463 Result(s)
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Chapter and Conference Paper
Analysis of Significant Cell Differences Between Cancer Patients and Healthy Individuals
At the end of 2019, a global outbreak of a new coronavirus ravaged the world, and to this day, many people’s bodies are still deeply affected by the virus. In order to find out if there is a correlation betwee...
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Chapter and Conference Paper
An Efficient Scheduling Algorithm for Multi-mode Tasks on Near-Data Processing SSDs
Near-Data Processing (NDP) architectures have been proposed to alleviate the large overhead of data movement between the host and the Computational Storage Device (CSD) by offloading tasks to the CSD. In NDP a...
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Chapter and Conference Paper
Multi-view Neighbor-Enriched Contrastive Learning Framework for Bundle Recommendation
Bundle recommendation aims to recommend a group of items with a similar theme to users. The previous methods devoted to alleviating the data sparsity problem. However, they either modeled the intuitive interac...
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Chapter and Conference Paper
Ranking Enhanced Supervised Contrastive Learning for Regression
Supervised contrastive learning has shown promising results in image classification tasks where the representations are pulled together if they share same labels or otherwise pushed apart. Such dispersion proc...
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Chapter and Conference Paper
Dynamic Path Planning Based on Traffic Flow Prediction and Traffic Light Status
Traffic flow prediction and path planning are crucial components of effective intelligent transportation systems research. The intelligent transportation system can optimize vehicle driving routes by utilizing...
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Chapter and Conference Paper
MHDF: Multi-source Heterogeneous Data Progressive Fusion for Fake News Detection
Social media platforms are inundated with an extensive volume of unverified information, most of which originates from heterogeneous data from a variety of diverse sources, spreading rapidly and widely, thereb...
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Chapter and Conference Paper
Optimizing GNN Inference Processing on Very Long Vector Processor
Graph Neural Network (GNN) has shown great success in graph learning. However, within the complexity of the real-world tasks and the big graph datasets, current GNN models become increasingly bigger and more c...
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Chapter and Conference Paper
An Efficient Transformer Inference Engine on DSP
The transformer is one of the most important algorithms in the Natural Language Processing(NLP) field and widely used in computer vision recently. Due to the huge computation requirements, the current transfor...
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Chapter and Conference Paper
Hardness of Graph-Structured Algebraic and Symbolic Problems
In this paper, we study the hardness of solving graph-structured linear systems with coefficients over a finite field \(\mathbb {Z}_p\)
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Chapter and Conference Paper
AutoQ: An Automata-Based Quantum Circuit Verifier
We present a specification language and a fully automated tool named AutoQ for verifying quantum circuits symbolically. The tool implements the automata-based algorithm from [14] and extends it with the capabilit...
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Chapter and Conference Paper
Interconnected Neural Linear Contextual Bandits with UCB Exploration
Contextual multi-armed bandit algorithms are widely used to solve online decision-making problems. However, traditional methods assume linear rewards and low dimensional contextual information, leading to high...
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Chapter and Conference Paper
A NUMA-Aware Parallel Truss Decomposition Algorithm for Large Scale Graphs
Truss decomposition algorithm is to decompose a graph into a hierarchical subgraph structure. A k-truss ( \(k \ge 2\) ...
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Chapter and Conference Paper
Domain-Level Pairwise Semantic Interaction for Aspect-Based Sentiment Classification
Aspect-based sentiment classification (ABSC) is a very challenging subtask of sentiment analysis (SA) and suffers badly from the class-imbalance. Existing methods only process sentences independently, without ...
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Chapter and Conference Paper
A Multimodal Fusion Model Based on Hybrid Attention Mechanism for Gesture Recognition
Gesture recognition based on multimodal information plays a significant role in the field of human-computer interaction. In recent years, although many researchers devoted themselves to the related work in thi...
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Chapter and Conference Paper
Unsupervised Domain Adaptation for 3D Medical Image with High Efficiency
Domain adaptation is a fundamental problem in the 3D medical image process. The current methods mainly cut the 3D image into 2D slices and then use 2D CNN for processing, which may ignore the inter-slice infor...
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Chapter and Conference Paper
Towards Synthetic Multivariate Time Series Generation for Flare Forecasting
One of the limiting factors in training data-driven, rare-event prediction algorithms is the scarcity of the events of interest resulting in an extreme imbalance in the data. There have been many methods intro...
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Chapter and Conference Paper
Structure-Enhanced Graph Representation Learning for Link Prediction in Signed Networks
Link prediction in signed networks has attracted widespread attention from researchers recently. Existing studies usually learn a representation vector for each node, which is used for link prediction tasks, b...
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Chapter and Conference Paper
A Multi-task Kernel Learning Algorithm for Survival Analysis
Survival analysis aims to predict the occurring times of certain events of interest. Most existing methods for survival analysis either assume specific forms for the underlying stochastic processes or linear h...
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Chapter and Conference Paper
Probabilistic Model Checking of Randomized Java Code
Java PathFinder (JPF) and PRISM are the most popular model checkers for Java code and systems that exhibit random behaviour, respectively. Our tools make it possible to use JPF and PRISM together. For the firs...
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Chapter and Conference Paper
A Meta-path Based Graph Convolutional Network with Multi-scale Semantic Extractions for Heterogeneous Event Classification
Heterogeneous social events modeling in large and noisy data sources is an important task for applications such as international situation assessment and disaster relief. Accurate and interpretable classificat...