Developing Algorithms To Detect Incidents On Freeways From Loop Detector And Vehicle Re-Identification Data,
2019
Old Dominion University
Developing Algorithms To Detect Incidents On Freeways From Loop Detector And Vehicle Re-Identification Data, Biraj Adhikari
Civil & Environmental Engineering Theses & Dissertations
A new approach for testing incident detection algorithms has been developed and is presented in this thesis. Two new algorithms were developed and tested taking California #7, which is the most widely used algorithm to date, and SVM (Support Vector Machine), which is considered one of the best performing classifiers, as the baseline for comparisons. Algorithm #B in this study uses data from Vehicle Re-Identification whereas the other three algorithms (California #7, SVM and Algorithm #A) use data from a double loop detector for detection of an incident. A microscopic traffic simulator is used for modeling three types of incident …
A Review On Swarm Intelligence And Evolutionary Algorithms For Solving Flexible Job Shop Scheduling Problems,
2019
Singapore Management University
A Review On Swarm Intelligence And Evolutionary Algorithms For Solving Flexible Job Shop Scheduling Problems, Kaizhou Gao, Zhiguang Cao, Le Zhang, Zhenghua Chen, Yuyan Han, Quanke Pan
Research Collection School Of Computing and Information Systems
Flexible job shop scheduling problems (FJSP) have received much attention from academia and industry for many years. Due to their exponential complexity, swarm intelligence (SI) and evolutionary algorithms (EA) are developed, employed and improved for solving them. More than 60% of the publications are related to SI and EA. This paper intents to give a comprehensive literature review of SI and EA for solving FJSP. First, the mathematical model of FJSP is presented and the constraints in applications are summarized. Then, the encoding and decoding strategies for connecting the problem and algorithms are reviewed. The strategies for initializing algorithms? population …
Volumetric Optimization Of Freight Cargo Loading: Case Study Of A Smu Forwarder,
2019
Singapore Management University
Volumetric Optimization Of Freight Cargo Loading: Case Study Of A Smu Forwarder, Tristan Lim, Michael Ser Chong Ping, Mark Goh, Shi Ying Jacelyn Tan
Research Collection School Of Computing and Information Systems
Purpose: Freight forwarders faces a challenging environment of high market volatility and margin compression risks. Hence, strategic consideration is given to undertaking capacity management and transport asset ownership to achieve longer term cost leadership. Doing so will also help to address management issues, such as better control of potential transport disruptions, improve scheduling flexibility and efficiency, and provide service level enhancement.Design/methodology/approach: The case company currently hastruck resource which is unprofitable, and the firm’s schedulers are having difficulty optimizing the loading capacity. We apply Genetic Algorithm (GA) to undertake volumetric optimization of truckcapacity and to build an easy-to-use platform to help …
A Resource Constrained Shortest Paths Approach To Reducing Personal Pollution Exposure,
2019
Portland State University
A Resource Constrained Shortest Paths Approach To Reducing Personal Pollution Exposure, Elling Payne
REU Final Reports
As wildfires surge in frequency and impact in the Pacific Northwest, in tandem with increasingly traffic-choked roads, personal exposure to harmful airborne pollutants is a rising concern. Particularly at risk are school-age children, especially those living in disadvantaged communities near major motorways and industrial centers. Many of these children must walk to school, and the choice of route can effect exposure. Route-planning applications and frameworks utilizing computational shortest paths methods have been proposed which consider personal exposure with reasonable success, but few have focused on pollution exposure, and all have been limited in scalability or geographic scope. This paper addresses …
Can Algorithms Help Us Decide Who To Trust?,
2019
Singapore Management University
Can Algorithms Help Us Decide Who To Trust?, David De Cremer, Jack Mcguire, Yorck Hesselbarth, Ke M Mai
Research Collection Lee Kong Chian School Of Business
The use of artificial intelligence (AI) and algorithms is increasing within organizations to manage business processes, hire employees, and automate routine organizational decision making. This comes as no surprise, since the application of simple linear algorithms have been shown to outperform human judgment in the accuracy of many administrative tasks. A 2017 Accenture survey also revealed that 85% of executives want to invest more extensively in AI-related technologies over the next three years.
Distributed Similarity Queries In Metric Spaces,
2019
Zhejiang University
Distributed Similarity Queries In Metric Spaces, Keyu Yang, Xin Ding, Yuanliang Zhang, Lu Chen, Baihua Zheng, Yunjun Gao
Research Collection School Of Computing and Information Systems
Similarity queries, including range queries and k nearest neighbor (kNN) queries, in metric spaces have applications in many areas such as multimedia retrieval, computational biology and location-based services. With the growing volumes of data, a distributed method is required. In this paper, we propose an Asynchronous Metric Distributed System (AMDS), to support efficient metric similarity queries in the distributed environment. AMDS uniformly partitions the data with the pivot-mapping technique to ensure the load balancing, and employs publish/subscribe communication model to asynchronous process large scale of queries. The employment of asynchronous processing model also improves robustness and efficiency of AMDS. In …
Grammar-Based Procedurally Generated Village Creation Tool,
2019
California Polytechnic State University, San Luis Obispo
Grammar-Based Procedurally Generated Village Creation Tool, Kevin Matthew Graves
Computer Engineering
This project is a 3D village generator tool for Unity. It consists of three components: a building, mountain, and river generator. All of these generators use grammar-based procedural generation in order to create a unique and logical village and landscape each time the program is run.
Methodology For Comparison Of Algorithms For Real-World Multi-Objective Optimization Problems: Space Surveillance Network Design,
2019
Air Force Institute of Technology
Methodology For Comparison Of Algorithms For Real-World Multi-Objective Optimization Problems: Space Surveillance Network Design, Troy B. Dontigney
Theses and Dissertations
Space Situational Awareness (SSA) is an activity vital to protecting national and commercial satellites from damage or destruction due to collisions. Recent research has demonstrated a methodology using evolutionary algorithms (EAs) which is intended to develop near-optimal Space Surveillance Network (SSN) architectures in the sense of low cost, low latency, and high resolution. That research is extended here by (1) developing and applying a methodology to compare the performance of two or more algorithms against this problem, and (2) analyzing the effects of using reduced data sets in those searches. Computational experiments are presented in which the performance of five …
Social Recommendation With Optimal Limited Attention,
2019
Singapore Management University
Social Recommendation With Optimal Limited Attention, Xin Wang, Wenwu Zhu, Chenghao Liu
Research Collection School Of Computing and Information Systems
Social recommendation has been playing an important role in suggesting items to users through utilizing information from social connections. However, most existing approaches do not consider the attention factor causing the constraint that people can only accept a limited amount of information due to the limited strength of mind, which has been discovered as an intrinsic physiological property of human by social science. We address this issue by resorting to the concept of limited attention in social science and combining it with machine learning techniques in an elegant way. When introducing the idea of limited attention into social recommendation, two …
Rethinking Algorithmic Bias Through Phenomenology And Pragmatism,
2019
Worcester State University
Rethinking Algorithmic Bias Through Phenomenology And Pragmatism, Johnathan C. Flowers
Computer Ethics - Philosophical Enquiry (CEPE) Proceedings
In 2017, Amazon discontinued an attempt at developing a hiring algorithm which would enable the company to streamline its hiring processes due to apparent gender discrimination. Specifically, the algorithm, trained on over a decade’s worth of resumes submitted to Amazon, learned to penalize applications that contained references to women, that indicated graduation from all women’s colleges, or otherwise indicated that an applicant was not male. Amazon’s algorithm took up the history of Amazon’s applicant pool and integrated it into its present “problematic situation,” for the purposes of future action. Consequently, Amazon declared the project a failure: even after attempting to …
Difference Between Algorithmic Processing And The Process Of Lifeworld (Lebenswelt),
2019
Technische Universität Braunschweig
Difference Between Algorithmic Processing And The Process Of Lifeworld (Lebenswelt), Domenico Schneider
Computer Ethics - Philosophical Enquiry (CEPE) Proceedings
The following article compares the temporality of the life-world with the digital processing. The temporality of the life-world is determined to be stretched and spontaneous. The temporality of the digital is given by discrete step-by-step points of time. Most ethical issues can be traced back to a mismatch of these two ways of processing. This creates a foundation for the ethics of the digital processing. Methodologically, phenomenological considerations are merged with media-philosophical considerations in the article.
Scalable Community Detection Using Distributed Louvain Algorithm,
2019
University of New Orleans
Scalable Community Detection Using Distributed Louvain Algorithm, Naw Safrin Sattar
LSU New Orleans Theses and Dissertations
Community detection (or clustering) in large-scale graph is an important problem in graph mining. Communities reveal interesting characteristics of a network. Louvain is an efficient sequential algorithm but fails to scale emerging large-scale data. Developing distributed-memory parallel algorithms is challenging because of inter-process communication and load-balancing issues. In this work, we design a shared memory-based algorithm using OpenMP, which shows a 4-fold speedup but is limited to available physical cores. Our second algorithm is an MPI-based parallel algorithm that scales to a moderate number of processors. We also implement a hybrid algorithm combining both. Finally, we incorporate dynamic load-balancing in …
Deep Learning On Graphs Using Graph Convolutional Networks,
2019
San Jose State University
Deep Learning On Graphs Using Graph Convolutional Networks, Saurabh Mithe
Master's Projects
Graphs are a powerful way to model network data with the objects as nodes and the relationship between the various objects as links. Such graphs contain a plethora of valuable information about the underlying data which can be extracted, analyzed, and visualized using Machine Learning (ML). The challenge to this task is that graphs are non-Euclidean structures which means that they cannot be directly used with ML techniques because ML techniques only work with Euclidean structures like grids or sequences. In order to overcome this challenge, the graph structure first needs to be encoded into an equivalent Euclidean representation in …
Online Local Communities,
2019
San Jose State University
Online Local Communities, Mrudula Murali
Master's Projects
A community in a network is a group of nodes that are densely and closely connected to each other, get sparsely connected to the nodes outside the community. Finding communities in a large network helps solve many real-world problems. But detecting such communities in a complex network by focusing on the whole network is not feasible. Instead, we focus on finding communities around one or more seed node(s) of interest. Therefore, in this project, we find local communities. Moreover, we consider the online setting where the whole graph is unknown in the beginning and we get a stream of edges, …
Network Alignment In Heterogeneous Social Networks,
2019
San Jose State University
Network Alignment In Heterogeneous Social Networks, Priyanka Kasbekar
Master's Projects
Online Social Networks (OSN) have numerous applications and an ever growing user base. This has led to users being a part of multiple social networks at the same time. Identifying a similar user from one social network on another social network will give in- formation about a user’s behavior on different platforms. It further helps in community detection and link prediction tasks. The process of identifying or aligning users in multiple networks is called Network Alignment. More the information we have about the nodes / users better the results of Network Alignment. Unlike other related work in this field that …
Geometric Problems In Robot Exploration,
2019
Louisiana State University and Agricultural and Mechanical College
Geometric Problems In Robot Exploration, Wyatt Preston Clements
LSU Doctoral Dissertations
Robots are increasingly utilized to perform tasks in today's world. This has varied from vacuuming to building advanced structures. With robots being used for tasks such as these, new challenges are introduced. Problems that have been previously researched to be performed, either theoretically or implemented, need to be redesigned to be able to better handle these challenges. In this thesis, I will discuss multiple problems that have previously been researched and I have redesigned to be possible to be implemented by robots or that I have developed a new way for the robots to solve the problem. I focus on …
Recovering Depth From Stereo Without Using Any Oculomotor Information,
2019
School of Psychology, Higher School of Economics
Recovering Depth From Stereo Without Using Any Oculomotor Information, Tadamasa Sawada
MODVIS Workshop
The human visual system uses binocular disparity to perceive depth within 3D scenes. It is commonly assumed that the visual system needs oculomotor information about the relative orientation of the two eyes to perceive depth on the basis of binocular disparity. The necessary oculomotor information can be obtained from an efference copy of the oculomotor signals, or from a 2D distribution of the vertical disparity, specifically, from the vertical component of binocular disparity. It is known that oculomotor information from the efference copy and from the vertical disparity distribution can affect the perception of depth based on binocular disparity. But, …
Machine Learning Pipeline For Exoplanet Classification,
2019
Southern Methodist University
Machine Learning Pipeline For Exoplanet Classification, George Clayton Sturrock, Brychan Manry, Sohail Rafiqi
SMU Data Science Review
Planet identification has typically been a tasked performed exclusively by teams of astronomers and astrophysicists using methods and tools accessible only to those with years of academic education and training. NASA’s Exoplanet Exploration program has introduced modern satellites capable of capturing a vast array of data regarding celestial objects of interest to assist with researching these objects. The availability of satellite data has opened up the task of planet identification to individuals capable of writing and interpreting machine learning models. In this study, several classification models and datasets are utilized to assign a probability of an observation being an exoplanet. …
Automate Nuclei Detection Using Neural Networks,
2019
Southern Methodist University
Automate Nuclei Detection Using Neural Networks, Jonathan Flores, Thejas Prasad, Jordan Kassof, Robert Slater
SMU Data Science Review
Nuclei identification is a pivotal first step in many areas of biomedical research. Pathologists often observe images containing microscopic nuclei as part of their day to day jobs. During research, pathologists must identify nuclei characteristics from microscopic images such as: volume of nuclei, size, density and individual position within image. The pathology field can benefit from image detection enhancements done through the use of computer image segmentation techniques. This research presents methods that can be used to identify all the cell nuclei contained in images. Multiple techniques were experimented with such as edge detection and Convolutional Neural Networks with U-Net …
Clustering Of Multiple Instance Data.,
2019
University of Louisville
Clustering Of Multiple Instance Data., Andrew D. Karem
Electronic Theses and Dissertations
An emergent area of research in machine learning that aims to develop tools to analyze data where objects have multiple representations is Multiple Instance Learning (MIL). In MIL, each object is represented by a bag that includes a collection of feature vectors called instances. A bag is positive if it contains at least one positive instance, and negative if no instances are positive. One of the main objectives in MIL is to identify a region in the instance feature space with high correlation to instances from positive bags and low correlation to instances from negative bags -- this region is …
