This work integrates Monadic Second-Order (MSO) logic into Spatio-Temporal Heterogeneous Hypergraphs (STHH) to advance Neuro-Symbolic AI.By bridging higher-ordered symbolic logic with neural computations, STHH offers a novel framework for knowledge representation and learning.Evaluations on a custom agricultural dataset show that the proposed STHH outperforms state-of-the-art hypergraph models across F1-score, accuracy, and AUC metrics.Despite challenges such as limited standardized datasets, this study underscores the potential of integrating higher-ordered symbolic logic into neural systems to achieve robust and interpretable AI.

Vehicular visible light communication (V VLC) systems, when combined with reconfigurable intelligent surfaces (RIS), present promising opportunities for improving communication reliability and efficiency in vehicle to vehicle (V2V) environments. Nevertheless, safeguarding these systems at the physical layer remains a critical challenge, particularly given their exposure to jamming threats. In this study, we investigate the physical layer security performance of RIS assisted V2V VLC systems under jamming scenarios, employing realistic V2V VLC channel models. We develop a methodology to examine the impact of various security strategies in mitigating the adverse effects of jamming. Our analysis examines key parameters including the signal to noise ratio SNR, the secure communication rate and the count of RIS units. Simulation results confirm that the proposed security systems significantly enhance the resilience of V2V VLC networks in the presence of jamming attacks. These results offer useful perspectives for the reliable design structure and deployment of RIS based V2V VLC systems in practical vehicular communication settings.

Urban areas globally face escalating challenges in traffic congestion, air pollution, and inefficient transportation systems. Traditional traffic management strategies are increasingly inadequate for handling the growing complexity of urban mobility. To address these issues, this paper presents the development of an ontology for Intelligent Transportation Systems (ITS) aimed at enhancing traffic management and safety through sustainable practices. The research problem focuses on the lack of comprehensive and standardized ontologies that integrate diverse data sources, support real-time decision-making, and incorporate sustainability considerations. Utilizing SUMO Eclipse for simulations and tools like Protégé and OwlReady2 for ontology development, the project integrates environmental considerations and vehicular communication protocols. The ontology’s structure encompasses five main concepts: Environment, Communication, System, WeatherState, and ValuePartition.

International audience

Visible light communication (VLC) is a rapidly growing wireless communication technology for the Internet of Things (IoT) that offers high data rates and low latency, making it ideal for massive connectivity. Efficient resource allocation is essential in VLC networks to minimize inter-symbol and cochannel interferences, which can greatly improve network performance and user satisfaction. This paper focuses on an indoor IoT-based VLC system that utilizes photodetectors (PDs) on users’ cell phones as receivers, with the goal of maximizing system performances and reducing power consumption by selectively activating some PDs while deactivating others. However, this objective presents a challenge due to the inherent non-convex nature of the multi-objective optimization problem, which cannot be solved by analytical means. To address this, we propose an enhanced Aquila optimization (EAO) scheme that improves upon the Aquila Optimizer (AO) by incorporating a fitness distance balance (FDB) function. We evaluate our proposed EAO in various scenarios under different settings, considering both capacity and fairness metrics. Through simulations, we demonstrate the effectiveness of our approach and its superiority over classical algorithms such as Aquila Optimizer (AO), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) in finding the optimal solution. Our results confirm that the proposed EAO algorithm can efficiently optimize the system capacity and ensure fairness among all users, providing a promising solution for indoor VLC systems.

In order to provide the agricultural industry with the infrastructure it needs to take advantage of advanced technology, such as big data, the cloud, and the internet of things (IoT); smart farming is a management concept that focuses on providing the infrastructure necessary to track, monitor, automate, and analyse operations. To represent the knowledge extracted from the primary data collected is of utmost importance. An agricultural ontology framework for smart agriculture systems is presented in this study. The knowledge graph is represented as a lattice to capture and perform reasoning on spatio-temporal agricultural data.

International audience

In this paper, we propose a probabilistic MLN-based model for spatial context disambiguation. This model serves as a solution for the problem of incomplete knowledge in High-level task planning. By applying the state of the art MLN probabilistic reasoning such as MCSAT, we determine the concept class of the current spatial context of the robot and contribute by combining semantic spatial relations with observed data at different timesteps. The inherent uncertainty of robot dynamic environments makes the proposed approach suitable to deal with partial observability and sensing limitations of robots. Simulation experiments and evaluation results are presented to validate our model.

The contribution of this paper is articulated around a new software design approach of autonomous control systems for connected vehicle platoons. Our control system is distributed and real-time based on object-oriented component-based method of design that brakes with the industrial traditions subject to cyclic OS-free approaches. We illustrate our design by relevant case studies of the longitudinal speed control widely studied in industrial and academic research around automotive platooning. Our software is mainly implemented using the Ada standard of programming (in particular the annexes D and E of real-time and distributed systems). The distribution in our software is managed by the versatile middleware PolyORB. The control scenarios and communication aspects covered by the case studies are animated by wheeled robot prototypes commanded by single-board ARM Cortex computers under real-time Linux kernels.

Automating functional size measurement (FSM) is important for organizations needing to measure a large number of projects within a short timeframe, provided, of course, that the results automatically generated are accurate, particularly when such measurement is based on an international measurement standard. A literature review has shown that very little work has been conducted on verifying measurement results produced by FSM automation. This paper presents a verification protocol designed to provide evidence of the accuracy of an automated FSM tool using the COSMIC ISO 19761 measurement standard. Also included are: an example of its use for the verification of an AUTOSAR-based FSM automation prototype tool developed at ESTACA, and a case study on the application of the proposed verification protocol on another prototype tool developed at Renault S.A.

With the development of applications called "intelligent", one of the challenges of research on multimodality in ambient intelligence environments is the elaboration of architectural solutions that respond and adapt to different types of constraints in the human robotic interaction.

People communicate with each other using different ways, such as words, gestures, etc. to give information about their status, emotions and intentions. But how may this information be described in a way that autonomous systems (e.g. Robots) can react with a human being in a given environment?

COSMIC-based functional size measurement (FSM) procedures should all produce the same measurement results, no matter what design decisions are made when modeling the functional requirements of the software to be measured. When the FSM procedure proposed in [14] is applied to different models of the same functional requirements, the measurement results vary. This problem is addressed, and a refined FSM procedure for real-time embedded software is proposed. The revised rules of this refined FSM procedure are based on an analysis of the issues detected in [14], and a solution to the variance issue is offered as a result.

To obtain the functional size of software and reduce measurement variance caused by the interpretations of individual measurers, a number of measurement procedureshave been designed based on measurement methods approved as international standards. To date, most of these procedures have targeted Management Information Systems software, while only a few were designed for real-time and reactiveembedded software. In this paper, we propose a functional size measurement (FSM) procedure for real-time embedded software, based on the COSMIC method, version 3.0.1 (ISO 19761), and with requirements documented using the Simulinkmodel. The design of this FSM procedure is based on the mapping of key concepts in both Simulink and COSMIC, and the identification of the mapping rules for extracting the information stored in the Simulink files that is required formeasurement. This procedure therefore provides the foundation for automating the measurement of software requirements documented using Simulink. The backgroundstudy for this procedure was conducted at Renault SAS using ECU (Electronic Control Unit) functional requirements expressed with the Simulink tool.

This paper demonstrates the design of a fault- tolerant ubiquitous MM computing system. Our ubiquitous multimodal multimedia (MM) computing system selects the appropriate media and modalities based on the user's context and user's profile. This paper demonstrates the design of a fault-tolerant ubiquitous MM computing system. The requirements analysis is undertaken by considering the quality attributes desired by different stakeholders. We use the attribute-driven design method in the requirement analysis and Architecture Tradeoffs Analysis Method in evaluating the system architecture. In this paper we conduct all tests and explain results through stochastic colored Petri Net specification, citing pre- and postcondition of each scenario.

Machine learning (ML) allows a machine to acquire basic and decision-making knowledge so that it can be delegated with tasks that are otherwise left to human for implementation. Incremental learning is a form of continuous ML wherein the knowledge acquisition can go on indefinitely for as long as there is some new knowledge to learn. In effect, a machine becomes smarter after gaining added knowledge from training. Our paper details the design of an incremental ML component of a ubiquitous multimodal multimedia computing system. Our system chooses the media and modalities that are appropriate for the user situation; the situation itself is based upon user's context, user's profile and the user's environment and hardware profile. The user context itself is dependent on the user's location, the noise level in the workplace and the presence or absence of other people in the vicinity, and the user's computing device. If applicable, the user's special need (i.e. handicap) is also taken into consideration in the media and modality selection. Incremental ML allows our system to become fault-tolerant, capable of finding replacement to a missing or defective component

In this paper, we present our work on a ubiquitous context-sensitive multimodal multimedia computing system that progressively acquires machine knowledge. This ubiquitous computing system supports an automatic selection of media and modalities deemed appropriate for the user's context and user's profile. The ability of the system to do so constitutes its acquired knowledge. The decision making for media/modality selection takes into account if the user has some special needs due to disability. The architecture of the system is designed to be pervasive and is conceived to resist failure. In case of one or more components being missing or found defective, the machine would resist failure by reconfiguring itself dynamically in the architectural level. It finds alternative replacement to the failed component using its acquired knowledge.

In this paper, we proposed a context-sensitive incremental learning paradigm of an ubiquitous multimodal multimedia computing system. An ubiquitous computing environment supports a busy and mobile user's need of being able to work on his task anytime and anywhere he wants. Along with user's data (his profile, task, and application registry) the machine-acquired intelligence needs to be transported as well in order that the user could continue working on an intelligent environment. Machine intelligence is acquired through incremental learning. In a context-sensitive environment that has a rich selection of modalities and media for data input and output, an intelligent computing system could determine the I/O devices appropriate for the user's setting after considering the user's location, the noise level in the environment, and the presence or absence of other people in the vicinity. Every new setting (pre-condition scenario) produces a new I/O devices configuration (post-condition scenario) suited for the setting; each new scenario knowledge gets stored onto knowledge database. Overtime, the machine would have enough knowledge to deal with whatever context scenario that comes up.

A design methodology for multimodal-controlled application has been developed using Wizard-of-Oz simulations as the principal mechanism for evaluating and getting input for dialogue design. This methodology may enable multimodal application developers to support dialogues that are optimal with respect to naturalness, especially on a pragmatic level, given the technical restrictions.

The paper deals with a neural network adaptive controller designed for constrained redundant robot manipulators. The controller has been determined using extended cartesian space to ensure minimum joint positions of the robot and to take into account mechanical constraints like joint limitations. The proposed approach guarantees a good minimization of any performance criterion, subject to either equality or inequality constraints while achieving the end-effector task. It verifies the repeatability property for closed or cyclic trajectories and avoids the computation of the inverse or pseudoinverse Jacobian extended matrix. Several neural networks are used to approximate separately the elements of the dynamical model of the robot manipulator written in cartesian space. Adaptation laws are derived for each network to ensure stability of the closed loop system. Simulations results demonstrate a good performance of the proposed controller.

We propose a new solution to the inverse kinematic problem of redundant robots subject to a set of criteria and constraints. First, a study of existing methods leads us to develop an on-line algorithm based on an adaptive neural network. This solution needs only a few iterations to converge, offers substantially better accuracy, verifies the repeatability propriety for the closed trajectory and avoids the computation of the inverse or pseudoinverse Jacobian matrix. Our approach guarantees a good minimization of any performance criterion subject to either equality or inequality constraints while achieving the end-effector task. Then, our method can solve the inverse kinematic problem of a redundant robot for it to follow a desired trajectory while avoiding moved or fixed obstacles.

In this paper, a penalty approach which deals with a constrained optimization to solve the inverse kinematic of redundant robot manipulators is considered. An optimization procedure using neural networks is formulated, it produces on-line position and velocity trajectories in joint space from position and orientation trajectories in Cartesian space. This new method offers substantially better accuracy and guarantees a good minimization of a performance function subject to joint limitations while achieving the end-effector task.