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The field of education has undergone a significant revolution with the advent of intelligent systems and technology, which aim to personalize the learning experience, catering to the unique needs and abilities of individual learners. In this pursuit, a ...

Repairing deep neural networks (DNNs) to maintain its performance during deployment presents significant challenges due to the potential occurrence of unknown but common environmental corruptions. Most existing DNN repair methods only focus on repairing ...

Multi-planar magnetic resonance imaging (MRI) can provide comprehensive 3D structural information for disease diagnosis. Compared to multi-source MRI, multi-planar MRI scans target areas in the human body from different directions. This atypical ...

Hand gesture recognition is becoming a more prevalent mode of human-computer interaction, especially as cameras proliferate across everyday devices. Despite continued progress in this field, gesture customization is often underexplored. Customization is ...

Accurate segmentation of echocardiograph images is essential for the diagnosis of cardiovascular diseases. Recent advances in deep learning have opened a possibility for automated cardiac image segmentation. However, the data-driven ...

Learning with limited labelled data, such as prompting, in-context learning, fine-tuning, meta-learning, or few-shot learning, aims to effectively train a model using only a small amount of labelled samples. However, these approaches have been observed to ...

With the emergence of smartphones, Android has become a widely used mobile operating system. However, it is vulnerable when encountering various types of attacks. Every day, new malware threatens the security of users’ devices and private data. Many ...

Automated machine learning (AutoML) streamlines the creation of ML models, but few specialized methods have approached the challenging domain of time series forecasting. Deep neural networks (DNNs) often deliver state-of-the-art predictive performance ...

Spurious correlations are brittle associations between certain attributes of inputs and target variables, such as the correlation between an image background and an object class. Deep image classifiers often leverage them for predictions, leading to poor ...

Deep neural network based Outlier Detection (DOD) has seen a recent surge of attention thanks to the many advances in deep learning. In this paper, we consider a critical-yet-understudied challenge with unsupervised DOD, that is, effective hyperparameter ...

Complex urban road networks with high vehicle occupancy frequently face severe traffic congestion. Designing an effective strategy for managing multiple traffic lights plays a crucial role in managing congestion. However, most current traffic light ...

Reinforcement learning (RL) has shown great promise in optimizing long-term user interest in recommender systems. However, existing RL-based recommendation methods need a large number of interactions for each user to learn the recommendation policy. The ...

Enhancing the performance of recommendation systems through joint modeling of user-item interactions and knowledge graph (KG) information using Graph Neural Networks (GNN) has shown promising results. However, due to the cold-start problem and ...

Few-shot learning aims to use a limited amount of data to complete the model’s training, but complex models often face overfitting. To mitigate overfitting risks, we propose a new meta-learning method termed Meta-Pruning, which diverges from ...

The rapid expansion of single-cell sequencing-based research has motivated a proliferation of variant callers on the sequencing data. Due to the differences on calling strategies, these callers often exhibit varying performance when applied across ...

State-of-the-art Symbolic Regression (SR) algorithms employ evolutionary techniques to fulfill the task of generating a concise mathematical expression that fulfills an objective. A common objective is to fit to a dataset of input-output pairs, in which ...

Multilingual semantic retrieval involves retrieving semantically relevant content to a query irrespective of the language. Compared to monolingual and bilingual semantic retrieval, multilingual semantic retrieval requires a stronger alignment approach to ...

Temporal graphs, depicting time-evolving node connections through temporal edges, are extensively utilized in domains where temporal connection patterns are essential, such as recommender systems, financial networks, healthcare, and sensor networks. ...

With the growing number of available machine learning models and the emergence of model-sharing platforms, model reuse has become a significant approach to harnessing the power of artificial intelligence. One of the key issues to realizing model reuse ...

IEEE 802.15.4-based industrial wireless sensor-actuator networks (WSANs) have been widely deployed to connect sensors, actuators, and controllers in industrial facilities. Configuring an industrial WSAN to meet the application-specified quality of service ...