Otman Basir

The rapid spread of diseases like COVID-19 highlights the need for adaptable monitoring systems to support public health measures such as mask compliance and social distancing. This study presents the CB-OWL-ViT framework: a Cluster-Based Open-World Localization Vision Transformer for mask detection and social distance estimation. It incorporates homography-based distance estimation for effective deployment with monocular cameras. The innovative integration of open-world vision-language detection with a clustering-based strategy enhances mask-wearing assessments, enabling adaptability without retraining. Evaluations on datasets including Kaggle, Roboflow, and a new dataset from the University of Waterloo show that CB-OWL-ViT improves mask detection precision by 0.37 and F1-score by 0.2 compared to the baseline. The homography module achieves a Mean Absolute Error of 0.1116 in distance estimation, and real-world tests demonstrate a recall of 0.98 for detecting noncompliance in the “Without Mask” class. This framework is a practical solution for large-scale disease monitoring across various settings.

Traffic scene understanding is essential for enabling autonomous vehicles to accurately perceive and interpret their environment, thereby ensuring safe navigation. This paper presents a novel framework that transforms a single frontal-view camera image into a concise natural language description, effectively capturing spatial layouts, semantic relationships, and driving-relevant cues. The proposed model leverages a hybrid attention mechanism to enhance spatial and semantic feature extraction and integrates these features to generate contextually rich and detailed scene descriptions. To address the limited availability of specialized datasets in this domain, a new dataset derived from the BDD100K dataset has been developed, with comprehensive guidelines provided for its construction. Furthermore, the study offers an in-depth discussion of relevant evaluation metrics, identifying the most appropriate measures for this task. Extensive quantitative evaluations using metrics such as CIDEr and SPICE, complemented by human judgment assessments, demonstrate that the proposed model achieves strong performance and effectively fulfills its intended objectives on the newly developed dataset.

Artificial intelligence systems are increasingly embedded as persistent, closed-loop components within cyber-physical, social, and institutional processes. Rather than producing isolated outputs, such systems operate continuously under feedback, adaptation, and scale, reshaping physical flows, human behavior, and institutional practice over time. In these settings, socially unacceptable outcomes rarely arise from singular faults or explicit policy violations. Instead, they emerge through cumulative execution trajectories enabled by repetition, concurrency, and feedback. This paper advances the formal foundation of the Social Responsibility Stack (SRS) by making its central requirement explicit: responsibility is fundamentally a reachability property of system execution. A system is responsible iff its execution semantics prevent entry into inadmissible global configurations, regardless of local performance gains or optimization objectives. Responsibility failures are therefore not objective-level errors, but execution-level failures of trajectory control. To operationalize this perspective, we introduce Petri nets as an execution-level formalism for responsible autonomous systems. We show how SRS value commitments correspond to forbidden markings, safeguards to structural constraints on transition firing, auditing to monitoring of reachability pressure, and governance to legitimate modification of execution structure. Embedding Petri-net reachability within the SRS architecture internalizes responsibility as a structural invariant rather than an external objective or post-hoc mechanism. These results establish the Social Responsibility Stack as an executable responsibility architecture and position reachability-based execution semantics as a necessary foundation for responsible autonomy in feedback-rich cyber-physical and socio-technical systems.

Automatic detection of traffic accidents has a crucial effect on improving transportation, public safety, and path planning. Many lives can be saved by the consequent decrease in the time between when the accidents occur and when rescue teams are dispatched, and much travelling time can be saved by notifying drivers to select alternative routes. This problem is challenging mainly because of the rareness of accidents and spatial heterogeneity of the environment. This paper studies deep representation of loop detector data using long‐short term memory (LSTM) network for automatic detection of freeway accidents. The LSTM‐based framework increases class separability in the encoded feature space while reducing the dimension of data. The experiments on real accident and loop detector data collected from the Twin Cities Metro freeways of Minnesota demonstrate that deep representation of traffic flow data using LSTM network has the potential to detect freeway accidents in less than 18 min with a true positive rate of 0.71 and a false positive rate of 0.25 which outperforms other competing methods in the same arrangement.

Cloud computing environments often have to deal with random-arrival computational workloads that vary in resource requirements and demand high Quality of Service (QoS) obligations. It is typical that a Service-Level-Agreement (SLA) is employed to govern the QoS obligations of the cloud computing service provider to the client. A typical challenge service-providers face every day is maintaining a balance between the limited resources available for computing and the high QoS requirements of varying random demands. Any imbalance in managing these conflicting objectives may result in either dissatisfied clients and potentially significant commercial penalties, or an over-resourced cloud computing environment that can be significantly costly to acquire and operate. Thus, scheduling the clients' workloads as they arrive at the environment to ensure their timely execution has been a central issue in cloud computing. Various approaches have been reported in the literature to address this problem: Shortest-Queue, Join-Idle-Queue, Round Robin, MinMin, MaxMin, and Least Connection, to name a few. However, optimization strategies of such approaches fail to capture QoS obligations and their associated commercial penalties. This paper presents an approach for service-level driven load scheduling and balancing in multi-tier environments. Joint scheduling and balancing operations are employed to distribute and schedule jobs among the resources, such that the total waiting time of client jobs is minimized, and thus the potential of a penalty to be incurred by the service provider is mitigated. A penalty model is used to quantify the penalty the service provider incurs as a function of the jobs' total waiting time. A Virtual-Queue abstraction is proposed to facilitate optimal job scheduling at the tier level. This problem is NP-complete, a genetic algorithm is proposed for computing job schedules.

Traffic incident detection and localization is an important application in traffic management systems. The ability to detect and localize traffic incidents enables a timely response to accidents and facilitates effective and efficient traffic flow management. This paper presents a sensor-network based approach for tackling the problem of incident localization. Traffic count sensors, which tend to be an element of the road infrastructure, are used as the source of traffic sensory data. Such sensors come in a variety of types and capabilities, providing the potential for complementary and redundant information gathering. Thus, it is conceivable to fuse such sensory information to achieve insightful and accurate incident detection and localization. In this context, the Dempster-Shafer (DS) theory of evidence is used as the foundation for fusing traffic sensory data. In this paper, a traffic model generator and two traffic-counting sensory systems are employed for acquiring traffic data pertinent to the distribution of cars on a given road segment. Experimental analysis on the performance of the proposed approach is provided.