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.

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.

Due to the enormous volume of data on the web, it is hard for the user to retrieve effective and useful information within the right time. Thus, it has become a need to generate a brief summary from a large amount of textual data according to the user profile. In this context, text summarization is used to identify important information within text documents. It aims to generate shorter versions of the source text, by including only the relevant and salient information. In recent years, the research on summarization techniques based on topic modeling techniques has become a hot topic among researchers thanks to their ability to classify, understand a large text corpora and extract important topics on the text. However, existing studies do not provide the support of personalization when generating summaries because they need to know not only which documents are most helpful to the users, but also which topics and keywords are more or less related to the user' interests. Thus, existing studies lack of the support of adaptive user modeling for user applications in the emerging areas of automatic summarization, topic modeling and visualization. In this context, we propose a new approach of automated text summarization based on topic modeling techniques and taking into account the user's profile which helps to semantically extract relevant topics of textual documents, summarizing information according to the user' topics interests and finally visualize them through a hyper-graph Experiments have been conducted to measure the effectiveness of our solution compared to existing summarizing approaches based on text content. The results show the superiority of our approach.

Summary In the era of Internet Technology (IT), uncertainty management is a challenge in many fields. These include e‐commerce, social and sensor networks, scientific data production and mining, object tracking, data integration, geo‐located services, and recently Internet and Web of Things. Due to the uncertain data published on the web, web resources are diverse. Hence, identical resources could be available from heterogeneous platforms and heterogeneous resources could represent the same objects. These resources are hugely heterogeneous, conflict, inconsistent, or have incompatible formats. This uncertainty is inherently related to many facts, such as information extraction and integration. Hence, with web resources proliferation on the web, referencing through the uncertain web has become increasingly difficult. The traditional techniques used for the classical web could not handle uncertain navigation. Generally, it's implicitly represented, decided randomly, or even neglected. Harnessing these uncertain resources to their full potential in order to handle the uncertain navigation, raises major challenges that relate to each phase of their life cycle: creation, representation, and navigation. In this article, we establish a probabilistic approach to model and interpret uncertain web resources. We present operators to compute response uncertainty. Finally, we create algorithms in order to validate resources and achieve uncertain hypertext navigation.

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.

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.

International audience

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.

Traditional access control models aim to prevent data leakage via direct accesses.A direct access occurs when a requester poses his query directly on the desired object.However, these models fail to protect sensitive data from being accessed with inference channels.An inference channel is produced by the combination of the legitimate response which a user receives from the system and metadata.Detecting and removing inference in database systems guarantee a highquality design in terms of data secrecy and privacy.Parting from the fact that data distribution exacerbates inference problem, we give in this paper a survey of the current and emerging research on the inference problem in both centralized and distributed database systems and highlighting research directions in this field.

A gradual evolution of the Internet, allowing it to extend beyond the electronic world to the physical world by interconnecting various devices and sensors that can communicate with each other and share data. The field of healthcare monitoring Systems have experienced significant changes using this promising new technology, called Internet of Things (IoT). In this paper, we propose a new platform for health care, which we have called the 'Semantic Medical IoT platform'. This platform is designed primarily for the semantization of the Internet of things in the medical and healthcare field. It proposes solutions for the problems of the interoperability of medical devices, the integration of massive and heterogeneous data, and the personalized visualization of these data. The platform also provides various services, like: Multi-type functional communication service, the significant exploitation of the localization feature provided by the connected objects, the simplification of medical texts for the patients and the effective integration of the social media technology. The SM-IoT Platform also defines new contract-based security policies to ensure the confidentiality of patient's health information.

Today, numerous technological advances in electronic and information technologies are rapidly transforming our modern life. Industries are being completely transformed and health systems are not oblivious to these changes. All these new technologies are opening up a wide range of new opportunities and challenges for researchers, physicians and patients. The emergent paradigm of Internet of Things is one of the promising technologies introduced into the world of health care. In fact, IoT has been widely applied to interconnect available medical devices and sensors which allow patients to take control of their health condition in real time, also physicians to accurately remotely monitor the health of their patients. The ultimate goal of achieving high quality of healthcare practices depends on the ability to effectively integrate data incoming from heterogeneous sources, share the collected data while keeping their security and privacy, use powerful data analytics tools to extract useful information from these data, and the ability to have an expressive and personalized visualization. In this paper, we propose the SM-IoT platform, an IoT-based platform for intelligent and personalized healthcare, dedicated to patients, as well as caregivers. The aim of this platform is to improve the remote patient monitoring and promote healthcare services. SM-IoT platform is able to collect data from heterogeneous information sources, integrate them by using a flexible semantic web, store them in the cloud for further analysis, visualized these data with user-friendly interfaces and facilitate their sharing by taking into account their privacy aspect.

Distributed graph processing has become a very popular research topic recently, particularly in domains such as the analysis of social networks, web graphs and spatial networks. In this context, graph partitioning is an important task. Several partitioning algorithms have been proposed, such as DFEP, JABEJA and POWERGRAPH, but they are limited to static graphs only. In fact, they do not consider dynamic graphs in which vertices and edges are added and/or removed. In this paper, we propose a graph partitioning method for large dynamic graphs. We present an implementation of the proposed approach on top of the AKKA framework, and we experimentally show that our approach is efficient in the case of large dynamic graphs.