Artificial Intelligence and Robotics Commons^™

Proactive And Reactive Coordination Of Non-Dedicated Agent Teams Operating In Uncertain Environments, Pritee Agrawal, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Domains such as disaster rescue, security patrolling etc. often feature dynamic environments where allocations of tasks to agents become ineffective due to unforeseen conditions that may require agents to leave the team. Agents leave the team either due to arrival of high priority tasks (e.g., emergency, accident or violation) or due to some damage to the agent. Existing research in task allocation has only considered fixed number of agents and in some instances arrival of new agents on the team. However, there is little or no literature that considers situations where agents leave the team after task allocation. To that …

An Analysis Of The Application Of Simplified Silhouette To The Evaluation Of K-Means Clustering Validity, Fei Wang, Hector-Hugo Franco-Penya, John D. Kelleher, John Pugh, Robert J. Ross

Conference papers

Silhouette is one of the most popular and effective internal measures for the evaluation of clustering validity. Simplified Silhouette is a computationally simplified version of Silhouette. However, to date Simplified Silhouette has not been systematically analysed in a specific clustering algorithm. This paper analyses the application of Simplified Silhouette to the evaluation of k-means clustering validity and compares it with the k-means Cost Function and the original Silhouette from both theoretical and empirical perspectives. The theoretical analysis shows that Simplified Silhouette has a mathematical relationship with both the k-means Cost Function and the original Silhouette, while empirically, we show that …

Motion-Capture-Based Hand Gesture Recognition For Computing And Control, Andrew Gardner

Doctoral Dissertations

This dissertation focuses on the study and development of algorithms that enable the analysis and recognition of hand gestures in a motion capture environment. Central to this work is the study of unlabeled point sets in a more abstract sense. Evaluations of proposed methods focus on examining their generalization to users not encountered during system training.

In an initial exploratory study, we compare various classification algorithms based upon multiple interpretations and feature transformations of point sets, including those based upon aggregate features (e.g. mean) and a pseudo-rasterization of the capture space. We find aggregate feature classifiers to be balanced across …

Deshadownet: A Multi-Context Embedding Deep Network For Shadow Removal, Liangqiong Qu, Jiandong Tian, Shengfeng He, Yandong Tang, Rynson W. H. Lau

Research Collection School Of Computing and Information Systems

Shadow removal is a challenging task as it requires the detection/annotation of shadows as well as semantic understanding of the scene. In this paper, we propose an automatic and end-to-end deep neural network (DeshadowNet) to tackle these problems in a unified manner. DeshadowNet is designed with a multi-context architecture, where the output shadow matte is predicted by embedding information from three different perspectives. The first global network extracts shadow features from a global view. Two levels of features are derived from the global network and transferred to two parallel networks. While one extracts the appearance of the input image, the …

Speech Based Machine Learning Models For Emotional State Recognition And Ptsd Detection, Debrup Banerjee

Electrical & Computer Engineering Theses & Dissertations

Recognition of emotional state and diagnosis of trauma related illnesses such as posttraumatic stress disorder (PTSD) using speech signals have been active research topics over the past decade. A typical emotion recognition system consists of three components: speech segmentation, feature extraction and emotion identification. Various speech features have been developed for emotional state recognition which can be divided into three categories, namely, excitation, vocal tract and prosodic. However, the capabilities of different feature categories and advanced machine learning techniques have not been fully explored for emotion recognition and PTSD diagnosis. For PTSD assessment, clinical diagnosis through structured interviews is a …

Adviser+: Toward A Usable Web-Based Algorithm Portfolio Deviser, Hoong Chuin Lau, Mustafa Misir, Xiang Li Li, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

The present study offers a more user-friendly and parallelized version of a web-based algorithm portfolio generator, called ADVISER. ADVISER is a portfolio generation tool to deliver a group of configurations for a given set of algorithms targeting a particular problem. The resulting configurations are expected to be diverse such that each can perform well on a certain type of problem instances. One issue with ADVISER is that it performs portfolio generation on a single-core which results in long waiting times for the users. Besides that, it lacks of a reporting system with visualizations to tell more about the generated portfolios. …

Poster: Unobtrusive User Verification Using Piezoelectric Energy Harvesting, Dong Ma, Guohao Lan, Weitao Xu, Mahbub Hassan, Wen Hu

Research Collection School Of Computing and Information Systems

With the capability to harvest energy from low frequency motions or vibrations, piezoelectric energy harvesting has become a promising solution to achieve self-powered wearable system. Apart from generating energy to power the wearable devices, the output electricity signal of the PEH can also be used as an information source as it reflects the activity or motion patterns of the user. In this paper, we have designed and built an insole-based user authentication system by leveraging the AC voltage generated by the PEH during human walking. Meanwhile, the generated power is also collected and stored, which could be later used as …

A Unified Framework For Vehicle Rerouting And Traffic Light Control To Reduce Traffic Congestion, Zhiguang Cao, Siwei Jiang, Jie Zhang, Hongliang Guo

Research Collection School Of Computing and Information Systems

As the number of vehicles grows rapidly each year, more and more traffic congestion occurs, becoming a big issue for civil engineers in almost all metropolitan cities. In this paper, we propose a novel pheromone-based traffic management framework for reducing traffic congestion, which unifies the strategies of both dynamic vehicle rerouting and traffic light control. Specifically, each vehicle, represented as an agent, deposits digital pheromones over its route, while roadside infrastructure agents collect the pheromones and fuse them to evaluate real-time traffic conditions as well as to predict expected road congestion levels in near future. Once road congestion is predicted, …

Incentivizing The Use Of Bike Trailers For Dynamic Repositioning In Bike Sharing Systems, Supriyo Ghosh, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Bike Sharing System (BSS) is a green mode of transportation that is employed extensively for short distance travels in major cities of the world. Unfortunately, the users behaviour driven by their personal needs can often result in empty or full base stations, thereby resulting in loss of customer demand. To counter this loss in customer demand, BSS operators typically utilize a fleet of carrier vehicles for repositioning the bikes between stations. However, this fuel burning mode of repositioning incurs a significant amount of routing, labor cost and further increases carbon emissions. Therefore, we propose a potentially self-sustaining and environment friendly …

How Artificial Intelligence Is Impacting Manufacturing Industry, Deepak Srinivasan, Maitreyi Ramesh Swaroop, Balaji Rajaram, Sri Krishan Iyer

Research Collection School Of Computing and Information Systems

In this survey, we study the impact of Artificial Intelligence (AI) on manufacturing sector. AI methods can be utilized to make new thoughts several ways: by delivering novel mixes of wellknown thoughts; by investigating the capability of theoretical spaces; and by making changes that empower the era of unexplored thoughts. AI will have less trouble in displaying the era of new thoughts than in automating their assessment. We describe the advances that have been made on AI in manufacturing industry. We close with how to overcome the issues in this area.

Question Type Recognition Using Natural Language Input, Aishwarya Soni

Master's Projects

Recently, numerous specialists are concentrating on the utilization of Natural Language Processing (NLP) systems in various domains, for example, data extraction and content mining. One of the difficulties with these innovations is building up a precise Question and Answering (QA) System. Question type recognition is the most significant task in a QA system, for example, chat bots. Organization such as National Institute of Standards (NIST) hosts a conference series called as Text REtrieval Conference (TREC) series which keeps a competition every year to encourage and improve the technique of information retrieval from a large corpus of text. When a user …

Improving Text Classification With Word Embedding, Lihao Ge

Master's Projects

One challenge in text classification is that it is hard to make feature reduction basing upon the meaning of the features. An improper feature reduction may even worsen the classification accuracy. Word2Vec, a word embedding method, has recently been gaining popularity due to its high precision rate of analyzing the semantic similarity between words at relatively low computational cost. However, there are only a limited number of researchers focusing on feature reduction using Word2Vec. In this project, we developed a Word2Vec based method to reduce the feature size while increasing the classification accuracy. The feature reduction is achieved by loosely …

Estimation Of Train Driver Workload: Extracting Taskload Measures From On-Train-Data-Recorders, Nora Balfe, Katie Crowley, Brendan Smith, Luca Longo

Conference papers

This paper presents a method to extract train driver taskload from downloads of on-train-data-recorders (OTDR). OTDR are in widespread use for the purposes of condition monitoring of trains, but they may also have applications in operations monitoring and management. Evaluation of train driver workload is one such application. The paper describes the type of data held in OTDR recordings and how they can be transformed into driver actions throughout a journey. Example data from 16 commuter journeys are presented, which highlights the increased taskload during arrival at stations. Finally, the possibilities and limitations of the data are discussed.

Solving Algorithmic Problems In Finitely Presented Groups Via Machine Learning, Jonathan Gryak

Dissertations, Theses, and Capstone Projects

Machine learning and pattern recognition techniques have been successfully applied to algorithmic problems in free groups. In this dissertation, we seek to extend these techniques to finitely presented non-free groups, in particular to polycyclic and metabelian groups that are of interest to non-commutative cryptography.

As a prototypical example, we utilize supervised learning methods to construct classifiers that can solve the conjugacy decision problem, i.e., determine whether or not a pair of elements from a specified group are conjugate. The accuracies of classifiers created using decision trees, random forests, and N-tuple neural network models are evaluated for several non-free groups. …

Travel Mode Identification With Smartphone Sensors, Xing Su

Dissertations, Theses, and Capstone Projects

Personal trips in a modern urban society typically involve multiple travel modes. Recognizing a traveller's transportation mode is not only critical to personal context-awareness in related applications, but also essential to urban traffic operations, transportation planning, and facility design. While the state of the art in travel mode recognition mainly relies on large-scale infrastructure-based fixed sensors or on individuals' GPS devices, the emergence of the smartphone provides a promising alternative with its ever-growing computing, networking, and sensing powers. In this thesis, we propose new algorithms for travel mode identification using smartphone sensors. The prototype system is built upon the latest …

Robot Society

SIGNED: The Magazine of The Hong Kong Design Institute

Can the emerging field of social robotics deliver on its promise to revolutionise the way we use tech?

Roborodentia Robot (Duct Tape Craze), Tarrant J. Starck

Computer Science and Software Engineering

Roborodentia is an annual autonomous robotics competition held at Cal Poly in April. In 2017, Roborodentia was a head-to-head double elimination tournament with the winner being the robot that moves more rings onto the scoring pegs. For this year’s competition, I designed, built, programmed, and tested a robot.

Neural Network Ai For Fightingice, Alan D. Robison

Computer Engineering

Game AI in the fighting game genre, along the lines of Street Fighter, Mortal Kombat and Tekken, is traditionally script-based, with hard-coded reactions to various situations. Though this approach is often easy to understand and tweak, it requires substantial time and understanding of the game to implement in a way that is challenging and satisfying for the player due to the very large possibility space. This paper explores the use of neural networks as an alternative approach by implementing and training a network to select an action to take each frame based on the game state.

Tackling The Interleaving Problem In Activity Discovery, Eoin Rogers, Robert J. Ross, John D. Kelleher

Conference papers

Activity discovery (AD) is the unsupervised process of discovering activities in data produced from streaming sensor networks that are recording the actions of human subjects. One major challenge for AD systems is interleaving, the tendency for people to carry out multiple activities at a time a parallel. Following on from our previous work, we continue to investigate AD in interleaved datasets, with a view towards progressing the state-of-the-art for AD.

Time-Series Link Prediction Using Support Vector Machines, Proceso L. Fernandez Jr, Jan Miles Co

Department of Information Systems & Computer Science Faculty Publications

The prominence of social networks motivates developments in network analysis, such as link prediction, which deals with predicting the existence or emergence of links on a given network. The Vector Auto Regression (VAR) technique has been shown to be one of the best for time-series based link prediction. One VAR technique implementation uses an unweighted adjacency matrix and five additional matrices based on the similarity metrics of Common Neighbor, Adamic-Adar, Jaccard’s Coefficient, Preferential Attachment and Research Allocation Index. In our previous work, we proposed the use of the Support Vector Machines (SVM) for such prediction task, and, using the same …