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Artificial intelligence

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Articles 931 - 960 of 1090

Full-Text Articles in Physical Sciences and Mathematics

Bringing Defensive Artificial Intelligence Capabilities To Mobile Devices, Kevin Chong, Ahmed Ibrahim Jan 2018

Bringing Defensive Artificial Intelligence Capabilities To Mobile Devices, Kevin Chong, Ahmed Ibrahim

Australian Information Security Management Conference

Traditional firewalls are losing their effectiveness against new and evolving threats today. Artificial intelligence (AI) driven firewalls are gaining popularity due to their ability to defend against threats that are not fully known. However, a firewall can only protect devices in the same network it is deployed in, leaving mobile devices unprotected once they leave the network. To comprehensively protect a mobile device, capabilities of an AI-driven firewall can enhance the defensive capabilities of the device. This paper proposes porting AI technologies to mobile devices for defence against today’s ever-evolving threats. A defensive AI technique providing firewall-like capability is being …

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Ethics And Bias In Machine Learning: A Technical Study Of What Makes Us “Good”, Ashley Nicole Shadowen Dec 2017

Ethics And Bias In Machine Learning: A Technical Study Of What Makes Us “Good”, Ashley Nicole Shadowen

Student Theses

The topic of machine ethics is growing in recognition and energy, but bias in machine learning algorithms outpaces it to date. Bias is a complicated term with good and bad connotations in the field of algorithmic prediction making. Especially in circumstances with legal and ethical consequences, we must study the results of these machines to ensure fairness. This paper attempts to address ethics at the algorithmic level of autonomous machines. There is no one solution to solving machine bias, it depends on the context of the given system and the most reasonable way to avoid biased decisions while maintaining the …

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On Improvised Music, Computational Creativity And Human-Becoming, Arto Artinian, Adam James Wilson Dec 2017

On Improvised Music, Computational Creativity And Human-Becoming, Arto Artinian, Adam James Wilson

Publications and Research

Music improvisation is an act of human-becoming: of self-expression—an articulation of histories and memories that have molded its participants—and of exploration—a search for unimagined structures that break with the stale norms of majoritarian culture. Given that the former objective may inhibit the latter, we propose an integration of human musical improvisers and deliberately flawed creative software agents that are designed to catalyze the development of human-ratified minoritarian musical structures.

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The Birds Of A Feather Research Challenge, Todd W. Neller Nov 2017

The Birds Of A Feather Research Challenge, Todd W. Neller

Computer Science Faculty Publications

Neller presented a set of research challenges for undergraduates that allow an excellent formative experience of research, writing, peer review, and potential presentation and publication through a top-tier conference. The focus problem is the analysis of a newly-designed solitaire card game, Birds of a Feather, so potentials for discovery abound. Open access talk slides, research code, solvability data sets, research tutorial videos, and more are also available at http://cs.gettysburg.edu/~tneller/puzzles/boaf .

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The Transformation Of Science With Hpc, Big Data, And Ai, Jeffrey Kirk Oct 2017

The Transformation Of Science With Hpc, Big Data, And Ai, Jeffrey Kirk

Commonwealth Computational Summit

High performance computing has matured into an indispensable tool for not only academic research and government labs and agencies, but also for many industry sectors: energy, manufacturing, healthcare, financial services, even digital content creation. More recently, the advent of Big Data has enabled the use of HPC techniques for large scale data analysis, expanding the scope of HPC and the reach of it into more research and enterprise use cases. Since 2012, a new regime of data-driven analytics, deep learning, has erupted in popularity, fueled by both the massive performance increases in HPC technologies and in the explosive rate of …

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Investigating Genetic Algorithm Optimization Techniques In Video Games, Nathan Ambuehl Aug 2017

Investigating Genetic Algorithm Optimization Techniques In Video Games, Nathan Ambuehl

Undergraduate Honors Theses

Immersion is essential for player experience in video games. Artificial Intelligence serves as an agent that can generate human-like responses and intelligence to reinforce a player’s immersion into their environment. The most common strategy involved in video game AI is using decision trees to guide chosen actions. However, decision trees result in repetitive and robotic actions that reflect an unrealistic interaction. This experiment applies a genetic algorithm that explores selection, crossover, and mutation functions for genetic algorithm implementation in an isolated Super Mario Bros. pathfinding environment. An optimized pathfinding AI can be created by combining an elitist selection strategy with …

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An Intelligent Multimodal Upper-Limb Rehabilitation Robotic System, Alexandros Lioulemes Aug 2017

An Intelligent Multimodal Upper-Limb Rehabilitation Robotic System, Alexandros Lioulemes

Computer Science and Engineering Dissertations - Archive

A traffic accident, a battlefield injury, or a stroke can lead to brain or musculoskeletal injuries that impact motor and cognitive functions and can drastically change a person's life. In such situations, rehabilitation plays a critical role in the ability of the patient to partially or totally regain motor function, but the optimal training approach remains unclear. Robotic technologies are recognized as powerful tools to promote neuroplasticity and stimulate motor re-learning. Moreover, they deliver high-intensity, repetitive, active and task-oriented training; in addition, they provide objective measurements for patient evaluation. The primary focus of this research is to investigate the development …

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Online Multitask Relative Similarity Learning, Shuji Hao, Peilin Zhao, Yong Liu, Steven C. H. Hoi, Chunyan Miao Aug 2017

Online Multitask Relative Similarity Learning, Shuji Hao, Peilin Zhao, Yong Liu, Steven C. H. Hoi, Chunyan Miao

Research Collection School Of Computing and Information Systems

Relative similarity learning (RSL) aims to learn similarity functions from data with relative constraints. Most previous algorithms developed for RSL are batch-based learning approaches which suffer from poor scalability when dealing with real world data arriving sequentially. These methods are often designed to learn a single similarity function for a specific task. Therefore, they may be sub-optimal to solve multiple task learning problems. To overcome these limitations, we propose a scalable RSL framework named OMTRSL (Online Multi-Task Relative Similarity Learning). Specifically, we first develop a simple yet effective online learning algorithm for multi-task relative similarity learning. Then, we also propose …

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Learning To Hallucinate Face Images Via Component Generation And Enhancement, Yibing Song, Jiawei Zhang, Shengfeng He, Linchao Bao, Qingxiong Yang Aug 2017

Learning To Hallucinate Face Images Via Component Generation And Enhancement, Yibing Song, Jiawei Zhang, Shengfeng He, Linchao Bao, Qingxiong Yang

Research Collection School Of Computing and Information Systems

We propose a two-stage method for face hallucination. First, we generate facial components of the input image using CNNs. These components represent the basic facial structures. Second, we synthesize fine-grained facial structures from high resolution training images. The details of these structures are transferred into facial components for enhancement. Therefore, we generate facial components to approximate ground truth global appearance in the first stage and enhance them through recovering details in the second stage. The experiments demonstrate that our method performs favorably against state-of-the-art methods.

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Deepfacade: A Deep Learning Approach To Facade Parsing, Hantang Liu, Jialiang Zhang, Jianke Zhu, Steven C. H. Hoi Aug 2017

Deepfacade: A Deep Learning Approach To Facade Parsing, Hantang Liu, Jialiang Zhang, Jianke Zhu, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

The parsing of building facades is a key component to the problem of 3D street scenes reconstruction, which is long desired in computer vision. In this paper, we propose a deep learning based method for segmenting a facade into semantic categories. Man-made structures often present the characteristic of symmetry. Based on this observation, we propose a symmetric regularizer for training the neural network. Our proposed method can make use of both the power of deep neural networks and the structure of man-made architectures. We also propose a method to refine the segmentation results using bounding boxes generated by the Region …

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Proactive And Reactive Coordination Of Non-Dedicated Agent Teams Operating In Uncertain Environments, Pritee Agrawal, Pradeep Varakantham Aug 2017

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 …

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Object Detection Meets Knowledge Graphs, Yuan Fang, Kingsley Kuan, Jie Lin, Cheston Tan, Vijay Chandrasekhar Aug 2017

Object Detection Meets Knowledge Graphs, Yuan Fang, Kingsley Kuan, Jie Lin, Cheston Tan, Vijay Chandrasekhar

Research Collection School Of Computing and Information Systems

Object detection in images is a crucial task in computer vision, with important applications ranging from security surveillance to autonomous vehicles. Existing state-of-the-art algorithms, including deep neural networks, only focus on utilizing features within an image itself, largely neglecting the vast amount of background knowledge about the real world. In this paper, we propose a novel framework of knowledge-aware object detection, which enables the integration of external knowledge such as knowledge graphs into any object detection algorithm. The framework employs the notion of semantic consistency to quantify and generalize knowledge, which improves object detection through a re-optimization process to achieve …

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Modeling Trajectories With Recurrent Neural Networks, Hao Wu, Ziyang Chen, Weiwei Sun, Baihua Zheng, Wei Wang Aug 2017

Modeling Trajectories With Recurrent Neural Networks, Hao Wu, Ziyang Chen, Weiwei Sun, Baihua Zheng, Wei Wang

Research Collection School Of Computing and Information Systems

Modeling trajectory data is a building block for many smart-mobility initiatives. Existing approaches apply shallow models such as Markov chain and inverse reinforcement learning to model trajectories, which cannot capture the long-term dependencies. On the other hand, deep models such as Recurrent Neura lNetwork (RNN) have demonstrated their strength of modeling variable length sequences. However, directly adopting RNN to model trajectories is not appropriate because of the unique topological constraints faced by trajectories. Motivated by these findings, we design two RNN-based models which can make full advantage of the strength of RNN to capture variable length sequence and meanwhile to …

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Mechanism Design For Strategic Project Scheduling, Pradeep Varakantham, Na Fu Aug 2017

Mechanism Design For Strategic Project Scheduling, Pradeep Varakantham, Na Fu

Research Collection School Of Computing and Information Systems

Organizing large scale projects (e.g., Conferences, IT Shows, F1 race) requires precise scheduling of multiple dependent tasks on common resources where multiple selfish entities are competing to execute the individual tasks. In this paper, we consider a well studied and rich scheduling model referred to as RCPSP (Resource Constrained Project Scheduling Problem). The key change to this model that we consider in this paper is the presence of selfish entities competing to perform individual tasks with the aim of maximizing their own utility. Due to the selfish entities in play, the goal of the scheduling problem is no longer only …

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Game Specific Approaches To Monte Carlo Tree Search For Dots And Boxes, Jared Prince Jun 2017

Game Specific Approaches To Monte Carlo Tree Search For Dots And Boxes, Jared Prince

Mahurin Honors College Capstone Experience/Thesis Projects

In this project, a Monte Carlo tree search player was designed and implemented for the child’s game dots and boxes, the computational burden of which has left traditional artificial intelligence approaches like minimax ineffective. Two potential improvements to this player were implemented using game-specific information about dots and boxes: the lack of information for decision-making provided by the net score and the inherent symmetry in many states. The results of these two approaches are presented, along with details about the design of the Monte Carlo tree search player. The first improvement, removing net score from the state information, was proven …

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Robot Society Jun 2017

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?

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Investigating Trust And Trust Recovery In Human-Robot Interactions, Abigail L. Thomson May 2017

Investigating Trust And Trust Recovery In Human-Robot Interactions, Abigail L. Thomson

Celebration of Learning

As artificial intelligence and robotics continue to advance and be used in increasingly different functions and situations, it is important to look at how these new technologies will be used. An important factor in how a new resource will be used is how much it is trusted. This experiment was conducted to examine people’s trust in a robotic assistant when completing a task, how mistakes affect this trust, and if the levels of trust exhibited with a robot assistant were significantly different than if the assistant were human. The task was to watch a computer simulation of the three-cup monte …

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Country 2.0: Upgrading Cities With Smart Technologies, Steven M. Miller May 2017

Country 2.0: Upgrading Cities With Smart Technologies, Steven M. Miller

Asian Management Insights

Advancements in technology are being used to transform our cities into smart cities, but the process is not without its risks.

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A Study On The Effects Of Mutation On Populations Using Strategies While Playing Iterative Prisoner's Dilemma, Ramses Romulus De Guzman Reyes May 2017

A Study On The Effects Of Mutation On Populations Using Strategies While Playing Iterative Prisoner's Dilemma, Ramses Romulus De Guzman Reyes

Theses and Dissertations

This thesis examines the effects different types of mutation and mutation rates have on populations using strategies while playing the Iterative Prisoners Dilemma (IPD). The system used in order to conduct this study was used in Leas et al. (2016), which uses genetic algorithms as a means of studying memory and its impact on populations playing IPD. For this study, experiments are organized into three different environments: Control, Static and Dynamic. The Control Environment focuses on analyzing the system and forming initial results. The Static Environment focuses on studying the effects of different rates on strategic populations playing IPD, while …

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Impact Of Artificial Intelligence, Robotics, And Machine Learning On Sales And Marketing, Keng Siau, Y. Yang May 2017

Impact Of Artificial Intelligence, Robotics, And Machine Learning On Sales And Marketing, Keng Siau, Y. Yang

Research Collection School Of Computing and Information Systems

AI, robotics, and machine learning are impacting the field of sales and marketing in an unprecedented way. A perfect storm is brewing! On one hand, online retail stores like Amazon are crushing the bricks and mortar stores. Sales and marketing professionals in bricks and mortar stores are facing a grim future. On the other hand, AI, robotics, and machine learning are replacing sales and marketing professionals in online stores. In fact, salespersons and marketers are predicted to be among the first to be replaced by robots. In a face-to-face environment, human may still prefer to interact with another human. In …

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Machine Learning Csc 461, Amanda Izenstark Apr 2017

Machine Learning Csc 461, Amanda Izenstark

Library Impact Statements

No abstract provided.

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A Sandbox In Which To Learn And Develop Soar Agents, Daniel Lugo Mar 2017

A Sandbox In Which To Learn And Develop Soar Agents, Daniel Lugo

Theses and Dissertations

It is common for military personnel to leverage simulations (and simulators) as cost-effective tools to train and become proficient at various tasks (e.g., flying an aircraft and/or performing a mission, among others). These training simulations often need to represent humans within the simulated world in a realistic manner. Realistic implies creating simulated humans that exhibit behaviors that mimic real-world decision making and actions. Typically, to create the decision-making logic, techniques developed from the domain of artificial intelligence are used. Although there are several approaches to developing intelligent agents; we focus on leveraging and open source project called Soar, to define …

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Playful Ai Education, Todd W. Neller Feb 2017

Playful Ai Education, Todd W. Neller

Computer Science Faculty Publications

In this talk, Neller shared how games can serve as a fun means of teaching not only game-tree search in Artificial Intelligence (AI), but also such diverse topics as constraint satisfaction, logical reasoning, planning, uncertain reasoning, machine learning, and robotics. He observed that teachers teach best when they enjoy what they share and encouraged AI educators present to teach to their unique strengths and enthusiasms.

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Streaming Classification With Emerging New Class By Class Matrix Sketching, Xin Mu, Feida Zhu, Juan Du, Ee-Peng Lim, Zhi-Hua Zhou Feb 2017

Streaming Classification With Emerging New Class By Class Matrix Sketching, Xin Mu, Feida Zhu, Juan Du, Ee-Peng Lim, Zhi-Hua Zhou

Research Collection School Of Computing and Information Systems

Streaming classification with emerging new class is an important problem of great research challenge and practical value. In many real applications, the task often needs to handle large matrices issues such as textual data in the bag-of-words model and large-scale image analysis. However, the methodologies and approaches adopted by the existing solutions, most of which involve massive distance calculation, have so far fallen short of successfully addressing a real-time requested task. In this paper, the proposed method dynamically maintains two low-dimensional matrix sketches to 1) detect emerging new classes; 2) classify known classes; and 3) update the model in the …

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Collective Multiagent Sequential Decision Making Under Uncertainty, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau Feb 2017

Collective Multiagent Sequential Decision Making Under Uncertainty, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Multiagent sequential decision making has seen rapid progress with formal models such as decentralized MDPs and POMDPs. However, scalability to large multiagent systems and applicability to real world problems remain limited. To address these challenges, we study multiagent planning problems where the collective behavior of a population of agents affects the joint-reward and environment dynamics. Our work exploits recent advances in graphical models for modeling and inference with a population of individuals such as collective graphical models and the notion of finite partial exchangeability in lifted inference. We develop a collective decentralized MDP model where policies can be computed based …

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Recurrent Neural Networks With Auxiliary Labels For Cross-Domain Opinion Target Extraction, Ying Ding, Jianfei Yu, Jing Jiang Feb 2017

Recurrent Neural Networks With Auxiliary Labels For Cross-Domain Opinion Target Extraction, Ying Ding, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

Opinion target extraction is a fundamental task in opinion mining. In recent years, neural network based supervised learning methods have achieved competitive performance on this task. However, as with any supervised learning method, neural network based methods for this task cannot work well when the training data comes from a different domain than the test data. On the other hand, some rule-based unsupervised methods have shown to be robust when applied to different domains. In this work, we use rule-based unsupervised methods to create auxiliary labels and use neural network models to learn a hidden representation that works well for …

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Enhanced Prediction Of Network Attacks Using Incomplete Data, Jacob D. Arthur Jan 2017

Enhanced Prediction Of Network Attacks Using Incomplete Data, Jacob D. Arthur

CCAC Theses and Dissertations

For years, intrusion detection has been considered a key component of many organizations’ network defense capabilities. Although a number of approaches to intrusion detection have been tried, few have been capable of providing security personnel responsible for the protection of a network with sufficient information to make adjustments and respond to attacks in real-time. Because intrusion detection systems rarely have complete information, false negatives and false positives are extremely common, and thus valuable resources are wasted responding to irrelevant events. In order to provide better actionable information for security personnel, a mechanism for quantifying the confidence level in predictions is …

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How China Is Preparing For An Ai-Powered Future, Yujia He Jan 2017

How China Is Preparing For An Ai-Powered Future, Yujia He

Patterson School of Diplomacy and International Commerce Faculty Publications

U.S. leadership in artificial intelligence (AI) research and development (R&D) may be challenged by China’s recent and centralized policy planning aimed at rapidly developing AI-related technologies. The Chinese government supports AI as a strategic area supported by high-level policies with ambitious and quantifiable targets, inter-ministry coordination, government funding for research and development, support for workforce development, and suggestions for international collaboration and expansion. Still, as a relative latecomer in the global AI race, China faces numerous challenges to implement its policy planning, including short-term skills gap and long-term institutional constraints.

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Learning Conditional Preference Networks From Optimal Choices, Cory Siler Jan 2017

Learning Conditional Preference Networks From Optimal Choices, Cory Siler

Theses and Dissertations--Computer Science

Conditional preference networks (CP-nets) model user preferences over objects described in terms of values assigned to discrete features, where the preference for one feature may depend on the values of other features. Most existing algorithms for learning CP-nets from the user's choices assume that the user chooses between pairs of objects. However, many real-world applications involve the the user choosing from all combinatorial possibilities or a very large subset. We introduce a CP-net learning algorithm for the latter type of choice, and study its properties formally and empirically.

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Special Issue: Neutrosophic Theories Applied In Engineering, Florentin Smarandache, Jun Ye Jan 2017

Special Issue: Neutrosophic Theories Applied In Engineering, Florentin Smarandache, Jun Ye

Branch Mathematics and Statistics Faculty and Staff Publications

Neutrosophic sets and logic are generalizations of fuzzy and intuitionistic fuzzy sets and logic. Neutrosophic sets and logic are gaining significant attention in solving many real life decision making problems that involve uncertainty, impreciseness, vagueness, incompleteness, inconsistent, and indeterminacy. They have been applied in computational intelligence, multiple criteria decision making, image processing, medical diagnoses, etc. This Special Issue presents original research papers that report on state-of-the-art and recent advancements in neutrosophic sets and logic in soft computing, artificial intelligence, big and small data mining, decision making problems, and practical achievements.

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