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Articles 1 - 12 of 12
Full-Text Articles in Physical Sciences and Mathematics
Distributing Complementary Resources Across Multiple Periods With Stochastic Demand, Shih-Fen Cheng, John Tajan, Hoong Chuin Lau
Distributing Complementary Resources Across Multiple Periods With Stochastic Demand, Shih-Fen Cheng, John Tajan, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
In this paper, we evaluate whether the robustness of a market mechanism that allocates complementary resources could be improved through the aggregation of time periods in which resources are consumed. In particular, we study a multi-round combinatorial auction that is built on a general equilibrium framework. We adopt the general equilibrium framework and the particular combinatorial auction design from the literature, and we investigate the benefits and the limitation of time-period aggregation when demand-side uncertainties are introduced. By using simulation experiments, we show that under stochastic conditions the performance variation of the process decreases as the time frame length (time …
Recursive Pattern Based Hybrid Supervised Training, Kiruthika Ramanathan, Sheng Uei Guan
Recursive Pattern Based Hybrid Supervised Training, Kiruthika Ramanathan, Sheng Uei Guan
Research Collection School Of Computing and Information Systems
We propose, theorize and implement the Recursive Pattern-based Hybrid Supervised (RPHS) learning algorithm. The algorithm makes use of the concept of pseudo global optimal solutions to evolve a set of neural networks, each of which can solve correctly a subset of patterns. The pattern-based algorithm uses the topology of training and validation data patterns to find a set of pseudo-optima, each learning a subset of patterns. It is therefore well adapted to the pattern set provided. We begin by showing that finding a set of local optimal solutions is theoretically equivalent, and more efficient, to finding a single global optimum …
Video Event Detection Using Motion Relativity And Visual Relatedness, Feng Wang, Yu-Gang Jiang, Chong-Wah Ngo
Video Event Detection Using Motion Relativity And Visual Relatedness, Feng Wang, Yu-Gang Jiang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Event detection plays an essential role in video content analysis. However, the existing features are still weak in event detection because: i) most features just capture what is involved in an event or how the event evolves separately, and thus cannot completely describe the event; ii) to capture event evolution information, only motion distribution over the whole frame is used which proves to be noisy in unconstrained videos; iii) the estimated object motion is usually distorted by camera movement. To cope with these problems, in this paper, we propose a new motion feature, namely Expanded Relative Motion Histogram of Bag-ofVisual-Words …
Generating Robust Schedules Subject To Resource And Duration Uncertainties, Na Fu, Hoong Chuin Lau, Fei Xiao
Generating Robust Schedules Subject To Resource And Duration Uncertainties, Na Fu, Hoong Chuin Lau, Fei Xiao
Research Collection School Of Computing and Information Systems
We consider the Resource-Constrained Project Scheduling Problem with minimal and maximal time lags under resource and duration uncertainties. To manage resource uncertainties, we build upon the work of Lambrechts et al 2007 and develop a method to analyze the effect of resource breakdowns on activity durations. We then extend the robust local search framework of Lau et al 2007 with additional considerations on the impact of unexpected resource breakdowns to the project makespan, so that partial order schedules (POS) can absorb both resource and duration uncertainties. Experiments show that our proposed model is capable of addressing the uncertainty of resources, …
A Heuristic Method For Job-Shop Scheduling With An Infinite Wait Buffer: From One-Machine To Multi-Machine Problems, Z. J. Zhao, J. Kim, M. Luo, Hoong Chuin Lau, S. S. Ge
A Heuristic Method For Job-Shop Scheduling With An Infinite Wait Buffer: From One-Machine To Multi-Machine Problems, Z. J. Zhao, J. Kim, M. Luo, Hoong Chuin Lau, S. S. Ge
Research Collection School Of Computing and Information Systems
Through empirical comparison of classical job shop problems (JSP) with multi-machine consideration, we find that the objective to minimize the sum of weighted tardiness has a better wait property compared with the objective to minimize the makespan. Further, we test the proposed Iterative Minimization Micro-model (IMM) heuristic method with the mixed integer programming (MIP) solution by CPLEX. For multi-machine problems, the IMM heuristic method is faster and achieves a better solution. Finally, for a large problem instance with 409 jobs and 30 types of machines, IMM-heuristic method is compared with ProModel and we find that the heuristic method is slightly …
Relationship Preserving Auction For Repeated E-Procurement, Park J., Lee J., Lau H.
Relationship Preserving Auction For Repeated E-Procurement, Park J., Lee J., Lau H.
Research Collection School Of Computing and Information Systems
While e-procurement auction has helped firms to achieve lower procurement costs, auction mechanisms that prevail at present in procurement markets need to address an important issue that concerns the ability to maintain long term relationships with the partners, especially in repeated e-procurement settings. In this paper, we propose a Relationship Preserving Auction (RPA) mechanism that augments the conventional auction mechanism with a bidder relationship scoring model. Our proposed mechanism gives increased chances of winning to the bidders who have bidden at relatively competitive price but had comparatively less wins so far. Keeping these bidders in the auction over time will …
H-Dpop: Using Hard Constraints For Search Space Pruning In Dcop, Akshat Kumar, Adrian Petcu, Boi Faltings
H-Dpop: Using Hard Constraints For Search Space Pruning In Dcop, Akshat Kumar, Adrian Petcu, Boi Faltings
Research Collection School Of Computing and Information Systems
In distributed constraint optimization problems, dynamic programming methods have been recently proposed (e.g. DPOP). In dynamic programming many valuations are grouped together in fewer messages, which produce much less networking overhead than search. Nevertheless, these messages are exponential in size. The basic DPOP always communicates all possible assignments, even when some of them may be inconsistent due to hard constraints. Many real problems contain hard constraints that significantly reduce the space of feasible assignments. This paper introduces H-DPOP, a hybrid algorithm that is based on DPOP, which uses Constraint Decision Diagrams (CDD) to rule out infeasible assignments, and thus compactly …
Linear Relaxation Techniques For Task Management In Uncertain Settings, Pradeep Varakantham, Stephen F. Smith
Linear Relaxation Techniques For Task Management In Uncertain Settings, Pradeep Varakantham, Stephen F. Smith
Research Collection School Of Computing and Information Systems
In this paper, we consider the problem of assisting a busy user in managing her workload of pending tasks. We assume that our user is typically oversubscribed, and is invariably juggling multiple concurrent streams of tasks (or work flows) of varying importance and urgency. There is uncertainty with respect to the duration of a pending task as well as the amount of follow-on work that may be generated as a result of executing the task. The user’s goal is to be as productive as possible; i.e., to execute tasks that realize the maximum cumulative payoff. This is achieved by enabling …
Multi-View Ear Recognition Based On B-Spline Pose Manifold Construction, Zhiyuan Zhang, Heng Liu
Multi-View Ear Recognition Based On B-Spline Pose Manifold Construction, Zhiyuan Zhang, Heng Liu
Research Collection School Of Computing and Information Systems
In this work, multi-view ear recognition problems are examined in detail. A new multi-view ear recognition approach based on B-Spline pose manifold construction in discriminative projection space which is formed by null kernel discriminant analysis (NKDA) feature extraction is presented. Many experiments and comparisons are provided to show the effectiveness of our multi-view ear recognition approach.
Electric Elves: What Went Wrong And Why, Milind Tambe, Emma Bowring, Jonathan Pearce, Pradeep Reddy Varakantham, Paul Scerri, David V. Pynadath
Electric Elves: What Went Wrong And Why, Milind Tambe, Emma Bowring, Jonathan Pearce, Pradeep Reddy Varakantham, Paul Scerri, David V. Pynadath
Research Collection School Of Computing and Information Systems
Software personal assistants continue to be a topic of significant research interest. This article outlines some of the important lessons learned from a successfully-deployed team of personal assistant agents (Electric Elves) in an office environment. In the Electric Elves project, a team of almost a dozen personal assistant agents were continually active for seven months. Each elf (agent) represented one person and assisted in daily activities in an actual office environment. This project led to several important observations about privacy, adjustable autonomy, and social norms in office environments. In addition to outlining some of the key lessons learned we outline …
Enhancing Recursive Supervised Learning Using Clustering And Combinatorial Optimization (Rsl-Cc), Kiruthika Ramanathan, Sheng Uei Guan
Enhancing Recursive Supervised Learning Using Clustering And Combinatorial Optimization (Rsl-Cc), Kiruthika Ramanathan, Sheng Uei Guan
Research Collection School Of Computing and Information Systems
The use of a team of weak learners to learn a dataset has been shown better than the use of one single strong learner. In fact, the idea is so successful that boosting, an algorithm combining several weak learners for supervised learning, has been considered to be one of the best off-the-shelf classifiers. However, some problems still remain, including determining the optimal number of weak learners and the overfitting of data. In an earlier work, we developed the RPHP algorithm which solves both these problems by using a combination of genetic algorithm, weak learner and pattern distributor. In this paper, …
The Oil Drilling Model And Iterative Deepening Genetic Annealing Algorithm For The Traveling Salesman Problem, Hoong Chuin Lau, Fei Xiao
The Oil Drilling Model And Iterative Deepening Genetic Annealing Algorithm For The Traveling Salesman Problem, Hoong Chuin Lau, Fei Xiao
Research Collection School Of Computing and Information Systems
In this work, we liken the solving of combinatorial optimization problems under a prescribed computational budget as hunting for oil in an unexplored ground. Using this generic model, we instantiate an iterative deepening genetic annealing (IDGA) algorithm, which is a variant of memetic algorithms. Computational results on the traveling salesman problem show that IDGA is more effective than standard genetic algorithms or simulated annealing algorithms or a straightforward hybrid of them. Our model is readily applicable to solve other combinatorial optimization problems.