Effective monitoring of essential road infrastructure is critical to ensure economic growth and community connectivity, as deterioration from climate change and intensive use can compromise safety and hamper emergency services. To address these critical issues, we propose optimized approaches for road damage detection using YOLOv5, YOLOv8, and YOLOv10 models, with fine-tuning and hyperparameter optimization to improve detection accuracy. Our approaches achieve good overall accuracy (in terms of F1-score) by combining models of different sizes and frameworks using ensemble methods. Moreover, to enhance the speed of inference, parallel processing techniques are used. In order to obtain fast detection performance, small models, such as YOLOv5-small and YOLOv8-nano were given priority in modeling to balance between detection accuracy and inference speed. In the context of the 2024 IEEE BigData Optimized Road Damage Detection Challenge (ORDDC’2024), our approach achieved an F1 score greater than 0.7 with an inference speed of 0.0432 seconds per image, meeting the ORDDC’s accuracy and inference speed goal.

Monitoring public facilities is essential for governments to establish safer, cleaner, and more resilient cities, ultimately benefiting all citizens. One promising approach for public facility surveillance is to employ visual machine-learning methods on street scenes. The potential development and advancements in such state-of-the-art methods require the availability of visual datasets annotated with several public facility defects (e.g., illegal dumping, graffiti, street potholes, and damaged traffic signs). Towards this, we introduce "StreetLens", a new dataset of videos capturing urban streets with plentiful annotations for vision-based public facility monitoring. This dataset includes four-and-a-half hours of videos recorded by smartphone cameras placed in moving vehicles in the suburbs of three different cities.

Technology advances in areas such as sensors, IoT, and robotics, enable new collaborative applications (e.g., autonomous devices). A primary requirement for such collaborations is to have a secure system that enables information sharing and information flow protection. A policy-based management system is a key mechanism for secure selective sharing of protected resources. However, policies in each party of a collaborative environment cannot be static as they have to adapt to different contexts and situations. One advantage of collaborative applications is that each party in the collaboration can take advantage of the knowledge of the other parties for learning or enhancing its own policies. We refer to this learning mechanism as policy transfer. The design of a policy transfer framework has challenges, including policy conflicts and privacy issues. Policy conflicts typically arise because of differences in the obligations of the parties, whereas privacy issues result because of data sharing constraints for sensitive data. Hence, the policy transfer framework should be able to tackle such challenges by considering minimal sharing of data and supporting policy adaptation to address conflict. In the paper, we propose a framework that aims at addressing such challenges. We introduce a formal definition of the policy transfer problem for attribute-based access control policies. We then introduce the transfer methodology which consists of three sequential steps. Finally, we report experimental results.

Policy-based access control systems are crucial for secure information sharing in collaborative applications. However, policy management needs to be flexible in order to adapt to different environments and be able to support policy evolution. However, when dealing with large sets of evolving policies, it is critical that policies meet certainpolicy quality requirements. Policy sets must be complete, free of inconsistencies, and relevant. In this paper, we propose a framework to analyze policies to determine whether they meet such requirements. Our framework uses provenance techniques to collect comprehensive data about actions which were either triggered due to a network context or a user (i.e., a human or a device) action. The framework includes two approaches for policy analysis: structure-based and classification-based. For the structure-based approach, we designed tree structures to organize and assess the policy set efficiently. For the classification-based approach, we employed the classification techniques to learn the characteristics of policies and predict their quality. In addition, the framework includes the policy evolution module which mainly consists of recommendation and re-evaluation services for policy changes which both aim at fulfilling the policy quality requirements. The analysis framework has been implemented and experimental results from the prototype are reported.

Policy systems are critical for managing missions and collaborative activities carried out by coalitions involving different organizations. Conventional policy-based management approaches are not suitable for next-generation coalitions that will involve not only humans, but also autonomous computing devices and systems. It is critical that those parties be able to generate and customize policies based on contexts and activities. This paper introduces a novel approach for the autonomic generation of policies by autonomous parties. The framework combines context free grammars, answer set programs, and inductionbased learning. It allows a party to generate its own policies, based on a grammar and some semantic constraints, by learning from examples. The paper also outlines initial experiments in the use of such a symbolic approach and outlines relevant research challenges, ranging from explainability to quality assessment of policies.

Policy-based mechanisms are used to implement desired autonomic behavior of a managed system in a distributed environment. For modern dynamically changing systems, policy-based mechanisms tend to be too rigid, and quickly lose their efficacy when conditions of the autonomous system change during its operation. In this paper, we propose a generative policy framework that can generate policies for an autonomous system when conditions change. For changed conditions, the policy generation manager dynamically generates new set of policies optimized for the new situation. As a use case, we demonstrate how our generative policy framework generates policies for selecting optimal data for an AI model training. The policies are dynamically generated based on the availability and trustworthiness of data in a coalition environment.

We design and implement a query language for a secure, interoperable, and multi-granular provenance framework, referred to as QL-SimP. Our language supports two provenance representations (relational and graph-based) due to its independence from the underlying provenance representation. It also supports various queries that can be utilized for difference provenance applications. We integrate Computational Research Infrastructure for Science (CRIS) — a real-world system for managing scientific data — with our provenance framework, thus making it possible to query CRIS provenance information by using our provenance language. We have also evaluated our provenance queries based on relational and graph databases.

Online knowledge production sites, such as Wikipedia or Stack Overflow, are dominated by small groups of contributors. How does this affect knowledge production and its quality? Does the persistent presence of some key contributors among the most productive members improve or not the quality of the knowledge, considered in the aggregate? The present paper considers these issues by correlating week-by-week value changes in contribution unevenness, elite resilience (stickiness), and content quality. The goal is to detect if and how changes in social structural variables may influence the quality of the knowledge produced by online knowledge production sites. The paper addresses such question by an extensive data analysis carried out on the datasets of two representative sites: Wikipedia and Stack Overflow. Results from the analysis show that on Stack Overflow both unevenness and elite stickiness have a curvilinear effect on quality. Quality is optimized at specific levels of elite stickiness and unevenness. At the same time, on Wikipedia, quality increases linearly with a decline on entropy, overall, and with an increase in stickiness in the maturation phase, after an entropy peak is reached.

Due to data intensive and sophisticated tasks in scientific experiments, workflows have been widely used to enable repetitive task automation and data reproducibility. This yields to the need for effective and efficient search mechanisms for scientific workflows discovery as workflow retrieval systems require a model which fulfills several requirements: unification, accuracy, and rich representations. Motivated by the recent uptake in provenance based models for scientific workflow discovery, in this paper, we propose a provenance-based architecture for retrieving workflows. Specifically, the paper presents an architecture which transforms data provenance into workflows and then organizes data into a set of indexes to support efficient querying mechanisms. The architecture enables composite queries supporting three types of search criteria: keywords of workflow tasks, workflow structure patterns, and metadata about workflows-e.g., how often a workflow was used.

Successful collaborations require information and resource sharing and thus adequate access control policy management systems that control sharing among the collaborating entities. Such management systems need to be flexible in order to adapt to different environments and thus be able to support access control policy evolution. However, when dealing with large sets of evolving policies it is critical that policies meet certain "policy quality requirements". Specifically, policies of interest must be up-to-date, complete, free of inconsistencies, relevant. In this paper, we propose an approach to analyze policies in order to determine whether policies meet such requirements. Our approach is based on the use of provenance techniques that collect comprehensive data about actions executed by users in the context of workflows, that is, sets of tasks executed according to some ordering by users. Provenance data are used by services that support various types of analysis to determine whether the policies of interest verify the quality requirements.

We propose a provenance framework which includes an expressive provenance model able to represent the provenance of any data object captured with various granularities. The model is represented according to relational and graph specifications. The framework is interoperable with two standard provenance models: OPM and PROV. In addition, the framework captures access control policies for data objects and secures the provenance storage itself. We have integrated our framework with CRIS - a real world system for managing scientific data.