Internship M2 in Computer Science

FUTURE – Framework for Uncertainty-aware and Trustworthy Unified Reasoning in Enabled CPS

Abstract

Cyber-Physical Systems (CPS) are increasingly deployed in safety-critical settings such as manufacturing, robotics, autonomous infrastructures, and intelligent buildings [1]. Smart Building systems, in particular, rely on dense networks of heterogeneous sensors to monitor energy consumption, indoor environmental quality, and occupant comfort. These systems must operate under heterogeneous data sources, incomplete information,sensor unreliability, and unpredictable disturbances [2]. Traditional AI-based decision systems provide deterministic outputs, often without expressing confidence levels or handling conflicting evidence [3].

FUTURE project aims to develop an uncertainty-aware and trust-oriented decision-making framework combining uncertainty modelling, AI-based predictions, and Dempster–Shafer theory. By integrating belief-function reasoning with multi-source evidence fusion, FUTURE enhances the resilience of CPS decision pipelines.

A realistic CPS scenario specifically, a Smart Building environment will serve as the experimental validation context, leveraging sensor data to evaluate uncertainty, reliability, and evidence-based decision performance.

Keywords :

Artificial Intelligence, Belief Functions Theory, Cyber-Physical Systems, Smart building, Data Fusion, Heterogenuous Sources.

Research work

Scientific Fields :

Artificial Intelligence, Trustworthy AI, Uncertainty Modelling, System Engineering, Information Fusion.

Work Program/Objectives:

The work program of FUTURE is structured around eight major tasks, each corresponding to a key component of the proposed uncertainty-aware decision-making framework. These tasks form a coherent pipeline, from data importation to experimental validation, and are described below.

1. Data Importation – As a first step, we identify a publicly accessible and representative dataset that reflects real-world smart-building sensor deployments. Such datasets must capture multiple sensing modalities relevant to CPS applications, enabling the evaluation of uncertainty-aware learning, reliability assessment, and evidence fusion in realistic operating conditions. For the FUTURE project, the selected test dataset is the CU-BEMS Smart Building Energy and IAQ Data1, which provides multi-sensor recordings of energy consumption, indoor air quality, and environmental variables across an operational smart- building environment.

2. Data Cleaning, Normalisation, and Harmonisation – Preprocess the raw data through noise filtering, unit conversion, timestamp alignment, and signal normalisation.

3. Uncertainty Modeling of Data – Estimate the data uncertainty of multi- heterogeneous sources.

4. Source-Aware Categorisation of Data – Organise the processed data into meaningful categories.

5. Development of an Uncertainty-Aware AI Model – Implement and train uncertain Neural Network or an evidential deep learning model capable to produce both predictions and associated uncertainty estimates.

6. Transformation of Model Outputs into Belief Masses – Convert the outputs of the AI model into to representative class-specific supports together with residual ignorance caused by uncertainty.

7. Evidence Fusion – Combine belief masses using different composition strategies depending the detected level of conflict.

8. Decision-Making under Uncertainty – Evaluate how uncertainty and conflicting evidence influence the robustness of the final decision.

Expected Scientific/Technical Output:

Scientific Contributions

•     A unified uncertainty-aware decision framework for CPS.

•     A novel integration of belief-function theory.

•     Experimental analysis of DS-based fusion in real CPS environments, particularly in Smart Building systems.

•     A reproducible methodology supporting trustworthy AI research.

•     A scientific research report.

Technical Contributions

•     A fully implemented Python prototype of FUTURE.

•     A validated CPS case study.

Impact

FUTURE strengthens resilience in CPS, reduces decision errors under uncertainty, and provides a reference framework for integrating uncertainty-aware AI in industrial settings.

Lab presentation :

CESI LINEACT (UR 7527), Laboratory for Digital Innovation for Businesses and Learning to Support the Competitiveness of Territories, anticipates and accompanies the technological mutations of sectors and services related to industry and construction. The historical proximity of CESI with companies is a determining element for our research activities. It has led us to focus our efforts on applied research close to companies and in partnership with them. A human-centered approach coupled with the use of technologies, as well as territorial networking and links with training, have enabled the construction of cross-cutting research; it puts humans, their needs and their uses, at the center of its issues and addresses the technological angle through these contributions. Its research is organized according to two interdisciplinary scientific teams and several application areas:

• Team 1 "Learning and Innovating" mainly concerns Cognitive Sciences, Social Sciences and Management Sciences, Training Techniques and those of Innovation. The main scientific objectives are the understanding of the effects of the environment, and more particularly of situations instrumented by technical objects (platforms, prototyping workshops, immersive systems...) on learning, creativity and innovation processes.

•Team 2 "Engineering and Digital Tools" mainly concerns Digital Sciences and Engineering. The main scientific objectives focus on modeling, simulation, optimization and data analysis of cyber physical systems. Research work also focuses on decision support tools and on the study of human-system interactions in particular through digital twins coupled with virtual or augmented environments.

These two teams develop and cross their research in application areas such as:

•     Industry 5.0,

•     Construction 4.0 and Sustainable City,

•     Digital Services.

Areas supported by research platforms, mainly that in Rouen dedicated to Factory 5.0 and those in Nanterre dedicated to Factory 5.0 and Construction 4.0.

Previous Work in the Laboratory

FUTURE builds upon my earlier research contributions at CESI [2]. It represents a first attempt to formally integrate uncertainty modelling with previous work, particularly the research conducted on distributed and federated learning [4]. In this direction, FUTURE extends and strengthens the decision-making techniques previously developed within federated learning frameworks by incorporating belief reasoning and uncertainty- aware mechanisms.

Link to the research axes of the research team involved:

FUTURE project aims to enhance techniques developed in Resilient and Safe Systems (R2S) within team 2 of CESI LINEACT.

References

[1] Samir Ouchani. Secure and Reliable Smart Cyber-Physical Systems HDR Thesis, CNAM Paris, France.

[2] Safa Ben Ayed, Malika Ben Khalifa, and Samir Ouchani. Modeling distributed and flexible PHM framework based on the belief function theory. In Artificial Intelligence Applications and Innovations - 20th IFIP WG 12.5 International Conference, AIAI 2024, Corfu, Greece, June 27-30, 2024, Proceedings, Part I, volume 711, pages 160–173. Springer, 2024.

[3] Md Mehedi Hasan, Moloud Abdar, Abbas Khosravi, Uwe Aickelin, Pietro Lio, Ibrahim Hossain, Ashikur Rahman, and Saeid Nahavandi. Survey on leveraging uncertainty estimation towards trustworthy deep neural networks: The case of reject option and post-training processing. ACM Comput. Surv., 57(9), 2025.

[4] Souhila Badra Guendouzi, Samir Ouchani, Hiba Al Assaad, and Madeleine El-Zaher. Ensuring the federation correctness: Formal verification of federated learning in industrial cyber-physical systems. Future Gener. Comput. Syst., 166:107675, 2025.

Modalities :

Review of applications and interview. All enthusiastic individuals are encouraged to apply. Application should include (

in one file PDF) :

•     Detailed Curriculum Vitae of the candidate. In case of a break in academic studies, please provide an explanation;

•     A motivation letter;

•     Transcripts of MASTER I and/or II and/or corresponding grade reports;

•     BSc/MSc/Ing. certificates;

•     Two recommendation letters.

Requirements:

The candidate should possess a Master student or equivalent in Software Engineering or Computer Science. She/He should have some knowledge and experience in a number of the following points:

Scientific Skills

• Probability, uncertainty modelling, and evidence-based reasoning.

• Fundamentals of machine learning and classification.

• Understanding of CPS architectures.

Technical Skills

• Programming environments.

• Python programming (NumPy, SciPy, Pandas).

• PyTorch or TensorFlow for deep learning.

• Dempster–Shafer libraries (e.g., pyds).

• Data cleaning techniques.

• Git version control.

Interpersonal Skills

• Analytical thinking and autonomy

• Problem-solving

• Ability to work in interdisciplinary teams.

• Scientific communication and reporting.