Realtime Object Detection Via Deep Learning-Based Pipelines,
2019
Bryant University
Realtime Object Detection Via Deep Learning-Based Pipelines, James G. Shanahan, Liang Dai
Information Systems and Analytics Department Faculty Conference Proceedings
Ever wonder how the Tesla Autopilot system works (or why it fails)? In this tutorial we will look under the hood of self-driving cars and of other applications of computer vision and review state-of-the-art tech pipelines for object detection such as two-stage approaches (e.g., Faster R-CNN) or single-stage approaches (e.g., YOLO/SSD). This is accomplished via a series of Jupyter Notebooks that use Python, OpenCV, Keras, and Tensorflow. No prior knowledge of computer vision is assumed (although it will be help!). To this end we begin this tutorial with a review of computer vision and traditional approaches to object detection such …
Design And Modeling Of A New Biomimetic Soft Robotic Jellyfish Using Ipmc-Based Electroactive Polymers,
2019
University of Nevada, Las Vegas
Design And Modeling Of A New Biomimetic Soft Robotic Jellyfish Using Ipmc-Based Electroactive Polymers, Zakai J. Olsen, Kwang J. Kim
Mechanical Engineering Faculty Research
Smart materials and soft robotics have been seen to be particularly well-suited for developing biomimetic devices and are active fields of research. In this study, the design and modeling of a new biomimetic soft robot is described. Initial work was made in the modeling of a biomimetic robot based on the locomotion and kinematics of jellyfish. Modifications were made to the governing equations for jellyfish locomotion that accounted for geometric differences between biology and the robotic design. In particular, the capability of the model to account for the mass and geometry of the robot design has been added for better …
Liability For Ai Decision-Making: Some Legal And Ethical Considerations,
2019
William & Mary Law School
Liability For Ai Decision-Making: Some Legal And Ethical Considerations, Iria Giuffrida
Faculty Publications
No abstract provided.
Emotion-Aware Chat Machine: Automatic Emotional Response Generation For Human-Like Emotional Interaction,
2019
Huazhong University of Science and Technology
Emotion-Aware Chat Machine: Automatic Emotional Response Generation For Human-Like Emotional Interaction, Wei Wei, Jiayi Liu, Xianling Mao, Guibing Guo, Feida Zhu, Pan Zhou, Yuchong Hu
Research Collection School Of Computing and Information Systems
The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions. However, this challenge is not well addressed in the literature, since most of the approaches neglect the emotional information conveyed by a post while generating responses. This article addresses this problem by proposing a unified end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post for generating more intelligent responses with appropriately expressed emotions. Extensive experiments on real-world data demonstrate that the proposed method outperforms the state-of-the-art methods in terms …
Predicting Audience Engagement Across Social Media Platforms In The News Domain,
2019
Hamad Bin Khalifa University
Predicting Audience Engagement Across Social Media Platforms In The News Domain, Kholoud Khalil Aldous, Jisun An, Bernard J. Jansen
Research Collection School Of Computing and Information Systems
We analyze cross-platform factors for posts on both single and multiple social media platforms for numerous news outlets to better predict audience engagement, precisely the number of likes and comments. We collect 676,779 social media posts from 53 news outlets during eight months on four social media platforms (Facebook, Instagram, Twitter, and YouTube), along with the associated comments (more than 31 million) and the number of likes (more than 840 million). We develop a framework for predicting the audience engagement based on both linguistic features of the post and social media platform factors. Among other findings, results show that content …
Stylistic Features Usage: Similarities And Differences Using Multiple Social Networks,
2019
Singapore Management University
Stylistic Features Usage: Similarities And Differences Using Multiple Social Networks, Kholoud Khalil Aldous, Jisun An, Bernard J. Jansen
Research Collection School Of Computing and Information Systems
User engagement on social networks is essential for news outlets where they often distribute online content. News outlets simultaneously leverage multiple social media platforms to reach their overall audience and to increase marketshare. In this research, we analyze ten common stylistic features indicative of user engagement for news postings on multiple social media platforms. We display the stylistic features usage differences of news posts from various news sources. Results show that there are differences in the usage of stylistic features across social media platforms (Facebook, Instagram, Twitter, and YouTube). Online news outlets can benefit from these findings in building guidelines …
Gender And Racial Diversity In Commercial Brands' Advertising Images On Social Media,
2019
Singapore Management University
Gender And Racial Diversity In Commercial Brands' Advertising Images On Social Media, Jisun An, Haewoon Kwak
Research Collection School Of Computing and Information Systems
Gender and racial diversity in the mediated images from the media shape our perception of different demographic groups. In this work, we investigate gender and racial diversity of 85,957 advertising images shared by the 73 top international brands on Instagram and Facebook. We hope that our analyses give guidelines on how to build a fully automated watchdog for gender and racial diversity in online advertisements.
When Players Affect Target Values: Modeling And Solving Dynamic Partially Observable Security Games,
2019
Singapore Management University
When Players Affect Target Values: Modeling And Solving Dynamic Partially Observable Security Games, Xinrun Wang, Milind Tambe, Branislav Bosanky, Bo An
Research Collection School Of Computing and Information Systems
Most of the current security models assume that the values of targets/areas are static or the changes (if any) are scheduled and known to the defender. Unfortunately, such models are not sufficient for many domains, where actions of the players modify the values of the targets. Examples include wildlife scenarios, where the attacker can increase value of targets by secretly building supporting facilities. To address such security game domains with player-affected values, we first propose DPOS3G, a novel partially observable stochastic Stackelberg game where target values are determined by the players’ actions; the defender can only partially observe these targets’ …
Artificial Intelligence And The Challenge For Rural Medicine,
2019
Marshall University
Artificial Intelligence And The Challenge For Rural Medicine, James Denvir
Marshall Journal of Medicine
Recent advances in artificial intelligence, machine learning, and deep learning are beginning to have an impact on everyday experiences, from natural language processing used in automated telephone call centers to semi-autonomous vehicles. These techniques have also been applied to medical care. In this editorial we discuss applications of AI to medicine and argue for a proactive approach to include rural medicine in this paradigm shift.
Deepcon-Pre: Improved Protein Contact Map Prediction Using Inverse Covariance And Deep Residual Networks,
2019
University of Missouri-St. Louis
Deepcon-Pre: Improved Protein Contact Map Prediction Using Inverse Covariance And Deep Residual Networks, Nachammai Palaniappan
Theses
As with most domains where machine learning methods are applied, correct feature engineering is critical when developing deep learning algorithms for solving the protein folding problem. Unlike the domains such as computer vision and natural language processing, feature engineering is not rigorously studied towards solving the protein folding problem. A recent research has highlighted that input features known as precision matrix are most informative for predicting inter-residue contact map, the key for building three-dimensional models. In this work, we study the significance of the precision matrix feature when very deep residual networks are trained. Using a standard dataset of 3456 …
Detecting Digitally Forged Faces In Online Videos,
2019
University of South Florida
Detecting Digitally Forged Faces In Online Videos, Neilesh Sambhu
USF Tampa Graduate Theses and Dissertations
We use Rossler’s FaceForensics dataset of 1004 online videos and their corresponding forged counterparts [1] to investigate the ability to distinguish digitally forged facial images from original images automatically with deep learning. The proposed convolutional neural network is much smaller than the current state-of-the-art solutions. Nevertheless, the network maintains a high level of accuracy (99.6%), all while using the entire FaceForensics dataset and not including any temporal information. We implement majority voting and show the impact on accuracy (99.67%), where only 1 video of 300 is misclassified. We examine why the model misclassified this one video. In terms of tuning …
Classifying Fiction And Non-Fiction Works Using Machine Learning,
2019
Illinois Mathematics and Science Academy
Classifying Fiction And Non-Fiction Works Using Machine Learning, Rachna Gupta '21
Student Publications & Research
The objective of this project was to create a program that can determine whether an unknown text is a work of fiction or non-fiction using machine learning. Various datasets of speeches, ebooks, poems, scientific papers, and texts from Project Gutenberg and the Wolfram Example Data were utilized to train and test a Markov Chain machine learning model. A microsite was deployed with the final product that returns a probability of fictionality based on input from the user with 95% accuracy.
Court Record In The Age Of Artificial Intelligence,
2019
William & Mary Law School
Court Record In The Age Of Artificial Intelligence, Fredric I. Lederer
Popular Media
No abstract provided.
Automated Morgan Keenan Classification Of Observed Stellar Spectra Collected By The Sloan Digital Sky Survey Using A Single Classifier,
2019
Central Washington University
Automated Morgan Keenan Classification Of Observed Stellar Spectra Collected By The Sloan Digital Sky Survey Using A Single Classifier, Michael J. Brice, Răzvan Andonie
All Faculty Scholarship for the College of the Sciences
The classification of stellar spectra is a fundamental task in stellar astrophysics. Stellar spectra from the Sloan Digital Sky Survey are applied to standard classification methods, k-nearest neighbors and random forest, to automatically classify the spectra. Stellar spectra are high dimensional data and the dimensionality is reduced using astronomical knowledge because classifiers work in low dimensional space. These methods are utilized to classify the stellar spectra into a complete Morgan Keenan classification (spectral and luminosity) using a single classifier. The motion of stars (radial velocity) causes machine-learning complications through the feature matrix when classifying stellar spectra. Due to the nature …
Protein Inter-Residue Distance Prediction Using Residual And Capsule Networks,
2019
University of Missouri-St. Louis
Protein Inter-Residue Distance Prediction Using Residual And Capsule Networks, Andrew Dillon
Theses
The protein folding problem, also known as protein structure prediction, is the task of building three-dimensional protein models given their one-dimensional amino acid sequence. New methods that have been successfully used in the most recent CASP challenge have demonstrated that predicting a protein's inter-residue distances is key to solving this problem. Various deep learning algorithms including fully convolutional neural networks and residual networks have been developed to solve the distance prediction problem. In this work, we develop a hybrid method based on residual networks and capsule networks. We demonstrate that our method can predict distances more accurately than the algorithms …
Update Frequency And Background Corpus Selection In Dynamic Tf-Idf Models For First Story Detection,
2019
Technological University Dublin
Update Frequency And Background Corpus Selection In Dynamic Tf-Idf Models For First Story Detection, Fei Wang, Robert J. Ross, John D. Kelleher
Conference papers
First Story Detection (FSD) requires a system to detect the very first story that mentions an event from a stream of stories. Nearest neighbour-based models, using the traditional term vector document representations like TF-IDF, currently achieve the state of the art in FSD. Because of its online nature, a dynamic term vector model that is incrementally updated during the detection process is usually adopted for FSD instead of a static model. However, very little research has investigated the selection of hyper-parameters and the background corpora for a dynamic model. In this paper, we analyse how a dynamic term vector model …
Development Of Spatiotemporal Congestion Pattern Observation Model Using Historical And Near Real Time Data,
2019
Kennesaw State University
Development Of Spatiotemporal Congestion Pattern Observation Model Using Historical And Near Real Time Data, Betty Kretlow
Master of Science in Computer Science Theses
Traffic congestion is not foreign to major metropolitan areas. Congestion in large cities often is associated with dense land developments and continued economic growth. In general, congestion can be classified into two categories: recurring and nonrecurring. Recurring congestion often occurs at certain parts of highway networks, referred to as bottleneck locations. Nonrecurring congestion, on the other hand, can be caused by different reasons, including work zones, special events, accidents, inclement weather, poor signal timing, etc. The work presented here demonstrates an approach to effectively identifying spatiotemporal patterns of traffic congestion at a network level. The Metro Atlanta highway network was …
Automatic Inference Of Causal Reasoning Chains From Student Essays,
2019
DePaul University
Automatic Inference Of Causal Reasoning Chains From Student Essays, Simon Mark Hughes
College of Computing and Digital Media Dissertations
While there has been an increasing focus on higher-level thinking skills arising from the Common Core Standards, many high-school and middle-school students struggle to combine and integrate information from multiple sources when writing essays. Writing is an important learning skill, and there is increasing evidence that writing about a topic develops a deeper understanding in the student. However, grading essays is time consuming for teachers, resulting in an increasing focus on shallower forms of assessment that are easier to automate, such as multiple-choice tests. Existing essay grading software has attempted to ease this burden but relies on shallow lexico-syntactic features …
Experiences Of Using Intelligent Virtual Assistants By Visually Impaired Students In Online Higher Education,
2019
University of South Florida
Experiences Of Using Intelligent Virtual Assistants By Visually Impaired Students In Online Higher Education, Michele R. Forbes
USF Tampa Graduate Theses and Dissertations
In today’s world, the attainment of higher education impacts the acquisition of competitive employment and, thus, quality of life. As a group, persons with disabilities continually fall behind others in such academic progress, requiring new efforts to support their earning of advanced credentials. Though highly beneficial for these individuals, obtaining a degree comes with elevated levels of stress. As enrollment of students with disabilities grows in all formats of higher education, those involved must understand the stress endured by these students and how to diminish it. Theories speculate that technology, such as intelligent virtual assistants, may be a viable tool …
Artificial Intelligence, Real Impact,
2019
Singapore Management University
Artificial Intelligence, Real Impact, Singapore Management University
Perspectives@SMU
AI use in China continues to push innovation envelopes, but technology must be utilised and updated with expert advice
