ACM Transactions on Intelligent Systems and Technology

This article presents FLGC, a simple yet effective fully linear graph convolutional network for semi-supervised and unsupervised learning. Instead of using gradient descent, we train FLGC based on computing a global optimal closed-form solution with a ...

Traffic lights optimization is one of the principal components to lessen the traffic flow and travel time in an urban area. The present article seeks to introduce a novel procedure to design the traffic lights in a city using evolutionary-based ...

Three-dimensional (3D) modeling of non-linear objects from stylized sketches is a challenge even for computer graphics experts. The extrapolation of object parameters from a stylized sketch is a very complex and cumbersome task. In the present study, we ...

Customer volume prediction is crucial for a variety of urban applications, such as store location selection. So far, the key challenge lies in how to fuse multiple modalities from different data sources, on account of the massive amount of data accessible,...

In this article, we study knowledge concept recommendation in Massive Open Online Courses (MOOCs) in an explainable manner. Knowledge concepts, composing course units (e.g., videos) in MOOCs, refer to topics and skills that students are expected to ...

Quantitative trading (QT), which refers to the usage of mathematical models and data-driven techniques in analyzing the financial market, has been a popular topic in both academia and financial industry since 1970s. In the last decade, reinforcement ...

Deep neural networks are vulnerable to adversarial examples, even in the black-box setting where the attacker is only accessible to the model output. Recent studies have devised effective black-box attacks with high query efficiency. However, such ...

Nowadays, graph structure data has played a key role in machine learning because of its simple topological structure, and therefore, the graph representation learning methods have attracted great attention. And it turns out that the low-dimensional ...

Manifold learning is a widely used technique for dimensionality reduction as it can reveal the intrinsic geometric structure of data. However, its performance decreases drastically when data samples are contaminated by heavy noise or occlusions, which ...

The rising use of online media has changed social customs of the public. Users have become gradually accustomed to sharing daily experiences and publishing personal opinions on social networks. Social data carrying with emotions and attitudes have ...