Artificial Intelligence and Robotics Commons^™

Generating Music With Sentiments, Chunhui Bao

Dissertations and Theses Collection (Open Access)

In this thesis, I focus on the music generation conditional on human sentiments such as positive and negative. As there are no existing large-scale music datasets annotated with sentiment labels, generating high-quality music conditioned on sentiments is hard. I thus build a new dataset consisting of the triplets of lyric, melody and sentiment, without requiring any manual annotations. I utilize an automated sentiment recognition model (based on the BERT trained on Edmonds Dance dataset) to "label'' the music according to the sentiments recognized from its lyrics. I then train the model of generating sentimental music and call the method Sentimental …

Learning Knowledge-Enriched Company Embeddings For Investment Management, Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Relationships between companies serve as key channels through which the effects of past stock price movements and news events propagate and influence future price movements. Such relationships can be implicitly found in knowledge bases or explicitly represented as knowledge graphs. In this paper, we propose KnowledgeEnriched Company Embedding (KECE), a novel multi-stage attentionbased dynamic network embedding model combining multimodal information of companies with knowledge from Wikipedia and knowledge graph relationships from Wikidata to generate company entity embeddings that can be applied to a variety of downstream investment management tasks. Experiments on an extensive set of real-world stock prices and news …

Fleet Sizing And Allocation For On-Demand Last-Mile Transportation Systems, Karmel Shehadeh, Hai Wang, Peter Zhang

Research Collection School Of Computing and Information Systems

The last-mile problem refers to the provision of travel service from the nearest public transportation node to home or other destination. Last-Mile Transportation Systems (LMTS), which have recently emerged, provide on-demand shared transportation. In this paper, we investigate the fleet sizing and allocation problem for the on-demand LMTS. Specifically, we consider the perspective of a last-mile service provider who wants to determine the number of servicing vehicles to allocate to multiple last-mile service regions in a particular city. In each service region, passengers demanding last-mile services arrive in batches, and allocated vehicles deliver passengers to their final destinations. The passenger …

Towards Enriching Responses With Crowd-Sourced Knowledge For Task-Oriented Dialogue, Yingxu He, Lizi Liao, Zheng Zhang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Task-oriented dialogue agents are built to assist users in completing various tasks. Generating appropriate responses for satisfactory task completion is the ultimate goal. Hence, as a convenient and straightforward way, metrics such as success rate, inform rate etc., have been widely leveraged to evaluate the generated responses. However, beyond task completion, there are several other factors that largely affect user satisfaction, which remain under-explored. In this work, we focus on analyzing different agent behavior patterns that lead to higher user satisfaction scores. Based on the findings, we design a neural response generation model EnRG. It naturally combines the power of …

Artificial Intelligence As Augmenting Automation: Implications For Employment, F. Ted Tschang, Esteve Almirall

Research Collection Lee Kong Chian School Of Business

There has been great concern in recent years that artificial intelligence (AI) may cause widespread unemployment, but proponents say that AI augments existing jobs. Both of these positions have substance, but there is a need is to articulate the mechanisms by which AI may actually do both, and in the process, transform work and business organizations alike. We use economic studies showing past transformations automation wrought on the structure of employment and skills (such as the favouring of nonroutine skills) to articulate a ground for discussion. We then use case evidence of AI and automation to show how AI is …

Span-Level Emotion Cause Analysis With Neural Sequence Tagging, Xiangju Li, Wei Gao, Shi Feng, Daling Wang, Shafiq Joty

Research Collection School Of Computing and Information Systems

This paper addresses the task of span-level emotion cause analysis (SECA). It is a finer-grained emotion cause analysis (ECA) task, which aims to identify the specific emotion cause span(s) behind certain emotions in text. In this paper, we formalize SECA as a sequence tagging task for which several variants of neural network-based sequence tagging models to extract specific emotion cause span(s) in the given context. These models combine different types of encoding and decoding approaches. Furthermore, to make our models more "emotionally sensitive'', we utilize the multi-head attention mechanism to enhance the representation of context. Experimental evaluations conducted on two …

Predicting Anti-Asian Hateful Users On Twitter During Covid-19, Jisun An, Haewoon Kwak, Claire Seungeun Lee, Bogang Jun, Yong-Yeol Ahn

Research Collection School Of Computing and Information Systems

We investigate predictors of anti-Asian hate among Twitter users throughout COVID-19. With the rise of xenophobia and polarization that has accompanied widespread social media usage in many nations, online hate has become a major social issue, attracting many researchers. Here, we apply natural language processing techniques to characterize social media users who began to post anti-Asian hate messages during COVID-19. We compare two user groups—those who posted anti-Asian slurs and those who did not—with respect to a rich set of features measured with data prior to COVID-19 and show that it is possible to predict who later publicly posted anti-Asian …

Stock Market Trend Forecasting Based On Multiple Textual Features: A Deep Learning Method, Zhenda Hu, Zhaoxia Wang, Seng-Beng Ho, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Stock market trend forecasting is a valuable and challenging research task for both industry and academia. In order to explore the influence of stock news information on the stock market trend, a textual embedding construction method is proposed to encode multiple textual features, including topic features, sentiment features, and semantic features extracted from stock news textual content. In addition, a deep learning method is designed by using financial data and multiple textual features obtained from multiple news textual embeddings for short-term stock market trend prediction. For evaluation, extensive experiments on real stock market data are conducted. The experimental results illustrate …

Self-Supervised Multi-Class Pre-Training For Unsupervised Anomaly Detection And Segmentation In Medical Images, Yu Tian, Fengbei Liu, Guansong Pang, Yuanhong Chen, Yuyuan Liu, Johan W. Verjans, Rajvinder Singh

Research Collection School Of Computing and Information Systems

Unsupervised anomaly detection (UAD) that requires only normal (healthy) training images is an important tool for enabling the development of medical image analysis (MIA) applications, such as disease screening, since it is often difficult to collect and annotate abnormal (or disease) images in MIA. However, heavily relying on the normal images may cause the model training to overfit the normal class. Self-supervised pre-training is an effective solution to this problem. Unfortunately, current self-supervision methods adapted from computer vision are sub-optimal for MIA applications because they do not explore MIA domain knowledge for designing the pretext tasks or the training process. …

Development Of Sensor, Sensory System And Signal Processing Algorithm For Intelligent Sensing Applications, Xingzhe Zhang

Dissertations

Sensors have been receiving significant attention in the last decade and the demand for sensory systems has increased in recent years due to the rapid growth in the field of artificial intelligence (AI). Sensors can improve people’s awareness by providing them with real-time information on the environment and their immediate health conditions. This dissertation presents the fulfilment of three main projects and focuses on the development of a sensor, a sensory system, and a sensor signal recognition system for AI applications by employing printed electronics, analog circuit design, and digital signal processing techniques.

In the first project, a multi-channel stethograph …

The Ratio Method: Addressing Complex Tort Liability In The Fourth Industrial Revolution, Harrison C. Margolin, Grant H. Frazier

St. Mary's Law Journal

Emerging technologies of the Fourth Industrial Revolution show fundamental promise for improving productivity and quality of life, though their misuse may also cause significant social disruption. For example, while artificial intelligence will be used to accelerate society’s processes, it may also displace millions of workers and arm cybercriminals with increasingly powerful hacking capabilities. Similarly, human gene editing shows promise for curing numerous diseases, but also raises significant concerns about adverse health consequences related to the corruption of human and pathogenic genomes.

In most instances, only specialists understand the growing intricacies of these novel technologies. As the complexity and speed of …

Generating Synthetic Training Data For Deep Learning-Based Uav Trajectory Prediction, Brendan T. Morris, Stefan Becker, Ronny Hug, Wolfgang Huebner, Michael Arens

Electrical & Computer Engineering Faculty Research

Deep learning-based models, such as recurrent neural networks (RNNs), have been applied to various sequence learning tasks with great success. Following this, these models are increasingly replacing classic approaches in object tracking applications for motion prediction. On the one hand, these models can capture complex object dynamics with less modeling required, but on the other hand, they depend on a large amount of training data for parameter tuning. Towards this end, we present an approach for generating synthetic trajectory data of unmanned-aerial-vehicles (UAVs) in image space. Since UAVs, or rather quadrotors are dynamical systems, they can not follow arbitrary trajectories. …

Research On Cgf-Oriented Intention Recognition Behavioral Modeling Framework, Xu Kai, Yunxiu Zeng, Wansen Wu, Quanjun Yin, Yabing Zha

Journal of System Simulation

Abstract: As an important cognitive behavior in Computer Generated Forces (CGF), Intention Recognition reasons the temporal relations between actions of friends and enemies to recognize their true intentions, and provides the observer with far more focused decision-making ability. In order to further formalize the modeling of CGF-oriented intention recognition, the paper reviews the worldwide research development from 1980s, along with the designs and implementations of different methods. Following the theory of Situation Awareness, the paper analyzes the situation awareness process of CGF, its impacting factors and constraints and proposes a generalized intention recognition framework considering different problem characteristics, constraints and …

Identification Of Main Steam Temperature System Based On Improved Particle Swarm Optimization, Zhenqian Cao, Yin Jiang, Jinhua Zhang

Journal of System Simulation

Abstract: Establishing an accurate mathematical model of main steam temperature is the basis of improving the performance of control system. Aiming at the problems of early maturity and slow convergence in traditional particle swarm optimization (PSO) algorithm in model identification, an improved PSO algorithm with shrinkage factor is proposed. The algorithm improves the global optimization capability and convergence speed of the algorithm by adjusting the shrinkage factor. The on-site operating data of a 350 MW circulating fluidized bed (CFB) boiler in a power plant in Shanxi province are used in the identification of the main steam model parameters, and the …

Dqn-Based Path Planning Method And Simulation For Submarine And Warship In Naval Battlefield, Xiaodong Huang, Haitao Yuan, Bi Jing, Liu Tao

Journal of System Simulation

Abstract: To realize multi-agent intelligent planning and target tracking in complex naval battlefield environment, the work focuses on agents (submarine or warship), and proposes a simulation method based on reinforcement learning algorithm called Deep Q Network (DQN). Two neural networks with the same structure and different parameters are designed to update real and predicted Q values for the convergence of value functions. An ε-greedy algorithm is proposed to design an action selection mechanism, and a reward function is designed for the naval battlefield environment to increase the update velocity and generalization ability of Learning with Experience Replay (LER). Simulation results …

Research On Path Tracking Control Strategy Of Four-Wheel Steering Intelligent Vehicle, Jingbo Zhao, Liangpeng Zhu, Chengye Liu

Journal of System Simulation

Abstract: Aiming at the instability of path tracking control of intelligent vehicle at high speed, a path tracking control strategy of four-wheel steering combined with differential braking is proposed. In the upper layer, the front wheel active steering controller is designed based on the path tracking model. In the lower layer, the integrated controller of active rear steering and additional yaw moment is designed using the sliding mode control method. The additional yaw moment is transformed into the control of single wheel by designing differential braking distribution strategy. Simulation results show that the tracking accuracy of the combined control strategy …

Fixed-Time Event-Triggered Formation Control For Multiple Uavs, Ye Shuai, Guoping Jiang, Yingjiang Zhou, Liu Shang

Journal of System Simulation

Abstract: To solve the quadrotor UAV formation control problem, an event-triggered fixed time formation control algorithm of multi quadrotor UAV system is studied. For the attitude loop control problem of UAV, the switching fixed time sliding surface is selected to make the system reach the equilibrium point in a fixed time when the system state is on the sliding surface. A fixed-time sliding mode controller is designed so that the system state which is not on the sliding surface can reach the sliding surface within a fixed time. In view of the position loop control problem of UAV, the event-driven …

Virus Propagation And System Simulation Based On Cellular Automata Model, Lijuan Zhang, Fuchang Wang, Zhengang Li

Journal of System Simulation

Abstract: A susceptible-latent-infected-cured-immune virus spreading model is established according to the characteristics of epidemic transmission and the actual urban spatial map model. Individuals in the environment are regarded as agents, and the spreading mechanism is established according to the principle of cellular. The effects of different strategies and different characteristics of virus spreading on epidemic have been studied. The role and effect of important factors on epidemic prevention and control method are discussed, and the model is validated taking Shijiazhuang epidemic as an example . The results show that the number of initial latent, the infectivity of disease, vaccination proportion …

Research On Accurate Gesture Recognition Algorithm In Complex Environment Based On Machine Vision, Xu Sheng, Wenyu Feng, Zhicheng Liu, Xintao Tu, Minrui Fei, Kun Zhang

Journal of System Simulation

Abstract: To address the issue of cross infection caused by elevator public buttons during COVID-19, a software algorithm based on machine vision for non-contact control of public buttons by gesture recognition is designed. In order to improve the accuracy of gesture recognition, an improved YOLOv4 algorithm is proposed. A Ghost module is designed based on attention mechanism, and the ResBlock module in YOLOv4 is improved to Ghost module. The experimental results show that, in the task of gesture recognition, the detection speed is improved by 14% and the detection accuracy is improved by 0.1% compared with the original model. The …

Simulation Of Civil Aircraft Takeoff Scenario Based On Mbse, Liangyu Zhao, Junjie Ye, He Qi, Guo Wei, Zhao Yong

Journal of System Simulation

Abstract: Aiming at the interactions and flaws of design being difficult to be fully discovered, the requirements being hard to be traced, the early verification of system design being difficult to be realized and so on, a Model-Based System Engineering (MBSE) method is adopted to realize the simulation of civil aircraft take-off scenario. Based on the analysis of civil aircraft takeoff scenario requirements, the civil aircraft takeoff scenario simulation architecture, take-off scenario discrete logic model, and continuous physical simulation model are established. The method of fusing the SysML model and Simulink model and the 3D visualization of simulation data …