Haibo Zhang 교수

Recently, an increasing number of laws have governed the useability of users’ privacy. For example, Article 17 of the General Data Protection Regulation (GDPR), the right to be forgotten, requires machine learning applications to remove a portion of data from a dataset and retrain it if the user makes such a request. Furthermore, from the security perspective, training data for machine learning models, i.e., data that may contain user privacy, should be effectively protected, including appropria

This exploratory investigation aims to discuss current status and challenges, especially in aspect of security and trust problems, of digital supply chain management system with applying some advanced information technologies, such as Internet of Things, cloud computing and blockchain, for improving various system performance and properties, i.e. transparency, visibility, accountability, traceability and reliability. This paper introduces the general histories and definitions, in terms of inform

For software engineering, if two code fragments are closely similar with minor modifications or even identical due to a copy-paste behavior, that is called software/code clone. Code clones can cause trouble in software maintenance and debugging process because identifying all copied compromised code fragments in other locations is time-consuming. Researchers have been working on code clone detection issues for a long time, and the discussion mainly focuses on software engineering management and

Deep learning has become one of the most popular research topics today. Researchers have developed cutting-edge learning algorithms and frameworks around deep learning, applying them to a wide range of fields to solve real-world problems. However, we are more concerned about the security risks associated with deep learning models themselves—such as adversarial attacks, which will be discussed in this article. Attackers can use the deep learning model to create the conditions for an attack, malic

In the context of the booming digital economy, integrating industry and education in vocational colleges is crucial for cultivating high-quality technical talent. Currently, there are issues with the collaboration between vocational colleges and enterprises, including outdated collaboration mechanisms, insufficient faculty, and a lack of practical training resources. Three typical models have emerged in practice: the order-based talent co-education model, which customizes training plans through

With the rapid development of informationization in colleges and universities, colleges and universities had established a lot of information systems. In order to use these systems to achieve single sign-on and unified authorization and certification, a unified information system platform for identity authentication platform need to be built. Based on the CAS technology, this paper took the library information system as an example, and studied how to realize the unified authentication in the inf

Adversarial attacks present a significant security risk to image recognition tasks. Defending against these attacks in a real-life setting can be compared to the way antivirus software works, with a key consideration being how well the defense can adapt to new and evolving attacks. Another important factor is the resources involved in terms of time and cost for training defense models and updating the model database. Training many models that are specific to each type of attack can be time-consu

ObjectiveA large number of design follow-ups and actual operation data show that there is a significant deviation between the required capacity and the design capacity of metro main transformers, and a low load rate operation phenomenon of the main transformer is usually existing. The capacity calculation of main transformers involves complex load composition and statistical methods, with numerous complex influencing factors and high calculation difficulty. However, the determination of the capa

This paper discusses key challenges of data processing in the field of artificial intelligence (AI), specifically in dealing with unstructured data and adapting to market changes. We propose a novel AI risk assessment framework by developing a multi-model hybrid scoring system that integrates machine learning and deep learning, focusing on random forests and long Short-Term memory (LSTM) networks. Experimental validation shows that our framework performs more effectively in accurate risk classif

As the new power system accelerates to clean and low-carbon transition, the rise of new energy penetration and the complexity of load structure lead to short-term load forecasting facing the dual challenges of data noise interference and small sample characteristics. Traditional data enhancement methods are difficult to capture the dynamic dependence characteristics of time series data, and are prone to destroying the distribution consistency, which restricts the prediction model accuracy. To th