Digital Commons Network^™

Artificial Intelligence And Data Mining: Algorithms And Applications, Jianhong Xia, Fuding Xie, Yong Zhang, Craig Caulfield

Research outputs 2013

Artificial intelligence and data mining techniques have been used in many domains to solve classification, segmentation, association, diagnosis, and prediction problems. The overall aim of this special issue is to open a discussion among researchers actively working on algorithms and applications. The issue covers a wide variety of problems for computational intelligence, machine learning, time series analysis, remote sensing image mining, and pattern recognition. After a rigorous peer review process, 20 papers have been selected from 38 submissions. The accepted papers in this issue addressed the following topics: (i) advanced artificial intelligence and data mining techniques; (ii) computational intelligence in …

Improving Performance By Re-Rating In The Dynamic Estimation Of Rater Reliability, Alexey Tarasov, Sarah Jane Delany, Brian Macnamee

Conference papers

Nowadays crowdsourcing is widely used in supervised machine learning to facilitate the collection of ratings for unlabelled training sets. In order to get good quality results it is worth rejecting results from noisy/unreliable raters, as soon as they are discovered. Many techniques for filtering unreliable raters rely on the presentation of training instances to the raters identified as most accurate to date. Early in the process, the true rater reliabilities are not known and unreliable raters may be used as a result. This paper explores improving the quality of ratings for train- ing instances by performing re-rating. The re-rating relies …

Coupling Numerical Simulation And Pattern Recognition To Model Production And Evaluate Carbon Dioxide Injection In Shale Gas Reservoir, Amirmasoud Kalantari-Dahaghi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Massive multi-cluster, multi-stage hydraulic fractures have significantly increased the complexity of the flow behavior in shale. This has translated into multiple challenges in the modeling of production from shale wells.

Most commonly used numerical techniques for modeling production from shale wells are Explicit Hydraulic Fracture (EHF) and Stimulated Reservoir Volume (SRV). Model setup for the EHF technique is long and laborious and its implementation is computationally expensive, such that it becomes impractical to model beyond a single pad. On the other hand, identifying the extent and conductivity of SRV is a challenging proposition. SRV technique is commonly used to simplify …

Sensor Feature Selection And Combination For Stress Identification Using Combinatorial Fusion, Yong Deng, Zhonghai Wu, Chao-Hsien Chu, Qixun Zhang, D. Frank Hsu

Research Collection School Of Computing and Information Systems

The identification of stressfulness under certain driving condition is an important issue for safety, security and health. Sensors and systems have been placed or implemented as wearable devices for drivers. Features are extracted from the data collected and combined to predict symptoms. The challenge is to select the feature set most relevant for stress. In this paper, we propose a feature selection method based on the performance and the diversity between two features. The feature sets selected are then combined using a combinatorial fusion. We also compare our results with other combination methods such as naïve Bayes, support vector machine, …

Concept Drift Datasets, Patrick Lindstrom

Doctoral

This zip file contains the datasets used in the PhD thesis:

Lindstrom, P., 2013. Handling Concept Drift in the Context of Expensive Labels. Technological University Dublin. For more information about the datasets please see the README file and the aforementioned thesis.

Automated Parameter Tuning Framework For Heterogeneous And Large Instances: Case Study In Quadratic Assignment Problem, Linda Lindawati, Zhi Yuan, Hoong Chuin Lau, Feida Zhu

Research Collection School Of Computing and Information Systems

This paper is concerned with automated tuning of parameters of algorithms to handle heterogeneous and large instances. We propose an automated parameter tuning framework with the capability to provide instance-specific parameter configurations. We report preliminary results on the Quadratic Assignment Problem (QAP) and show that our framework provides a significant improvement on solutions qualities with much smaller tuning computational time.

Clustering Of Search Trajectory And Its Application To Parameter Tuning, Linda Lindawati, Hoong Chuin Lau, David Lo

Research Collection School Of Computing and Information Systems

This paper is concerned with automated classification of Combinatorial Optimization Problem instances for instance-specific parameter tuning purpose. We propose the CluPaTra Framework, a generic approach to CLUster instances based on similar PAtterns according to search TRAjectories and apply it on parameter tuning. The key idea is to use the search trajectory as a generic feature for clustering problem instances. The advantage of using search trajectory is that it can be obtained from any local-search based algorithm with small additional computation time. We explore and compare two different search trajectory representations, two sequence alignment techniques (to calculate similarities) as well as …

On The Recognition Of Emotion From Physiological Data, Warren Creemers

Theses: Doctorates and Masters

This work encompasses several objectives, but is primarily concerned with an experiment where 33 participants were shown 32 slides in order to create ‗weakly induced emotions‘. Recordings of the participants‘ physiological state were taken as well as a self report of their emotional state. We then used an assortment of classifiers to predict emotional state from the recorded physiological signals, a process known as Physiological Pattern Recognition (PPR). We investigated techniques for recording, processing and extracting features from six different physiological signals: Electrocardiogram (ECG), Blood Volume Pulse (BVP), Galvanic Skin Response (GSR), Electromyography (EMG), for the corrugator muscle, skin temperature …

Uncertain Congestion Games With Assorted Human Agent Populations , Pradeep Reddy Varakantham, Asrar Ahmed, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

Congestion games model a wide variety of real-world resource congestion problems, such as selfish network routing, traffic route guidance in congested areas, taxi fleet optimization and crowd movement in busy areas. However, existing research in congestion games assumes: (a) deterministic movement of agents between resources; and (b) perfect rationality (i.e. maximizing their own expected value) of all agents. Such assumptions are not reasonable in dynamic domains where decision support has to be provided to humans. For instance, in optimizing the performance of a taxi fleet serving a city, movement of taxis can be involuntary or nondeterministic (decided by the specific …

Interpreting Individual Classifications Of Hierarchical Networks, Will Landecker, Michael David Thomure, Luis M.A. Bettencourt, Melanie Mitchell, Garrett T. Kenyon, Steven P. Brumby

Computer Science Faculty Publications and Presentations

Hierarchical networks are known to achieve high classification accuracy on difficult machine-learning tasks. For many applications, a clear explanation of why the data was classified a certain way is just as important as the classification itself. However, the complexity of hierarchical networks makes them ill-suited for existing explanation methods. We propose a new method, contribution propagation, that gives per-instance explanations of a trained network's classifications. We give theoretical foundations for the proposed method, and evaluate its correctness empirically. Finally, we use the resulting explanations to reveal unexpected behavior of networks that achieve high accuracy on visual object-recognition tasks using well-known …

Identification Of Tcp Protocols, Juan Shao

School of Computing: Dissertations, Theses, and Student Research

Recently, many new TCP algorithms, such as BIC, CUBIC, and CTCP, have been deployed in the Internet. Investigating the deployment statistics of these TCP algorithms is meaningful to study the performance and stability of the Internet. Currently, there is a tool named Congestion Avoidance Algorithm Identification (CAAI) for identifying the TCP algorithm of a web server and then for investigating the TCP deployment statistics. However, CAAI using a simple k-NN algorithm can not achieve a high identification accuracy. In this thesis, we comprehensively study the identification accuracy of five popular machine learning models. We find that the random forest model …

Knowledge-Based Exploration For Reinforcement Learning In Self-Organizing Neural Networks, Teck-Hou Teng, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Exploration is necessary during reinforcement learning to discover new solutions in a given problem space. Most reinforcement learning systems, however, adopt a simple strategy, by randomly selecting an action among all the available actions. This paper proposes a novel exploration strategy, known as Knowledge-based Exploration, for guiding the exploration of a family of self-organizing neural networks in reinforcement learning. Specifically, exploration is directed towards unexplored and favorable action choices while steering away from those negative action choices that are likely to fail. This is achieved by using the learned knowledge of the agent to identify prior action choices leading to …

Lagrangian Relaxation For Large-Scale Multi-Agent Planning, Geoffrey J. Gordon, Pradeep Varakantham, William Yeoh, Hoong Chuin Lau, Ajay S. Aravamudhan, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

Multi-agent planning is a well-studied problem with various applications including disaster rescue, urban transportation and logistics, both for autonomous agents and for decision support to humans. Due to computational constraints, existing research typically focuses on one of two scenarios: unstructured domains with many agents where we are content with heuristic solutions, or domains with small numbers of agents or special structure where we can provide provably near-optimal solutions. By contrast, in this paper, we focus on providing provably near-optimal solutions for domains with large numbers of agents, by exploiting a common domain-general property: if individual agents each have limited influence …

Agent-Based Virtual Humans In Co-Space: An Evaluative Study, Yilin Kang, Ah-Hwee Tan, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Co-Space refers to interactive virtual environment modelled after the real world in terms of look-and-feel, functionalities and services. We have developed a 3D virtual world named Nanyang Technological University (NTU) CoSpace populated with virtual human characters. Three key requirements of realistic virtual humans in the virtual world have been identified, namely (1) autonomy: agents can function on their own; (2) interactivity: agents can interact naturally with players; and (3) personality: agents can exhibit human traits and characteristics. Working towards these challenges, we propose a brain-inspired agent architecture that integrates goal-directed autonomy, natural language interaction and human-like personality. We conducted an …

Knowledge-Driven Autonomous Commodity Trading Advisor, Yee Pin Lim, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

The myth that financial trading is an art has been mostly destroyed in the recent decade due to the proliferation of algorithmic trading. In equity markets, algorithmic trading has already bypass human traders in terms of traded volume. This trend seems to be irreversible, and other asset classes are also quickly becoming dominated by the machine traders. However, for asset that requires deeper understanding of physicality, like the trading of commodities, human traders still have significant edge over machines. The primary advantage of human traders in such market is the qualitative expert knowledge that requires traders to consider not just …

Lagrangian Relaxation For Large-Scale Multi-Agent Planning, Geoff Gordon, Pradeep Varakantham, William Yeoh, Hoong Chuin Lau, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

Multi-agent planning is a well-studied problem with various applications including disaster rescue, urban transportation and logistics, both for autonomous agents and for decision support to humans. Due to computational constraints, existing research typically focuses on one of two scenarios: unstructured domains with many agents where we are content with heuristic solutions, or domains with small numbers of agents or special structure where we can provide provably near-optimal solutions. By contrast, in this paper, we focus on providing provably near-optimal solutions for domains with large numbers of agents, by exploiting a common domain-general property: if individual agents each have limited influence …

Effect Of Business Intelligence And It Infrastructure Flexibility On Organizational Agility, Xiaofeng Chen, Keng Siau

Research Collection School Of Computing and Information Systems

There is a growing use of business intelligence (BI) for better management decisions in different industries. However, empirical studies on BI are still scarce in academic research. This research investigates BI from an organizational agility perspective. Organizational agility is the ability to sense and respond to market opportunities and threats with speed. Drawing on systems theory and literature on organizational agility, business intelligence, and IT infrastructure flexibility, we hypothesize that BI use and IT infrastructure flexibility are two major antecedents to organizational agility. We developed a research model to examine the effect of BI use and IT infrastructure flexibility on …

Investigating Intelligent Agents In A 3d Virtual World, Yilin Kang, Fiona Fui-Hoon Nah, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Web 3.0 involves "intelligent" web applications that utilize natural language processing, machine-based learning and reasoning, and intelligent techniques to analyze and understand user behavior. In this research, we empirically assess a specific form of Web 3.0 application in the form of intelligent agents that offer assistance to users in the virtual world. Using media naturalness theory, we hypothesize that the use of intelligent agents in the virtual world can enhance user experience by offering a more natural way of communication and assistance to users. We are interested to test if media naturalness theory holds in the context of intelligent agents …

Agent-Based Virtual Humans In Co-Space: An Evaluative Study, Yilin Kang, Ah-Hwee Tan, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Co-Space refers to interactive virtual environment modelled after the real world in terms of look-and-feel, functionalities and services. We have developed a 3D virtual world named Nan yang Technological University (NTU) Co-Space populated with virtual human characters. Three key requirements of realistic virtual humans in the virtual world have been identified, namely (1) autonomy: agents can function on their own, (2) interactivity: agents can interact naturally with players, and (3) personality: agents can exhibit human traits and characteristics. Working towards these challenges, we propose a brain-inspired agent architecture that integrates goal-directed autonomy, natural language interaction and human-like personality. We conducted …

A Mechanism For Organizing Last-Mile Service Using Non-Dedicated Fleet, Shih-Fen Cheng, Duc Thien Nguyen, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Unprecedented pace of urbanization and rising income levels have fueled the growth of car ownership in almost all newly formed megacities. Such growth has congested the limited road space and significantly affected the quality of life in these megacities. Convincing residents to give up their cars and use public transport is the most effective way in reducing congestion; however, even with sufficient public transport capacity, the lack of last-mile (from the transport hub to the destination) travel services is the major deterrent for the adoption of public transport. Due to the dynamic nature of such travel demands, fixed-size fleets will …

A System For Bidirectional Robotic Pathfinding, Tesca Fitzgerald

Computer Science Faculty Publications and Presentations

The accuracy of an autonomous robot's intended navigation can be impacted by environmental factors affecting the robot's movement. When sophisticated localizing sensors cannot be used, it is important for a pathfinding algorithm to provide opportunities for landmark usage during route execution, while balancing the efficiency of that path. Although current pathfinding algorithms may be applicable, they often disfavor paths that balance accuracy and efficiency needs. I propose a bidirectional pathfinding algorithm to meet the accuracy and efficiency needs of autonomous, navigating robots.

Cognitive Architectures And Autonomy: Commentary And Response, Włodzisław Duch, Ah-Hwee Tan, Stan Franklin

Research Collection School Of Computing and Information Systems

This paper provides a very useful and promising analysis and comparison of current architectures of autonomous intelligent systems acting in real time and specific contexts, with all their constraints. The chosen issue of Cognitive Architectures and Autonomy is really a challenge for AI current projects and future research. I appreciate and endorse not only that challenge but many specific choices and claims; in particular: (i) that “autonomy” is a key concept for general intelligent systems; (ii) that “a core issue in cognitive architecture is the integration of cognitive processes ....”; (iii) the analysis of features and capabilities missing in current …

Benchmarking Still-To-Video Face Recognition Via Partial And Local Linear Discriminant Analysis On Cox-S2v Dataset, Zhiwu Huang, S. Shan, H. Zhang, S. Lao, A. Kuerban, X. Chen

Research Collection School Of Computing and Information Systems

In this paper, we explore the real-world Still-to-Video (S2V) face recognition scenario, where only very few (single, in many cases) still images per person are enrolled into the gallery while it is usually possible to capture one or multiple video clips as probe. Typical application of S2V is mug-shot based watch list screening. Generally, in this scenario, the still image(s) were collected under controlled environment, thus of high quality and resolution, in frontal view, with normal lighting and neutral expression. On the contrary, the testing video frames are of low resolution and low quality, possibly with blur, and captured under …

A Generalized Cluster Centroid Based Classifier For Text Categorization, Guansong Pang, Shengyi Jiang

Research Collection School Of Computing and Information Systems

In this paper, a Generalized Cluster Centroid based Classifier (GCCC) and its variants for text categorization are proposed by utilizing a clustering algorithm to integrate two wellknown classifiers, i.e., the K-nearest-neighbor (KNN) classifier and the Rocchio classifier. KNN, a lazy learning method, suffers from inefficiency in online categorization while achieving remarkable effectiveness. Rocchio, which has efficient categorization performance, fails to obtain an expressive categorization model due to its inherent linear separability assumption. Our proposed method mainly focuses on two points: one point is that we use a clustering algorithm to strengthen the expressiveness of the Rocchio model; another one is …

Self-Regulating Action Exploration In Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

The basic tenet of a learning process is for an agent to learn for only as much and as long as it is necessary. With reinforcement learning, the learning process is divided between exploration and exploitation. Given the complexity of the problem domain and the randomness of the learning process, the exact duration of the reinforcement learning process can never be known with certainty. Using an inaccurate number of training iterations leads either to the non-convergence or the over-training of the learning agent. This work addresses such issues by proposing a technique to self-regulate the exploration rate and training duration …

Uncertain Congestion Games With Assorted Human Agent Populations, Asrar Ahmed, Pradeep Reddy Varakantham, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

Congestion games model a wide variety of real-world resource congestion problems, such as selfish network routing, traffic route guidance in congested areas, taxi fleet optimization and crowd movement in busy areas. However, existing research in congestion games assumes: (a) deterministic movement of agents between resources; and (b) perfect rationality (i.e. maximizing their own expected value) of all agents. Such assumptions are not reasonable in dynamic domains where decision support has to be provided to humans. For instance, in optimizing the performance of a taxi fleet serving a city, movement of taxis can be involuntary or nondeterministic (decided by the specific …

Bidder Behaviors In Repeated B2b Procurement Auctions, Jong Han Park, Jae Kyu Lee, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

B2B auctions play a key role in a firm's procurement process. Even though it is known that repetition is a key characteristic of procurement auctions, traditional auctioneers typically have not put in place a suitable mechanism that supports repetitive auctions effectively. In this paper, we empirically investigate what has taken place in repeated procurement auctions based on real world data from a major outsourcing company of MRO (Maintenance, Repair and Operations) items in Korea. From this empirical study, we discovered the followings. First, we discovered that the repeated bidders contribute majority of all bids, and that the number of new …

Improving Patient Flow In Emergency Department Through Dynamic Priority Queue, Kar Way Tan, Chao Wang, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Most queuing problems are based on FIFO, LIFO, or static priority queues; very few address dynamic priority queues. In this paper, we present a case in a hospital’s emergency department (ED) where the queuing process can be modeled as a time-varying M/M/s queue with re-entrant patients. In order to improve patient flow in the department, we propose the use of a dynamic priority queue to dispatch patients to consultation with doctors. We test our proposed model using simulation and our experimental results show that a dynamic priority queue is effective in reducing the length of stay (LOS) of patients and …

Dynamic Stochastic Orienteering Problems For Risk-Aware Applications, Hoong Chuin Lau, William Yeoh, Pradeep Varakantham, Duc Thien Nguyen

Research Collection School Of Computing and Information Systems

Orienteering problems (OPs) are a variant of the well-known prize-collecting traveling salesman problem, where the salesman needs to choose a subset of cities to visit within a given deadline. OPs and their extensions with stochastic travel times (SOPs) have been used to model vehicle routing problems and tourist trip design problems. However, they suffer from two limitations travel times between cities are assumed to be time independent and the route provided is independent of the risk preference (with respect to violating the deadline) of the user. To address these issues, we make the following contributions: We introduce (1) a dynamic …

Toward Large-Scale Agent Guidance In An Urban Taxi Service, Agussurja Lucas, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Empty taxi cruising represents a wastage of resources in the context of urban taxi services. In this work, we seek to minimize such wastage. An analysis of a large trace of taxi operations reveals that the services’ inefficiency is caused by drivers’ greedy cruising behavior. We model the existing system as a continuous time Markov chain. To address the problem, we propose that each taxi be equipped with an intelligent agent that will guide the driver when cruising for passengers. Then, drawing from AI literature on multiagent planning, we explore two possible ways to compute such guidance. The first formulation …