ディペンダブルシステムデザインのための感度分析アプローチに関する研究

広島大学(Hiroshima University)博士(工学)Doctor of Engineeringdoctora

A note on sensitivity analysis for PH approximation (New Developments on Mathematical Decision Making Under Uncertainty)

This paper presents the moment-based approximation for model depandability when the uncertainty of model parameters is considered. The propagation of uncertainty of model parameters can be estimated by regarding the model parameters as random variables. However, statistical model often involves the non-exponential distribution such as Weibull distribution, which leads to the high computation cost of uncertainty analysis. In this paper, we focus on the Phase-type(PH) distribution to overcome the difficulty of computation for model contains Weibull distribution

PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection

Node-level graph anomaly detection (GAD) plays a critical role in identifying anomalous nodes from graph-structured data in various domains such as medicine, social networks, and e-commerce. However, challenges have arisen due to the diversity of anomalies and the dearth of labeled data. Existing methodologies - reconstruction-based and contrastive learning - while effective, often suffer from efficiency issues, stemming from their complex objectives and elaborate modules. To improve the efficiency of GAD, we introduce a simple method termed PREprocessing and Matching (PREM for short). Our approach streamlines GAD, reducing time and memory consumption while maintaining powerful anomaly detection capabilities. Comprising two modules - a pre-processing module and an ego-neighbor matching module - PREM eliminates the necessity for message-passing propagation during training, and employs a simple contrastive loss, leading to considerable reductions in training time and memory usage. Moreover, through rigorous evaluations of five real-world datasets, our method demonstrated robustness and effectiveness. Notably, when validated on the ACM dataset, PREM achieved a 5% improvement in AUC, a 9-fold increase in training speed, and sharply reduce memory usage compared to the most efficient baseline.Comment: Accepted by IEEE International Conference of Data Mining 2023 (ICDM 2023

Systematic Evaluation of the Safety Threshold for Allograft Macrovesicular Steatosis in Cadaveric Liver Transplantation

Background: Currently, 30% macrovesicular steatosis (MaS) content is usually assigned empirically as the boundary between “use” and “refuse” a donor liver for liver transplantation (LT); however, this cut-off is questionable due to the lack of systemic evidence of the efficiency relative to prognosis prediction. Clinicians have tried to identify the threshold for optimized utilization of marginal steatotic allografts, but controversy exists among different studies.Aim: Our study aimed to systematically determine an acceptable donor MaS content cut-off without incurring extra risk in liver transplantation, using meta-analysis.Methods: The relevant literature reporting the relationship between MaS content and post-transplant mortality/morbidity was searched and retrieved in Pubmed, Embase, and ISI Web of Science.Results: Nine studies were enrolled into the final analysis. A categorical comparison revealed that patients who received allografts with moderate steatosis (MaS content >30%) had significantly higher risks of graft failure/dysfunction, but not of mortality. Dose-response analysis showed that donor MaS content affected the graft failure/dysfunction in a non-linear relationship. Risks associated with MaS content in terms of poorer outcomes were independent of other risk covariates for liver transplantation. A non-significant increase in risk of inferior post-transplant outcomes was observed in patients who received allografts with a MaS content <35%. The risks of post-transplant graft failure and dysfunction increased with severe donor MaS content infiltration, without a consistent relationship.Conclusions: The threshold of allograft MaS content can be safely extended to 35% without additional risk burden on post-transplant inferior outcomes. Clarification on “the effects of stratification” for MaS content can provide theoretical evidence for further optimal utilization of marginal steatotic allografts in liver transplantation

Experimental and simulation study on nonlinear pitch control of Seagull underwater glider

1008-1015The Seagull underwater glider, developed by the Shanghai Jiao Tong University, is designed as a test-bed glider for the development and validation of various algorithms to enhance the glider’s long-term autonomy. In this paper, an adaptive backstepping control (ABC) method is proposed for the nonlinear pitch control of the underwater glider gliding in the vertical plane. The linear quadratic regulator (LQR) control and proportional-integral-derivative (PID) control are applied and evaluated with the ABC method to control a glider in saw-tooth motion. Simulation results demonstrate inherent effectiveness and superiority of the LQR or PID based method. According to Lyapunov stability theory, the ABC control scheme is derived to ensure the tracking errors asymptotically converge to zero. The ABC controller has been implemented on Seagull underwater glider, and verified in field experiments in the Qiandao Lake, Zhejiang

Pull-Type Security Patch Management in Intrusion Tolerant Systems: Modeling and Analysis

In this chapter, we introduce a stochastic framework to evaluate the system availability of an intrusion tolerant system (ITS), where the system undergoes patch management with a periodic vulnerability checking strategy, i.e., pull-type patch management. In particular, a composite stochastic reward net (SRN) is developed to capture the overall system behaviors, including vulnerability discovery, intrusion tolerance, and reactive maintenance operations. Furthermore, two kinds of availability criteria, the interval availability and the steady-state availability of the system, are formulated by applying the phase-type (PH) approximation to solve the Markov regenerative process (MRGP) model derived from the composite SRN. Numerical experiments are conducted to investigate the effects of the vulnerability checking interval on the system availability

Action Sensitivity Learning for Temporal Action Localization

Temporal action localization (TAL), which involves recognizing and locating action instances, is a challenging task in video understanding. Most existing approaches directly predict action classes and regress offsets to boundaries, while overlooking the discrepant importance of each frame. In this paper, we propose an Action Sensitivity Learning framework (ASL) to tackle this task, which aims to assess the value of each frame and then leverage the generated action sensitivity to recalibrate the training procedure. We first introduce a lightweight Action Sensitivity Evaluator to learn the action sensitivity at the class level and instance level, respectively. The outputs of the two branches are combined to reweight the gradient of the two sub-tasks. Moreover, based on the action sensitivity of each frame, we design an Action Sensitive Contrastive Loss to enhance features, where the action-aware frames are sampled as positive pairs to push away the action-irrelevant frames. The extensive studies on various action localization benchmarks (i.e., MultiThumos, Charades, Ego4D-Moment Queries v1.0, Epic-Kitchens 100, Thumos14 and ActivityNet1.3) show that ASL surpasses the state-of-the-art in terms of average-mAP under multiple types of scenarios, e.g., single-labeled, densely-labeled and egocentric.Comment: Accepted to ICCV 202

XTQA: Span-Level Explanations of the Textbook Question Answering

Textbook Question Answering (TQA) is a task that one should answer a diagram/non-diagram question given a large multi-modal context consisting of abundant essays and diagrams. We argue that the explainability of this task should place students as a key aspect to be considered. To address this issue, we devise a novel architecture towards span-level eXplanations of the TQA (XTQA) based on our proposed coarse-to-fine grained algorithm, which can provide not only the answers but also the span-level evidences to choose them for students. This algorithm first coarsely chooses top $M$ paragraphs relevant to questions using the TF-IDF method, and then chooses top $K$ evidence spans finely from all candidate spans within these paragraphs by computing the information gain of each span to questions. Experimental results shows that XTQA significantly improves the state-of-the-art performance compared with baselines. The source code is available at https://github.com/keep-smile-001/opentqaComment: 10 page