Graph anomaly detection (GAD) has various applications in finance, healthcare, and security. Graph Neural Networks (GNNs) are now the primary method for GAD, treating it as a task of semi-supervised node classification (normal vs. anomalous). However, ...

• We propose a neural architecture search method for the design of physics-informed neural networks, namely NAS-PINN, to automatically search the optimum neural architecture for solving PDEs.
• By relaxing the discrete search space into a ...

Physics-informed neural network (PINN) has been a prevalent framework for solving PDEs since proposed. By incorporating the physical information into the neural network through loss functions, it can predict solutions to PDEs in an unsupervised ...

Federated Learning (FL) enables a large number of data owners (a.k.a. FL clients) to jointly train a machine learning model without disclosing private local data. The importance of local data samples to the FL model vary widely. This is exacerbated by ...

Human mesh reconstruction (HMR) from monocular video is the key step to many mixed reality and robotic applications. Although existing methods show promising results by capturing frames' temporal information, these methods predict human mesh with the ...

Vision-based hand gesture recognition in human-computer interface design has useful applications in virtual-reality, gaming control, communication through sign language, medical rehabilitation etc. In many scenarios, such applications are deployed on ...

The two-dimensional bin packing problem (2DBP) is a critical optimization problem in the furniture production and glass cutting industries, where the objective is to cut smaller-sized items from a minimum number of large standard-sized raw materials. In ...

The acquisition of explicit user feedback (e.g., ratings) in real-world recommender systems is often hindered by the need for active user involvement. To mitigate this issue, implicit feedback (e.g., clicks) generated during user browsing is exploited ...

This paper investigates the problem of synthesizing proactive defense systems in which the defender can allocate deceptive targets and modify the cost of actions for the attacker who aims to compromise security assets in this system. We model the ...

We initiate the study of how to perturb the reward in a zero-sum Markov game with two players to induce a desirable Nash equilibrium, namely arbitrating. Such a problem admits a bi-level optimization formulation. The lower level requires solving the Nash ...

Graph neural networks (GNNs) have received remarkable success in link prediction (GNNLP) tasks. Existing efforts first predefine the subgraph for the whole dataset and then apply GNNs to encode edge representations by leveraging the neighborhood ...

Data imbalance is still so far a challenging issue in data classification. In literature, cost-sensitive approach has been used to deal with such a challenge. Despite its interesting results, the manual design of cost matrices is still the main ...

This paper presents a new evolutionary method and tool called BMLDS (Bi-level Multi-Label Detection of Smells) that optimizes a population of classifier chains for the multi-label detection of smells. As the chain is sensitive to the labels' (i.e., smell ...

For few-shot learning, minimizing the empirical risk cannot reach the optimal hypothesis from image to its label due to the effect of overfitting. Therefore, most of the existing work leverages a set of base classes with sufficient labeled samples to ...

In contrast to offline working fashions, two research paradigms are devised for online learning: (1) Online Meta-Learning (OML)[6, 20, 26] learns good priors over model parameters (or learning to learn) in a sequential setting where tasks are revealed ...

Multi-level evolution is a bottom-up robotic design paradigm which decomposes the design problem into layered sub-tasks that involve concurrent search for appropriate materials, component geometry and overall morphology. Each of the three layers operate ...

This paper describes the use of bi-level optimization as well as the well-known store-and-forward model for optimization of transport network. The following software products are applied: MATLAB with an additional tool box for modeling and optimization ...

In this paper, we ask a question whether evolutionary algorithms can evolve cryptographic algorithms when no precise design criteria are given. Our strategy utilizes Cartesian Genetic Programming in the bi-level optimization setting with multiple ...

¹With the implementation of dynamic tariffs, the electricity retailer may define distinct energy prices along the day. These tariff schemes encourage consumers to adopt different patterns of consumption with potential savings and enable the retailer to ...

Supply-chain management problems are common to most industries and they involve a hierarchy of subtasks, which must be coordinated well to arrive at an overall optimal solution. Such problems involve a hierarchy of decision-makers, each having its own ...

In general, there are two sorts of strategies of energy saving and CO₂ emissions reduction in transportation systems using wireless sensor network for transportation. They are single-level optimization solutions and the bi-level optimization ones. The bi-...