Artificial Intelligence and Robotics Commons^™

Motor Control Systems Analysis, Design, And Optimization Strategies For A Lightweight Excavation Robot, Austin Jerold Crawford

Graduate Theses and Dissertations

This thesis entails motor control system analysis, design, and optimization for the University of Arkansas NASA Robotic Mining Competition robot. The open-loop system is to be modeled and simulated in order to achieve a desired rapid, yet smooth response to a change in input. The initial goal of this work is to find a repeatable, generalized step-by-step process that can be used to tune the gains of a PID controller for multiple different operating points. Then, sensors are to be modeled onto the robot within a feedback loop to develop an error signal and to make the control system self-corrective …

Design Of Artificial Swarms Using Network Motifs: A Simulation Study, Khoinguyen Trinh

Mechanical Engineering Undergraduate Honors Theses

The objective of this research is to develop a new approach in engineering complex swarm systems with desired characteristics based on the theory of network motifs – subgraphs that repeat themselves (patterns) among various networks. System engineering has traditionally followed a top-down methodology which creates a framework for the system and adds additional features to meet specific design requirements. Meanwhile, complex swarm systems, such as ant colonies and bird flocks, are formed via a bottom-up manner where the system-level structure directly emerges from the interactions and behaviors among individuals. The behaviors of these individuals cannot be directly controlled, which makes …

Beyond Autonomy: The Self And Life Of Social Agents, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Agents have gained popularity nowadays as virtual assistants and companions of their human users supporting daily activities in many aspects of personal life. Designed to be sociable, an agent engages its user(s) to communicate and even develop friendships. Rather than just as a lifeless toy, it is supposed to be perceived as an individual with its own personality, experiences, and social life. In this paper, we seek to highlight self-hood as another dimension that characterizes an agent. Besides levels of autonomy and reasoning, an agent can be defined based on its capacity to process and reflect on its own self …

The Challenges Of Creating Engaging Content: Results From A Focus Group Study Of A Popular News Media Organization, Kholoud Khalil Aldous, Jisun An, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

The process of content creation for distribution via social media platforms is not a trivial one for social media editors as the goal of creating both serious and engaging content is challenging, with no clear or differing guidelines or rules across and between platforms. For creators of serious content, such as news organizations, advertisers, or educational institutions, engagement has a deeper meaning beyond likes, shares, etc. that is aimed at the audience actually processing the underlying content associated with a social media post. In this research, we report findings from a group study that aimed to understand the process and …

Clustering Of Multiple Instance Data., Andrew D. Karem

Electronic Theses and Dissertations

An emergent area of research in machine learning that aims to develop tools to analyze data where objects have multiple representations is Multiple Instance Learning (MIL). In MIL, each object is represented by a bag that includes a collection of feature vectors called instances. A bag is positive if it contains at least one positive instance, and negative if no instances are positive. One of the main objectives in MIL is to identify a region in the instance feature space with high correlation to instances from positive bags and low correlation to instances from negative bags -- this region is …

Online Learning And Planning For Crowd-Aware Service Robot Navigation, Anoop Aroor

Dissertations, Theses, and Capstone Projects

Mobile service robots are increasingly used in indoor environments (e.g., shopping malls or museums) among large crowds of people. To efficiently navigate in these environments, such a robot should be able to exhibit a variety of behaviors. It should avoid crowded areas, and not oppose the flow of the crowd. It should be able to identify and avoid specific crowds that result in additional delays (e.g., children in a particular area might slow down the robot). and to seek out a crowd if its task requires it to interact with as many people as possible. These behaviors require the ability …

Seeing Eye To Eye: A Machine Learning Approach To Automated Saccade Analysis, Maigh Attre

Honors Scholar Theses

Abnormal ocular motility is a common manifestation of many underlying pathologies particularly those that are neurological. Dynamics of saccades, when the eye rapidly changes its point of fixation, have been characterized for many neurological disorders including concussions, traumatic brain injuries (TBI), and Parkinson’s disease. However, widespread saccade analysis for diagnostic and research purposes requires the recognition of certain eye movement parameters. Key information such as velocity and duration must be determined from data based on a wide set of patients’ characteristics that may range in eye shapes and iris, hair and skin pigmentation [36]. Previous work on saccade analysis has …

A Bystander's Dilemma: Participatory Design Study Of Privacy Expectations For Smart Home Devices, Oriana Mcdonough

Renée Crown University Honors Thesis Projects - All

Traditional homes have become increasingly filled with Internet-connected devices, turning them into “smart homes.” Currently, research around privacy concerns with smart home devices has focused on the end users. The goal for our research is to understand the perceptions and desired privacy mechanisms from the perspective of a different stakeholder, i.e., the bystanders. Bystanders in this context are individuals who are not the owner or primary user of smart home devices but are potentially affected by the device usage, such as house guests or family members. In order to understand this, we conducted a focus group study with co-design activities …

Watersheds For Semi-Supervised Classification, Aditya Challa, Sravan Danda, B. S.Daya Sagar, Laurent Najman

Journal Articles

Watershed technique from mathematical morphology (MM) is one of the most widely used operators for image segmentation. Recently watersheds are adapted to edge weighted graphs, allowing for wider applicability. However, a few questions remain to be answered - How do the boundaries of the watershed operator behave? Which loss function does the watershed operator optimize? How does watershed operator relate with existing ideas from machine learning. In this letter, a framework is developed, which allows one to answer these questions. This is achieved by generalizing the maximum margin principle to maximum margin partition and proposing a generic solution, morphMedian, resulting …

Analysis Literatures Of Machine Learning And Neural Networks For Real Time Scheduling, Phong Nguyenho, Mark Nguyen

Recent Advances in Real-Time Systems

Real time scheduling problems are present in every aspect of software development. An optimized real time scheduling scheme would determine the performance of an operating system. There are many different approaches that real time scheduling researchers developed to tackle scheduling problems in many computer systems that have great important roles in keeping our modern society running smoothly. Neural-network real time scheduling is one of those approaches that can solve many computer scheduling problems. As computing technology advanced, more and more real time scheduling problems arise that need new solutions to keep up with the demand of faster computer systems. In …

Multi-Resolution Spatio-Temporal Change Analyses Of Hydro-Climatological Variables In Association With Large-Scale Oceanic-Atmospheric Climate Signals, Kazi Ali Tamaddun

UNLV Theses, Dissertations, Professional Papers, and Capstones

The primary objective of the work presented in this dissertation was to evaluate the change patterns, i.e., a gradual change known as the trend, and an abrupt change known as the shift, of multiple hydro-climatological variables, namely, streamflow, snow water equivalent (SWE), temperature, precipitation, and potential evapotranspiration (PET), in association with the large-scale oceanic-atmospheric climate signals. Moreover, both observed datasets and modeled simulations were used to evaluate such change patterns to assess the efficacy of the modeled datasets in emulating the observed trends and shifts under the influence of uncertainties and inconsistencies. A secondary objective of this study was to …

The Affective Perceptual Model: Enhancing Communication Quality For Persons With Pimd, Jadin Tredup

UNLV Theses, Dissertations, Professional Papers, and Capstones

Methods for prolonged compassionate care for persons with Profound Intellectual and Multiple Disabilities (PIMD) require a rotating cast of import people in the subjects life in order to facilitate interaction with the external environment. As subjects continue to age, dependency on these people increases with complexity of communications while the quality of communication decreases. It is theorized that a machine learning (ML) system could replicate the attuning process and replace these people to promote independence. This thesis extends this idea to develop a conceptual and formal model and system prototype.

The main contributions of this thesis are: (1) proposal of …

Designing A General Education Course On The Societal Impacts Of Artificial Intelligence, Vincent Rollins

Honors Theses

Most colleges, including UTC, already offer an artificial intelligence course (CPSC 4440) as part of their computer science curricula. Such courses are meant to explain the technology behind these elaborate systems, but these courses often neglect extensive coverage of the real-world impacts of the technology itself. UTC also offers a course entitled “Ethical and Social Issues in Computing” that does convey the importance behind the advances of computer technology and its impacts, but this course is practically available only to computer science majors. There is no generalized and widely available course that covers the technological, economic, cultural, philosophical/theological, and ethical …

Classification Of Vegetation In Aerial Imagery Via Neural Network, Gevand Balayan

UNLV Theses, Dissertations, Professional Papers, and Capstones

This thesis focuses on the task of trying to find a Neural Network that is best suited for identifying vegetation from aerial imagery. The goal is to find a way to quickly classify items in an image as highly likely to be vegetation(trees, grass, bushes and shrubs) and then interpolate that data and use it to mark sections of an image as vegetation. This has practical applications as well. The main motivation of this work came from the effort that our town takes in conserving water. By creating an AI that can easily recognize plants, we can better monitor the …

Transparency And Communication Patterns In Human-Robot Teaming, Shan Lakhmani

Electronic Theses and Dissertations

In anticipation of the complex, dynamic battlefields of the future, military operations are increasingly demanding robots with increased autonomous capabilities to support soldiers. Effective communication is necessary to establish a common ground on which human-robot teamwork can be established across the continuum of military operations. However, the types and format of communication for mixed-initiative collaboration is still not fully understood. This study explores two approaches to communication in human-robot interaction, transparency and communication pattern, and examines how manipulating these elements with a robot teammate affects its human counterpart in a collaborative exercise. Participants were coupled with a computer-simulated robot to …

Re-Org: An Online Repositioning Guidance Agent, Muralidhar Konda, Pradeep Varakantham, Aayush Saxena, Meghna Lowalekar

Research Collection School Of Computing and Information Systems

No abstract provided.

Neural Multimodal Belief Tracker With Adaptive Attention For Dialogue Systems, Zheng Zhang, Lizi Liao, Minlie Huang, Xiaoyan Zhu, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Multimodal dialogue systems are attracting increasing attention with a more natural and informative way for human-computer interaction. As one of its core components, the belief tracker estimates the user's goal at each step of the dialogue and provides a direct way to validate the ability of dialogue understanding. However, existing studies on belief trackers are largely limited to textual modality, which cannot be easily extended to capture the rich semantics in multimodal systems such as those with product images. For example, in fashion domain, the visual appearance of clothes play a crucial role in understanding the user's intention. In this …

Organizing For Artificial Intelligence (Ai) Technologies, Sukti Ghosh

Research Collection Lee Kong Chian School Of Business

This study focuses on organisation design choices as tools for addressing the management challenges of commercialising AI technologies for competitive advantage. It explores how design choices address fundamental problems of organising in such context, illustrating notable design features observed. Additionally, it examines external alignment and internal coherence reiterating interdependencies in organisation’s design choices, when adapting to exogenous changes due to emerging AI technologies.

Robust Factorization Machine: A Doubly Capped Norms Minimization, Chenghao Liu, Teng Zhang, Jundong Li, Jianwen Yin, Peilin Zhao, Jianling Sun, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Factorization Machine (FM) is a general supervised learning framework for many AI applications due to its powerful capability of feature engineering. Despite being extensively studied, existing FM methods have several limitations in common. First of all, most existing FM methods often adopt the squared loss in the modeling process, which can be very sensitive when the data for learning contains noises and outliers. Second, some recent FM variants often explore the low-rank structure of the feature interactions matrix by relaxing the low-rank minimization problem as a trace norm minimization, which cannot always achieve a tight approximation to the original one. …

The Challenge Of Collaborative Iot-Based Inferencing In Adversarial Settings, Archan Misra, Dulanga Kaveesha Weerakoon Weerakoon Mudiyanselage, Kasthuri Jayarajah

Research Collection School Of Computing and Information Systems

In many practical environments, resource-constrained IoT nodes are deployed with varying degrees of redundancy/overlap--i.e., their data streams possess significant spatiotemporal correlation. We posit that collaborative inferencing, whereby individual nodes adjust their inferencing pipelines to incorporate such correlated observations from other nodes, can improve both inferencing accuracy and performance metrics (such as latency and energy overheads). However, such collaborative models are vulnerable to adversarial behavior by one or more nodes, and thus require mechanisms that identify and inoculate against such malicious behavior. We use a dataset of 8 outdoor cameras to (a) demonstrate that such collaborative inferencing can improve people counting …

A Homophily-Free Community Detection Framework For Trajectories With Delayed Responses, Chung-Kyun Han, Shih-Fen Cheng, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

No abstract provided.

Deep Neural Network Architectures For Music Genre Classification, Kai Middlebrook, Shyam Sudhakaran, Kunal Sonar, David Guy Brizan

Creative Activity and Research Day - CARD

With the recent advancements in technology, many tasks in fields such as computer vision, natural language processing, and signal processing have been solved using deep learning architectures. In the audio domain, these architectures have been used to learn musical features of songs to predict: moods, genres, and instruments. In the case of genre classification, deep learning models were applied to popular datasets--which are explicitly chosen to represent their genres--and achieved state-of-the-art results. However, these results have not been reproduced on less refined datasets. To this end, we introduce an un-curated dataset which contains genre labels and 30-second audio previews for …

Examining The Limits Of Predictability Of Human Mobility, Vaibhav Klukarni, Abhijit Mahalunkar, Benoit Garbinato, John D. Kelleher

Articles

We challenge the upper bound of human-mobility predictability that is widely used to corroborate the accuracy of mobility prediction models. We observe that extensions of recurrent-neural network architectures achieve significantly higher prediction accuracy, surpassing this upper bound. Given this discrepancy, the central objective of our work is to show that the methodology behind the estimation of the predictability upper bound is erroneous and identify the reasons behind this discrepancy. In order to explain this anomaly, we shed light on several underlying assumptions that have contributed to this bias. In particular, we highlight the consequences of the assumed Markovian nature of …

Building Consumer Trust In The Cloud: An Experimental Analysis Of The Cloud Trust Label Approach, Lisa Van Der Werff, Grace Fox, Ieva Masevic, Vincent C. Emeakaroha, John P. Morrison, Theo Lynn

Department of Computer Science Publications

The lack of transparency surrounding cloud service provision makes it difficult for consumers to make knowledge based purchasing decisions. As a result, consumer trust has become a major impediment to cloud computing adoption. Cloud Trust Labels represent a means of communicating relevant service and security information to potential customers on the cloud service provided, thereby facilitating informed decision making. This research investigates the potential of a Cloud Trust Label system to overcome the trust barrier. Specifically, it examines the impact of a Cloud Trust Label on consumer perceptions of a service and cloud service provider trustworthiness and trust in the …

New Clock-Driven Algorithm Based On Separation Of Synaptic Conductance Computation, Zhijie Wang, Peng Xia, Han Fang, Xiaochun Gu

Journal of System Simulation

Abstract: In order to reduce the computing time when simulating the biologic neural network, an efficient clock-driven algorithm based on the separation of synaptic conductance computation is presented. It is found that the calculation of the synaptic state variables can be separated into two independent parts: one called conductance coefficient related with the pre-synaptic neuron, and the other called synaptic current. By introducing the data structure of the virtual synapse cluster to storing sequences of synaptic conductance coefficient, the former part can be calculated independently according to the spiking states of pre-synaptic neuron at each time step. When calculating the …

Grasping Simulation Of Linkage Underactuated Mechanical Finger, Xiali Li, Tianyi Lan, Licheng Wu

Journal of System Simulation

Abstract: For verifying the design rationality and the property of a linkage underactuated finger, Solidworks is utilized to simulate the grasp operation of the finger with some different objects at the different positions. The simulations can be used to analyze the grasping range, the underactuated characteristic, the uniformity of grasp motion and the mechanical property. The finger mechanism contains springs that brings adaptive grasp capability of different objects. The results show that the finger can make suitable capability for grasping the objects whose size can vary from 0.106 to 0.851 times of the finger length. And the finger can give …

Modeling And Simulation Of False Report Filtering Scheme Based On Position In Wireless Sensor Networks, Zhixiong Liu, Limiao Li

Journal of System Simulation

Abstract: In wireless sensor networks, the adversary can inject false reports from compromised nodes. Previous security designs cannot detect faked reports that are forged coordinately by a group of compromised nodes. Furthermore, in sparse areas, some events failed to be reported to sink. This paper proposes a position based filtering scheme (PFS). It derives the optimal coverage degree ω, and the nodes are deployed accordingly. After deployment, each node distributes its position to some other nodes. When a report is generated for an observed event, it must carry t distinct MACs (Message Authentication Codes) along with positions of all detecting …

Multi-Chromosome Genetic Algorithm For Multiple Traveling Salesman Problem, Duofu Ye, Liu Gang, He Bing

Journal of System Simulation

Abstract: A multi-traveling salesman model with time window is established, and two objective functions for the number of traveling salesmen and the sum of travel time are designed. A multi - chromosome coding method is designed to develop complex mutation operator tree, which overcomes the problem of large searching space of traditional genetic algorithms. The performances of algorithms are compared by simulation, and the simulation results show that the genetic algorithm with complex multi-chromosome mutation tree can balance the two objective functions of the number of TSP and total travel time well, improve the algorithm of travel speed, and reduce …

Virtual Scene Simulation Of Pilot Response In Taking-Off And Landing Processes Of Carrier-Based Aircraft, Ke Peng, Chenglin Xu, Songyang Liu, Minggao Li, Zhao Xin

Journal of System Simulation

Abstract: A research and implementation method based on biomechanics simulation and virtual scene simulation was put forward to investigate the dynamics responses of pilots during the taking off and landing processes of carrier-based aircraft. The human biomechanics calculation of pilots and 3D virtual scene simulation of flight scene were combined to design and develop the integrated virtual simulation platform. This platform has the functions of data management and analysis, human biological dynamics calculation, synchronous analysis of key results, 3D virtual scene simulation of taking-off and landing processes and human response. The verification was carried out using the experiments from …

Modeling And Timing Simulation Of Turbojet Engine Starting Process, Wang Lei, Liying Yang, Hongda Zhang, Yuqing He

Journal of System Simulation

Abstract: The turbojet engine is complicated in its starting process and requires collaboration and cooperation involving all the execution structures, thus higher demand is put forward for the control system. How to model the engine starting process, and then accurately describe and analyze the impact of actuator states on engine performance directly affect the engine control system performance. According to the working principle of the JetCat-P400 German turbojet starting process, the rotor speed mathematical model of the turbojet engine starting process is constructed by the component method. The parameters of the model are identified by combining the experimental data of …