Active Control of Camera Parameters and Algorithm Selection for Object Detection

In this thesis, we quantitatively investigate the effect of camera parameters, shutter speed and voltage gain, on the performance of several popular object detection algorithms, under various illumination conditions. Our experimental results indicate a significant difference in sensitivity of the evaluated algorithms to these camera parameters. Based on the experimental benchmark results, a novel active control of camera parameters method and an algorithm selection extension are proposed. In empirical evaluation, our active control approach outperforms the conventional auto-exposure method for most algorithms. Also, the proposed algorithm selection extension has demonstrated the capability of selecting a proper tuple, in order to deal with varying light conditions

Byzantine Attack and Defense in Cognitive Radio Networks: A Survey

The Byzantine attack in cooperative spectrum sensing (CSS), also known as the spectrum sensing data falsification (SSDF) attack in the literature, is one of the key adversaries to the success of cognitive radio networks (CRNs). In the past couple of years, the research on the Byzantine attack and defense strategies has gained worldwide increasing attention. In this paper, we provide a comprehensive survey and tutorial on the recent advances in the Byzantine attack and defense for CSS in CRNs. Specifically, we first briefly present the preliminaries of CSS for general readers, including signal detection techniques, hypothesis testing, and data fusion. Second, we analyze the spear and shield relation between Byzantine attack and defense from three aspects: the vulnerability of CSS to attack, the obstacles in CSS to defense, and the games between attack and defense. Then, we propose a taxonomy of the existing Byzantine attack behaviors and elaborate on the corresponding attack parameters, which determine where, who, how, and when to launch attacks. Next, from the perspectives of homogeneous or heterogeneous scenarios, we classify the existing defense algorithms, and provide an in-depth tutorial on the state-of-the-art Byzantine defense schemes, commonly known as robust or secure CSS in the literature. Furthermore, we highlight the unsolved research challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral

WIPI: A New Web Threat for LLM-Driven Web Agents

With the fast development of large language models (LLMs), LLM-driven Web Agents (Web Agents for short) have obtained tons of attention due to their superior capability where LLMs serve as the core part of making decisions like the human brain equipped with multiple web tools to actively interact with external deployed websites. As uncountable Web Agents have been released and such LLM systems are experiencing rapid development and drawing closer to widespread deployment in our daily lives, an essential and pressing question arises: "Are these Web Agents secure?". In this paper, we introduce a novel threat, WIPI, that indirectly controls Web Agent to execute malicious instructions embedded in publicly accessible webpages. To launch a successful WIPI works in a black-box environment. This methodology focuses on the form and content of indirect instructions within external webpages, enhancing the efficiency and stealthiness of the attack. To evaluate the effectiveness of the proposed methodology, we conducted extensive experiments using 7 plugin-based ChatGPT Web Agents, 8 Web GPTs, and 3 different open-source Web Agents. The results reveal that our methodology achieves an average attack success rate (ASR) exceeding 90% even in pure black-box scenarios. Moreover, through an ablation study examining various user prefix instructions, we demonstrated that the WIPI exhibits strong robustness, maintaining high performance across diverse prefix instructions

Multi-trajectories of triglyceride-glucose index and lifestyle with cardiovascular disease: A cohort study

Background: Previous studies using trajectory models focused on examining the longitudinal changes in triglyceride-glucose (TyG) levels and lifestyle scores separately, without exploring the joint evolution of these two factors. This study aimed to identify the multi-trajectories of TyG levels and lifestyle scores and assess their association with the risk of cardiovascular disease (CVD). Methods: The study enrolled 47,384 participants from three health surveys of the Kailuan Study. The TyG index was computed as Ln [fasting triglycerides (mg/dL) × fasting blood glucose (mg/dL)/2], and the lifestyle scores were derived from five factors, including smoking, alcohol consumption, physical activity, sedentary behaviors, and salt intake. A group-based multi-trajectory model was adopted to identify multi-trajectories of TyG levels and lifestyle scores. The association of identified multi-trajectories with incident CVD was examined using Cox proportional hazard model. Results: Five distinct multi-trajectories of TyG levels and lifestyle scores were identified. During a median follow-up period of 10.98 years, 3042 participants developed CVD events (2481 strokes, 616 myocardial infarctions, and 55 co-current stroke and myocardial infarctions). In comparison to group 3 with the lowest TyG levels and the best lifestyle scores, the highest CVD risk was observed in group 5 characterized by the highest TyG levels and moderate lifestyle scores (HR = 1.76, 95% CI: 1.50–2.05). Group 2 with higher TyG levels and the poorest lifestyle scores had a 1.45-fold (95% CI 1.26–1.66) risk of CVD, and group 1 with lower TyG levels and poorer lifestyle scores had a 1.33-fold (95% CI 1.17–1.50) risk of CVD. Group 4, with moderate TyG levels and better lifestyle scores, exhibited the lowest CVD risk (HR = 1.32, 95% CI: 1.18–1.47). Conclusions: Distinct multi-trajectories of TyG levels and lifestyle scores corresponded to differing CVD risks. The CVD risk caused by a high level TyG trajectory remained increased despite adopting healthier lifestyles. These findings underscored the significance of evaluating the combined TyG and lifestyle patterns longitudinally, and implementing early interventions to reduce CVD risk by lowering TyG levels

Constituency Parsing using LLMs

Constituency parsing is a fundamental yet unsolved natural language processing task. In this paper, we explore the potential of recent large language models (LLMs) that have exhibited remarkable performance across various domains and tasks to tackle this task. We employ three linearization strategies to transform output trees into symbol sequences, such that LLMs can solve constituency parsing by generating linearized trees. We conduct experiments using a diverse range of LLMs, including ChatGPT, GPT-4, OPT, LLaMA, and Alpaca, comparing their performance against the state-of-the-art constituency parsers. Our experiments encompass zero-shot, few-shot, and full-training learning settings, and we evaluate the models on one in-domain and five out-of-domain test datasets. Our findings reveal insights into LLMs' performance, generalization abilities, and challenges in constituency parsing