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 …
Machine Learning To Predict College Course Success,
2018
Southern Methodist University
Machine Learning To Predict College Course Success, Anthony R.Y. Dalton, Justin Beer, Sriharshasai Kommanapalli, James S. Lanich Ph.D.
SMU Data Science Review
In this paper, we present an analysis of the predictive ability of machine learning on the success of students in college courses in a California Community College. The California Legislature passed assembly bill 705 in order to place students in non-remedial coursework, based on high school transcripts, to increase college completion. We utilize machine learning methods on de-identified student high school transcript data to create predictive algorithms on whether or not the student will be successful in college-level English and Mathematics coursework. To satisfy the bill’s requirements, we first use exploratory data analysis on applicable transcript variables. Then we use …
Analytics For Novel Consumer Insights (A Three Essay Dissertation),
2018
University of South Florida
Analytics For Novel Consumer Insights (A Three Essay Dissertation), Utkarsh Shrivastava
USF Tampa Graduate Theses and Dissertations
Both literature and practice have investigated how the vast amount of ever increasing customer information can inform marketing strategy and decision making. However, the customer data is often susceptible to modeling bias and misleading findings due to various factors including sample selection and unobservable variables. The available analytics toolkit has continued to develop but in the age of nearly perfect information, the customer decision making has also evolved. The dissertation addresses some of the challenges in deriving valid and useful consumer insights from customer data in the digital age. The first study addresses the limitations of traditional customer purchase measures …
Predicting River Stage Using Recurrent Neural Networks,
2018
Louisiana State University and Agricultural and Mechanical College
Predicting River Stage Using Recurrent Neural Networks, Eric Rohli
LSU Master's Theses
River stage prediction is an important problem in the water transportation industry. Accurate river stage predictions provide crucial information to barge and tow boat operators, port terminal captains, and lock management officials. Shallow river levels caused by prolonged drought impact the loading capacity of barges and tow boats. High river levels caused by excessive rainfall or snowmelt allow for greater tow capacities but make downstream transportation and lock management risky. Current academic river height prediction systems utilize either time series statistical analysis or machine learning algorithms to forecast future river heights, but systems that combine these two areas often limit …
Creating Real-Time Dynamic Knowledge Graphs,
2018
Wright State University - Main Campus
Creating Real-Time Dynamic Knowledge Graphs, Swati Padhee, Sarasi Lalithsena, Amit P. Sheth
Kno.e.sis Publications
No abstract provided.
Is Information Systems Misuse Always Bad? A New Perspective On Is Misuse In Hospitals Under The Context Of Disasters,
2018
Dakota State University
Is Information Systems Misuse Always Bad? A New Perspective On Is Misuse In Hospitals Under The Context Of Disasters, Dheyaaldin Alsalman
Masters Theses & Doctoral Dissertations
Although the extant literature has investigated how individuals engage in inappropriate behaviors based on the rational choice theory (RCT) (e.g., computer misconduct), the neutralization theory (e.g., IS security policies violation), and workarounds under normal situations, it has given little consideration to how individuals are involved in misuse of information systems with a good intention under the context of disasters. To fill this research gap, we propose a selfless misuse model, which offers a theoretical explanation for the concept of individuals’ selfless misuse intention under uncertainty caused by disasters. In this study, we show why employees make decisions to misuse the …
Hybrid Recommender For Online Petitions With Social Network And Psycholinguistic Features,
2018
Dakota State University
Hybrid Recommender For Online Petitions With Social Network And Psycholinguistic Features, Ahmed Elnoshokaty
Masters Theses & Doctoral Dissertations
The online petition has become one of the most important channels of civic participation. Most of the state-of-the-art online platforms, however, tend to use simple indicators (such as popularity) to rank petitions, hence creating a situation where the most popular petitions dominate the rank and attract most people’s attention. For the petitions which focus on specific issues, they are often in a disadvantageous position on the list. For example, a petition for local environment problem may not be seen by many people who are really concerned with it, simply because it takes multiple pages to reach it. Therefore, the simple …
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, …
Context Recovery In Location-Based Social Networks,
2018
Singapore Management University
Context Recovery In Location-Based Social Networks, Wen Haw Chong
Dissertations and Theses Collection (Open Access)
This dissertation addresses context recovery in Location-Based Social Networks (LBSN), which are platforms where users post content from various locations. With this general LBSN definition, many existing social media platforms that support user-generated location relevant content using mobile devices could also qualify as LBSNs. Context recovery for such user posts refers to recovering the venue and the semantic contexts of these user posts. Such information is useful for user profiling and to support various applications such as venue recommendation and location- based advertising.
An Assessment Of Users’ Cyber Security Risk Tolerance In Reward-Based Exchange,
2018
Singapore Management University
An Assessment Of Users’ Cyber Security Risk Tolerance In Reward-Based Exchange, Xinhui Zhan, Fiona Fui-Hoon Nah, Maggie X. Cheng
Research Collection School Of Computing and Information Systems
This study examines users’ risk-taking behavior in software downloads. We are interested in quantifying the degree of risks that users are willing to take in the cyber security context. We propose conducting an experiment using Amazon’s Mechanical Turk to assess the degree of risks that people are willing to take for monetary gains when they download software from uncertified sources.
Probabilistic Guided Exploration For Reinforcement Learning In Self-Organizing Neural Networks,
2018
Singapore Management University
Probabilistic Guided Exploration For Reinforcement Learning In Self-Organizing Neural Networks, Peng Wang, Weigui Jair Zhou, Di Wang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Exploration is essential in reinforcement learning, which expands the search space of potential solutions to a given problem for performance evaluations. Specifically, carefully designed exploration strategy may help the agent learn faster by taking the advantage of what it has learned previously. However, many reinforcement learning mechanisms still adopt simple exploration strategies, which select actions in a pure random manner among all the feasible actions. In this paper, we propose novel mechanisms to improve the existing knowledgebased exploration strategy based on a probabilistic guided approach to select actions. We conduct extensive experiments in a Minefield navigation simulator and the results …
Striving To Earn More: A Survey Of Work Strategies And Tool Use Among Crowd Workers,
2018
Carnegie Mellon University
Striving To Earn More: A Survey Of Work Strategies And Tool Use Among Crowd Workers, Toni Kaplan, Susumu Saito, Kotaro Hara, Jeffrey P. Bigham
Research Collection School Of Computing and Information Systems
Earning money is a primary motivation for workers on Amazon Mechanical Turk, but earning a good wage is difficult because work that pays well is not easily identified and can be time-consuming to find. We explored the strategies that both low- and high-earning workers use to find and complete tasks via a survey of 360 workers. Nearly all workers surveyed had earning money as their primary goal, and workers used many of the same tools (browser extensions and scripts) and strategies in an attempt to earn more money, regardless of earning level. However, high-earning workers used more tools, were more …
Autonomous Agents In Snake Game Via Deep Reinforcement Learning,
2018
Singapore Management University
Autonomous Agents In Snake Game Via Deep Reinforcement Learning, Zhepei Wei, Di Wang, Ming Zhang, Ah-Hwee Tan, Chunyan Miao, You Zhou
Research Collection School Of Computing and Information Systems
Since DeepMind pioneered a deep reinforcement learning (DRL) model to play the Atari games, DRL has become a commonly adopted method to enable the agents to learn complex control policies in various video games. However, similar approaches may still need to be improved when applied to more challenging scenarios, where reward signals are sparse and delayed. In this paper, we develop a refined DRL model to enable our autonomous agent to play the classical Snake Game, whose constraint gets stricter as the game progresses. Specifically, we employ a convolutional neural network (CNN) trained with a variant of Q-learning. Moreover, we …
Face Detection Using Deep Learning: An Improved Faster Rcnn Approach,
2018
DeepIR Inc
Face Detection Using Deep Learning: An Improved Faster Rcnn Approach, Xudong Sun, Pengcheng Wu, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
In this paper, we present a new face detection scheme using deep learning and achieve the state-of-the-art detection performance on the well-known FDDB face detection benchmark evaluation. In particular, we improve the state-of-the-art Faster RCNN framework by combining a number of strategies, including feature concatenation, hard negative mining, multi-scale training, model pre-training, and proper calibration of key parameters. As a consequence, the proposed scheme obtained the state-of-the-art face detection performance and was ranked as one of the best models in terms of ROC curves of the published methods on the FDDB benchmark
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 …
Efficient Representative Subset Selection Over Sliding Windows,
2018
National University of Singapore
Efficient Representative Subset Selection Over Sliding Windows, Yanhao Wang, Yuchen Li, Kian-Lee Tan
Research Collection School Of Computing and Information Systems
Representative subset selection (RSS) is an important tool for users to draw insights from massive datasets. Existing literature models RSS as submodular maximization to capture the "diminishing returns" property of representativeness, but often only has a single constraint, which limits its applications to many real-world problems. To capture the recency issue and support various constraints, we formulate dynamic RSS as maximizing submodular functions subject to general d -knapsack constraints (SMDK) over sliding windows. We propose a KnapWindow framework (KW) for SMDK. KW utilizes KnapStream (KS) for SMDK in append-only streams as a subroutine. It maintains a sequence of checkpoints and …
Deeptravel: A Neural Network Based Travel Time Estimation Model With Auxiliary Supervision,
2018
Fudan University
Deeptravel: A Neural Network Based Travel Time Estimation Model With Auxiliary Supervision, Hanyuan Zhang, Hao Wu, Weiwei Sun, Baihua Zheng
Research Collection School Of Computing and Information Systems
Estimating the travel time of a path is of great importance to smart urban mobility. Existing approaches are either based on estimating the time cost of each road segment or designed heuristically in a non-learning-based way. The former is not able to capture many cross-segment complex factors while the latter fails to utilize the existing abundant temporal labels of the data, i.e., the time stamp of each trajectory point. In this paper, we leverage on new development of deep neural networks and propose a novel auxiliary supervision model, namely DeepTravel, that can automatically and effectively extract different features, as well …
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 …
Searching For The X-Factor: Exploring Corpus Subjectivity For Word Embeddings,
2018
Singapore Management University
Searching For The X-Factor: Exploring Corpus Subjectivity For Word Embeddings, Maksim Tkachenko, Chong Cher Chia, Hady W. Lauw
Research Collection School Of Computing and Information Systems
We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this to be the case indeed. Moreover, based on the discovery of the outsized role that sentiment words play on subjectivity-sensitive tasks such as sentiment classification, we develop a novel word embedding SentiVec which is infused with sentiment information from a lexical resource, and is shown to outperform baselines on such tasks.
Role Of Social Media In Public Accounting Firms,
2018
Singapore Management University
Role Of Social Media In Public Accounting Firms, Brenda Eschenbrenner, Fiona Fui-Hoon Nah, Zhiwei Lu
Research Collection School Of Computing and Information Systems
Social media has been widely used for both professional and personal communications. Businesses recognize the importance of social media and are using them to fulfill various business objectives. In this paper, we focus on analyzing the business objectives of public accounting firms that have both a firm-wide main page and a career page on Facebook. More specifically, we compare the business objectives they are achieving with their firm-wide main pages versus career pages. We not only find differences in the objectives that are being achieved, but also identify other objectives that are not actively being pursued on either page but …
