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Articles 8461 - 8490 of 11355

Full-Text Articles in Artificial Intelligence and Robotics

A Collaboration Between Neural Networks And Reinforcement Learning: Applying Concepts To A Brick Breaking Game, Bryce Kadrlik Feb 2020

A Collaboration Between Neural Networks And Reinforcement Learning: Applying Concepts To A Brick Breaking Game, Bryce Kadrlik

Augsburg Honors Review

The intent of this work is to explore the interactions of artificial neural networks and digital games. It details the development of an artificial neural network trained upon a brick breaking game like the Atari game Breakout. This network was designed with the goals of not dropping the ball and maximizing the game score. Full game and network integration was not completed. However, two versions of the network were developed to move the paddle to the right or left based on the ball's point of impact on the paddle. In preliminary testing using manual inputs, these networks eventually learned to …

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Evaluation Of Text Mining Techniques Using Twitter Data For Hurricane Disaster Resilience, Joshua Eason, Sathish Kumar Feb 2020

Evaluation Of Text Mining Techniques Using Twitter Data For Hurricane Disaster Resilience, Joshua Eason, Sathish Kumar

SDSU Data Science Symposium

Data obtained from social media microblogging websites such as Twitter provide the unique ability to collect and analyze conversations of the public in order to gain perspective on the thoughts and feelings of the general public. Sentiment and volume analysis techniques were applied to the dataset in order to gain an understanding of the amount and level of sentiment associated with certain disaster-related tweets, including a topical analysis of specific terms. This study showed that disaster-type events such as a hurricane can cause some strong negative sentiment in the period of time directly preceding the event, but ultimately returns quickly …

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A Monte Carlo Approach To Closing The Reality Gap, Damian Lyons, James Finocchiaro, Michael Novitzky, Christopher Korpela Feb 2020

A Monte Carlo Approach To Closing The Reality Gap, Damian Lyons, James Finocchiaro, Michael Novitzky, Christopher Korpela

Faculty Publications

We propose a novel approach to the ’reality gap’ problem, i.e., modifying a robot simulation so that its performance becomes more similar to observed real world phenomena. This problem arises whether the simulation is being used by human designers or in an automated policy development mechanism. We expect that the program/policy is developed using simulation, and subsequently deployed on a real system. We further assume that the program includes a monitor procedure with scalar output to determine when it is achieving its performance objectives. The proposed approach collects simulation and real world observations and builds conditional probability functions. These are …

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Singapore’S National Ai Strategy, Singapore Management University Feb 2020

Singapore’S National Ai Strategy, Singapore Management University

Perspectives@SMU

The island state is banking on industry-wide projects and building an AI ecosystem to transform its economy

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Noise Reduction Of Eeg Signals Using Autoencoders Built Upon Gru Based Rnn Layers, Esra Aynali Feb 2020

Noise Reduction Of Eeg Signals Using Autoencoders Built Upon Gru Based Rnn Layers, Esra Aynali

Dissertations

Understanding the cognitive and functional behaviour of the brain by its electrical activity is an important area of research. Electroencephalography (EEG) is a method that measures and record electrical activities of the brain from the scalp. It has been used for pathology analysis, emotion recognition, clinical and cognitive research, diagnosing various neurological and psychiatric disorders and for other applications. Since the EEG signals are sensitive to activities other than the brain ones, such as eye blinking, eye movement, head movement, etc., it is not possible to record EEG signals without any noise. Thus, it is very important to use an …

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Solving Online Threat Screening Games Using Constrained Action Space Reinforcement Learning, Sanket Shah, Arunesh Sinha, Pradeep Varakantham, Andrew Perrault, Millind Tambe Feb 2020

Solving Online Threat Screening Games Using Constrained Action Space Reinforcement Learning, Sanket Shah, Arunesh Sinha, Pradeep Varakantham, Andrew Perrault, Millind Tambe

Research Collection School Of Computing and Information Systems

Large-scale screening for potential threats with limited resources and capacity for screening is a problem of interest at airports, seaports, and other ports of entry. Adversaries can observe screening procedures and arrive at a time when there will be gaps in screening due to limited resource capacities. To capture this game between ports and adversaries, this problem has been previously represented as a Stackelberg game, referred to as a Threat Screening Game (TSG). Given the significant complexity associated with solving TSGs and uncertainty in arrivals of customers, existing work has assumed that screenees arrive and are allocated security resources at …

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Gdface: Gated Deformation For Multi-View Face Image Synthesis, Xuemiao Xu, Keke Li, Cheng Xu, Shengfeng He Feb 2020

Gdface: Gated Deformation For Multi-View Face Image Synthesis, Xuemiao Xu, Keke Li, Cheng Xu, Shengfeng He

Research Collection School Of Computing and Information Systems

Photorealistic multi-view face synthesis from a single image is an important but challenging problem. Existing methods mainly learn a texture mapping model from the source face to the target face. However, they fail to consider the internal deformation caused by the change of poses, leading to the unsatisfactory synthesized results for large pose variations. In this paper, we propose a Gated Deformable Face Synthesis Network to model the deformation of faces that aids the synthesis of the target face image. Specifically, we propose a dual network that consists of two modules. The first module estimates the deformation of two views …

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Robust Neural Machine Translation, Abdul Rafae Khan Feb 2020

Robust Neural Machine Translation, Abdul Rafae Khan

Dissertations, Theses, and Capstone Projects

This thesis aims for general robust Neural Machine Translation (NMT) that is agnostic to the test domain. NMT has achieved high quality on benchmarks with closed datasets such as WMT and NIST but can fail when the translation input contains noise due to, for example, mismatched domains or spelling errors. The standard solution is to apply domain adaptation or data augmentation to build a domain-dependent system. However, in real life, the input noise varies in a wide range of domains and types, which is unknown in the training phase. This thesis introduces five general approaches to improve NMT accuracy and …

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Zero-Shot Ingredient Recognition By Multi-Relational Graph Convolutional Network, Jingjing Chen, Liangming Pan, Zhipeng Wei, Xiang Wang, Chong-Wah Ngo, Tat-Seng Chua Feb 2020

Zero-Shot Ingredient Recognition By Multi-Relational Graph Convolutional Network, Jingjing Chen, Liangming Pan, Zhipeng Wei, Xiang Wang, Chong-Wah Ngo, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Recognizing ingredients for a given dish image is at the core of automatic dietary assessment, attracting increasing attention from both industry and academia. Nevertheless, the task is challenging due to the difficulty of collecting and labeling sufficient training data. On one hand, there are hundred thousands of food ingredients in the world, ranging from the common to rare. Collecting training samples for all of the ingredient categories is difficult. On the other hand, as the ingredient appearances exhibit huge visual variance during the food preparation, it requires to collect the training samples under different cooking and cutting methods for robust …

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Five Challenges In Cloud-Enabled Intelligence And Control, Tarek Abdelzaher, Yifan Hao, Kasthuri Jayarajah, Archan Misra, Per Skarin, Shuochao Yao, Dulanga Kaveesha Weerakoon Weerakoon Mudiyanselage, Karl-Erik Arzen Feb 2020

Five Challenges In Cloud-Enabled Intelligence And Control, Tarek Abdelzaher, Yifan Hao, Kasthuri Jayarajah, Archan Misra, Per Skarin, Shuochao Yao, Dulanga Kaveesha Weerakoon Weerakoon Mudiyanselage, Karl-Erik Arzen

Research Collection School Of Computing and Information Systems

The proliferation of connected embedded devices, or the Internet of Things (IoT), together with recent advances in machine intelligence, will change the profile of future cloud services and introduce a variety of new research problems, both in cloud applications and infrastructure layers. These problems are centered around empowering individually resource-limited devices to exhibit intelligent behavior, both in sensing and control, thanks to a judicious utilization of cloud resources. Cloud services will enable learning from data, performing inference, and executing control, all with assurances on outcomes. The paper discusses such emerging services and outlines five resulting new research directions towards enabling …

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Topic Modeling On Document Networks With Adjacent-Encoder, Ce Zhang, Hady W. Lauw Feb 2020

Topic Modeling On Document Networks With Adjacent-Encoder, Ce Zhang, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Oftentimes documents are linked to one another in a network structure,e.g., academic papers cite other papers, Web pages link to other pages. In this paper we propose a holistic topic model to learn meaningful and unified low-dimensional representations for networked documents that seek to preserve both textual content and network structure. On the basis of reconstructing not only the input document but also its adjacent neighbors, we develop two neural encoder architectures. Adjacent-Encoder, or AdjEnc, induces competition among documents for topic propagation, and reconstruction among neighbors for semantic capture. Adjacent-Encoder-X, or AdjEnc-X, extends this to also encode the network structure …

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Short-Term Electricity Price Forecasting In Deregulated Electricity Market Based On Enhanced Artificial Intelligence Techniques, Pourdaryaei Alireza Feb 2020

Short-Term Electricity Price Forecasting In Deregulated Electricity Market Based On Enhanced Artificial Intelligence Techniques, Pourdaryaei Alireza

Student Works (2020-2029)

Electricity price forecasting is considered as one of prime factors for operation, planning and scheduling of price-setter market participants. However, possessing time variant, non-linear and non-stationary behaviors make the electricity price a complex signal. The main challenge in this area is providing highly accurate and efficient day-ahead price forecasting. A suitable feature selection technique, which is able to model the interacting features and nonlinearities of the forecast processes, is still required although researches have been performed for day-ahead forecasting. In this research, a hybrid electricity price forecasting methodology is proposed using two-stage feature selection method and optimization using adaptive neuro-fuzzy …

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Bounding Regret In Empirical Games, Steven Jecmen, Arunesh Sinha, Zun Li, Long Tran-Thanh Feb 2020

Bounding Regret In Empirical Games, Steven Jecmen, Arunesh Sinha, Zun Li, Long Tran-Thanh

Research Collection School Of Computing and Information Systems

Empirical game-theoretic analysis refers to a set of models and techniques for solving large-scale games. However, there is a lack of a quantitative guarantee about the quality of output approximate Nash equilibria (NE). A natural quantitative guarantee for such an approximate NE is the regret in the game (i.e. the best deviation gain). We formulate this deviation gain computation as a multi-armed bandit problem, with a new optimization goal unlike those studied in prior work. We propose an efficient algorithm Super-Arm UCB (SAUCB) for the problem and a number of variants. We present sample complexity results as well as extensive …

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Generating Realistic Stock Market Order Streams, Junyi Li, Xintong Wang, Yaoyang Lin, Arunesh Sinha, Michael P. Wellman Feb 2020

Generating Realistic Stock Market Order Streams, Junyi Li, Xintong Wang, Yaoyang Lin, Arunesh Sinha, Michael P. Wellman

Research Collection School Of Computing and Information Systems

We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks. We model the order stream as a stochastic process with finite history dependence, and employ a conditional Wasserstein GAN to capture history dependence of orders in a stock market. We test our approach with actual market and synthetic data on a number of different statistics, and find the generated data to be close to real data.

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Multi-Level Head-Wise Match And Aggregation In Transformer For Textual Sequence Matching, Shuohang Wang, Yunshi Lan, Yi Tay, Jing Jiang, Jingjing Liu Feb 2020

Multi-Level Head-Wise Match And Aggregation In Transformer For Textual Sequence Matching, Shuohang Wang, Yunshi Lan, Yi Tay, Jing Jiang, Jingjing Liu

Research Collection School Of Computing and Information Systems

Transformer has been successfully applied to many natural language processing tasks. However, for textual sequence matching, simple matching between the representation of a pair of sequences might bring in unnecessary noise. In this paper, we propose a new approach to sequence pair matching with Transformer, by learning head-wise matching representations on multiple levels. Experiments show that our proposed approach can achieve new state-of-the-art performance on multiple tasks that rely only on pre-computed sequence-vectorrepresentation, such as SNLI, MNLI-match, MNLI-mismatch, QQP, and SQuAD-binary

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Sentiment Analysis For Airline Services On Twitter Using Deep Learning With Word Embedding, Nour El Daim El Khalifa Mawada Mohamed Feb 2020

Sentiment Analysis For Airline Services On Twitter Using Deep Learning With Word Embedding, Nour El Daim El Khalifa Mawada Mohamed

Student Works (2020-2029)

The use of social media platform in the airline industries have increased rapidly to allow analysis introduce the quality and performance of the services. The role of Sentiment Analysis (SA) is to classify people's opinions into different categories, such as positive and negative from text, using existing algorithms. However, existing approaches such as the Bag of Words (BOW) model is frequently used for text classification, where a document is mapped to a feature vector before the construction of the actual model, using machine learning techniques, like Logistical Regression and Support Vector algorithms. This problem has led to low accuracy in …

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Stochastically Robust Personalized Ranking For Lsh Recommendation Retrieval, Dung D. Le, Hady W. Lauw Feb 2020

Stochastically Robust Personalized Ranking For Lsh Recommendation Retrieval, Dung D. Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Locality Sensitive Hashing (LSH) has become one of the most commonly used approximate nearest neighbor search techniques to avoid the prohibitive cost of scanning through all data points. For recommender systems, LSH achieves efficient recommendation retrieval by encoding user and item vectors into binary hash codes, reducing the cost of exhaustively examining all the item vectors to identify the topk items. However, conventional matrix factorization models may suffer from performance degeneration caused by randomly-drawn LSH hash functions, directly affecting the ultimate quality of the recommendations. In this paper, we propose a framework named SRPR, which factors in the stochasticity of …

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Deepdualmapper: A Gated Fusion Network For Automatic Map Extraction Using Aerial Images And Trajectories, Hao Wu, Hanyuan Zhang, Xinyu Zhang, Weiwei Sun, Baihua Zheng, Yuning Jiang Feb 2020

Deepdualmapper: A Gated Fusion Network For Automatic Map Extraction Using Aerial Images And Trajectories, Hao Wu, Hanyuan Zhang, Xinyu Zhang, Weiwei Sun, Baihua Zheng, Yuning Jiang

Research Collection School Of Computing and Information Systems

Automatic map extraction is of great importance to urban computing and location-based services. Aerial image and GPS trajectory data refer to two different data sources that could be leveraged to generate the map, although they carry different types of information. Most previous works on data fusion between aerial images and data from auxiliary sensors do not fully utilize the information of both modalities and hence suffer from the issue of information loss. We propose a deep convolutional neural network called DeepDualMapper which fuses the aerial image and trajectory data in a more seamless manner to extract the digital map. We …

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Multi-Level Fine-Scaled Sentiment Sensing With Ambivalence Handling, Zhaoxia Wang, Seng-Beng Ho, Erik Cambria Feb 2020

Multi-Level Fine-Scaled Sentiment Sensing With Ambivalence Handling, Zhaoxia Wang, Seng-Beng Ho, Erik Cambria

Research Collection School Of Computing and Information Systems

Social media represent a rich source of information, such as critiques, feedback, and other opinions posted online by Internet users. Such information is typically a good reflection of users’ sentiments and attitudes towards various services, topics, or products. Sentiment analysis has become an increasingly important natural language processing (NLP) task to help users make sense of what is happening in the Internet blogosphere and it can be useful for companies as well as public organizations. However, most existing sentiment analysis techniques are only able to analyze data at the aggregate level, merely providing a binary classification (positive vs. negative), and …

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Beyond Reasonable Doubt: A Proposal For Undecidedness Blocking In Abstract Argumentation, Pierpaolo Dondio, Luca Longo Jan 2020

Beyond Reasonable Doubt: A Proposal For Undecidedness Blocking In Abstract Argumentation, Pierpaolo Dondio, Luca Longo

Articles

In Dung’s abstract semantics, the label undecided is always propagated from the attacker to the attacked argument, unless the latter is also attacked by an accepted argument. In this work we propose undecidedness blocking abstract argumentation semantics where the undecided label is confined to the strong connected component where it was generated and it is not propagated to the other parts of the argumentation graph. We show how undecidedness blocking is a fundamental reasoning pattern absent in abstract argumentation but present in similar fashion in the ambiguity blocking semantics of Defeasible logic, in the beyond reasonable doubt legal principle or …

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Preface To The Special Issue On Advances In Argumentation In Artificial Intelligence, Pierpaolo Dondio, Luca Longo, Stefano Bistarelli Jan 2020

Preface To The Special Issue On Advances In Argumentation In Artificial Intelligence, Pierpaolo Dondio, Luca Longo, Stefano Bistarelli

Articles

Now at the forefront of automated reasoning, argumentation has become a key research topic within Artificial Intelligence. It involves the investigation of those activities for the production and exchange of arguments, where arguments are attempts to persuade someone of something by giving reasons for accepting a particular conclusion or claim as evident. The study of argumentation has been the focus of attention of philosophers and scholars, from Aristotle and classical rhetoric to the present day. The computational study of arguments has emerged as a field of research in AI in the last two decades, mainly fuelled by the interest from …

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Harnessing Artificial Intelligence Capabilities To Improve Cybersecurity, Sherali Zeadally, Erwin Adi, Zubair Baig, Imran A. Khan Jan 2020

Harnessing Artificial Intelligence Capabilities To Improve Cybersecurity, Sherali Zeadally, Erwin Adi, Zubair Baig, Imran A. Khan

Information Science Faculty Publications

Cybersecurity is a fast-evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the original motives for carrying out cyberattacks largely remain unchanged, cybercriminals have become increasingly sophisticated with their techniques. Traditional cybersecurity solutions are becoming inadequate at detecting and mitigating emerging cyberattacks. Advances in cryptographic and Artificial Intelligence (AI) techniques (in particular, machine learning and deep learning) show promise in enabling cybersecurity experts to counter the ever-evolving threat posed by adversaries. Here, we explore AI's …

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Research On The Strategy Of Adaptive Uvls Based On Rtds Simulation, Zhao Dan, Man Ji, Yunling Ni, Dengxin Liu, Weidong Wang Jan 2020

Research On The Strategy Of Adaptive Uvls Based On Rtds Simulation, Zhao Dan, Man Ji, Yunling Ni, Dengxin Liu, Weidong Wang

Journal of System Simulation

Abstract: Aiming at the improvement of the slow and unstable system voltage recovery after the low voltage load shedding load protected by centralized station area, the traditional low voltage load shedding load strategy will be researched. Considering the factors such as the power shortage and the important grade of the load, the power factor of the load to be cut is introduced, and the low load shedding model is established by minimizing the amount of cut load and minimizing the reactive power. The research is transformed into a belt Constrained multi-objective optimization research. The multi- objective particle swarm optimization algorithm …

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Design Of Air Defense Missile Weapon System Simulation Platform Based On Xsim Platform, Kaizhi Ruan, Qingqing Yuan, Wenhua Zhai, Zhiqiang Zhang Jan 2020

Design Of Air Defense Missile Weapon System Simulation Platform Based On Xsim Platform, Kaizhi Ruan, Qingqing Yuan, Wenhua Zhai, Zhiqiang Zhang

Journal of System Simulation

Abstract: Taking the system simulation technology which applied to the scheme argumentation, optimization design, flight test forecast, battle effectiveness evaluation of air defense missile weapon system as background, the design method of air defense missile weapon system simulation platform based on Xsim platform was put forward. The total configuration design, model design and simulation process design of the platform were discussed. An simulation platform of an air defense missile weapon system was accomplished. The result proves the platform can simulate the battle process of air defense missile weapon system, support the simulation work of air defense weapon at different …

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Analysis And Optimization Of Combustion Characteristics Of Cement Kiln Cooperatively Disposing Domestic Refuse, Jingbing Wu, Hanqing Tang, Xu Jun Jan 2020

Analysis And Optimization Of Combustion Characteristics Of Cement Kiln Cooperatively Disposing Domestic Refuse, Jingbing Wu, Hanqing Tang, Xu Jun

Journal of System Simulation

Abstract: Because the traditional methods can hardly analyze the complex combustion characteristics of cement kiln mixed with domestic refuse, a data mining technology is introduced. A domestic cement plant is selected as the object, and its operating data and relevant parameters are collected. The influence coefficient of each parameter on coal consumption and NOx emission is analyzed by using Stability Selection algorithm. The mathematical model of coal consumption and NOx emission is established with Random Forest algorithm, and the key optimization parameters and their optimal values are obtained by K-means clustering algorithm. The result shows that this method …

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An Enhanced Multi-Modal Function Optimization Fireworks Algorithm Base On Loser-Out Tournament, Xiaoning Shen, Wang Qian, Huang Yao, You Xuan Jan 2020

An Enhanced Multi-Modal Function Optimization Fireworks Algorithm Base On Loser-Out Tournament, Xiaoning Shen, Wang Qian, Huang Yao, You Xuan

Journal of System Simulation

Abstract: An enhanced multi-modal fireworks algorithm based on the loser-out tournament is proposed. A new position-based mapping rule is used to map the explosion sparks beyond the upper boundary of the explosion space to the area near the upper boundary, and to map the one below the lower boundary to the area near the lower boundary. A strategy which adaptively adjusts the number of explosion sparks is introduced to better balance the global and local search abilities of the algorithm. The 28 functions in the CEC2013 standard test function set are selected to the test. Experimental results show that the …

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A Xor-Based Visual Cryptography Scheme For (2, N) Access Structure With Ideal Structure Division, Yuqiao Cheng, Zhengxin Fu, Bin Yu Jan 2020

A Xor-Based Visual Cryptography Scheme For (2, N) Access Structure With Ideal Structure Division, Yuqiao Cheng, Zhengxin Fu, Bin Yu

Journal of System Simulation

Abstract: We propose a XOR-based visual cryptography scheme for (2, n) access structures. According to the definition of ideal access structure, the relationship of shares among the minimal qualified subsets is analyzed. And based on it, a division algorithm of access structures is presented with the theory of graph. By this approach, we can obtain the least number of ideal access structures. Additionally the processes of secret sharing and recovering are given. Experimental results show that this scheme can achieve a perfect secret recovery. Compared with existing schemes, the pixel expansion of our paper is the best.

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Study On Hardware-In-Loop Simulation Of Space-Feed Low-Frequency Guidance With Turntable External, Linpeng Wang, Chaolei Wang, Yuting Dai Jan 2020

Study On Hardware-In-Loop Simulation Of Space-Feed Low-Frequency Guidance With Turntable External, Linpeng Wang, Chaolei Wang, Yuting Dai

Journal of System Simulation

Abstract: Low-frequency detection, tracking and guidance of stealthy targets need new requirements for hardware-in-loop simulation verification technology. Turntable built-in usually produces electromagnetic interference to seeker. A turntable external method for space- feed low-frequency guidance of hardware in- loop simulation system is proposed. The space-feed low-frequency guidance simulation model is established, and the influence of turntable electromagnetic interference on the seeker is completely eliminated by the turntable external. The simulation environment of non-inertial space motion is constructed to solve the information fusion problem of multiple spaces for hardware-in-loop simulation. The feasibility of the simulation method is verified. The results show that …

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Study On Three-Dimensional Scene Sar Radio Frequency Simulation Technology, Guijie Diao, Ni Hong, Yang Liang Jan 2020

Study On Three-Dimensional Scene Sar Radio Frequency Simulation Technology, Guijie Diao, Ni Hong, Yang Liang

Journal of System Simulation

Abstract: Synthetic Aperture Radar (SAR) radio frequency simulation technology for three-dimensional scene is significant for SAR system test and the research of signal processing algorithms. A SAR radio frequency signal simulation is the core technology. Based on preliminary SAR frequency simulation scheme, a real-time SAR radio frequency signal simulation method for three-dimensional scene is proposed, and key parameters such as backward scattering coefficient, range between radar and target, shielding factor, antenna pattern weighting factor are calculated in real time according to the flight path information of SAR radar platform. Finally, the test results proved the validity of the method.

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Matching Between Mac Address And Object Based On Rssi Change Sequence, Zhang Liang, Kaifeng Hao Jan 2020

Matching Between Mac Address And Object Based On Rssi Change Sequence, Zhang Liang, Kaifeng Hao

Journal of System Simulation

Abstract: The connection between real people and the MAC of the communications device is of high value to public and network security. A better solution was proposed to improve the existing methods. MAC and real-time RSSI changes of the communication device were obtained by multiple Wi-Fi probes, then the RSSI status change sequence was constructed. The distance between object and multiple Wi-Fi probes was obtained by the object tracking, and the sequence of distance state change was constructed. After two kinds of sequences were compared, the optimal result was selected as the matching result between the moving object and the …

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