1,655 Result(s)
within
**mo Wang
Algorithm Analysis and Problem Complexity
Computer Communication Networks
Information Systems Applications (incl. Internet)
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Chapter and Conference Paper
Multi-Asset Market Making via Multi-Task Deep Reinforcement Learning
Market making (MM) is a trading activity by an individual market participant or a member firm of an exchange that buys and sells same securities with the primary goal of profiting on the bid-ask spread, which ...
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Chapter and Conference Paper
Improved Migrating Birds Optimization Algorithm to Solve Hybrid Flowshop Scheduling Problem with Lot-Streaming of Random Breakdown
An improved migrating birds optimization (IMBO) algorithm is proposed to solve the hybrid flowshop scheduling problem with lot-streaming of random breakdown (RBHLFS) with the aim of minimizing the total flow t...
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Chapter and Conference Paper
Auditing for Core Stability in Participatory Budgeting
We consider the participatory budgeting problem where each of n voters specifies additive utilities over m candidate projects with given sizes, and the goal is to choose a subset of projects (i.e., a committee) w...
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Chapter and Conference Paper
An Integrated Approach to Produce Robust Deep Neural Network Models with High Efficiency
Deep Neural Networks (DNNs) need to be both efficient and robust for practical uses. Quantization and structure simplification are promising ways to adapt DNNs to mobile devices, and adversarial training is on...
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Chapter and Conference Paper
On Tree Equilibria in Max-Distance Network Creation Games
We study the Nash equilibrium and the price of anarchy in the max-distance network creation game. The network creation game, first introduced and studied by Fabrikant et al. [18], is a classic model for real-worl...
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Chapter and Conference Paper
An IoT Ontology Class Recommendation Method Based on Knowledge Graph
Ontology is a formal representation of a domain using a set of concepts of the domain and how these concepts are related. Class is one of the components of an ontology for describing the concepts of the system...
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Chapter and Conference Paper
A Property-Based Method for Acquiring Commonsense Knowledge
Commonsense knowledge is crucial in a variety of AI applications. However, one kind of commonsense knowledge that has not received attention is that of properties of actions denoted by verbs. To address this l...
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Chapter and Conference Paper
GASKT: A Graph-Based Attentive Knowledge-Search Model for Knowledge Tracing
Knowledge tracking (KT) is a fundamental tool to customize personalized learning paths for students so that they can take charge of their own learning pace. The main task of KT is to model the learning state o...
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Chapter and Conference Paper
Extracting Prerequisite Relations Among Wikipedia Concepts Using the Clickstream Data
A prerequisite relation describes a basic dependency relation between concepts in education, cognition and other fields. Especially, prerequisite relations among concepts play a very important role in various ...
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Chapter and Conference Paper
Multi-hop Learning Promote Cooperation in Multi-agent Systems
The behavior of individuals maximizing their own benefits in some systems leads to decline of system performance. A challenge remains to promote and maintain cooperation between selfish individuals in multi-ag...
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Chapter and Conference Paper
Identification of Critical Nodes in Urban Transportation Network Through Network Topology and Server Routes
The identification of critical nodes has great practical significance to the urban transportation network (UTN) due to its contribution to enhancing the efficient operation of UTN. Several existing studies hav...
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Chapter and Conference Paper
Additive Noise Model Structure Learning Based on Rank Statistics
To examine the structural learning of the additive noise model in causal discovery, a new algorithm RCS (Rank-Correlation-Statistics) is proposed in combination with the rank correlation method. This algorithm...
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Chapter and Conference Paper
Improved Partitioning Graph Embedding Framework for Small Cluster
Graph embedding is a crucial method to produce node features that can be used for various machine learning tasks. Because of the large number of embedded parameters in large graphs, a single machine cannot loa...
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Chapter and Conference Paper
An Ensemble Fuzziness-Based Online Sequential Learning Approach and Its Application
Traditional deep learning algorithms are difficult to deploy on most IoT terminal devices due to their limited computing power. To solve this problem, this paper proposes a novel ensemble fuzziness-based onlin...
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Chapter and Conference Paper
Research on Innovation Trends of AI Applied to Medical Instruments Using Informetrics Based on Multi-sourse Information
COVID-19 accelerates the application of AI in medical field, and the global research and development on AI technology applied to medical instruments also become more important and noticeable. The innovation tr...
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Chapter and Conference Paper
Optimization of Remote Desktop with CNN-based Image Compression Model
Remote desktop systems become commonly used for users to enhance the efficiency of their daily tasks commonly. In this work, we propose an expanded image compression model with convolutional neural network (CNN) ...
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Chapter and Conference Paper
Node-Image CAE: A Novel Embedding Method via Convolutional Auto-encoder and High-Order Proximities
The network embedding method, which mainly aims to generate low-dimensional embedding vectors for nodes in a network, has received great passion from researchers and provides excellent tools for application an...
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Chapter and Conference Paper
Community Detection in Dynamic Networks: A Novel Deep Learning Method
Dynamic community detection has become a hot spot of researches, which helps detect the revolving relationships of complex systems. In view of the great value of dynamic community detection, various kinds of d...
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Chapter and Conference Paper
Clustering Massive-Categories and Complex Documents via Graph Convolutional Network
In recent years, a significant amount of text data are being generated on the Internet and in digital applications. Clustering the unlabeled documents becomes an essential task in many areas such as automated ...
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Chapter and Conference Paper
Knowledge Distillation via Channel Correlation Structure
Knowledge distillation (KD) has been one of the most popular techniques for model compression and acceleration, where a compact student model can be trained under the guidance of a large-capacity teacher model...
