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Articles 1 - 30 of 35
Full-Text Articles in Engineering
A Structure First Image Inpainting Approach Based On Self-Organizing Map (Som), Bo Chen, Zhaoxia Wang, Ming Bai, Quan Wang, Zhen Sun
A Structure First Image Inpainting Approach Based On Self-Organizing Map (Som), Bo Chen, Zhaoxia Wang, Ming Bai, Quan Wang, Zhen Sun
Research Collection School Of Computing and Information Systems
This paper presents a structure first image inpainting method based on self-organizing map (SOM). SOM is employed to find the useful structure information of the damaged image. The useful structure information which includes relevant edges of the image is used to simulate the structure information of the lost or damaged area in the image. The structure information is described by distinct or indistinct curves in an image in this paper. The obtained target curves separate the damaged area of the image into several parts. As soon as each part of the damaged image is restored respectively, the damaged image is …
Topical Summarization Of Web Videos By Visual-Text Time-Dependent Alignment, Song Tan, Hung-Khoon Tan, Chong-Wah Ngo
Topical Summarization Of Web Videos By Visual-Text Time-Dependent Alignment, Song Tan, Hung-Khoon Tan, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Search engines are used to return a long list of hundreds or even thousands of videos in response to a query topic. Efficient navigation of videos becomes difficult and users often need to painstakingly explore the search list for a gist of the search result. This paper addresses the challenge of topical summarization by providing a timeline-based visualization of videos through matching of heterogeneous sources. To overcome the so called sparse-text problem of web videos, auxiliary information from Google context is exploited. Google Trends is used to predict the milestone events of a topic. Meanwhile, the typical scenes of web …
Map Estimation For Graphical Models By Likelihood Maximization, Akshat Kumar, Shlomo Zilberstein
Map Estimation For Graphical Models By Likelihood Maximization, Akshat Kumar, Shlomo Zilberstein
Research Collection School Of Computing and Information Systems
Computing a maximum a posteriori (MAP) assignment in graphical models is a crucial inference problem for many practical applications. Several provably convergent approaches have been successfully developed using linear programming (LP) relaxation of the MAP problem. We present an alternative approach, which transforms the MAP problem into that of inference in a finite mixture of simple Bayes nets. We then derive the Expectation Maximization (EM) algorithm for this mixture that also monotonically increases a lower bound on the MAP assignment until convergence. The update equations for the EM algorithm are remarkably simple, both conceptually and computationally, and can be implemented …
Automobile Exhaust Gas Detection Based On Fuzzy Temperature Compensation System, Zhiyong Wang, Hao Ding, Fufei Hao, Zhaoxia Wang, Zhen Sun, Shujin Li
Automobile Exhaust Gas Detection Based On Fuzzy Temperature Compensation System, Zhiyong Wang, Hao Ding, Fufei Hao, Zhaoxia Wang, Zhen Sun, Shujin Li
Research Collection School Of Computing and Information Systems
A temperature compensation scheme of detecting automobile exhaust gas based on fuzzy logic inference is presented in this paper. The principles of the infrared automobile exhaust gas analyzer and the influence of the environmental temperature on analyzer are discussed. A fuzzy inference system is designed to improve the measurement accuracy of the measurement equipment by reducing the measurement errors caused by environmental temperature. The case studies demonstrate the effectiveness of the proposed method. The fuzzy compensation scheme is promising as demonstrated by the simulation results in this paper.
Assessing Value Creation And Value Capture In Digital Business Ecosystems, Ravi S. Sharma, Francis Pereira, Narayan Ramasubbu, Margaret Tan, F. Ted Tschang
Assessing Value Creation And Value Capture In Digital Business Ecosystems, Ravi S. Sharma, Francis Pereira, Narayan Ramasubbu, Margaret Tan, F. Ted Tschang
Research Collection Lee Kong Chian School of Business
Interest in business modeling of technology enterprises – the activity of designing the architecture for revenues, costs, products and/or services delivery and the overall value of an enterprise – has risen to prominence with the global crossing of the Internet chasm. However, as several studies have pointed out (c.f., Osterwalder, Pigneur & Tucci, 2005; Teece 2010; Zott & Amit, 2010), the investigations of business models and their fit with the strategy of an enterprise, have received little scholarly attention. In this article we formulate a framework, called ADVISOR, for modeling the business strategies of enterprises in the Interactive Digital Media …
Wireless Sensing Without Sensors: An Experimental Study Of Motion/Intrusion Detection Using Rf Irregularity, Wei Qi Lee, Winston K. G. Seah, Hwee-Pink Tan, Zexi Yao
Wireless Sensing Without Sensors: An Experimental Study Of Motion/Intrusion Detection Using Rf Irregularity, Wei Qi Lee, Winston K. G. Seah, Hwee-Pink Tan, Zexi Yao
Research Collection School Of Computing and Information Systems
Motion and intrusion detection are often cited as wireless sensor network (WSN) applications with typical configurations comprising clusters of wireless nodes equipped with motion sensors to detect human motion. Currently, WSN performance is subjected to several constraints, namely radio irregularity and finite on-board computation/energy resources. Radio irregularity in radio frequency (RF) propagation rises to a higher level in the presence of human activity due to the absorption effect of the human body. In this paper, we investigate the feasibility of monitoring RF transmission for the purpose of intrusion detection through experimentation. With empirical data obtained from the Crossbow TelosB platform …
Data-Driven Approaches To Community-Contributed Video Applications, Xiao Wu, Chong-Wah Ngo, Wan-Lei Zhao
Data-Driven Approaches To Community-Contributed Video Applications, Xiao Wu, Chong-Wah Ngo, Wan-Lei Zhao
Research Collection School Of Computing and Information Systems
With the prosperity of video-sharing websites such as YouTube, the amount of community-contributed video has increased dramatically. Reportedly more than 65,000 new videos were uploaded to YouTube every day in July 2006 and it's estimated that 20 hours of new videos were uploaded to the site every minute in May 2009. In addition to the huge volume of video data, the social Web provides rich contextual and social resources associated with videos. These resources include title, tags, thumbnails, related videos, and user and community information, as illustrated in Figure 1. While billions of user-generated videos accompanied with rich-media information have …
Event Study Method For Validating Agent-Based Trading Simulations, Shih-Fen Cheng
Event Study Method For Validating Agent-Based Trading Simulations, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
In this paper, we introduce how one can validate an event-centric trading simulation platform that is built with multi-agent technology. The issue of validation is extremely important for agent-based simulations, but unfortunately, so far there is no one universal method that would work in all domains. The primary contribution of this paper is a novel combination of event-centric simulation design and event study approach for market dynamics generation and validation. In our event-centric design, the simulation is progressed by announcing news events that affect market prices. Upon receiving these events, event-aware software agents would adjust their views on the market …
Decentralized Resource Allocation And Scheduling Via Walrasian Auctions With Negotiable Agents, Huaxing Chen, Hoong Chuin Lau
Decentralized Resource Allocation And Scheduling Via Walrasian Auctions With Negotiable Agents, Huaxing Chen, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
This paper is concerned with solving decentralized resource allocation and scheduling problems via auctions with negotiable agents by allowing agents to switch their bid generation strategies within the auction process, such that a better system wide performance is achieved on average as compared to the conventional walrasian auction running with agents of fixed bid generation strategy. We propose a negotiation mechanism embedded in auctioneer to solicit bidders’ change of strategies in the process of auction. Finally we benchmark our approach against conventional auctions subject to the real-time large-scale dynamic resource coordination problem to demonstrate the effectiveness of our approach.
On The Annotation Of Web Videos By Efficient Near-Duplicate Search, Wan-Lei Zhao, Xiao Wu, Chong-Wah Ngo
On The Annotation Of Web Videos By Efficient Near-Duplicate Search, Wan-Lei Zhao, Xiao Wu, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
With the proliferation of Web 2.0 applications, usersupplied social tags are commonly available in social media as a means to bridge the semantic gap. On the other hand, the explosive expansion of social web makes an overwhelming number of web videos available, among which there exists a large number of near-duplicate videos. In this paper, we investigate techniques which allow effective annotation of web videos from a data-driven perspective. A novel classifier-free video annotation framework is proposed by first retrieving visual duplicates and then suggesting representative tags. The significance of this paper lies in the addressing of two timely issues …
A Decision Theoretic Approach To Data Leakage Prevention, Janusz Marecki, Mudhakar Srivastava, Pradeep Reddy Varakantham
A Decision Theoretic Approach To Data Leakage Prevention, Janusz Marecki, Mudhakar Srivastava, Pradeep Reddy Varakantham
Research Collection School Of Computing and Information Systems
In both the commercial and defense sectors a compelling need is emerging for rapid, yet secure, dissemination of information. In this paper we address the threat of information leakage that often accompanies such information flows. We focus on domains with one information source (sender) and many information sinks (recipients) where: (i) sharing is mutually beneficial for the sender and the recipients, (ii) leaking a shared information is beneficial to the recipients but undesirable to the sender, and (iii) information sharing decisions of the sender are determined using imperfect monitoring of the (un)intended information leakage by the recipients.We make two key …
Effect Of Human Biases On Human-Agent Teams, Praveen Paruchuri, Pradeep Reddy Varakantham, Katia Sycara, Paul Scerri
Effect Of Human Biases On Human-Agent Teams, Praveen Paruchuri, Pradeep Reddy Varakantham, Katia Sycara, Paul Scerri
Research Collection School Of Computing and Information Systems
As human-agent teams get increasingly deployed in the real-world, agent designers need to take into account that humans and agents have different abilities to specify preferences. In this paper, we focus on how human biases in specifying preferences for resources impacts the performance of large, heterogeneous teams. In particular, we model the inclination of humans to simplify their preference functions and to exaggerate their utility for desired resources, and show the effect of these biases on the team performance. We demonstrate this on two different problems, which are representative of many resource allocation problems addressed in literature. In both these …
The Bi-Objective Master Physician Scheduling Problem, Aldy Gunawan, Hoong Chuin Lau
The Bi-Objective Master Physician Scheduling Problem, Aldy Gunawan, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Physician scheduling is the assignment of physicians to perform different duties in the hospital timetable. In this paper, the goals are to satisfy as many physicians’ preferences and duty requirements as possible while ensuring optimum usage of available resources. We present a mathematical programming model to represent the problem as a bi-objective optimization problem. Three different methods based on ε–Constraint Method, Weighted-Sum Method and HillClimbing algorithm are proposed. These methods were tested on a real case from the Surgery Department of a large local government hospital, as well as on randomly generated problem instances. The strengths and weaknesses of the …
Distributed Route Planning And Scheduling Via Hybrid Conflict Resolution, Ramesh Thangarajoo, Hoong Chuin Lau
Distributed Route Planning And Scheduling Via Hybrid Conflict Resolution, Ramesh Thangarajoo, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
In this paper, we discuss the problem of route planning and scheduling by a group of agents. Each agent is responsible for designing a route plan and schedule over a geographical network, and the goal is to obtain a conflict-free plan/schedule that optimizes a global objective. We present a hybrid conflict resolution method that involves coalition formation and distributed constraint satisfaction depending on the level of coupling between agents. We show how this approach can be effectively applied to solve a distributed convoy movement planning problem.
Investigating Perceptions Of A Location-Based Annotation System, Huynh Nhu Hop Quach, Khasfariyati Razikin, Dion Hoe-Lian Goh, Thi Nhu Quynh Kim, Tan Phat Pham, Yin-Leng Theng, Ee-Peng Lim
Investigating Perceptions Of A Location-Based Annotation System, Huynh Nhu Hop Quach, Khasfariyati Razikin, Dion Hoe-Lian Goh, Thi Nhu Quynh Kim, Tan Phat Pham, Yin-Leng Theng, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
We introduce MobiTOP, a Web-based system for organizing and retrieving hierarchical location-based annotations. Each annotation contains multimedia content (such as text, images, video) associated with a location, and users are able to annotate existing annotations to an arbitrary depth, in effect creating a hierarchy. An evaluation was conducted on a group of potential users to ascertain their perceptions of the usability of the application. The results were generally positive and the majority of the participants saw MobiTOP as a useful platform to share location-based information. We conclude with implications of our work and opportunities for future research.
Coherent Bag-Of Audio Words Model For Efficient Large-Scale Video Copy Detection, Yang Liu, Wan-Lei Zhao, Chong-Wah Ngo, Chang-Sheng Xu, Han-Qing Lu
Coherent Bag-Of Audio Words Model For Efficient Large-Scale Video Copy Detection, Yang Liu, Wan-Lei Zhao, Chong-Wah Ngo, Chang-Sheng Xu, Han-Qing Lu
Research Collection School Of Computing and Information Systems
Current content-based video copy detection approaches mostly concentrate on the visual cues and neglect the audio information. In this paper, we attempt to tackle the video copy detection task resorting to audio information, which is equivalently important as well as visual information in multimedia processing. Firstly, inspired by bag-of visual words model, a bag-of audio words (BoA) representation is proposed to characterize each audio frame. Different from naive singlebased modeling audio retrieval approaches, BoA is a highlevel model due to its perceptual and semantical property. Within the BoA model, a coherency vocabulary indexing structure is adopted to achieve more efficient …
Co-Reranking By Mutual Reinforcement For Image Search, Ting Yao, Tao Mei, Chong-Wah Ngo
Co-Reranking By Mutual Reinforcement For Image Search, Ting Yao, Tao Mei, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Most existing reranking approaches to image search focus solely on mining “visual” cues within the initial search results. However, the visual information cannot always provide enough guidance to the reranking process. For example, different images with similar appearance may not always present the same relevant information to the query. Observing that multi-modality cues carry complementary relevant information, we propose the idea of co-reranking for image search, by jointly exploring the visual and textual information. Co-reranking couples two random walks, while reinforcing the mutual exchange and propagation of information relevancy across different modalities. The mutual reinforcement is iteratively updated to constrain …
Hybrid Time-Frequency Domain Analysis For Inverter-Fed Induction Motor Fault Detection, T. W. Chua, W. W. Tan, Zhaoxia Wang, C. S. Chang
Hybrid Time-Frequency Domain Analysis For Inverter-Fed Induction Motor Fault Detection, T. W. Chua, W. W. Tan, Zhaoxia Wang, C. S. Chang
Research Collection School Of Computing and Information Systems
The detection of faults in an induction motor is important as a part of preventive maintenance. Stator current is one of the most popular signals used for utility-supplied induction motor fault detection as a current sensor can be installed nonintrusively. In variable speeds operation, the use of an inverter to drive the induction motor introduces noise into the stator current so stator current based fault detection techniques become less reliable. This paper presents a hybrid algorithm, which combines time and frequency domain analysis, for broken rotor bar and bearing fault detection. Cluster information obtained by using Independent Component Analysis (ICA) …
Effective Heuristic Methods For Finding Non-Optimal Solutions Of Interest In Constrained Optimization Models, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau
Effective Heuristic Methods For Finding Non-Optimal Solutions Of Interest In Constrained Optimization Models, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
This paper introduces the SoI problem, that of finding nonoptimal solutions of interest for constrained optimization models. SoI problems subsume finding FoIs (feasible solutions of interest), and IoIs (infeasible solutions of interest). In all cases, the interest addressed is post-solution analysis in one form or another. Post-solution analysis of a constrained optimization model occurs after the model has been solved and a good or optimal solution for it has been found. At this point, sensitivity analysis and other questions of import for decision making (discussed in the paper) come into play and for this purpose the SoIs can be of …
On The Sampling Of Web Images For Learning Visual Concept Classifiers, Shiai Zhu, Gang Wang, Chong-Wah Ngo, Yu-Gang Jiang
On The Sampling Of Web Images For Learning Visual Concept Classifiers, Shiai Zhu, Gang Wang, Chong-Wah Ngo, Yu-Gang Jiang
Research Collection School Of Computing and Information Systems
Visual concept learning often requires a large set of training images. In practice, nevertheless, acquiring noise-free training labels with sufficient positive examples is always expensive. A plausible solution for training data collection is by sampling the largely available user-tagged images from social media websites. With the general belief that the probability of correct tagging is higher than that of incorrect tagging, such a solution often sounds feasible, though is not without challenges. First, user-tags can be subjective and, to certain extent, are ambiguous. For instance, an image tagged with “whales” may be simply a picture about ocean museum. Learning concept …
An Analysis Of Extreme Price Shocks And Illiquidity Among Systematic Trend Followers, Bernard Lee, Shih-Fen Cheng, Annie Koh
An Analysis Of Extreme Price Shocks And Illiquidity Among Systematic Trend Followers, Bernard Lee, Shih-Fen Cheng, Annie Koh
Research Collection Lee Kong Chian School Of Business
We construct an agent-based model to study the interplay between extreme price shocks and illiquidity in the presence of systematic traders known as trend followers. The agent-based approach is particularly attractive in modeling commodity markets because the approach allows for the explicit modeling of production, capacities, and storage constraints. Our study begins by using the price stream from a market simulation involving human participants and studies the behavior of various trend-following strategies, assuming initially that their participation will not impact the market. We notice an incremental deterioration in strategy performance as and when strategies deviate further and further from the …
Anytime Planning For Decentralized Pomdps Using Expectation Maximization, Akshat Kumar, Shlomo Zilberstein
Anytime Planning For Decentralized Pomdps Using Expectation Maximization, Akshat Kumar, Shlomo Zilberstein
Research Collection School Of Computing and Information Systems
Decentralized POMDPs provide an expressive framework for multi-agent sequential decision making. While finite-horizon DECPOMDPs have enjoyed signifcant success, progress remains slow for the infinite-horizon case mainly due to the inherent complexity of optimizing stochastic controllers representing agent policies. We present a promising new class of algorithms for the infinite-horizon case, which recasts the optimization problem as inference in a mixture of DBNs. An attractive feature of this approach is the straightforward adoption of existing inference techniques in DBNs for solving DEC-POMDPs and supporting richer representations such as factored or continuous states and actions. We also derive the Expectation Maximization (EM) …
Towards Finding Robust Execution Strategies For Rcpsp/Max With Durational Uncertainty, Na Fu, Pradeep Varakantham, Hoong Chuin Lau
Towards Finding Robust Execution Strategies For Rcpsp/Max With Durational Uncertainty, Na Fu, Pradeep Varakantham, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Resource Constrained Project Scheduling Problems with minimum and maximum time lags (RCPSP/max) have been studied extensively in the literature. However, the more realistic RCPSP/max problems — ones where durations of activities are not known with certainty – have received scant interest and hence are the main focus of the paper. Towards addressing the significant computational complexity involved in tackling RCPSP/max with durational uncertainty, we employ a local search mechanism to generate robust schedules. In this regard, we make two key contributions: (a) Introducing and studying the key properties of a new decision rule to specify start times of activities with …
Open Innovation In Platform Competition, Mei Lin
Open Innovation In Platform Competition, Mei Lin
Research Collection School Of Computing and Information Systems
We examine the competition between a proprietary platform and an open platform,where each platform holds a two-sided market consisted of app developers and users.The open platform cultivates an innovative environment by inviting public efforts todevelop the platform itself and permitting distribution of apps outside of its own appmarket; the proprietary platform restricts apps sales solely within its app market. Weuse a game theoretic model to capture this competitive phenomenon and analyze theimpact of growth of the open source community on the platform competition. We foundthat growth of the open community mitigates the platform rivalry, and balances the developernetwork sizes on …
Point-Based Backup For Decentralized Pompds: Complexity And New Algorithms, Akshat Kumar, Shlomo Zilberstein
Point-Based Backup For Decentralized Pompds: Complexity And New Algorithms, Akshat Kumar, Shlomo Zilberstein
Research Collection School Of Computing and Information Systems
Decentralized POMDPs provide an expressive framework for sequential multi-agent decision making. Despite their high complexity, there has been significant progress in scaling up existing algorithms, largely due to the use of point-based methods. Performing point-based backup is a fundamental operation in state-of-the-art algorithms. We show that even a single backup step in the multi-agent setting is NP-Complete. Despite this negative worst-case result, we present an efficient and scalable optimal algorithm as well as a principled approximation scheme. The optimal algorithm exploits recent advances in the weighted CSP literature to overcome the complexity of the backup operation. The polytime approximation scheme …
Pagesense: Style-Wise Web Page Advertising, Lusong Li, Tao Mei, Xiang Niu, Chong-Wah Ngo
Pagesense: Style-Wise Web Page Advertising, Lusong Li, Tao Mei, Xiang Niu, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
This paper presents an innovative style-wise advertising platform for web page. Web page “style” mainly refers to visual effects, such as color and layout. Unlike the most popular ad-network such as Google AdSense which needs publishers to change the original structure of their pages and define the position and style of the embedded ads manually, stylewise page advertising aims to automatically deliver styleconsistent ads at proper positions within the web page, without breaking the layout of the original page. Our system is motivated from the fact that almost 90% web pages contain blank regions without any content. Given a web …
Managing Media Rich Geo-Spatial Annotations For A Map-Based Mobile Application Using Clustering, Khasfariyati Razikin, Dion Hoe-Lian Goh, Ee Peng Lim, Aixin Sun, Yin-Leng Theng, Thi Nhu Quynh Kim, Kalyani Chatterjea, Chew-Hung Chang
Managing Media Rich Geo-Spatial Annotations For A Map-Based Mobile Application Using Clustering, Khasfariyati Razikin, Dion Hoe-Lian Goh, Ee Peng Lim, Aixin Sun, Yin-Leng Theng, Thi Nhu Quynh Kim, Kalyani Chatterjea, Chew-Hung Chang
Research Collection School Of Computing and Information Systems
With the prevalence of mobile devices that are equipped with wireless Internet capabilities and Global Positioning System (GPS) functionality, the creation and access of user-generated content are extended to users on the go. Such content are tied to real world objects, in the form of geospatial annotations, and it is only natural that these annotations are visualized using a map-based approach. However, viewing maps that are filled with annotations could hinder the serendipitous discovery of data, especially on the small screens of mobile devices. This calls for a need to manage the annotations. In this paper, we introduce a mobile …
Continuous Spatial Assignment Of Moving Users, Hou U Leong, Kyriakos Mouratidis, Nikos Mamoulis
Continuous Spatial Assignment Of Moving Users, Hou U Leong, Kyriakos Mouratidis, Nikos Mamoulis
Research Collection School Of Computing and Information Systems
Consider a set of servers and a set of users, where each server has a coverage region (i.e., an area of service) and a capacity (i.e., a maximum number of users it can serve). Our task is to assign every user to one server subject to the coverage and capacity constraints. To offer the highest quality of service, we wish to minimize the average distance between users and their assigned server. This is an instance of a well-studied problem in operations research, termed optimal assignment. Even though there exist several solutions for the static case (where user locations are fixed), …
A Self-Organizing Neural Architecture Integrating Desire, Intention And Reinforcement Learning, Ah-Hwee Tan, Yu-Hong Feng, Yew-Soon Ong
A Self-Organizing Neural Architecture Integrating Desire, Intention And Reinforcement Learning, Ah-Hwee Tan, Yu-Hong Feng, Yew-Soon Ong
Research Collection School Of Computing and Information Systems
This paper presents a self-organizing neural architecture that integrates the features of belief, desire, and intention (BDI) systems with reinforcement learning. Based on fusion Adaptive Resonance Theory (fusion ART), the proposed architecture provides a unified treatment for both intentional and reactive cognitive functionalities. Operating with a sense-act-learn paradigm, the low level reactive module is a fusion ART network that learns action and value policies across the sensory, motor, and feedback channels. During performance, the actions executed by the reactive module are tracked by a high level intention module (also a fusion ART network) that learns to associate sequences of actions …
An Analysis Of Extreme Price Shocks And Illiquidity Among Trend Followers, Bernard Lee, Shih-Fen Cheng, Annie Koh
An Analysis Of Extreme Price Shocks And Illiquidity Among Trend Followers, Bernard Lee, Shih-Fen Cheng, Annie Koh
Research Collection School Of Computing and Information Systems
We construct an agent-based model to study the interplay between extreme price shocks and illiquidity in the presence of systematic traders known as trend followers. The agent-based approach is particularly attractive in modeling commodity markets because the approach allows for the explicit modeling of production, capacities, and storage constraints. Our study begins by using the price stream from a market simulation involving human participants and studies the behavior of various trend-following strategies, assuming initially that their participation will not impact the market. We notice an incremental deterioration in strategy performance as and when strategies deviate further and further from the …