepts. Preliminary experiments highlight the quality and potential of our approach.

In today’s highly connected cyber-physical environments, users are becoming more and more concerned about their privacy and ask for more involvement in the control of their data. However, achieving effective involvement of users requires improving their privacy decision-making. This can be achieved by: (i) raising their awareness regarding the direct and indirect privacy risks they accept to take when sharing data with consumers; (ii) helping them in optimizing their privacy protection decisions to meet their privacy requirements while maximizing data utility. In this article, we address the second goal by proposing a user-centric multi-objective approach for context-aware privacy management in connected environments, denoted δ- Risk . Our approach features a new privacy risk quantification model to dynamically calculate and select the best protection strategies for the user based on her preferences and contexts. Computed strategies are optimal in that they seek to closely satisfy user requirements and preferences while maximizing data utility and minimizing the cost of protection. We implemented our proposed approach and evaluated its performance and effectiveness in various scenarios. The results show that δ- Risk delivers scalability and low-complexity in time and space. Besides, it handles privacy reasoning in real-time, making it able to support the user in various contexts, including ephemeral ones. It also provides the user with at least one best strategy per context.

In this article, we present a privacy-preserving technique for user-centric multi-release graphs. Our technique consists of sequentially releasing anonymized versions of these graphs under Blowfish Privacy. To do so, we introduce a graph model that is augmented with a time dimension and sampled at discrete time steps. We show that the direct application of state-of-the-art privacy-preserving Differential Private techniques is weak against background knowledge attacker models. We present different scenarios where randomizing separate releases independently is vulnerable to correlation attacks. Our method is inspired by Differential Privacy (DP) and its extension Blowfish Privacy (BP). To validate it, we show its effectiveness as well as its utility by experimental simulations.

International audience

Since the last decade, the emergence of affordable smart devices in web-connected environments (e.g., smart cities and buildings) has been a critical step towards improving user comfort. In this respect, notifying end-users about different essential events in their environment is of significant interest. Thus, the design of an efficient data dissemination strategy is of paramount importance. The thriving challenge would be to inform interested devices about an event and to avoid as far as possible the network collision problem. To overcome these limitations, we introduce a new generic and adaptive data Dissemination Strategy in Connected Environments (DSCE). Our strategy’s main thrust is reaching a high delivery ratio as well as a low network overhead by sending messages only to the interested devices. To do so, we rely on a semantic-based approach to define device interests and to identify salient messages. Moreover, to cope with the broadcast storm problem and to organize the scheduling of data dissemination, we provide a new waiting time measure for each device before rebroadcasting any received message. The performed experiments show that our strategy outperforms its competitors in terms of effectiveness and efficiency.

Over the last decade, the number of research and development projects on sensor network technology has grown exponentially. Events detection is among these research fields, it allows the monitoring of the environment. To build an interpretation to these events, the combination of sensor network and document corpus data is essential since document corpus provide significant amounts of important and valuable information (e.g., technical data sheets, maintenance reports, customer sheets). However, most information systems in connected environments do not support the interconnection of sensor network and document corpus data, hence, user has to look for an explanation by himself through multiple queries on both data sources which is indeed very tedious, time consuming and requires a huge compilation effort. In this paper, we show that recent researches on 5W1H question-answering ("What? Who? Where? When? Why? How?") are an interesting issue to facilitate tunnelling through heterogeneous data sources (sensor networks and document corpus) and the identification of relevant data for the purpose of explaining an event. Consequently, we propose ISEE (an Information System for Event Explanation), a framework for event interpretation based on (i) the semantic representation of a heterogeneous information system, (ii) the cross-analysis of both sensor network and document corpus data and (iii) 5W1H question-answering techniques.

The Latent Dirichlet Allocation (LDA) model [18] was originally developed and utilised for document modeling and topic extraction in Information Retrieval. To design high quality domain ontologies, effective and usable methodologies are needed to facilitate their building process. In this paper, we propose a new approach for semi-automatic ontology enriching from textual corpus based on LDA model. In our approach, LDA is adopted to provide efficient dimension reduction, able to capture semantic relationships between word-topic and topic-document in terms of probability distributions with minimum human intervention. We conducted several experiments with different model parameters and the corresponding behavior of the enriching technique was evaluated by domain experts. We also compared the results of our method with two existing learning methods using the same dataset. The study showed that our method outperforms the other methods in terms of recall and precision measures.

Recently, RESTful services, designed as resources, have seen their popularity rising and have shown their potential in composing reliable Web-scale environments (Web applications, Web platforms, Web of Things (WoT), etc.). However, discovering the necessary resources for a composition is becoming more challenging. This is due to 1) the growing number of published Web-based resources, and 2) the highly dynamic nature of the WoT environment, in which smart devices, connected to the Web platform, are exposed as resources. In this paper, we propose an automatic resource discovery, applicable in hybrid Web-based environments providing static linked resources always available on the Web, and connecting dynamic resources that can be connected to and removed from the Web at different time periods. The solution presents an indexing schema that makes resource discovery process fast, especially in large-scale environments. Experiments were conducted in different environment setups to test the effective performance of our approach.

Recent advances in sensor technology and information processing have allowed connected environments to impact various application domains. In order to detect events in these environments, existing works rely on the sensed data. However, these works are not re-usable since they statically define the targeted events (i.e., the definitions are hard to modify when needed). Here, we present a generic framework for event detection composed of (i) a representation of the environment; (ii) an event detection mechanism; and (iii) an Event Query Language (EQL) for user/framework interaction. This paper focuses on detailing the EQL which allows the definition of the data model components, handles instances of each component, protects the security/privacy of data/users, and defines/detects events. We also propose a query optimizer in order to handle the dynamicity of the environment and spatial/temporal constraints. We finally illustrate the EQL and conclude the paper with some future works.

Semantic web techniques (e.g., ontologies) have been recently adopted for sensor network modeling. However, existing works do not fully address these challenges: (i) representing different sensor types (e.g., mobile/static sensors) to enrich the network with different data and ensure better coverage; (ii) representing a variety of platforms (e.g., environments, devices) for sensor deployment, thus, integrating new components (e.g., mobile phones); (iii) representing the diverse data (scalar/multimedia) needed for various applications (e.g., event detection); and (iv) proposing a generic model to allow re-usability in various application domains. In this paper, we propose HSSN, an ontology that extends the Semantic Sensor Network (SSN) ontology which is already re-usable and considers various platforms. We extend the representation of sensors, sensed data, and deployment environments to cope with these challenges. We evaluate the consistency, accuracy, clarity, and performance of HSSN.

Nowadays, social media runs a significant portion of people's daily lives. Millions of people use social media applications to share photos. The huge volume of images shared on social media presents serious challenges and requires large computational infrastructure to ensure successful data processing. However, image gets distorted somehow during the processing, transmission, sharing or from a combination of many factors. So, there is a need to guarantee an acceptable delivery content, especially for image processing applications. In this paper, we present a framework developed to process a large amount of images in real-time while estimating the image quality. Our quality evaluation is measured based on two methods: Perceptual Coherence Measure and Structural Similarity Index. A set of experiments is conducted to evaluate our proposed approach.

The Semantic Web and the Linked Open Data (LOD) initiatives promote the integration and combination of RDF data on the Web.In some cases, data need to be analyzed and protected before publication in order to avoid the disclosure of sensitive information.However, existing RDF techniques do not ensure that sensitive information cannot be discovered since all RDF resources are linked in the Semantic Web and the combination of different datasets could produce or disclose unexpected sensitive information.In this context, we propose a framework, called RiAiR, which reduces the complexity of the RDF structure in order to decrease the interaction of the expert user for the classification of RDF data into identifiers, quasi-identifiers, etc.An intersection process suggests disclosure sources that can compromise the data.Moreover, by a generalization method, we decrease the connections among resources to comply with the main objectives of integration and combination of the Semantic Web.Results show a viability and high performance for a scenario where heterogeneous and linked datasets are present.

The ultimate goal of a Traffic Information System (TIS) consists in properly informing vehicles about road traffic conditions in order to reduce traffic jams and consequently CO2 emission while increasing the user comfort. Therefore, the design of an efficient aggregation protocol that combines correlated traffic information like location, speed and direction known as Floating Car Data (FCD) is of paramount importance. In this paper, we introduce a new TIS data aggregation protocol called Smart Directional Data Aggregation (SDDA) able to decrease the network overload while obtaining high accurate information on traffic conditions for large road sections. To this end, we introduce three levels of messages filtering: (i) filtering all FCD messages before the aggregation process based on vehicle directions and road speed limitations, (ii) integrating a suppression technique in the phase of information gathering in order to eliminate the duplicate data, and (iii) aggregating the filtered FCD data and then disseminating it to other vehicles. The performed experiments show that the SDDA outperforms existing approaches in terms of effectiveness and efficiency.

In the context of RDF document matching/integration, the datatype information, which is related to literal objects, is an important aspect to be analyzed in order to better determine similar RDF documents. In this paper, we present an R DF D atatype in F erring F ramework, called RDF-F, which provides two independent datatype inference processes: 1) a four-step process consisting of (i) a predicate information analysis (i.e., deduce the datatype from existing range property), (ii) an analysis of the object value itself by a pattern-matching process (i.e., recognize the object lexical space), (iii) a semantic analysis of the predicate name and its context, and (iv) generalization of Numeric and Binary datatypes to ensure the integration; and 2) a non-ambiguous lexical-space-matching process, where literal values are inferred by the modification of their representation, following new lexical spaces. We evaluated the performance and the accuracy of both processes with datasets from DBpedia. Results show that the execution time of both indicators is linear and their accuracy can increase up to 97.10 and 99.30%, respectively.

A Digital Ecosystem ($DE$) is a collaborative environment, consisting of a number of heterogeneous components collaborating together on the basis of mutual interests and benefits. A MicroGrid ($MG$) is an example of the$DE$, since it is a distributed power system consisting of a number of heterogeneous components (energy sources, electrical loads, and energy storage systems) having direct/indirect impacts on each other and consequently on the entire environment. In order to provide an appropriate collaborative model, it is essential to conceive a dedicated clustering algorithm aiming at gathering the components that have mutual benefits. To do so, we propose in this paper a$DE$cooperative model for$MG$management, that consists of two main modules: 1) theAlliances Builderand 2) theSeller2Buyer matcher. TheAlliances Builderprovides an appropriate clustering algorithm, aiming at gathering all the$DE$heterogeneous components, having similar needs and preferences. Once done, theSeller2Buyer matcheris applied inside each cluster and between clusters, targeting a better collaboration inside the$MG$. Conducted simulations showed that the proposed algorithms yield significant results.

Multimedia Sensor Networks (MSNs) have gained increasing attention in recent years from both academic and industrial sectors. Unlike scalar sensor networks, the data collected from MSNs are enriched with multimedia data which can be used for defining and detecting complex and more application-meaningful events. However, to do so, there are several processing tasks need to be executed such as multimedia data decoding, translating semantic information from multimedia data, and integrating multimedia data from several sensors. Combining these tasks into one single generic framework so to process and detect complex events in MSNs is of great interest. However, developing such a framework is challenging due to the infrastructure of MSNs (which includes heterogeneous sensors) and types of multimedia data (which are diverse). Also, events in MSNs are needed to be detected in a near real-time manner. In this study, we propose an ontology-based framework to support complex event modeling and detecting in MSNs. Our framework helps users model MSNs infrastructure, complex events, and data collected from MSNs. It is also able of translating semantic formation, detecting, and reporting the events in a near real-time manner. Our framework is validated by means of prototyping and simulation. The results show that it can detect complex multimedia events in a high-work load scenario with average detection latency for less than 625 milliseconds.

Multi-Agent Systems (MASs) have received much attention in recent years because of their advantages on modeling complex distributed systems, such Digital Ecosystems (DESs). Many existing modeling languages that support the design of such systems are based on ontologies to assist the representation of agents knowledge. However, in the context of DESs, there is still a need for more general conceptual models to represent the specific characteristics of DESs in terms of win-win interaction, engagement, equilibrium, and self-organization. Then, concepts such behavior, roles, rules, and environment are needed. This paper describes an ontology-based approach by proposing MAS2DES-Onto, as the conceptual model, which considers the essential static and dynamic aspects of MASs by a clear representation of their concepts and relationships to support the design and development of DESs. To validate and conduct experimental tests, we integrate MAS2DES-Onto into a framework to automatically generate MAS-based DESs. Results show the efficiency and effectiveness of our approach.

A MicroGrid (MG) is a distributed power system consisting of a number of heterogeneous components having direct/indirect impacts on each other. In order to provide an appropriate collaboration (from several perspectives) between components, we propose a ”Digital Ecosystem Cooperative Framework” called DECF. In this paper, we present the clustering algorithm of DECF designed to build Alliances by gathering all the DE heterogeneous components having similar needs and preferences. Conducted simulations showed that the proposed algorithms yield significant results.

The main objective of the VANET networks is to improve road safety as well as transport efficiency through the use of communications technology and the emergence of wireless devices at low cost. Thus, the design of an efficient dissemination protocol, that informs vehicles about interesting safety and nonsafety events, is of paramount importance. The thriving challenge would be to maximize the delivery ratio by avoiding as far as possible the broadcast storm problem. A scrutiny of the literature wealthy number of approaches highlights that all of them fail to fulfill with the "sine qua non" requirements that we introduce. In this paper and to palliate this shortage, we introduce a new infrastructure-less geocast approach, called DPMS (data Dissemination Protocol based on Map Splitting). The main thrust of DPMS stands in reaching a high delivery ratio as well as a high geocast precision by sending messages only to vehicles in the Zone of Relevance (ZOR) with a minimum overhead cost. Carried out experiments showed that DPMS outperforms its competitors in terms of effectiveness and efficiency.

Nowadays, most of the information exchanged in Internet (particularly through social networks) is in the form of multimedia data. Each actor in Internet (individuals, enterprises, territorial communities, etc.) becomes producer and consumer of contents. Development and increasing demand for multimedia data offer producers/consumers more choices and opportunities for sharing in collaborative environments. Nevertheless, social network platforms and other traditional collaborative environments present limitations regarding content selection and collection, categorization and classification, aggregation, linking and interoperability, usage control and privacy. In this context, this ongoing work proposes a MultiMedia-Oriented Digital EcoSystem (MMDES) as a new environment of collaboration, sharing, and computing of multimedia content and applications ensuring the benefits of all its participants. In this paper, we formally define MMDES components, objectives and rules through a dedicated ontology. We also present here a P2P architectural design for MMDES.

International audience

A land cover map that represents the land surface of the earth is based primarily on analysis of remotely sensed images. However, the rate of concordance of existing land cover maps is not high. This lack of concordance results from a difference in classification methods and observation conditions of remotely sensed images. Also, conducting field surveys around the world is unrealistic. Therefore, we use ground level photographs from photo-sharing sites instead of field surveys. We propose a method to classify areas into land cover types using image features, geo-tags, titles and tags. Additionally, we create the land cover map using classified photographs. We evaluate the method using ground truth created manually. Results show that the accuracy of the proposed method is about 70 percent in New York.

In this demo paper, we briefly present our experimental prototype, entitled EVIAC (EValuation of VIbration Accessibility), allowing visually impaired users to access simple contour-based images using vibrating touch screen technology. We provide an overview of the system's main functionalities and discuss some experimental results.

Outsourcing social multimedia documents is a growing practice among several companies in a way to shift their business globally. It is a cost-effective process where those companies tend to gain more profits disregarding eventual privacy risks. In fact, several case studies have showed that adversaries are capable of identifying individuals, whose identities need to be kept private, using the content of their multimedia documents. In this paper, we propose de-linkability, a privacy-preserving constraint to bound the amount of information outsourced that can be used to re-identify the individual. We also provide a sanitizing MD*-algorithm to enforce de-linkability and present a set of experiments to demonstrate its efficiency.

The Web has known a very fast evolution: going from the Web 1.0, known as Web of Documents where users are merely consumers of static information, to the more dynamic Web 2.0, known as social or collaborative Web where users produce and consume information simultaneously, and entering the more sophisticated Web 3.0, known as the Semantic Web by giving information a well-defined meaning so that it becomes more easily accessible by human users and automated processes. Fostering service intelligence and atomicity (the ability of autonomous services to interact automatically), remains one of the most upcoming challenges of the Semantic Web. This promotes the dawn of a new era: the Intelligent Web (Web 4.0), known as the Internet of Things (IoT), an extension of the Semantic Web where (physical/software) objects and services autonomously interact in a multimedia virtual environment, provided with embedded communication capabilities, common semantics and addressing schemes, promoting the concept of Digital Web Ecosystems where every where (human and software) agents collaborate, interact, compete, and evolve autonomously in order to automatically solve complex and dynamic problems. This paper briefly describes the recent evolution of the Web providing an overview of the technological breakthroughs contributing to this evolution, covering: knowledge bases and semantic data description, XML-based data representation and manipulation technologies (i.e., RDF, RDFS, OWL, and SPARQL) as well as the main challenges toward achieving the Intelligent Web: connectivity, semantic heterogeneity, collective knowledge management, collective intelligence, as well as data sustainability and evolution. We also present some of the main application domains characterizing the Intelligent (Semantic) Web, from information retrieval and content analysis, to systems status monitoring and improving business life-cycle through ubiquitous computing.

In this paper, we present a novel unsupervised algorithm, called CLimate and rEmote sensing Association patteRns Miner, for mining association patterns on heterogeneous time series from climate and remote sensing data integrated in a remote sensing information system developed to improve the monitoring of sugar cane fields. The system, called RemoteAgri, consists of a large database of climate data and low-resolution remote sensing images, an image preprocessing module, a time series extraction module, and time series mining methods. The preprocessing module was projected to perform accurate geometric correction, what is a requirement particularly for land and agriculture applications of satellite images. The time series extraction is accomplished through a graphical interface that allows easy interaction and high flexibility to users. The time series mining method transforms series to symbolic representation in order to identify patterns in a multitemporal satellite images and associate them with patterns in other series within a temporal sliding window. The validation process was achieved with agroclimatic data and NOAA-AVHRR images of sugar cane fields. Results show a correlation between agroclimatic time series and vegetation index images. Rules generated by our new algorithm show the association patterns in different periods of time in each time series, pointing to a time delay between the occurrences of patterns in the series analyzed, corroborating what specialists usually forecast without having the burden of dealing with many data charts.

The web services (WS) technology provides a comprehensive solution for representing, discovering, and invoking services in a wide variety of environments, including Service Oriented Architectures (SOA ) and grid computing systems. At the core of WS technology lie a number of XML-based standards, such as the Simple Object Access Protocol (SOAP), that have successfully ensured WS extensibility, transparency, and interoperability. Nonetheless, there is an increasing demand to enhance WS performance, which is severely impaired by XML's verbosity. SOAP communications produce considerable network traffic, making them unfit for distributed, loosely coupled, and heterogeneous computing environments such as the open Internet. Also, they introduce higher latency and processing delays than other technologies, like Java RMI and CORBA. WS research has recently focused on SOAP performance enhancement. Many approaches build on the observation that SOAP message exchange usually involves highly similar messages (those created by the same implementation usually have the same structure, and those sent from a server to multiple clients tend to show similarities in structure and content). Similarity evaluation and differential encoding have thus emerged as SOAP performance enhancement techniques. The main idea is to identify the common parts of SOAP messages, to be processed only once, avoiding a large amount of overhead. Other approaches investigate nontraditional processor architectures, including micro- and macrolevel parallel processing solutions, so as to further increase the processing rates of SOAP/XML software toolkits. This survey paper provides a concise, yet comprehensive review of the research efforts aimed at SOAP performance enhancement. A unified view of the problem is provided, covering almost every phase of SOAP processing, ranging over message parsing, serialization, deserialization, compression, multicasting, security evaluation, and data/instruction-level processing.

SOAP has been widely adopted as a simple, robust and extensible XML-based protocol for the exchange of messages among web services. Unfortunately, SOAP communications have two major performance-related drawbacks: i) verbosity, related to XML, that leads to increased network traffic, and ii) high computational burden of XML parsing and processing, that leads to high latency. In this paper, we address these two issues and introduce a novel framework for Differential SOAP Multicasting (DSM). The main idea consists in identifying the common pattern and differences between SOAP messages, modeled as trees, so as to multicast similar messages together. Our method is based on the well known concept of Tree Edit Distance, built upon a novel filter-differencing architecture to reduce message aggregation time, identifying only those messages which are relevant (i.e., similar enough) for similarity evaluation. In addition, our technique exploits a dedicated differencing output format specifically designed to carry the minimum amount of diff information, in the multicast message, so as to minimize the multicast message size, and therefore reducing the network traffic. The battery of simulation experiments conducted to evaluate our approach shows the relevance of our method in comparison with traditional and dedicated multicasting techniques.

In this paper, we propose a novel technique for privacy preserving in multimedia databases. Our technique is based on a multimedia co-occurrence matrix and a tree augmented naive Bayesian classifier (TAN) to detect possible data associations making confidential multimedia objects at risk.

Large amounts of images from medical exams are being stored in databases, so developing retrieval techniques is an important research problem. Retrieval based on the image visual content is usually better than using textual descriptions, as they seldom gives every nuances that the user may be interested in. Content-based image retrieval employs the similarity among images for retrieval. However, similarity is evaluated using numeric methods, and they often orders the images by similarity in a way rather distinct from the user's intention. In this paper, we propose a technique to allow expressing the user's preference over attributes associated to the images, so similarity queries can be refined by preference rules. Experiments performed over a dataset with computed tomography lung images shows that correctly expressing the user's preferences, the similarity query precision can increase from an average of 60% up to close to 100%, when enough interesting images exists in the database.

Existing XML query algebras are not fully appropriate to retrieve RSS news items mainly due to three reasons: 1) RSS is text rich and its content is dependent on the wording and verbification of the author, thus semantic aware operators are needed; 2) news items are dynamic and consequently time oriented retrieval is needed; 3) a news item may evolve through time, or overlap with other news items and hence identifying relationships between items is also needed. In this paper, we aim to solve these issues by providing a dedicated RSS algebra based on semantic-aware operators that consider RSS characteristics. The provided operators are application domain specific and can be tuned according to the user preferences. We also provide a set of query rewriting and equivalence rules that would be used during query simplification and optimization. In addition and in order to validate our proposal, we present here our prototype that allows a user to formulate RSS query using our operators.

XML data flow has reached beyond the world of computer science and has spread to other areas such as data communication, e-commerce and instant messaging. Therefore, manipulating this data by non expert programmers is becoming imperative. On one hand, Mashups have emerged a few years ago, providing users with visual tools for web data manipulation but not necessarily XML specific. Mashups have been leaning towards functional composition but no formal languages have yet been defined. On the other hand, visual languages for XML have been emerging since the standardization of XML, and mostly relying on querying XML data for extraction or structure transformations. These languages are mainly based on existing textual XML languages, have limited expressiveness and do not provide non expert programmers with means to manipulate XML data. In this paper, we define a generic visual language called XCDL based on Colored Petri Nets allowing non expert programmers to compose manipulation operations. The language is adapted to XML, providing users with means to compose XML oriented operations. The language core syntax is presented here along with an implemented prototype based on it.

An efficient authoring tool would provide support for automatic temporal formatting and modeling of multimedia presentations. Automatic temporal formatting is a process of converting the given presentation specifications into a required temporal format. This paper presents an algorithm that can convert a temporal layout into a dynamic petri net (DPN) which can represent iterative and interactive presentation components effectively. The prototype of the authoring tool extracts the temporal layout from any given SMIL file representation and uses the proposed algorithm to automatically convert it into a DPN. The DPN generated automatically at compile-time helps the run-time components in effectively playing out the presentation and it can also be used to verify the presentation specifications for several properties.

International audience

In this paper, we describe our Multimedia Context based Security Engine (MCSE) which is a Java Based Prototype able to integrate multimedia context in order to enforce access control policies. The prototype provides supervised access to a database containing sensitive viral images.

No abstract available.

The rapid development of information systems has lead in many ways to the definition of advanced authorization and access control models. Recent models have considered context (such as time, location, age, etc.) as key issue to allow flexible and dynamic policy specification. However, these models are application-dependent, text-based, complex to manage, and insufficient to deny unauthorized access in several cases. Multimedia-based context (user surrounding snapshot, his moves and gesture, etc.) reveals interesting information which is considered of complimentary importance to textual-based context and should be considered in several scenarios while defining access control conditions. In this paper, we extend current models with Multimedia Context-Based Conditions for Access Control Models based on: flexible multimedia data description, complex and multi-criteria conditions (Mcc), and an uncertainty resolver able to reduce potential risk related to the use of multimedia data and similarity functions. Our approach is capable of enforcing current access control models and providing easy-to-use access control policies. Here we present a set of experimental tests that we have conducted to validate the proposed approach.

A visual query is based on pictorial representation of conceptual entities and operations. One of the most important features used in visual queries is the shape. Despite its intuitive writing, a shape-based visual query usually suffers of a complexity processing related to two major parameters: 1-the imprecise user request, 2-shapes may undergo several types of transformation. Several methods are provided in the literature to assist the user during query writing. In this paper, we present a new cooperative approach based on the shape neighborhood concept allowing the user to rewrite a shape-based visual query according to his preferences with high flexibility in terms of including (or excluding) only some shape transformations and of result sorting

Providing a fully functional multimedia DBMS (MMDBMS) becomes an emergency with the recent development of distributed environments. In this paper we address the impact of using watermarking techniques traditionally used to preserve the intellectual or industrial property (IIP) in MMDBMS. Through a multimedia content and metadata based representation model called M2, we particularly study: 1) how to watermark all components of a multimedia description, and not only its raw data 2) how watermarking can guarantee the mapping between multimedia objects and their descriptors, avoiding accidental or malevolent mismatch inside crucial documents 3) how to preserve data significance and semantics when altering data for watermarking purposes. We illustrate our approach by providing an example in the medical domain

This paper explores our solution aiming to provide efficient retrieval of medical imaging. Depending on the user, the same image can be described through different views. In essence, an image can be described on the basis of either low-level properties, such as texture or color; contextual data, such as date of acquisition or author; or semantic content, such as real-world objects and relations. Our approach consists of providing a multispaced description model capable of integrating different facets (or views) of the medical image. Visual retrieval solutions are recommended and are the most appropriated for noncomputer-science users. However, current visual languages suffer from several problems, especially ambiguities generated by the user and/or the system, and imprecision at different levels of image description. In this paper, we expose our solution and demonstrate how spatial precision of medical image content and ambiguities can be resolved. An implementation called Medical Image Management System (MIMS) has been realized to prove our proposition. A set of tests has been deployed to validate our prototype.

The need for systems that can store, represent and provide efficient retrieval facilities for images of particular interest is becoming very high in medicine. In this respect, a lot of work has been done to integrate image data in standard data processing environments. The two different approaches that are used for the representation of images are the meta-data and the content-based approaches. Users in medicine need queries that use both content-based and meta-data representations of images or salient objects. In this paper, we first present a global image data model that supports both meta-data and low-level descriptions of images and their salient objects. This allows us to make multi-criteria image retrieval (context-, semantic- and content-based queries). Then, we present an image data repository model that captures all the data described in the model and permits the integration of heterogeneous operations in a DBMS. In particular, content-based operations (content-based join and selection) in combination with traditional ones can be carried out using our model.

Since the last two decades, image database management has been practiced using different image representation methods. In the literature, images are represented using two paradigms: the metadata-based and the content-based representations. Image retrieval using the metadata is done using the traditional database operations. However, image retrieval by its low-level features requires similarity-based operations. Practice has shown that both types of operations are needed for an efficient image database management system. Particularly in medical image databases, such a mixed form of retrieval is very important. We first present a global image data model that supports both metadata and low-level descriptions of images. We illustrate our work with real examples in the medical domain. Then, using an original image data repository model, we show how relational and similarity-based operations can be integrated. Both image and salient object are considered in our model. A prototype called MIMS (medical image management system) has been realized to validate the main aspects of our approach.

Images are usually retrieved on the basis of either low-level features, such as texture or color; context, such as date of acquisition or author; or high-level features, such as real-world objects and relations. We propose a data model for medical image retrieval. Our model integrates several types of image features and projects them to a set of spaces. Each image feature is then represented by several dimensions. Furthermore, the medical image has an evolutionary content that changes over time and according to several events. Our proposition allows medical users to easily describe the content evolution of many images. To achieve that, a spatial knowledge model is integrated into our approach that verifies the coherence between feature spaces and assists both system and user whenever precision is needed or specific ambiguity occurs. We briefly expose our Web-accessible implementation called MIMS (Medical Image Management System) developed with Java.

Medical Imaging suffers from different problems. This paper explores the authors' solution that aims to provide efficient retrieval of medical imaging. Depending on the user, the same image can be described through different views. In essence, an image can be described on the basis of either low-level properties, such as texture or color; context, such as date of acquisition or author; or semantic content, such as real-world objects and relations. The authors' approach consists of providing a global description solution capable of integrating different dimensions (or views) of a medical image. The description problem of medical images during both storage and retrieval processes is studied. Few proposed solutions take into consideration the heterogeneity of user competence (physician, researcher, student, etc.) and the necessity of a high expressive power for medical imaging description. For example, spatial content in terms of relationships in surgical or radiation therapy of brain tumors is very decisive because the location of a tumor has profound implications on a therapeutic decision. Visual solutions are recommended and are the most appropriated for non computer-scientist users. However, current visual languages suffer from several problems, especially ambiguities generated by the user and/or the system at different levels of image description, imprecision and no respect of the integrity of spatial relations. This framework exposes the authors' solution showing how this problematic can be resolved. An implementation has been realized to prove their proposition.