Flood detection networks often fail when fixed infrastructure such as gateways or cellular towers is damaged. Multi-agent systems (MAS) operating over ad hoc peer-to-peer networks offer a resilient alternative by enabling sensors to self-organize, reroute data, and forward alerts cooperatively. This work simulates and compares two MAS organizational models for IoT-based flood monitoring: Flat MAS and Federated MAS. Flat MAS rely on peer-to-peer communication, allowing sensors to exchange acknowledgments and reroute messages through neighbors during connectivity loss. Federated MAS use selected gateways to coordinate clusters and maintain inter-gateway failover links, improving system continuity. Results show that Flat MAS respond faster under partial failure, while Federated MAS achieve higher delivery success and recover more effectively from gateway outages.

Connected and autonomous vehicles across land, water, and air must often operate in dynamic, unpredictable environments with limited communication, no centralized control, and partial observability. These real-world constraints pose significant challenges for coordination, particularly when vehicles pursue individual objectives. To address this, we propose a decentralized Multi-Agent Reinforcement Learning (MARL) framework that enables vehicles, acting as agents, to communicate selectively based on local goals and observations. This goal-aware communication strategy allows agents to share only relevant information, enhancing collaboration while respecting visibility limitations. We validate our approach in complex multi-agent navigation tasks featuring obstacles and dynamic agent populations. Results show that our method significantly improves task success rates and reduces time-to-goal compared to non-cooperative baselines. Moreover, task performance remains stable as the number of agents increases, demonstrating scalability. These findings highlight the potential of decentralized, goal-driven MARL to support effective coordination in realistic multi-vehicle systems operating across diverse domains.

Timely and coordinated flood response is often hindered by fragmented data and delayed situational awareness. This paper presents a multi-agent system (MAS) that integrates geolocated social media posts and IoT sensor data to enable dynamic and high-confidence flood detection and alerting. Each agent is responsible for a specific function, including filtering noisy tweets, validating water level readings, and clustering incident reports. In a simulated urban flood scenario, the system significantly improved event coverage, reduced response time, and lowered false alarms by validating information across multiple sources. These results highlight the potential of intelligent agent collaboration to enhance real-time disaster monitoring and response.

This study investigates the effect of sampling techniques on cancer type classification across five categories: breast, kidney, colon, lung, and prostate. We evaluate the impact of three sampling strategies: (i) Simple Random Oversampling/Undersampling, (ii) Stratified Random Sampling, and (iii) Synthetic Minority Oversampling Technique (SMOTE) on the performance of seven different machine learning models. Results show that incorporating sampling noticeably improves model performance, with SMOTE achieving the highest gains. On average, sampling improved F1 scores by 11.44%, highlighting its critical role in addressing class imbalance in medical datasets. These findings provide a practical and reproducible approach to mitigate data imbalance in biomedical machine learning workflows.

Deep Reinforcement Learning (DRL) has become a critical approach for Object Tracking (OT) due to its ability to handle the sequential decision-making processes inherent in tracking tasks. By iteratively refining predictions and adapting to changes in object appearance or motion, DRL-based methods offer robust performance in complex tracking scenarios. However, most existing DRL-based OT methods have primarily focused on algorithmic framework design, often overlooking the optimization of configurations, such as action space, state space, reward function, and DRL algorithm. This oversight is significant, as optimal configuration choices can enhance DRL framework performance by up to 64%. Addressing this gap, our study investigates the impact of various configuration setups on the performance of DRL-based systems, specifically for Single Object Tracking (SOT) with a fixed camera view. Through theoretical analyses and experimentation, we demonstrate that appropriate configurations can improve tracking precision and system adaptability. The insights from this study provide a foundational guide for optimizing DRL applications in SOT with a fixed camera view, paving the way for more robust and efficient implementations in practical scenarios.

Object Tracking is essential for many computer vision applications, such as autonomous navigation, surveillance, and robotics. Unlike Passive Object Tracking (POT), which relies on static camera viewpoints to detect and track objects across consecutive frames, Active Object Tracking (AOT) requires a controller agent to actively adjust its viewpoint to maintain visual contact with a moving target in complex environments. Existing AOT solutions are predominantly single-agent-based, which struggle in dynamic and complex scenarios due to limited information gathering and processing capabilities, often resulting in suboptimal decision-making. Alleviating these limitations necessitates the development of a multi-agent system where different agents perform distinct roles and collaborate to enhance learning and robustness in dynamic and complex environments. Although some multi-agent approaches exist for AOT, they typically rely on external auxiliary agents, which require additional devices, making them costly. In contrast, we introduce the Collaborative System for Active Object Tracking (CSAOT), a method that leverages multi-agent deep reinforcement learning (MADRL) and a Mixture of Experts (MoE) framework to enable multiple agents to operate on a single device, thereby improving tracking performance and reducing costs. Our approach enhances robustness against occlusions and rapid motion while optimizing camera movements to extend tracking duration. We validated the effectiveness of CSAOT on various interactive maps with dynamic and stationary obstacles.

The rapid growth of both the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) results in a high demand for AI applications in devices. To achieve high levels of accuracy, AI applications typically require a large amount of annotated data. Accessing such data is challenging in various applications such as healthcare, finance and information security. Federated learning (FL) is one of the strategies that was proposed to overcome this challenge. Specifically, FL enables the AI model in the centralized system to be trained without any prior knowledge of the information on the devices. Recent FLs have the disadvantage that they are dependent upon a centralized system, and thus are susceptible to single points of failure. This paper proposes a strategy that employs FL in a decentralized environment where devices can communicate with each other to increase the accuracy of the AI model in each device. Furthermore, we evaluate the proposed strategy in the image classification task with the ResNet50 architecture and the CIFAR-10 dataset. The evaluation shows that the ResNet50 model trained in the decentralized environment can achieve comparable results to the model trained in the centralized environment.

Abstract Human values such as honesty, social responsibility, fairness, privacy, and the like are things considered important by individuals and society. Software systems, including mobile software applications (apps), may ignore or violate such values, leading to negative effects in various ways for individuals and society. While some works have investigated different aspects of human values in software engineering, this mixed-methods study focuses on honesty as a critical human value. In particular, we studied (i) how to detect honesty violations in mobile apps, (ii) the types of honesty violations in mobile apps, and (iii) the perspectives of app developers on these detected honesty violations. We first develop and evaluate 7 machine learning (ML) models to automatically detect violations of the value of honesty in app reviews from an end-user perspective. The most promising was a Deep Neural Network model with F1 score of 0.921. We then conducted a manual analysis of 401 reviews containing honesty violations and characterised honesty violations in mobile apps into 10 categories: unfair cancellation and refund policies; false advertisements; delusive subscriptions; cheating systems; inaccurate information; unfair fees; no service; deletion of reviews; impersonation; and fraudulent-looking apps. A developer survey and interview study with mobile developers then identified 7 key causes behind honesty violations in mobile apps and 8 strategies to avoid or fix such violations. The findings of our developer study also articulate the negative consequences that honesty violations might bring for businesses, developers, and users. Finally, the app developers’ feedback shows that our prototype ML-based models can have promising benefits in practice.

Machine reading comprehension (MRC) is a challenging task in natural language processing that demonstrates the language understanding of the machine. An approach to tackle this challenge requires the machine to answer the question about the given context when needed and abstain from answering when there is no answer. Recent works attempted to solve this challenge with various comprehensive neural network architectures for sequences such as SAN, U-Net, EQuANt, and others that were trained on the SQuAD 2.0 dataset containing unanswerable questions. However, the robustness of these approaches has not been evaluated. In this paper, we propose a data augmentation approach that converts answerable questions to unanswerable questions in the SQuAD 2.0 dataset by altering the entities in the question to its antonym from ConceptNet which is a semantic network. The augmented data is, then, fitted into the U-Net question answering model to evaluate the robustness of the model.

In many real-world applications such as the inspection of powerlines, the automated detection of anomalies can minimise damage and reduce costs that result from the presence of unknown anomalies. Technologies such as LiDAR scans obtained from Unmanned Aerial Vehicles (UAV) are becoming prominent due to the data depth they provide. In the context of powerline transmission, investigators must search for anomalous elements such as line defects or obstructions. Such occurrences are not always apparent and detecting them requires extensive analysis of data within vast areas of wilderness. Automating this process can reduce time and labor costs. We propose a methodology to define what constitutes an anomaly within mapped real-world scenes, and a technique to address different types of anomalies. The notion of unknowns and knowns composed of unknown to both human and machine, known to human and unknown to machine, unknown to human and known to machine, and known to both human and machine is considered to develop a novel framework that detects anomalous patterns. For the purpose of evaluation, we introduce synthetic anomalous data points through our data augmentation methods. Our framework achieved 63.78% accuracy in detecting the points known to the machine and unknown to the machine from the Sensat Urban validation scene. Within the scene, 78.22% of the incorrectly classified data were detected as unknown to the machine. Furthermore, our framework achieved 84.34% accuracy in detecting the synthetic data and 35.5% accuracy in detecting those data as anomalies.

Human values such as integrity, privacy, curiosity, security, and honesty are guiding principles for what people consider important in life. Such human values may be violated by mobile software applications (apps), and the negative effects of such human value violations can be seen in various ways in society. In this work, we focus on the human value of honesty. We present a model to support the automatic identification of violations of the value of honesty from app reviews from an end-user perspective. Beyond the automatic detection of honesty violations by apps, we also aim to better understand different categories of honesty violations expressed by users in their app reviews. The result of our manual analysis of our honesty violations dataset shows that honesty violations can be characterised into ten categories: unfair cancellation and refund policies; false advertisements; delusive subscriptions; cheating systems; inaccurate information; unfair fees; no service; deletion of reviews; impersonation; and fraudulent-looking apps. Based on these results, we argue for a conscious effort in developing more honest software artefacts including mobile apps, and the promotion of honesty as a key value in software development practices. Furthermore, we discuss the role of app distribution platforms as enforcers of ethical systems supporting human values, and highlight some proposed next steps for human values in software engineering (SE) research.