Artificial Intelligence and Robotics Commons^™

Predicting Energy Demand Peak Using M5 Model Trees, Sara S. Abdelkader, Katarina Grolinger, Miriam Am Capretz

Electrical and Computer Engineering Publications

Predicting energy demand peak is a key factor for reducing energy demand and electricity bills for commercial customers. Features influencing energy demand are many and complex, such as occupant behaviours and temperature. Feature selection can decrease prediction model complexity without sacrificing performance. In this paper, features were selected based on their multiple linear regression correlation coefficients. This paper discusses the capabilities of M5 model trees in energy demand prediction for commercial buildings. M5 model trees are similar to regression trees; however they are more suitable for continuous prediction problems. The M5 model tree prediction was developed based on a selected …

Energy Forecasting For Event Venues: Big Data And Prediction Accuracy, Katarina Grolinger, Alexandra L'Heureux, Miriam Am Capretz, Luke Seewald

Electrical and Computer Engineering Publications

Advances in sensor technologies and the proliferation of smart meters have resulted in an explosion of energy-related data sets. These Big Data have created opportunities for development of new energy services and a promise of better energy management and conservation. Sensor-based energy forecasting has been researched in the context of office buildings, schools, and residential buildings. This paper investigates sensor-based forecasting in the context of event-organizing venues, which present an especially difficult scenario due to large variations in consumption caused by the hosted events. Moreover, the significance of the data set size, specifically the impact of temporal granularity, on energy …

Silver Assistants For Aging-In-Place, Di Wang, Budhitama Subagdja, Yilin Kang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

In this demo, we present an assembly of silver assistants for supporting Aging-In-Place (AIP). The virtual agents are designed to serve around the clock to complement human care within the intelligent home environment. Residing in different platforms with ubiquitous access, the agents collaboratively provide holistic care to the elderly users. The demonstration is shown in a 3-D virtual home replicating a typical 5-room apartment in Singapore. Sensory inputs are stored in a knowledge base named Situation Awareness Model (SAM). Therefore, the capabilities of the agents can always be extended by expanding the knowledge defined in SAM. Using the simulation system, …

Preface To Wi-Iat 2015 Workshops And Demo/Posters, Ah-Hwee Tan, Yuefeng Li

Research Collection School Of Computing and Information Systems

This volume contains the papers selected for presentation at the workshops and demonstration/poster track as part of the 2015 IEEE/WIC/ACM International Conference on Web Intelligence (WI’15) and 2015 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT’15) held from 6 to 9 December 2015 in Singapore.

Coordinated Persuasion With Dynamic Group Formation For Collaborative Elderly Care, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Ageing in place demands a new paradigm of inhouse caregiving allowing many aspects of daily lives to be tackled by smart appliances and technologies. The important challenges include the effective provision of recommendations by multiple parties of caregiver constituting changes of the user's behavior. In this multiagent environment, interdependencies between agents become major issues to tackle. This paper presents an approach of dynamic group formation for autonomous caregiving agents to collaborate in recommending different aspects of well-being. The approach supports the agents to regulate the timing of their recommendations, prevent conflicting messages, and cooperate to make more effective persuasions. A …

Robust Execution Strategies For Project Scheduling With Unreliable Resources And Stochastic Durations, Na Fu, Hoong Chuin Lau, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

The resource-constrained project scheduling problem with minimum and maximum time lags (RCPSP/max) is a general model for resource scheduling in many real-world problems (such as manufacturing and construction engineering). We consider RCPSP/max problems where the durations of activities are stochastic and resources can have unforeseen breakdowns. Given a level of allowable risk, (Formula presented.), our mechanisms aim to compute the minimum robust makespan execution strategy. Robust makespan for an execution strategy is any makespan value that has a risk less than (Formula presented.). The risk for a makespan value, (Formula presented.) given an execution strategy, is the probability that a …

Building Crowd Movement Model Using Sample-Based Mobility Survey, Larry J. J. Lin, Shih-Fen Cheng, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Crowd simulation is a well-studied topic, yet it usually focuses on visualization. In this paper, we study a special class of crowd simulation, where individual agents have diverse backgrounds, ad hoc objectives, and non-repeating visits. Such crowd simulation is particularly useful when modeling human agents movement in leisure settings such as visiting museums or theme parks. In these settings, we are interested in accurately estimating aggregate crowd-related movement statistics. As comprehensive monitoring is usually not feasible for a large crowd, we propose to conduct mobility surveys on only a small group of sampled individuals. We demonstrate via simulation that we …

A Layered Hidden Markov Model For Predicting Human Trajectories In A Multi-Floor Building, Qian Li, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Tracking and modeling huge amount of users’ movement in a multi-floor building by using wireless devices is a challenging task, due to crowd movement complexity and signal sensing accuracy. In this paper, we use Layered Hidden Markov Model (LHMM) to fit the spatial-temporal trajectories (with large number of missing values). We decompose the problem into distinct layers that Hidden Markov Models (HMMs) are operated at different spatial granularities separately. Baum-Welch algorithm and Viterbi algorithm are used for finding the probable location sequences at each layer. By measuring the predicted result of trajectories, we compared the predicted results of both single …

Learning And Controlling Network Diffusion In Dependent Cascade Models, Jiali Du, Pradeep Varakantham, Akshat Kumar, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

Diffusion processes have increasingly been used to represent flow of ideas, traffic and diseases in networks. Learning and controlling the diffusion dynamics through management actions has been studied extensively in the context of independent cascade models, where diffusion on outgoing edges from a node are independent of each other. Our work, in contrast, addresses (a) learning diffusion taking management actions to alter the diffusion dynamics to achieve a desired outcome in dependent cascade models. A key characteristic of such dependent cascade models is the flow preservation at all nodes in the network. For example, traffic and people flow is preserved …

Coordinated Persuasion With Dynamic Group Formation For Collaborative Elderly Care, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Ageing in place demands a new paradigm of inhouse caregiving allowing many aspects of daily lives to be tackled by smart appliances and technologies. The important challenges include the effective provision of recommendations by multiple parties of caregiver constituting changes of the user’s behavior. In this multiagent environment, interdependencies between agents become major issues to tackle. This paper presents an approach of dynamic group formation for autonomous caregiving agents to collaborate in recommending different aspects of well-being. The approach supports the agents to regulate the timing of their recommendations, prevent conflicting messages, and cooperate to make more effective persuasions. A …

Oriented Object Proposals, Shengfeng He, Rynson W. H. Lau

Research Collection School Of Computing and Information Systems

In this paper, we propose a new approach to generate oriented object proposals (OOPs) to reduce the detection error caused by various orientations of the object. To this end, we propose to efficiently locate object regions according to pixelwise object probability, rather than measuring the objectness from a set of sampled windows. We formulate the proposal generation problem as a generative probabilistic model such that object proposals of different shapes (i.e., sizes and orientations) can be produced by locating the local maximum likelihoods. The new approach has three main advantages. First, it helps the object detector handle objects of different …

Preface: Wi 2015, Ah-Hwee Tan, Yuefeng Li, Ee-Peng Lim, Jie Zhang, Dell Zhang, Julita Vassileva

Research Collection School Of Computing and Information Systems

This volume contains the papers selected for presentation at the 2015 IEEE/WIC/ACM International Conference on Web Intelligence (WI’15), which was held from 6 to 9 December 2015 in Singapore, a city which welcomes people from different parts of the world to work and play. Following the tradition of WI conference in previous years, WI’15 was collocated with 2015 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT’15). Both WI’15 and IAT’15 were sponsored by the IEEE Computer Society, Web Intelligence Consortium (WIC), Association for Computing Machinery (ACM), and the Memetic Computing Society. The two collocated conferences were hosted by the Joint …

Preface Iat 2015, Ah-Hwee Tan, Yuefeng Li, Ee-Peng Lim, An Bo, Anita Raja, Sarvapali Ramchurn

Research Collection School Of Computing and Information Systems

This volume contains the papers selected for presentation at the 2015 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT’15), which was held from 6 to 9 December 2015 in Singapore, a city which welcomes people from different parts of the world to work and play. Following the tradition of IAT conference in previous years, IAT’15 was collocated with 2015 IEEE/WIC/ACM International Conference on Web Intelligence (WI’15). Both WI’15 and IAT’15 were sponsored by the IEEE Computer Society, Web Intelligence Consortium (WIC), Association for Computing Machinery (ACM), and the Memetic Computing Society. The two collocated conferences were hosted by the Joint …

Non-Intrusive Robust Human Activity Recognition For Diverse Age Groups, Di Wang, Ah-Hwee Tan, Daqing Zhang

Research Collection School Of Computing and Information Systems

—Many elderly prefer to live independently at their own homes. However, how to use modern technologies to ensure their safety presents vast challenges and opportunities. Being able to non-intrusively sense the activities performed by the elderly definitely has great advantages in various circumstances. Non-intrusive activity recognition can be performed using the embedded sensors in modern smartphones. However, not many activity recognition models are robust enough that allow the subjects to carry the smartphones in different pockets with unrestricted orientations and varying deviations. Moreover, to the best of our knowledge, no existing literature studied the difference between the youth and the …

Mlaas: Machine Learning As A Service, Mauro Ribeiro, Katarina Grolinger, Miriam Am Capretz

Electrical and Computer Engineering Publications

The demand for knowledge extraction has been increasing. With the growing amount of data being generated by global data sources (e.g., social media and mobile apps) and the popularization of context-specific data (e.g., the Internet of Things), companies and researchers need to connect all these data and extract valuable information. Machine learning has been gaining much attention in data mining, leveraging the birth of new solutions. This paper proposes an architecture to create a flexible and scalable machine learning as a service. An open source solution was implemented and presented. As a case study, a forecast of electricity demand was …

An Adaptive Markov Strategy For Effective Network Intrusion Detection, Jianye Hao, Yinxing Xue, Mahinthan Chandramohan, Yang Liu, Jun Sun

Research Collection School Of Computing and Information Systems

Network monitoring is an important way to ensure the security of hosts from being attacked by malicious attackers. One challenging problem for network operators is how to distribute the limited monitoring resources (e.g., intrusion detectors) among the network to detect attacks effectively, especially when the attacking strategies can be changing dynamically and unpredictable. To this end, we adopt Markov game to model the interactions between the network operator and the attacker and propose an adaptive Markov strategy (AMS) to determine how the detectors should be placed on the network against possible attacks to minimize the network’s accumulated cost over time. …

Gaussian Nonlinear Line Attractor For Learning Multidimensional Data, Theus H. Aspiras, Vijayan K. Asari, Wesam Sakla

Electrical and Computer Engineering Faculty Publications

The human brain’s ability to extract information from multidimensional data modeled by the Nonlinear Line Attractor (NLA), where nodes are connected by polynomial weight sets. Neuron connections in this architecture assumes complete connectivity with all other neurons, thus creating a huge web of connections. We envision that each neuron should be connected to a group of surrounding neurons with weighted connection strengths that reduces with proximity to the neuron. To develop the weighted NLA architecture, we use a Gaussian weighting strategy to model the proximity, which will also reduce the computation times significantly.

Once all data has been trained in …

Shineseniors: Personalized Services For Active Ageing-In-Place, Liming Bai, Alex I. Gavino, Wei Qi Lee, Jungyoon Kim, Na Liu, Hwee-Pink Tan, Hwee Xian Tan, Lee Buay Tan, Xiaoping Toh, Alvin Cerdena Valera, Elina Jia Yu, Alfred Wu, Mark S. Fox

Research Collection School Of Computing and Information Systems

Singapore faces a major challenge in providing care and support for senior citizens due to its rapidlyageing population and declining old-age support ratio. The concept of Ageing-in-Place was introduced by the Singapore government [1] to allow older people to live independently in their own homes and communities so that the need for institutionalised care will only be utilised when necessary. We have three fundamental questions that this project will answer: 1. How to make community care serviceseffective through innovations in care delivery? How to lower the cost of service delivery and improve 2. productivity of caregivers, by leveraging information and …

The Effectiveness Of Using A Modified “Beat Frequent Pick” Algorithm In The First International Roshambo Tournament, Proceso L. Fernandez Jr, Sony E. Valdez, Generino P. Siddayao

Department of Information Systems & Computer Science Faculty Publications

In this study, a bot is developed to compete in the first International RoShamBo Tournament test suite. The basic “Beat Frequent Pick (BFP)” algorithm was taken from the supplied test suite and was improved by adding a random choice tailored fit against the opponent's distribution of picks. A training program was also developed that finds the best performing bot variant by changing the bot's behavior in terms of the timing of the recomputation of the pick distribution. Simulation results demonstrate the significantly improved performance of the proposed variant over the original BFP. This indicates the potential of using the core …

Density Peaks Clustering Approach For Discovering Demand Hot Spots In City-Scale Taxi Fleet Dataset, Dongchang Liu, Shih-Fen Cheng, Yiping Yang

Research Collection School Of Computing and Information Systems

In this paper, we introduce a variant of the density peaks clustering (DPC) approach for discovering demand hot spots from a low-frequency, low-quality taxi fleet operational dataset. From the literature, the DPC approach mainly uses density peaks as features to discover potential cluster centers, and this requires distances between all pairs of data points to be calculated. This implies that the DPC approach can only be applied to cases with relatively small numbers of data points. For the domain of urban taxi operations that we are interested in, we could have millions of demand points per day, and calculating all-pair …

Inferring Door Locations From A Teammate's Trajectory In Stealth Human-Robot Team Operations, Jean Oh, Arne Suppe, Arne Suppe, Anthony Stentz, Martial Hebert

Research Collection School Of Computing and Information Systems

Robot perception is generally viewed as the interpretation of data from various types of sensors such as cameras. In this paper, we study indirect perception where a robot can perceive new information by making inferences from non-visual observations of human teammates. As a proof-of-concept study, we specifically focus on a door detection problem in a stealth mission setting where a team operation must not be exposed to the visibility of the team's opponents. We use a special type of the Noisy-OR model known as BN2O model of Bayesian inference network to represent the inter-visibility and to infer the locations of …

Modeling Flood Risk For An Urban Cbd Using Ahp And Gis, Proceso L. Fernandez Jr, Generino P. Siddayao, Sony E. Valdez

Department of Information Systems & Computer Science Faculty Publications

The Central Business District (CBD) of a city is the activity center of the city, typically locating the main commercial and cultural establishments, as well as acting as the center point of the city’s transportation network. Flood risk assessment for a CBD is crucial for proper city planning and maintenance. In this study, we model the flood risk for the CBD of Tuguegarao City, which is located in northern Philippines. To accomplish this, we identified important flood-related factors whose data are either easily available or may be collected through some automated process that we developed. We then surveyed experts to …

Reformulation Strategies Of Repeated References In The Context Of Robot Perception Errors In Situated Dialogue, Niels Schütte, John D. Kelleher, Brian Mac Namee

Conference papers

We performed an experiment in which human participants interacted through a natural language dialogue interface with a simulated robot to fulfil a series of object manipulation tasks. We introduced errors into the robot’s perception, and observed the resulting problems in the dialogues and their resolutions. We then introduced different methods for the user to request information about the robot’s understanding of the environment. In this work, we describe the effects that the robot’s perceptual errors and the information request options available to the participant had on the reformulation of the referring expressions the participants used when resolving a unsuccessful reference.

Designing Bus Transit Services For Routine Crowd Situations At Large Event Venues, Jianli Du, Shih-Fen Cheng, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We are concerned with the routine crowd management problem after a major event at a known venue. Without properly design complementary transport services, such sudden crowd build-ups will overwhelm the existing infrastructure. In this paper, we introduce a novel flow-rate based model to model the dynamic movement of passengers over the transportation flow network. Based on this basic model, an integer linear programming model is proposed to solve the bus transit problem permanently. We validate our model against a real scenario in Singapore, where a newly constructed mega-stadium hosts various large events regularly. The results show that the proposed approach …

A Survey On Artificial Intelligence-Based Modeling Techniques For High Speed Milling Processes, Amin Jahromi Torabi, Meng Joo Er, Xiang Li, Beng Siong Lim, Lianyin Zhai, Richard Jayadi Oentaryo, Gan Oon Peen, Jacek M. Zurada

Research Collection School Of Computing and Information Systems

The process of high speed milling is regarded as one of the most sophisticated and complicated manufacturing operations. In the past four decades, many investigations have been conducted on this process, aiming to better understand its nature and improve the surface quality of the products as well as extending tool life. To achieve these goals, it is necessary to form a general descriptive reference model of the milling process using experimental data, thermomechanical analysis, statistical or artificial intelligence (AI) models. Moreover, increasing demands for more efficient milling processes, qualified surface finishing, and modeling techniques have propelled the development of more …

Ad-Hoc Automated Teller Machine Failure Forecast And Field Service Optimization, Michelle L. F. Cheong, Ping Shung Koo, B. Chandra Babu

Research Collection School Of Computing and Information Systems

As part of its overall effort to maintain good customer service while managing operational efficiency and reducing cost, a bank in Singapore has embarked on using data and decision analytics methodologies to perform better ad-hoc ATM failure forecasting and plan the field service engineers to repair the machines. We propose using a combined Data and Decision Analytics Framework which helps the analyst to first understand the business problem by collecting, preparing and exploring data to gain business insights, before proposing what objectives and solutions can and should be done to solve the problem. This paper reports the work in analyzing …

A Visual Analysis Of Articulated Motion Complexity Based On Optical Flow And Spatial-Temporal Features, Beau Michael Christ

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

The understanding of motion is an important problem in computer vision with applications including crowd-flow analysis, video surveillance, and estimating three-dimensional structure. A less-explored problem is the visual characterization and quantification of motion complexity. An important motion class that is prevalent in living beings is articulated motion (segments connected by joints). At present, no known standardized measure for quantifying the complexity of articulated motion exists. Such a measure could facilitate advanced motion analysis with applications including video indexing, motion comparison, and advanced biological study of visual signals in organisms.

This dissertation presents an in-depth study of the development of several …

Message Passing For Collective Graphical Models, Tao Sun, Daniel Sheldon, Akshat Kumar

Research Collection School Of Computing and Information Systems

Collective graphical models (CGMs) are a formalism for inference and learning about a population of independent and identically distributed individuals when only noisy aggregate data are available. We highlight a close connection between approximate MAP inference in CGMs and marginal inference in standard graphical models. The connection leads us to derive a novel Belief Propagation (BP) style algorithm for collective graphical models. Mathematically, the algorithm is a strict generalization of BP—it can be viewed as an extension to minimize the Bethe free energy plus additional energy terms that are non-linear functions of the marginals. For CGMs, the algorithm is much …

An Adaptive Computational Model For Personalized Persuasion, Yilin Kang, Ah-Hwee Tan, Chunyan Miao

Research Collection School Of Computing and Information Systems

While a variety of persuasion agents have been created and applied in different domains such as marketing, military training and health industry, there is a lack of a model which can provide a unified framework for different persuasion strategies. Specifically, persuasion is not adaptable to the individuals’ personal states in different situations. Grounded in the Elaboration Likelihood Model (ELM), this paper presents a computational model called Model for Adaptive Persuasion (MAP) for virtual agents. MAP is a semi-connected network model which enables an agent to adapt its persuasion strategies through feedback. We have implemented and evaluated a MAP-based virtual nurse …

Probabilistic Inference Based Message-Passing For Resource Constrained Dcops, Supriyo Ghosh, Akshat Kumar, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Distributed constraint optimization (DCOP) is an important framework for coordinated multiagent decision making. We address a practically useful variant of DCOP, called resource-constrained DCOP (RC-DCOP), which takes into account agents’ consumption of shared limited resources. We present a promising new class of algorithm for RC-DCOPs by translating the underlying co- ordination problem to probabilistic inference. Using inference techniques such as expectation- maximization and convex optimization machinery, we develop a novel convergent message-passing algorithm for RC-DCOPs. Experiments on standard benchmarks show that our approach provides better quality than previous best DCOP algorithms and has much lower failure rate. Comparisons against an …