Understanding anomalous human behaviors at a fine-grained level remains a major challenge in complex scenarios. Existing video anomaly understanding (VAU) methods often rely on coarse frame-level cues or overlook structured modeling of individual actions, limiting their capacity for reasoning about human interactions and accountability. To address these challenges, we propose TargetVAU, a multimodal anomaly-aware reasoning framework designed for individual-level anomaly recognition and explanation. TargetVAU first extracts both global-level and human-centric visual features using a frozen Vision Transformer (ViT) encoder. An Anomaly-focused Temporal Sampler is then employed to select behaviorally informative frames via a density-aware strategy guided by predicted anomaly scores. A Spatio-Temporal Interaction Graph is constructed to explicitly model interactions among individuals across time and space. These structured representations are fused with prompt embeddings via a frozen Q-Former to form a unified semantic representation. Finally, a large language model fine-tuned with low-rank adaptation (LoRA) performs instruction-guided reasoning to identify anomalous individuals and generate natural language explanations. Extensive experiments on UCCD and HIVAU-70K demonstrate that TargetVAU significantly outperforms existing methods in both accuracy and interpretability, advancing the state of individual-level anomaly understanding in surveillance videos.

An unmanned aerial vehicle (UAV) swarm is a cluster system composed of multiple UAVs and is widely used in military and civilian fields. The UAV swarm has a large number of resources, complex functions, space-time constraints, and task-driven characteristics. However, existing UAV swarm task description methods are usually limited to a specific task and cannot adapt to detailed descriptions of dynamic and complex application scenarios. To this end, we propose a UAV swarm application scenario model based on meta-level theory. Specifically, we abstract three types of meta models from UAV application scenarios: mission meta-model, resource meta-model, and constraint meta-model. Based on this model, we design and implement a UAV swarm application scenario modeling language (ASML) to support the formal description and analysis of the model. Furthermore, we define the conversion rules from ASML to timed automata. We model a logistics handling application scenario and use the model checking tool UPPAAL to verify the correctness of the scenario.

In the domain of video surveillance, describing the behavior of each individual within the video is becoming increasingly essential, especially in complex scenarios with multiple individuals present. This is because describing each individual's behavior provides more detailed situational analysis, enabling accurate assessment and response to potential risks, ensuring the safety and harmony of public places. Currently, video-level captioning datasets cannot provide fine-grained descriptions for each individual's specific behavior. However, mere descriptions at the video-level fail to provide an in-depth interpretation of individual behaviors, making it challenging to accurately determine the specific identity of each individual. To address this challenge, we construct a human-centric video surveillance captioning dataset, which provides detailed descriptions of the dynamic behaviors of 7,820 individuals. Specifically, we have labeled several aspects of each person, such as location, clothing, and interactions with other elements in the scene, and these people are distributed across 1,012 videos. Based on this dataset, we can link individuals to their respective behaviors, allowing for further analysis of each person's behavior in surveillance videos. Besides the dataset, we propose a novel video captioning approach that can describe individual behavior in detail on a person-level basis, achieving state-of-the-art results.

Identifying and fixing bug reports with tactical vul-nerabilities in a timely and accurate manner is essential to ensure the security of the software architecture. Manually identifying the bug reports with tactical vulnerabilities is labor-intensive and challenging. This paper presents Itactivul, an approach to automatically identify bug reports with tactical vulnerabilities and recommend their tactical categories to guide the fix. Unlike the existing security bug report prediction approach, we are the first attempt to use deep learning to mine discriminative tactical text features only from the vulnerability descriptions of the National Vulnerability Database (NVD) and apply them to identify bug reports with tactical vulnerabilities. We evaluate Itactivul on three bug reports datasets gathered from three large-scale open-source projects, including Chromium, PHP, and Thunderbird. The experimental results show that Itactivul outperforms baselines by an average of 8.88 %, 13.58 %, and 6.61 % in the F1-score of three datasets, respectively. To improve the explainability of the features mined by Itactivul, we manually analyze the high-weight phrases extracted by using attention backtracking. The results show that Itactivul can mine key and potential tactical vulnerabilities text features.

In recent years, Internet of Things (IoT) has become one of the most important technologies in academia and industry. However, in many application scenarios, smart devices are distributed over a remote area without terrestrial communication systems. Considering the power of smart devices is limited, it is infeasible for terrestrial access networks to effectively receive the data of smart devices. Therefore, as a supplement to the terrestrial networks, Space-Air-Ground networks are critical to the Internet of Remote Things (IoRT). In this paper, we use high-altitude platforms (HAPs) to assist data transmission from smart devices to low earth orbit (LEO) satellites. Aiming at minimizing system power consumption, we propose an algorithm that jointly optimizes the resource allocation scheme and the HAP relay deployment. Since the problem is a mix-integer non-convex programming which is prohibitive to solve, our proposed algorithm divides it into two sub-problems to find a near-optimal solution with low computational complexity. Simulation results show that compared with average resource allocation scheme, our proposed algorithm can significantly reduce the system power consumption while satisfying the rate requirements of smart devices.

A flaky test is a test that both passes and fails periodically without any code changes, and its uncontrolled uncertainty will destroy the value of the test suites and even cause developers to distrust the test results. Recently, researches in the flaky test have received broad attention in the software test community to reduce the manual maintenance cost of flaky tests by developers. In this survey, we conducted comprehensive research progress on the flaky test We identified 31 relevant studies and summarized the following aspects of the flaky test: root causes and factors, analyzing the impact, detecting and classifying techniques, and fixing approaches. This survey also identifies open research challenges to be further explored in future work.

Low Earth Orbit (LEO) satellite networks play a major role to provide communication support for the regions beyond the coverage of terrestrial network systems. The positions of gateway stations have influence on the time delay, power consumption and throughput of LEO satellite networks, making it important to deploy the gateway stations at the best position. Aiming at minimizing the inter-satellite hop count, we are the first to study the deployment problem of gateway stations. The gateway deployment problem is formulated as a non-convex optimization problem. According to the characteristics of the deployment problem, we propose an improved genetic algorithm (GA) and an improved simulated annealing algorithm (SA) not only to get the optimal deployment location, but also to greatly reduce the computational complexity. Simulation results and analyses show that compared with random deployment, the optimal locations of the gateway stations obtained by the proposed algorithms can decrease 42.6% average hop count of inter-satellite links and increase 4.4% average capacity of feeder links.

Low Earth Orbit (LEO) satellites play a central role in Earth observation systems. Due to high-speed movement, LEO satellites have very limited contact time with earth stations (ESs). Satellites that store too much data need to wait until they reconnect to ESs. In this case, the system may not meet the delay requirement. Existing works have scheduled the data downloading from satellites to ESs and made use of inter-satellite links (ISLs) to offload data among other satellites. However, such algorithms only maximize the overall throughput but neglect the truth that energy is always a scarce resource for satellites. To solve this problem, based on the proposed enhanced superposed multi-power level multi-transmission (ESMLMT) graph, we propose an iterative algorithm to minimize the energy consumption of data transmission while maximizing the throughput and satisfying the delay requirement. It is shown by simulations that our proposed algorithm achieved significantly higher energy efficiency without data downloading throughput performance degradation in satellite systems.

Existing Android testing tools, such as Monkey, generate a large quantity and a wide variety of user events to expose latent GUI bugs in Android apps. However, even if a bug is found, a majority of the events thus generated are often redundant and bug-irrelevant. In addition, it is also time-consuming for developers to localize and replay the bug given a long and tedious event sequence (trace).