Sentence Compression With Reinforcement Learning,
2018
Singapore Management University
Sentence Compression With Reinforcement Learning, Liangguo Wang, Jing Jiang, Lejian Liao
Research Collection School Of Computing and Information Systems
Deletion-based sentence compression is frequently formulated as a constrained optimization problem and solved by integer linear programming (ILP). However, ILP methods searching the best compression given the space of all possible compressions would be intractable when dealing with overly long sentences and too many constraints. Moreover, the hard constraints of ILP would restrict the available solutions. This problem could be even more severe considering parsing errors. As an alternative solution, we formulate this task in a reinforcement learning framework, where hard constraints are used as rewards in a soft manner. The experiment results show that our method achieves competitive performance …
Organizational Change In The Artificial Intelligence Age – The Case Of Marketing And Sales,
2018
Singapore Management University
Organizational Change In The Artificial Intelligence Age – The Case Of Marketing And Sales, Yin Yang, Keng Siau
Research Collection School Of Computing and Information Systems
Artificial intelligence (AI), robotics, machine learning, and automation are revolutionizing the field of marketing and sales. Although the field of sales and marketing has been impacted by advanced technologies continuously, the impact of AI on sales and marketing field is expected to be transformational and revolutionary. The main threats pose by AI are job losses and upheaval of the society as a result (Siau, 2018; Siau and Wang, 2018). A report published in February 2016 by Citibank in partnership with the University of Oxford predicted that 47% of US jobs are at risk of automation. In UK, it is 35%. …
Trusting Artificial Intelligence In Healthcare,
2018
Singapore Management University
Trusting Artificial Intelligence In Healthcare, W. Wang, Keng Siau
Research Collection School Of Computing and Information Systems
Artificial Intelligence (AI) is able to perform at humans and even surpass human’s performances in some tasks. Recent cases about self-driving cars, cashier-free supermarket Amazon Go, and virtual assistants such as Apple’s Siri and Google Assistant have illustrated the current and future potential of AI. AI and its applications have infiltrated human’s work and daily life. It is inevitable that humans need to build a working relationship with AI and its applications. On one hand, humans can benefit from this new technology, for instance, a home robot can release housewife from mundane and monotonous tasks (Siau 2017, Siau 2018). On …
Ethical And Moral Issues With Ai,
2018
Singapore Management University
Ethical And Moral Issues With Ai, Weiyu Wang, Keng Siau
Research Collection School Of Computing and Information Systems
AI-based technology has achieved many great things, such as facial recognition, medical diagnosis, and self-driving cars. AI promises enormous benefits for economic growth, social development, as well as human well-being and safety improvement. However, the low-level of explainability, data security, data privacy, and ethical problems of AI-based technology also pose significant risks for users, developers, and governments. As the AI advances, one critical issue is how to address the ethical and moral challenges associated with AI. This study will focus on the ethics and morality issues that may be caused by AI, andmay arise because of AI. This research uses …
Cryptovisor: A Cryptocurrency Advisor Tool,
2018
Southern Methodist University
Cryptovisor: A Cryptocurrency Advisor Tool, Matthew Baldree, Paul Widhalm, Brandon Hill, Matteo Ortisi
SMU Data Science Review
In this paper, we present a tool that provides trading recommendations for cryptocurrency using a stochastic gradient boost classifier trained from a model labeled by technical indicators. The cryptocurrency market is volatile due to its infancy and limited size making it difficult for investors to know when to enter, exit, or stay in the market. Therefore, a tool is needed to provide investment recommendations for investors. We developed such a tool to support one cryptocurrency, Bitcoin, based on its historical price and volume data to recommend a trading decision for today or past days. This tool is 95.50% accurate with …
Feeling Ai,
2018
Vocational Training Council
Feeling Ai
SIGNED: The Magazine of The Hong Kong Design Institute
We all develop emotional connections to the devices we use; the smartphone that is a constant companion or the office printer that is a constant source of frustration. Soon, these machines might be able to respond in kind
Mining Temporal Activity Patterns On Social Media,
2018
University of New Mexico
Mining Temporal Activity Patterns On Social Media, Nikan Chavoshi
Computer Science ETDs
Social media provide communication networks for their users to easily create and share content. Automated accounts, called bots, abuse these platforms by engaging in suspicious and/or illegal activities. Bots push spam content and participate in sponsored activities to expand their audience. The prevalence of bot accounts in social media can harm the usability of these platforms, and decrease the level of trustworthiness in them. The main goal of this dissertation is to show that temporal analysis facilitates detecting bots in social media. I introduce new bot detection techniques which exploit temporal information. Since automated accounts are controlled by computer programs, …
Taxis Strike Back: A Field Trial Of The Driver Guidance System,
2018
Singapore Management University
Taxis Strike Back: A Field Trial Of The Driver Guidance System, Shih-Fen Cheng, Shashi Shekhar Jha, Rishikeshan Rajendram
Research Collection School Of Computing and Information Systems
Traditional taxi fleet operators world-over have been facing intense competitions from various ride-hailing services such as Uber and Grab (specific to the Southeast Asia region). Based on our studies on the taxi industry in Singapore, we see that the emergence of Uber and Grab in the ride-hailing market has greatly impacted the taxi industry: the average daily taxi ridership for the past two years has been falling continuously, by close to 20% in total. In this work, we discuss how efficient real-time data analytics and large-scale multi-agent optimization technology could potentially help taxi drivers compete against more technologically advanced service …
The Price Of Usability: Designing Operationalizable Strategies For Security Games,
2018
University of Southern California
The Price Of Usability: Designing Operationalizable Strategies For Security Games, Sara Marie Mccarthy, Corine M. Laan, Kai Wang, Phebe Vayanos, Arunesh Sinha, Milind Tambe
Research Collection School Of Computing and Information Systems
We consider the problem of allocating scarce security resources among heterogeneous targets to thwart a possible attack. It is well known that deterministic solutions to this problem being highly predictable are severely suboptimal. To mitigate this predictability, the game-theoretic security game model was proposed which randomizes over pure (deterministic) strategies, causing confusion in the adversary. Unfortunately, such mixed strategies typically involve randomizing over a large number of strategies, requiring security personnel to be familiar with numerous protocols, making them hard to operationalize. Motivated by these practical considerations, we propose an easy to use approach for computing strategies that are easy …
Modeling Contemporaneous Basket Sequences With Twin Networks For Next-Item Recommendation,
2018
Singapore Management University
Modeling Contemporaneous Basket Sequences With Twin Networks For Next-Item Recommendation, Duc Trong Le, Hady W. Lauw, Yuan Fang
Research Collection School Of Computing and Information Systems
Our interactions with an application frequently leave a heterogeneous and contemporaneous trail of actions and adoptions (e.g., clicks, bookmarks, purchases). Given a sequence of a particular type (e.g., purchases)-- referred to as the target sequence, we seek to predict the next item expected to appear beyond this sequence. This task is known as next-item recommendation. We hypothesize two means for improvement. First, within each time step, a user may interact with multiple items (a basket), with potential latent associations among them. Second, predicting the next item in the target sequence may be helped by also learning from another supporting sequence …
Non-Destructive Evaluation For Composite Material,
2018
Old Dominion University
Non-Destructive Evaluation For Composite Material, Desalegn Temesgen Delelegn
Electrical & Computer Engineering Theses & Dissertations
The Nondestructive Evaluation Sciences Branch (NESB) at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) has conducted impact damage experiments over the past few years with the goal of understanding structural defects in composite materials. The Data Science Team within the NASA LaRC Office of the Chief Information Officer (OCIO) has been working with the Non-Destructive Evaluation (NDE) subject matter experts (SMEs), Dr. Cheryl Rose, from the Structural Mechanics & Concepts Branch and Dr. William Winfree, from the Research Directorate, to develop computer vision solutions using digital image processing and machine learning techniques that can help identify …
Identification And Optimal Linear Tracking Control Of Odu Autonomous Surface Vehicle,
2018
Old Dominion University
Identification And Optimal Linear Tracking Control Of Odu Autonomous Surface Vehicle, Nadeem Khan
Mechanical & Aerospace Engineering Theses & Dissertations
Autonomous surface vehicles (ASVs) are being used for diverse applications of civilian and military importance such as: military reconnaissance, sea patrol, bathymetry, environmental monitoring, and oceanographic research. Currently, these unmanned tasks can accurately be accomplished by ASVs due to recent advancements in computing, sensing, and actuating systems. For this reason, researchers around the world have been taking interest in ASVs for the last decade. Due to the ever-changing surface of water and stochastic disturbances such as wind and tidal currents that greatly affect the path-following ability of ASVs, identification of an accurate model of inherently nonlinear and stochastic ASV system …
Using Eeg-Validated Music Emotion Recognition Techniques To Classify Multi-Genre Popular Music For Therapeutic Purposes,
2018
NUS High School of Mathematics and Science
Using Eeg-Validated Music Emotion Recognition Techniques To Classify Multi-Genre Popular Music For Therapeutic Purposes, Dejoy Shastikk Kumaran
The International Student Science Fair 2018
Music is observed to possess significant beneficial effects to human mental health, especially for patients undergoing therapy and older adults. Prior research focusing on machine recognition of the emotion music induces by classifying low-level music features has utilized subjective annotation to label data for classification. We validate this approach by using an electroencephalography-based approach to cross-check the predictions of music emotion made with the predictions from low-level music feature data as well as collected subjective annotation data. Collecting 8-channel EEG data from 10 participants listening to segments of 40 songs from 5 different genres, we obtain a subject-independent classification accuracy …
Perception & Perspective: An Analysis Of Discourse And Situational Factors In Reference Frame Selection,
2018
Technological University Dublin
Perception & Perspective: An Analysis Of Discourse And Situational Factors In Reference Frame Selection, Robert J. Ross, Kavita E. Thomas
Conference papers
To integrate perception into dialogue, it is necessary to bind spatial language descriptions to reference frame use. To this end, we present an analysis of discourse and situational factors that may influence reference frame choice in dialogues. We show that factors including spatial orientation, task, self and other alignment, and dyad have an influence on reference frame use. We further show that a computational model to estimate reference frame based on these features provides results greater than both random and greedy reference frame selection strategies.
Using Eeg-Validated Music Emotion Recognition Techniques To Classify Multi-Genre Popular Music For Therapeutic Purposes,
2018
NUS High School of Mathematics and Science
Using Eeg-Validated Music Emotion Recognition Techniques To Classify Multi-Genre Popular Music For Therapeutic Purposes, Dejoy Shastikk Kumaran
The International Student Science Fair 2018
Music is observed to possess significant beneficial effects to human mental health, especially for patients undergoing therapy and older adults. Prior research focusing on machine recognition of the emotion music induces by classifying low-level music features has utilized subjective annotation to label data for classification. We validate this approach by using an electroencephalography-based approach to cross-check the predictions of music emotion made with the predictions from low-level music feature data as well as collected subjective annotation data. Collecting 8-channel EEG data from 10 participants listening to segments of 40 songs from 5 different genres, we obtain a subject-independent classification accuracy …
Mind The Gap: Situated Spatial Language A Case-Study In Connecting Perception And Language,
2018
Technological University Dublin
Mind The Gap: Situated Spatial Language A Case-Study In Connecting Perception And Language, John D. Kelleher
Other
This abstract reviews the literature on computational models of spatial semantics and the potential of deep learning models as an useful approach to this challenge.
Combination Forecasting Of Stock Index Time Series Based On Cooperative Game Theory,
2018
Hunan Railway Professional Technology College, Zhuzhou 412001, China;
Combination Forecasting Of Stock Index Time Series Based On Cooperative Game Theory, Luo Wei
Journal of System Simulation
Abstract: In view of the characteristics of nonlinear, large amplitude, frequent fluctuations in China's stock market, a prediction method of intelligent composite stock index time series based on the cooperative game is presented. The prediction model of stock index time series is established by using neural network method based on the correlations among the various economic indicators, and the development trend and laws of stock index time series are established by using the improved ARIMA method. The two methods are combined by importing cooperative game method. Simulation results show that the prediction accuracy of the presented method is controlled …
Real-Time Simulator For Spatial Information Networks Based On Analog If Signal Processing,
2018
1. National Key Laboratory of Science and Technology on Communication, Chengdu, 611731, China;;
Real-Time Simulator For Spatial Information Networks Based On Analog If Signal Processing, Zeguo Yang, Ma Shang, Diaopeng Huang, Jianhao Hu, Lixiang Liu
Journal of System Simulation
Abstract: To solve the problem of real-time simulation of spatial information network with high dynamic network topology, a real-time simulator based on the IF signal processing is proposed. Compared with traditional channel simulator, it supports both the channel transmission characteristics like channel fading, Doppler shift, noise, and path delay, and the real-time simulation of dynamic network topology changes. The simulator supports 8~128 70 MHz IF (0~20 MHz signal bandwidth) emulated nodes with flexible link type configuration. The maximal fading depth is 100 dB, the maximal Doppler shift is 2 MHz, and the maximal path delay can reach up …
Research And Simulation Of Roots-Type Power Machine Control System Based On Fuzzy Pid,
2018
School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China;
Research And Simulation Of Roots-Type Power Machine Control System Based On Fuzzy Pid, Yan-Jun Xiao, Yonggeng Wang, Jing Ran, Feng Hua, Yongcong Li
Journal of System Simulation
Abstract: For utilizing the domestic low grade waste heat resources, a Roots-type steam engine is developed. To make the roots engine stably output electric energy, an efficient constant power control system needs to be designed. The controlled object characteristics of the roots engine are analyzed; the modeling of the control system is established; and on this basis the fuzzy control algorithm is introduced. The fuzzy adaptive PID controller for the roots power machine constant power output is designed and the related MATLAB simulation is carried out. The results show that the Roots type steam power machine with fuzzy adaptive PID …
Application Of Finite Element Modification And Model Order Reduction In Temperature Control System,
2018
1. China Jiliang University, Zhejiang Hangzhou 310018, China;;
Application Of Finite Element Modification And Model Order Reduction In Temperature Control System, Xiaona Wang, Ye Ying, Qiyue Xu, Sebastian Marin, Michael Hohmann, Shuliang Ye
Journal of System Simulation
Abstract: A modification and model order reduction (MOR) method based on finite element model is proposed, which can be used in the design of simulation platform of furnace temperature control system. Based on the step response test of furnace behavior and the modification of finite element model's key parameters in ANSYS, the model reflecting the actual characteristics of furnace is obtained. Based on the software tool called mor4ansys using Krylo subspace reduction method, the state space model is obtained. The MATLAB/Simulink simulation platform based on state space model is built for more research work on furnace temperature control design. Based …
