Anomaly Detection under Distribution Shift

Anomaly detection (AD) is a crucial machine learning task that aims to learn patterns from a set of normal training samples to identify abnormal samples in test data. Most existing AD studies assume that the training and test data are drawn from the same data distribution, but the test data can have large distribution shifts arising in many real-world applications due to different natural variations such as new lighting conditions, object poses, or background appearances, rendering existing AD methods ineffective in such cases. In this paper, we consider the problem of anomaly detection under distribution shift and establish performance benchmarks on four widely-used AD and out-of-distribution (OOD) generalization datasets. We demonstrate that simple adaptation of state-of-the-art OOD generalization methods to AD settings fails to work effectively due to the lack of labeled anomaly data. We further introduce a novel robust AD approach to diverse distribution shifts by minimizing the distribution gap between in-distribution and OOD normal samples in both the training and inference stages in an unsupervised way. Our extensive empirical results on the four datasets show that our approach substantially outperforms state-of-the-art AD methods and OOD generalization methods on data with various distribution shifts, while maintaining the detection accuracy on in-distribution data. Code and data are available at https://github.com/mala-lab/ADShift.Comment: Accepted at ICCV 202

How Paternalistic Leaders Motivate Employees’ Information Security Policy Compliance? Building Climate or Applying Sanctions

This paper studies the influencing mechanisms of Paternalistic Leadership in motivating employees’ Information Security Polices Compliance. We proposed that Sanctions and Information Security Climate can mediate the impact of different PL dimensions. Based on survey data from 760 participants, we found that, for different PL dimension, their influencing mechanism are different. The impact of AL dimension is partially mediated by employees’ perception of the Sanction, while the impact of BL dimension and ML dimension are partially mediated by employees’ perception of the Information Security Climate. Our research extends the existing literature by introducing the impact of specific leadership styles on employees’ ISP Compliance and discovering the mediating role of Sanction and Information Security Climate. New knowledge is also found about how each PL dimension affects employees’ Compliance in the information security context

Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection

Open-set supervised anomaly detection (OSAD) - a recently emerging anomaly detection area - aims at utilizing a few samples of anomaly classes seen during training to detect unseen anomalies (i.e., samples from open-set anomaly classes), while effectively identifying the seen anomalies. Benefiting from the prior knowledge illustrated by the seen anomalies, current OSAD methods can often largely reduce false positive errors. However, these methods treat the anomaly examples as from a homogeneous distribution, rendering them less effective in generalizing to unseen anomalies that can be drawn from any distribution. In this paper, we propose to learn heterogeneous anomaly distributions using the limited anomaly examples to address this issue. To this end, we introduce a novel approach, namely Anomaly Heterogeneity Learning (AHL), that simulates a diverse set of heterogeneous (seen and unseen) anomaly distributions and then utilizes them to learn a unified heterogeneous abnormality model. Further, AHL is a generic framework that existing OSAD models can plug and play for enhancing their abnormality modeling. Extensive experiments on nine real-world anomaly detection datasets show that AHL can 1) substantially enhance different state-of-the-art (SOTA) OSAD models in detecting both seen and unseen anomalies, achieving new SOTA performance on a large set of datasets, and 2) effectively generalize to unseen anomalies in new target domains.Comment: 18 pages, 5 figure

Effect of hip fracture on prognosis of acute cerebral infarction

OBJECTIVES: Hip fractures are a worldwide public health problem. The incidence of hip fracture is high among the elderly, and it is an important cause of death and disability in this population. This observational study aimed to investigate the effect of acute hip fracture on the recovery of neurological function and the prognosis of patients with acute cerebral infarction, as well as whether surgical treatment of combined acute fracture can improve the prognosis of patients. METHODS: Thirty patients with acute cerebral infarction combined with acute hip fracture, who were hospitalized in two hospitals between January 1, 2013 and December 31, 2019, were included. The patients did not undergo surgical treatment. The control group included patients with common acute cerebral infarction without hip fracture admitted in the same period. The neurological function recovery, hospitalization period, half a year recovery rate, incidence of complications, and one-year mortality rate between the two groups were compared. Eleven patients with acute cerebral infarction combined with hip fracture, who underwent surgical treatment, were selected and compared with those in the non-surgery group. RESULTS: Compared with patients with common acute cerebral infarction, the National Institutes of Health Stroke Scale score of those with acute cerebral infarction combined with hip fracture was higher (7.2±5.4 vs. 5.6%±4.3, p=0.034), the hospitalization period was prolonged (16.1±8.9% vs. 12.2±5.3, p=0.041), and the half a year recovery rate was lower (26.7% vs. 53.3%, p=0.016). Additionally, the incidence of pulmonary infection and lower extremity deep vein thrombosis was increased (30% vs. 11.7%, p=0.03; 6.7% vs. 0, p=0.043). The one-year mortality rate of patients with hip fracture was higher than that of patients with common cerebral infarction (23.3% vs. 6.7%, p=0.027). Compared with the non-surgical group, the good recovery rate after half a year of surgical treatment of the group with cerebral infarction and acute hip fracture had an increasing trend, while the hospitalization cycle, incidence of complications, and one-year mortality rate were all decreased, although this was not statistically significant. CONCLUSIONS: Acute cerebral infarction combined with hip fracture leads to worse neurological recovery, prolonged hospitalization period, increased complications, decreased patient prognosis, and increased one-year mortality. Surgical treatment improves the prognosis of patients with acute cerebral infarction. These findings may provide insights into the management of acute cerebral infarction

Will Security and Privacy Updates Affect Users’ Privacy Choices of Mobile Apps

There is a growing emphasis among users on safeguarding personal privacy and authorization for applications. To address this, Security and Privacy Updates (SPU) are employed to bolster app security, alleviate user apprehensions regarding security, and encourage users to share data and permissions with greater confidence. Based on the Protection Motivation Theory (PMT), we propose that SPU, an IT technology itself, has a dual effect on users’ privacy choices, security threat susceptibility and security response efficacy are the two key mediators to explain this phenomenon, and that this influencing process will be moderated by user’s privacy trade-off. We will investigate this process through a set of online experiments

Visual Prompt Multi-Modal Tracking

Visible-modal object tracking gives rise to a series of downstream multi-modal tracking tributaries. To inherit the powerful representations of the foundation model, a natural modus operandi for multi-modal tracking is full fine-tuning on the RGB-based parameters. Albeit effective, this manner is not optimal due to the scarcity of downstream data and poor transferability, etc. In this paper, inspired by the recent success of the prompt learning in language models, we develop Visual Prompt multi-modal Tracking (ViPT), which learns the modal-relevant prompts to adapt the frozen pre-trained foundation model to various downstream multimodal tracking tasks. ViPT finds a better way to stimulate the knowledge of the RGB-based model that is pre-trained at scale, meanwhile only introducing a few trainable parameters (less than 1% of model parameters). ViPT outperforms the full fine-tuning paradigm on multiple downstream tracking tasks including RGB+Depth, RGB+Thermal, and RGB+Event tracking. Extensive experiments show the potential of visual prompt learning for multi-modal tracking, and ViPT can achieve state-of-the-art performance while satisfying parameter efficiency. Code and models are available at https://github.com/jiawen-zhu/ViPT.Comment: Accepted by CVPR202