A Near-Optimal Change-Detection Based Algorithm For Piecewise-Stationary Combinatorial Semi-Bandits, 2020 Singapore Management University
A Near-Optimal Change-Detection Based Algorithm For Piecewise-Stationary Combinatorial Semi-Bandits, Huozhi Zhou, Lingda Wang, Lav N. Varshney, Ee Peng Lim
Research Collection School Of Information Systems
We investigate the piecewise-stationary combinatorial semi-bandit problem. Compared to the original combinatorial semi-bandit problem, our setting assumes the reward distributions of base arms may change in a piecewise-stationary manner at unknown time steps. We propose an algorithm, GLR-CUCB, which incorporates an eﬃcient combinatorial semi-bandit algorithm, CUCB, with an almost parameter-free change-point detector, the Generalized Likelihood Ratio Test (GLRT). Our analysis shows that the regret of GLR-CUCB is upper bounded by O(√NKT logT), where N is the number of piecewise-stationary segments, K is the number of base arms, and T is the number of time steps. As a complement, we ...
Camera Placement Meeting Restrictions Of Computer Vision, 2020 Purdue University
Camera Placement Meeting Restrictions Of Computer Vision, Sara Aghajanzadeh, Roopasree Naidu, Shuo-Han Chen, Caleb Tung, Abhinav Goel, Yung-Hsiang Lu, George K. Thiruvathukal
Computer Science: Faculty Publications and Other Works
In the blooming era of smart edge devices, surveillance cam- eras have been deployed in many locations. Surveillance cam- eras are most useful when they are spaced out to maximize coverage of an area. However, deciding where to place cam- eras is an NP-hard problem and researchers have proposed heuristic solutions. Existing work does not consider a signifi- cant restriction of computer vision: in order to track a moving object, the object must occupy enough pixels. The number of pixels depends on many factors (how far away is the object? What is the camera resolution? What is the focal length ...
Experimental Comparison Of Features And Classifiers For Android Malware Detection, 2020 Singapore Management University
Experimental Comparison Of Features And Classifiers For Android Malware Detection, Lwin Khin Shar, Biniam Fisseha Demissie, Mariano Ceccato, Wei Minn
Research Collection School Of Information Systems
Android platform has dominated the smart phone market for years now and, consequently, gained a lot of attention from attackers. Malicious apps (malware) pose a serious threat to the security and privacy of Android smart phone users. Available approaches to detect mobile malware based on machine learning rely on features extracted with static analysis or dynamic analysis techniques. Dif- ferent types of machine learning classi ers (such as support vector machine and random forest) deep learning classi ers (based on deep neural networks) are then trained on extracted features, to produce models that can be used to detect mobile malware ...
Visual Saliency Estimation And Its Applications, 2020 Utah State University
Visual Saliency Estimation And Its Applications, Fei Xu
All Graduate Theses and Dissertations
The human visual system can automatically emphasize some parts of the image and ignore the other parts when seeing an image or a scene. Visual Saliency Estimation (VSE) aims to imitate this functionality of the human visual system to estimate the degree of human attention attracted by different image regions and locate the salient object. The study of VSE will help us explore the way human visual systems extract objects from an image. It has wide applications, such as robot navigation, video surveillance, object tracking, self-driving, etc.
The current VSE approaches on natural images models generic visual stimuli based on ...
Children In 2077: Designing Children’S Technologies In The Age Of Transhumanism, 2020 Tampere University of Technology
Children In 2077: Designing Children’S Technologies In The Age Of Transhumanism, Oğuz Oz Buruk, Oğuzhan Özcan, Gökçe Elif Baykal, Tilbe Göksun, Selçuk Acar, Güler Akduman, Mehmet Aydın Baytaş, Ceylan Beşevli, Joe Best, Aykut Coşkun, Hüseyin Uğur Genç, Baki Kocaballi, Samuli Laato, Cássia Mota, Konstantinos Papangelis, Marigo Raftopoulos, Richard Ramchurn, Juan Sádaba, Mattia Thibault, Annika Wolff, Mert Yıldız
Presentations and other scholarship
What for and how will we design children’s technologies in the transhumanism age, and what stance will we take as designers? This paper aims to answer this question with 13 fictional abstracts from sixteen authors of different countries, institutions and disciplines. Transhumanist thinking envisions enhancing human body and mind by blending human biology with technological augmentations. Fundamentally, it seeks to improve the human species, yet the impacts of such movement are unknown and the implications on children’s lives and technologies were not explored deeply. In an age, where technologies can clearly be defined as transhumanist, such as under-skin ...
A Real-Time Feature Indexing System On Live Video Streams, 2020 Purdue University
A Real-Time Feature Indexing System On Live Video Streams, Aditya Chakraborty, Akshay Pawar, Hojoung Jang, Shunqiao Huang, Sripath Mishra, Shuo-Han Chen, Yuan-Hao Chang, George K. Thiruvathukal, Yung-Hsiang Lu
Computer Science: Faculty Publications and Other Works
Most of the existing video storage systems rely on offline processing to support the feature-based indexing on video streams. The feature-based indexing technique provides an effec- tive way for users to search video content through visual features, such as object categories (e.g., cars and persons). However, due to the reliance on offline processing, video streams along with their captured features cannot be searchable immediately after video streams are recorded. According to our investigation, buffering and storing live video steams are more time-consuming than the YOLO v3 object detector. Such observation motivates us to propose a real-time feature indexing (RTFI ...
New Approaches To Frequent And Incremental Frequent Pattern Mining, 2020 The Graduate Center, City University of New York
New Approaches To Frequent And Incremental Frequent Pattern Mining, Mehmet Bicer
All Dissertations, Theses, and Capstone Projects
Data Mining (DM) is a process for extracting interesting patterns from large volumes of data. It is one of the crucial steps in Knowledge Discovery in Databases (KDD). It involves various data mining methods that mainly fall into predictive and descriptive models. Descriptive models look for patterns, rules, relationships and associations within data. One of the descriptive methods is association rule analysis, which represents co-occurrence of items or events. Association rules are commonly used in market basket analysis. An association rule is in the form of X → Y and it shows that X and Y co-occur with a given level ...
Bubble-In Digital Testing System, 2020 California State University, San Bernardino
Bubble-In Digital Testing System, Chaz Hampton
Electronic Theses, Projects, and Dissertations
Bubble-In is a cloud-based test-taking system build for students and teachers. The Bubble-In system is a test-taking application that interfaces with a cloud server. The mobile applications have been built for Android and Apple devices and the webserver is hosted on Digital Ocean VPS run with Nginx. The Bubble-In application is equipped with anti-cheating mechanisms such as question-answer key scrambling, not allowing screenshots, screen recording, or leaving the application. The tests students take are sent to the webserver to be graded and have statistics calculated and displayed in easy to use format for the test creator. Instructors can use the ...
A Statistical Impulse Response Model Based On Empirical Characterization Of Wireless Underground Channel, Abdul Salam, Mehmet C. Vuran, Suat Irmak
Wireless underground sensor networks (WUSNs) are becoming ubiquitous in many areas. The design of robust systems requires extensive understanding of the underground (UG) channel characteristics. In this paper, an UG channel impulse response is modeled and validated via extensive experiments in indoor and field testbed settings. The three distinct types of soils are selected with sand and clay contents ranging from $13\%$ to $86\%$ and $3\%$ to $32\%$, respectively. The impacts of changes in soil texture and soil moisture are investigated with more than $1,200$ measurements in a novel UG testbed that allows flexibility in soil moisture control. Moreover ...
Evidence-Based Detection Of Pancreatic Canc, 2020 San Jose State University
Evidence-Based Detection Of Pancreatic Canc, Rajeshwari Deepak Chandratre
This study is an effort to develop a tool for early detection of pancreatic cancer using evidential reasoning. An evidential reasoning model predicts the likelihood of an individual developing pancreatic cancer by processing the outputs of a Support Vector Classifier, and other input factors such as smoking history, drinking history, sequencing reads, biopsy location, family and personal health history. Certain features of the genomic data along with the mutated gene sequence of pancreatic cancer patients was obtained from the National Cancer Institute (NIH) Genomic Data Commons (GDC). This data was used to train the SVC. A prediction accuracy of ~85 ...
Predicting Students’ Performance By Learning Analytics, 2020 San Jose State University
Predicting Students’ Performance By Learning Analytics, Sandeep Subhash Madnaik
The field of Learning Analytics (LA) has many applications in today’s technology and online driven education. Learning Analytics is a multidisciplinary topic for learn- ing purposes that uses machine learning, statistic, and visualization techniques . We can harness academic performance data of various components in a course, along with the data background of each student (learner), and other features that might affect his/her academic performance. This collected data then can be fed to a sys- tem with the task to predict the final academic performance of the student, e.g., the final grade. Moreover, it allows students to ...
Probabilistic And Machine Learning Enhancement To Conn Toolbox, 2020 San Jose State University
Probabilistic And Machine Learning Enhancement To Conn Toolbox, Gayathri Hanuma Ravali Kuppachi
Clinical depression is a state of mind where the person suffers from persevering and overpowering sorrow. Existing examinations have exhibited that the course of action of arrangement in the brain of patients with clinical depression has a weird framework topology structure. In the earlier decade, resting-state images of the brain have been under the radar a. Specifically, the topological relationship of the brain aligned with graph hypothesis has discovered a strong connection in patients experiencing clinical depression. However, the systems to break down brain networks still have a couple of issues to be unwound. This paper attempts to give a ...
Detection Of Mild Cognitive Impairment Using Diffusion Compartment Imaging, 2020 San Jose State University
Detection Of Mild Cognitive Impairment Using Diffusion Compartment Imaging, Matthew Jones
The result of applying the Neurite Orientation Density and Dispersion Index (NODDI) algorithm to improve the prediction accuracy for patients diagnosed with MCI is reported. Calculations were carried out using a collection of 68 patients (34 control and 34 with MCI) gathered from the Alzheimer’s Disease Neuroimaging Initiative database (ADNI). Patient data includes the use of high-resolution Magnetic Resonance Images as with as Diffusion Tensor Imaging. A Linear Regression accuracy of 83% was observed using the added NODDI summary statistic: Orientation Dispersion Index (ODI). A statistically significant difference in groups was found between control patients and patients with MCI ...
Pattern Analysis And Prediction Of Mild Cognitive Impairment Using The Conn Toolbox, 2020 San Jose State University
Pattern Analysis And Prediction Of Mild Cognitive Impairment Using The Conn Toolbox, Meenakshi Anbukkarasu
Alzheimer's is an irreversible neurodegenerative disorder described by dynamic psychological and memory defalcation. It has been accounted for that the pervasiveness of Alzheimer's is to increase by 4 times in a few years, where one in every 75 people will have this disorder. Hence, there is a critical requirement for the analysis of Alzheimer's at its beginning stage to diminish the difficulty of the overall medical complications. The initial state of Alzheimer’s is called Mild cognitive impairment (MCI), and hence it is a decent target for premature diagnosis and treatment of Alzheimer's. This project focuses ...
Video Synthesis From The Stylegan Latent Space, 2020 San Jose State University
Video Synthesis From The Stylegan Latent Space, Lei Zhang
Generative models have shown impressive results in generating synthetic images. However, video synthesis is still difficult to achieve, even for these generative models. The best videos that generative models can currently create are a few seconds long, distorted, and low resolution. For this project, I propose and implement a model to synthesize videos at 1024x1024x32 resolution that include human facial expressions by using static images generated from a Generative Adversarial Network trained on the human facial images. To the best of my knowledge, this is the first work that generates realistic videos that are larger than 256x256 resolution from single ...
Using Deep Learning And Linguistic Analysis To Predict Fake News Within Text, 2020 San Jose State University
Using Deep Learning And Linguistic Analysis To Predict Fake News Within Text, John Nguyen
The spread of information about current events is a way for everybody in the world to learn and understand what is happening in the world. In essence, the news is an important and powerful tool that could be used by various groups of people to spread awareness and facts for the good of mankind. However, as information becomes easily and readily available for public access, the rise of deceptive news becomes an increasing concern. The reason is due to the fact that it will cause people to be misled and thus could affect the livelihood of themselves or others. The ...
Ai Quantification Of Language Puzzle To Language Learning Generalization, 2020 San Jose State University
Ai Quantification Of Language Puzzle To Language Learning Generalization, Harita Shroff
Online language learning applications provide users multiple ways/games to learn a new language. Some of the ways include rearranging words in the foreign language sentences, filling in the blanks, providing flashcards, and many more. Primarily this research focused on quantifying the effectiveness of these games in learning a new language. Secondarily my goal for this project was to measure the effectiveness of exercises for transfer learning in machine translation. Currently, very little research has been done in this field except for the research conducted by the online platforms to provide assurance to their users . Machine learning has been ...
Housing Market Crash Prediction Using Machine Learning And Historical Data, 2020 San Jose State University
Housing Market Crash Prediction Using Machine Learning And Historical Data, Parnika De
The 2008 housing crisis was caused by faulty banking policies and the use of credit derivatives of mortgages for investment purposes. In this project, we look into datasets that are the markers to a typical housing crisis. Using those data sets we build three machine learning techniques which are, Linear regression, Hidden Markov Model, and Long Short-Term Memory. After building the model we did a comparative study to show the prediction done by each model. The linear regression model did not predict a housing crisis, instead, it showed that house prices would be rising steadily and the R-squared score of ...
Rehearsal Scheduling Problem, 2020 San Jose State University
Rehearsal Scheduling Problem, Thuan Bao
Scheduling is a common task that plays a crucial role in many industries such as manufacturing or servicing. In a competitive environment, effective scheduling is one of the key factors to reduce cost and increase productivity. Therefore, scheduling problems have been studied by many researchers over the past thirty years. Rehearsal scheduling problem (RSP) is similar to the popular resource-constrained project scheduling problem (RCPSP); however, it does not have activity precedence constraints and the resources’ availabilities are not fixed during processing time. RSP can be used to schedule rehearsal in theatre industry or to schedule group scheduling when each member ...
An Ai For A Modification Of Dou Di Zhu, 2020 San Jose State University
An Ai For A Modification Of Dou Di Zhu, Xuesong Luo
We describe our implementation of AIs for the Chinese game Dou Di Zhu. Dou Di Zhu is a three-player game played with a standard 52 card deck together with two jokers. One player acts as a landlord and has the advantage of receiving three extra cards, the other two players play as peasants. We designed and implemented a Deep Q-learning Neural Network (DQN) agent to play the Dou Di Zhu. At the same time, we also designed and made a pure Q-learning based agent as well as a Zhou rule-based agent to compare with our main agent. We show the ...