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Articles 1 - 6 of 6
Full-Text Articles in Theory and Algorithms
Robust Test Selection For Deep Neural Networks, Weifeng Sun, Meng Yan, Zhongxin Liu, David Lo
Robust Test Selection For Deep Neural Networks, Weifeng Sun, Meng Yan, Zhongxin Liu, David Lo
Research Collection School Of Computing and Information Systems
Deep Neural Networks (DNNs) have been widely used in various domains, such as computer vision and software engineering. Although many DNNs have been deployed to assist various tasks in the real world, similar to traditional software, they also suffer from defects that may lead to severe outcomes. DNN testing is one of the most widely used methods to ensure the quality of DNNs. Such method needs rich test inputs with oracle information (expected output) to reveal the incorrect behaviors of a DNN model. However, manually labeling all the collected test inputs is a labor-intensive task, which delays the quality assurance …
Deep Reinforcement Learning With Explicit Context Representation, Francisco Munguia-Galeano, Ah-Hwee Tan, Ze Ji
Deep Reinforcement Learning With Explicit Context Representation, Francisco Munguia-Galeano, Ah-Hwee Tan, Ze Ji
Research Collection School Of Computing and Information Systems
Though reinforcement learning (RL) has shown an outstanding capability for solving complex computational problems, most RL algorithms lack an explicit method that would allow learning from contextual information. On the other hand, humans often use context to identify patterns and relations among elements in the environment, along with how to avoid making wrong actions. However, what may seem like an obviously wrong decision from a human perspective could take hundreds of steps for an RL agent to learn to avoid. This article proposes a framework for discrete environments called Iota explicit context representation (IECR). The framework involves representing each state …
Maximizing Multifaceted Network Influence, Yuchen Li, Ju Fan, George V. Ovchinnikov, Panagiotis Karras
Maximizing Multifaceted Network Influence, Yuchen Li, Ju Fan, George V. Ovchinnikov, Panagiotis Karras
Research Collection School Of Computing and Information Systems
An information dissemination campaign is often multifaceted, involving several facets or pieces of information disseminating from different sources. The question then arises, how should we assign such pieces to eligible sources so as to achieve the best viral dissemination results? Past research has studied the problem of Influence Maximization (IM), which is to select a set of k promoters that maximizes the expected reach of a message over a network. However, in this classical IM problem, each promoter spreads out the same unitary piece of information. In this paper, we propose the Optimal Influential Pieces Assignment (OIPA) problem, which is …
Qos Routing Optimization Strategy Using Genetic Algorithm In Optical Fiber Communication Networks, Zhaoxia Wang, Zengqiang Chen, Zhuzhi Yuan
Qos Routing Optimization Strategy Using Genetic Algorithm In Optical Fiber Communication Networks, Zhaoxia Wang, Zengqiang Chen, Zhuzhi Yuan
Research Collection School Of Computing and Information Systems
This paper describes the routing problems in optical fiber networks, defines five constraints, induces and simplifies the evaluation function and fitness function, and proposes a routing approach based on the genetic algorithm, which includes an operator [OMO] to solve the QoS routing problem in optical fiber communication networks. The simulation results show that the proposed routing method by using this optimal maintain operator genetic algorithm (OMOGA) is superior to the common genetic algorithms (CGA). It not only is robust and efficient but also converges quickly and can be carried out simply, that makes it better than other complicated GA.
Cascade Artmap: Integrating Neural Computation And Symbolic Knowledge Processing, Ah-Hwee Tan
Cascade Artmap: Integrating Neural Computation And Symbolic Knowledge Processing, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
This paper introduces a hybrid system termed cascade adaptive resonance theory mapping (ARTMAP) that incorporates symbolic knowledge into neural-network learning and recognition. Cascade ARTMAP, a generalization of fuzzy ARTMAP, represents intermediate attributes and rule cascades of rule-based knowledge explicitly and performs multistep inferencing. A rule insertion algorithm translates if-then symbolic rules into cascade ARTMAP architecture. Besides that initializing networks with prior knowledge can improve predictive accuracy and learning efficiency, the inserted symbolic knowledge can be refined and enhanced by the cascade ARTMAP learning algorithm. By preserving symbolic rule form during learning, the rules extracted from cascade ARTMAP can be compared …
Inductive Neural Logic Network And The Scm Algorithm, Ah-Hwee Tan, Loo-Nin Teow
Inductive Neural Logic Network And The Scm Algorithm, Ah-Hwee Tan, Loo-Nin Teow
Research Collection School Of Computing and Information Systems
Neural Logic Network (NLN) is a class of neural network models that performs both pattern processing and logical inferencing. This article presents a procedure for NLN to learn multi-dimensional mapping of both binary and analog data. The procedure, known as the Supervised Clustering and Matching (SCM) algorithm, provides a means of inferring inductive knowledge from databases. In contrast to gradient descent error correction methods, pattern mapping is learned by an inductive NLN using fast and incremental clustering of input and output patterns. In addition, learning/encoding only takes place when both the input and output match criteria are satisfied in a …