相关论文: CIPM: Common Identification Process Model for Data…

Few-shot multimodal industrial anomaly detection is a critical yet underexplored task, offering the ability to quickly adapt to complex industrial scenarios. In few-shot settings, insufficient training samples often fail to cover the…

Process mining is a technology that helps understand, analyze, and improve processes. It has been present for around two decades, and although initially tailored for business processes, the spectrum of analyzed processes nowadays is…

While a great variety of 3D cameras have been introduced in recent years, most publicly available datasets for object recognition and pose estimation focus on one single camera. In this work, we present a dataset of 32 scenes that have been…

The California Innocence Project (CIP), a clinical law school program aiming to free wrongfully convicted prisoners, evaluates thousands of mails containing new requests for assistance and corresponding case files. Processing and…

Process discovery aims to derive process models from event logs, providing insights into operational behavior and forming a foundation for conformance checking and process improvement. However, models derived solely from event data may not…

Federated learning (FL) allows participants to jointly train a machine learning model without sharing their private data with others. However, FL is vulnerable to poisoning attacks such as backdoor attacks. Consequently, a variety of…

Process mining is the common name for a range of methods and approaches aimed at analysing and improving processes. Specifically, methods that aim to derive process models from event logs fall under the category of process discovery. Within…

Diffusion Models (DMs) are state-of-the-art generative models that learn a reversible corruption process from iterative noise addition and denoising. They are the backbone of many generative AI applications, such as text-to-image…

We introduce a Parametric Information Maximization (PIM) model for the Generalized Category Discovery (GCD) problem. Specifically, we propose a bi-level optimization formulation, which explores a parameterized family of objective functions,…

As digital transformation continues, enterprises are generating, managing, and storing vast amounts of data, while artificial intelligence technology is rapidly advancing. However, it brings challenges in information security and data…

Image provenance can represent crucial knowledge in criminal investigation and journalistic fact checking. In the last two decades, numerous algorithms have been proposed for obtaining information on the source camera and distribution…

Class-Incremental Learning (CIL) aims to train a reliable model with the streaming data, which emerges unknown classes sequentially. Different from traditional closed set learning, CIL has two main challenges: 1) Novel class detection. The…

Process mining is a field of computer science that deals with discovery and analysis of process models based on automatically generated event logs. Currently, many companies use this technology for optimization and improving their…

Model-Based Anomaly Detection has been a successful approach to identify deviations from the expected behavior of Cyber-Physical Production Systems. Since manual creation of these models is a time-consuming process, it is advantageous to…

Developing intelligent, interoperable Cyber Threat Information (CTI) sharing technologies can help build strong defences against modern cyber threats. CTIs allow the community to share information about cybercriminals' threats and…

In this work, we propose the first framework for integrating Differential Privacy (DP) and Contextual Integrity (CI). DP is a property of an algorithm that injects statistical noise to obscure information about individuals represented…

Image features from a small local region often give strong evidence in person re-identification task. However, CNN suffers from paying too much attention on the most salient local areas, thus ignoring other discriminative clues, e.g., hair,…

Cybersecurity of Industrial Control Systems (ICS) is drawing significant concerns as data communication increasingly leverages wireless networks. A lot of data-driven methods were developed for detecting cyberattacks, but few are focused on…

Model-sharing offers significant business value by enabling firms with well-established Machine Learning (ML) models to monetize and share their models with others who lack the resources to develop ML models from scratch. However, concerns…

The problem of merging databases arises in many government and commercial applications. Schema matching, a common first step, identifies equivalent fields between databases. We introduce a schema matching framework that builds nonparametric…