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Ontology-Based Driving Simulation for Traffic Lights Optimization
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 ...
Fully Linear Graph Convolutional Networks for Semi-Supervised and Unsupervised Classification
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 ...
Customer Volume Prediction Using Fusion of Shared-private Dynamic Weighting over Multiple Modalities
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,...
Reinforced Explainable Knowledge Concept Recommendation in MOOCs
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 ...
Reinforcement Learning for Quantitative Trading
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 ...
Saliency Attack: Towards Imperceptible Black-box Adversarial Attack
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 ...
Representation Learning of Enhanced Graphs Using Random Walk Graph Convolutional Network
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 ...
Robust Dimensionality Reduction via Low-rank Laplacian Graph Learning
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 ...
Hybrid Representation and Decision Fusion towards Visual-textual Sentiment
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 ...
Modeling Within-Basket Auxiliary Item Recommendation with Matchability and Ubiquity
Within-basket recommendation is to recommend suitable items for the current basket with some already known items. The within-basket auxiliary item recommendation (WBAIR) is to recommend auxiliary items based on the primary items in the basket. Such a task ...
Enough Waiting for the Couriers: Learning to Estimate Package Pick-up Arrival Time from Couriers’ Spatial-Temporal Behaviors
- Haomin Wen,
- Youfang Lin,
- Fan Wu,
- Huaiyu Wan,
- Zhongxiang Sun,
- Tianyue Cai,
- Hongyu Liu,
- Shengnan Guo,
- Jianbin Zheng,
- Chao Song,
- Lixia Wu
In intelligent logistics systems, predicting the Estimated Time of Pick-up Arrival (ETPA) of packages is a crucial task, which aims to predict the courier’s arrival time to all the unpicked-up packages at any time. Accurate prediction of ETPA can help ...
Fast Real-Time Video Object Segmentation with a Tangled Memory Network
In this article, we present a fast real-time tangled memory network that segments the objects effectively and efficiently for semi-supervised video object segmentation (VOS). We propose a tangled reference encoder and a memory bank organization mechanism ...
Toward Balancing the Efficiency and Effectiveness in k-Facility Relocation Problem
Facility Relocation (FR), which is an effort to reallocate the placement of facilities to adapt to the changes of urban planning, has remarkable impact on many areas. Existing solutions fail to guarantee the result quality on relocating k > 1 facilities. ...
Hyper-Laplacian Regularized Multi-View Clustering with Exclusive L21 Regularization and Tensor Log-Determinant Minimization Approach
Multi-view clustering aims to capture the multiple views inherent information by identifying the data clustering that reflects distinct features of datasets. Since there is a consensus in literature that different views of a dataset share a common latent ...
MaNIACS: Approximate Mining of Frequent Subgraph Patterns through Sampling
We present MaNIACS, a sampling-based randomized algorithm for computing high-quality approximations of the collection of the subgraph patterns that are frequent in a single, large, vertex-labeled graph, according to the Minimum Node Image-based (MNI) ...
A Discriminant Information Theoretic Learning Framework for Multi-modal Feature Representation
As sensory and computing technology advances, multi-modal features have been playing a central role in ubiquitously representing patterns and phenomena for effective information analysis and recognition. As a result, multi-modal feature representation is ...
Empirical Review of Various Thermography-based Computer-aided Diagnostic Systems for Multiple Diseases
The lifestyle led by today’s generation and its negligence towards health is highly susceptible to various diseases. Developing countries are at a higher risk of mortality due to late-stage presentation, inaccessible diagnosis, and high-cost treatment. ...
Reinforced PU-learning with Hybrid Negative Sampling Strategies for Recommendation
The data of recommendation systems typically only contain the purchased item as positive data and other un-purchased items as unlabeled data. To train a good recommendation model, in addition to the known positive information, we also need high-quality ...