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Artificial Intelligence and Robotics Commons™
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Articles 1 - 13 of 13
Full-Text Articles in Artificial Intelligence and Robotics
Leveraging Artificial Intelligence To Capture The Singapore Rideshare Market, Pradeep Varakantham
Leveraging Artificial Intelligence To Capture The Singapore Rideshare Market, Pradeep Varakantham
MITB Thought Leadership Series
BIKE-SHARING programmes face many of the issues encountered by their counterparts in the carsharing world. But in Singapore, there are a number of factors that have a unique impact on the industry. These include the regulatory structure and the significant fines for those companies who do not abide by these regulations. When this is combined with the competitive nature of the industry in one of the world's most dynamic cities, it becomes clear that first movers who leverage machine learning and prediction will come to dominate the industry
Comparing Elm With Svm In The Field Of Sentiment Classification Of Social Media Text Data, Zhihuan Chen, Zhaoxia Wang, Zhiping Lin, Ting Yang
Comparing Elm With Svm In The Field Of Sentiment Classification Of Social Media Text Data, Zhihuan Chen, Zhaoxia Wang, Zhiping Lin, Ting Yang
Research Collection School Of Computing and Information Systems
Machine learning has been used in various fields with thousands of applications. Extreme learning machine (ELM), which is the most recently developed machine learning algorithm, has become increasingly popular for its good generalization ability. However, it has been relatively less applied to the domain of social media. Support Vector Machine (SVM), another popular learning-based algorithm, has been applied for sentiment classification of social media text data and has obtained good results. This paper investigates and compares the capabilities of these two learning-based methods in the field of sentiment classification of social media. The results indicate that SVM can obtain good …
Knowledge-Aware Multimodal Dialogue Systems, Lizi Liao, Yunshan Ma, Xiangnan He, Richang Hong, Tat-Seng Chua
Knowledge-Aware Multimodal Dialogue Systems, Lizi Liao, Yunshan Ma, Xiangnan He, Richang Hong, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
By offering a natural way for information seeking, multimodal dialogue systems are attracting increasing attention in several domains such as retail, travel etc. However, most existing dialogue systems are limited to textual modality, which cannot be easily extended to capture the rich semantics in visual modality such as product images. For example, in fashion domain, the visual appearance of clothes and matching styles play a crucial role in understanding the user's intention. Without considering these, the dialogue agent may fail to generate desirable responses for users. In this paper, we present a Knowledge-aware Multimodal Dialogue (KMD) model to address the …
Interpretable Multimodal Retrieval For Fashion Products, Lizi Liao, Xiangnan He, Bo Zhao, Chong-Wah Ngo, Tat-Seng Chua
Interpretable Multimodal Retrieval For Fashion Products, Lizi Liao, Xiangnan He, Bo Zhao, Chong-Wah Ngo, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Deep learning methods have been successfully applied to fashion retrieval. However, the latent meaning of learned feature vectors hinders the explanation of retrieval results and integration of user feedback. Fortunately, there are many online shopping websites organizing fashion items into hierarchical structures based on product taxonomy and domain knowledge. Such structures help to reveal how human perceive the relatedness among fashion products. Nevertheless, incorporating structural knowledge for deep learning remains a challenging problem. This paper presents techniques for organizing and utilizing the fashion hierarchies in deep learning to facilitate the reasoning of search results and user intent. The novelty of …
Modeling Contemporaneous Basket Sequences With Twin Networks For Next-Item Recommendation, Duc Trong Le, Hady W. Lauw, Yuan Fang
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 …
Taxis Strike Back: A Field Trial Of The Driver Guidance System, Shih-Fen Cheng, Shashi Shekhar Jha, Rishikeshan Rajendram
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 …
Ai: Augmentation, More So Than Automation, Steven M. Miller
Ai: Augmentation, More So Than Automation, Steven M. Miller
Asian Management Insights
The take-up of Artificial Intelligence (AI)-enabled systems in organisations is expanding rapidly. Integrating AI-enabled automation with people into workplace processes and societal systems is a complex and evolving challenge. The articles takes a managerial perspective on how firms can effectively deploy human minds and intelligent machines in the workplace.
Evaluating Sequence Discovery Systems In An Abstraction-Aware Manner, Eoin Rogers, Robert J. Ross, John D. Kelleher
Evaluating Sequence Discovery Systems In An Abstraction-Aware Manner, Eoin Rogers, Robert J. Ross, John D. Kelleher
Conference papers
Activity discovery is a challenging machine learning problem where we seek to uncover new or altered behavioural patterns in sensor data. In this paper we motivate and introduce a novel approach to evaluating activity discovery systems. Pre-annotated ground truths, often used to evaluate the performance of such systems on existing datasets, may exist at different levels of abstraction to the output of the output produced by the system. We propose a method for detecting and dealing with this situation, allowing for useful ground truth comparisons. This work has applications for activity discovery, and also for related fields. For example, it …
User-Centric Privacy Preservation In Mobile And Location-Aware Applications, Mingming Guo
User-Centric Privacy Preservation In Mobile And Location-Aware Applications, Mingming Guo
FIU Electronic Theses and Dissertations
The mobile and wireless community has brought a significant growth of location-aware devices including smart phones, connected vehicles and IoT devices. The combination of location-aware sensing, data processing and wireless communication in these devices leads to the rapid development of mobile and location-aware applications. Meanwhile, user privacy is becoming an indispensable concern. These mobile and location-aware applications, which collect data from mobile sensors carried by users or vehicles, return valuable data collection services (e.g., health condition monitoring, traffic monitoring, and natural disaster forecasting) in real time. The sequential spatial-temporal data queries sent by users provide their location trajectory information. The …
Automatic Persona Generation (Apg): A Rationale And Demonstration, Soon-Gyo Jung, Joni Salminen, Haewoon Kwak, Jisun An, Bernard J Jansen
Automatic Persona Generation (Apg): A Rationale And Demonstration, Soon-Gyo Jung, Joni Salminen, Haewoon Kwak, Jisun An, Bernard J Jansen
Research Collection School Of Computing and Information Systems
We present Automatic Persona Generation (APG), a methodology and system for quantitative persona generation using large amounts of online social media data. The system is operational, beta deployed with several client organizations in multiple industry verticals and ranging from small-to-medium sized enterprises to large multi-national corporations. Using a robust web framework and stable back-end database, APG is currently processing tens of millions of user interactions with thousands of online digital products on multiple social media platforms, such as Facebook and YouTube. APG identifies both distinct and impactful user segments and then creates persona descriptions by automatically adding pertinent features, such …
An Lstm Model For Cloze-Style Machine Comprehension, Shuohang Wang, Jing Jiang
An Lstm Model For Cloze-Style Machine Comprehension, Shuohang Wang, Jing Jiang
Research Collection School Of Computing and Information Systems
Machine comprehension is concerned with teaching machines to answer reading comprehension questions. In this paper we adopt an LSTM-based model we designed earlier for textual entailment and propose two new models for cloze-style machine comprehension. In our first model, we treat the document as a premise and the question as a hypothesis, and use an LSTM with attention mechanisms to match the question with the document. This LSTM remembers the best answer token found in the document while processing the question. Furthermore, we observe some special properties of machine comprehension and propose a two-layer LSTM model. In this model, we …
Integrated Cooperation And Competition In Multi-Agent Decision-Making, Kyle Hollins Wray, Akshat Kumar, Shlomo Zilberstein
Integrated Cooperation And Competition In Multi-Agent Decision-Making, Kyle Hollins Wray, Akshat Kumar, Shlomo Zilberstein
Research Collection School Of Computing and Information Systems
Observing that many real-world sequential decision problems are not purely cooperative or purely competitive, we propose a new model—cooperative-competitive process (CCP)—that can simultaneously encapsulate both cooperation and competition.First, we discuss how the CCP model bridges the gap between cooperative and competitive models. Next, we investigate a specific class of group-dominant CCPs, in which agents cooperate to achieve a common goal as their primary objective, while also pursuing individual goals as a secondary objective. We provide an approximate solution for this class of problems that leverages stochastic finite-state controllers.The model is grounded in two multi-robot meeting and box pushing domains that …
Neutrosophic Operational Research - Vol. 3., Florentin Smarandache, Mohamed Abdel Basset, Victor Chang
Neutrosophic Operational Research - Vol. 3., Florentin Smarandache, Mohamed Abdel Basset, Victor Chang
Branch Mathematics and Statistics Faculty and Staff Publications
Foreword John R. Edwards This book is an excellent exposition of the use of Data Envelopment Analysis (DEA) to generate data analytic insights to make evidence-based decisions, to improve productivity, and to manage cost-risk and benefitopportunity in public and private sectors. The design and the content of the book make it an up-to-date and timely reference for professionals, academics, students, and employees, in particular those involved in strategic and operational decisionmaking processes to evaluate and prioritize alternatives to boost productivity growth, to optimize the efficiency of resource utilization, and to maximize the effectiveness of outputs and impacts to stakeholders. It …