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Articles 1 - 27 of 27
Full-Text Articles in Engineering
Examining The Externalities Of Highway Capacity Expansions In California: An Analysis Of Land Use And Land Cover (Lulc) Using Remote Sensing Technology, Serena E. Alexander, Bo Yang, Owen Hussey, Derek Hicks
Examining The Externalities Of Highway Capacity Expansions In California: An Analysis Of Land Use And Land Cover (Lulc) Using Remote Sensing Technology, Serena E. Alexander, Bo Yang, Owen Hussey, Derek Hicks
Mineta Transportation Institute Publications
There are over 590,000 bridges dispersed across the roadway network that stretches across the United States alone. Each bridge with a length of 20 feet or greater must be inspected at least once every 24 months, according to the Federal Highway Act (FHWA) of 1968. This research developed an artificial intelligence (AI)-based framework for bridge and road inspection using drones with multiple sensors collecting capabilities. It is not sufficient to conduct inspections of bridges and roads using cameras alone, so the research team utilized an infrared (IR) camera along with a high-resolution optical camera. In many instances, the IR camera …
Faster Multidimensional Data Queries On Infrastructure Monitoring Systems, Yinghua Qin, Gheorghi Guzun
Faster Multidimensional Data Queries On Infrastructure Monitoring Systems, Yinghua Qin, Gheorghi Guzun
Faculty Research, Scholarly, and Creative Activity
The analytics in online performance monitoring systems have often been limited due to the query performance of large scale multidimensional data. In this paper, we introduce a faster query approach using the bit-sliced index (BSI). Our study covers multidimensional grouping and preference top-k queries with the BSI, algorithms design, time complexity evaluation, and the query time comparison on a real-time production performance monitoring system. Our research work extended the BSI algorithms to cover attributes filtering and multidimensional grouping. We evaluated the query time with the single attribute, multiple attributes, feature filtering, and multidimensional grouping. To compare with the existing prior …
Drivers’ Response To Scenarios When Driving Connected And Automated Vehicles Compared To Vehicles With And Without Driver Assist Technology, Srinivas S. Pulugurtha, Raghuveer Gouribhatla
Drivers’ Response To Scenarios When Driving Connected And Automated Vehicles Compared To Vehicles With And Without Driver Assist Technology, Srinivas S. Pulugurtha, Raghuveer Gouribhatla
Mineta Transportation Institute Publications
Traffic related crashes cause more than 38,000 fatalities every year in the United States. They are the leading cause of death among drivers up to 54 years in age and incur $871 million in losses each year. Driver errors contribute to about 94% of these crashes. In response, automotive companies have been developing vehicles with advanced driver assistance systems (ADAS) that aid in various driving tasks. These features are aimed at enhancing safety by either warning drivers of a potential hazard or picking up certain driving maneuvers like maintaining the lane. These features are already part of vehicles with Driver …
Computer-Aided Diagnosis Of Low Grade Endometrial Stromal Sarcoma (Lgess), Xinxin Yang, Mark Stamp
Computer-Aided Diagnosis Of Low Grade Endometrial Stromal Sarcoma (Lgess), Xinxin Yang, Mark Stamp
Faculty Research, Scholarly, and Creative Activity
Low grade endometrial stromal sarcoma (LGESS) accounts for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients are initially misdiagnosed with leiomyoma, which is a type of benign tumor, also known as fibroids. In this research, uterine tissue biopsy images of potential LGESS patients are preprocessed using segmentation and stain normalization algorithms. We then apply a variety of classic machine learning and advanced deep learning models to classify tissue images as either benign or cancerous. For the classic techniques considered, the highest classification accuracy we attain is about 0.85, while our best deep learning model achieves an …
How Live Streaming And Twitch Have Changed The Gaming Industry, Krystal Ruiz
How Live Streaming And Twitch Have Changed The Gaming Industry, Krystal Ruiz
ART 108: Introduction to Games Studies
Live streaming in itself has become a booming industry in which its content consists of “streamers” who live broadcast numerous events and real-time interactions while simultaneously chatting with viewers drawing huge and increasing numbers (Adamovich). Twitch has especially excelled at garnering attention as one of the most popular live streaming platforms that focuses on broadcasting and viewing video game content (Adamovich). Twitch has grown rapidly within the last few years asserting its dominance as one of the major forces in the games industry and becoming a multi-billion-dollar industry (Adamovich). For example, according to Descrier, in 2016 there were approximately 292 …
Securing The Emerging Technologies Of Autonomous And Connected Vehicles, Shahab Tayeb, Matin Pirouz
Securing The Emerging Technologies Of Autonomous And Connected Vehicles, Shahab Tayeb, Matin Pirouz
Mineta Transportation Institute Publications
The Internet of Vehicles (IoV) aims to establish a network of autonomous and connected vehicles that communicate with one another through facilitation led by road-side units (RSUs) and a central trust authority (TA). Messages must be efficiently and securely disseminated to conserve resources and preserve network security. Currently, research in this area lacks consensus about security schemes and methods of disseminating messages. Furthermore, a current deficiency of information regarding resource optimization prevents further efficient development of this network. This paper takes an interdisciplinary approach to these issues by merging both cybersecurity and data science to optimize and secure the network. …
A Building Permit System For Smart Cities: A Cloud-Based Framework, Magdalini Eirinaki, Subhankar Dhar, Shishir Mathur, Adwait Kaley, Arpit Patel, Akshar Joshi, Dhvani Shah
A Building Permit System For Smart Cities: A Cloud-Based Framework, Magdalini Eirinaki, Subhankar Dhar, Shishir Mathur, Adwait Kaley, Arpit Patel, Akshar Joshi, Dhvani Shah
Faculty Publications
In this paper we propose a novel, cloud-based framework to support citizens and city officials in the building permit process. The proposed framework is efficient, user-friendly, and transparent with a quick turn-around time for homeowners. Compared to existing permit systems, the proposed smart city permit framework provides a pre-permitting decision workflow, and incorporates a data analytics and mining module that enables the continuous improvement of both the end user experience and the permitting and urban planning processes. This is enabled through a data mining-powered permit recommendation engine as well as a data analytics process that allow a gleaning of key …
Social Recommendations For Personalized Fitness Assistance, Saumil Dharia, Magdalini Eirinaki, Vijesh Jain, Jvalant Patel, Iraklis Varlamis, Jainikkumar Vora, Rizen Yamauchi
Social Recommendations For Personalized Fitness Assistance, Saumil Dharia, Magdalini Eirinaki, Vijesh Jain, Jvalant Patel, Iraklis Varlamis, Jainikkumar Vora, Rizen Yamauchi
Faculty Publications
Wearable technology allows users to monitor their activity and pursue a healthy lifestyle through the use of embedded sensors. Such wearables usually connect to a mobile application that allows them to set their profile and keep track of their goals. However, due to the relatively “high maintenance” of such applications, where a significant amount of user feedback is expected, users who are very busy, or not as self-motivated, stop using them after a while. It has been shown that accountability improves commitment to an exercise routine. In this work, we present the PRO-Fit framework, a personalized fitness assistant aiming at …
A Survey On Network Security-Related Data Collection Technologies, Huaqing Lin, Zheng Yan, Yu Chen, Lifang Zhang
A Survey On Network Security-Related Data Collection Technologies, Huaqing Lin, Zheng Yan, Yu Chen, Lifang Zhang
Faculty Publications, Information Systems & Technology
Security threats and economic loss caused by network attacks, intrusions, and vulnerabilities have motivated intensive studies on network security. Normally, data collected in a network system can reflect or can be used to detect security threats. We define these data as network security-related data. Studying and analyzing security-related data can help detect network attacks and intrusions, thus making it possible to further measure the security level of the whole network system. Obviously, the first step in detecting network attacks and intrusions is to collect security-related data. However, in the context of big data and 5G, there exist a number of …
Recommender Systems For Large-Scale Social Networks: A Review Of Challenges And Solutions, Magdalini Eirinaki, Jerry Gao, Iraklis Varlamis, Konstantinos Tserpes
Recommender Systems For Large-Scale Social Networks: A Review Of Challenges And Solutions, Magdalini Eirinaki, Jerry Gao, Iraklis Varlamis, Konstantinos Tserpes
Faculty Publications
Social networks have become very important for networking, communications, and content sharing. Social networking applications generate a huge amount of data on a daily basis and social networks constitute a growing field of research, because of the heterogeneity of data and structures formed in them, and their size and dynamics. When this wealth of data is leveraged by recommender systems, the resulting coupling can help address interesting problems related to social engagement, member recruitment, and friend recommendations.In this work we review the various facets of large-scale social recommender systems, summarizing the challenges and interesting problems and discussing some of the …
A Framework For Recommendation Of Highly Popular News Lacking Social Feedback, Nuno Moniz, Luís Torgo, Magdalini Eirinaki, Paula Branco
A Framework For Recommendation Of Highly Popular News Lacking Social Feedback, Nuno Moniz, Luís Torgo, Magdalini Eirinaki, Paula Branco
Faculty Publications
Social media is rapidly becoming the main source of news consumption for users, raising significant challenges to news aggregation and recommendation tasks. One of these challenges concerns the recommendation of very recent news. To tackle this problem, approaches to the prediction of news popularity have been proposed. In this paper, we study the task of predicting news popularity upon their publication, when social feedback is unavailable or scarce, and to use such predictions to produce news rankings. Unlike previous work, we focus on accurately predicting highly popular news. Such cases are rare, causing known issues for standard prediction models and …
Multi-Valued Sequences Generated By Power Residue Symbols Over Odd Characteristic Fields, Begum Nasima, Yasuyuki Nogami, Satoshi Uehara, Robert Morelos-Zaragoza
Multi-Valued Sequences Generated By Power Residue Symbols Over Odd Characteristic Fields, Begum Nasima, Yasuyuki Nogami, Satoshi Uehara, Robert Morelos-Zaragoza
Faculty Publications
This paper proposes a new approach for generating pseudo random multi-valued (including binary-valued) sequences. The approach uses a primitive polynomial over an odd characteristic prime field $\f{p}$, where p is an odd prime number. Then, for the maximum length sequence of vectors generated by the primitive polynomial, the trace function is used for mapping these vectors to scalars as elements in the prime field. Power residue symbol (Legendre symbol in binary case) is applied to translate the scalars to k-value scalars, where k is a prime factor of p-1. Finally, a pseudo random k-value sequence is obtained. Some important properties …
Robust And Agile System Against Fault And Anomaly Traffic In Software Defined Networks, Mihui Kim, Younghee Park, Rohit Kotalwar
Robust And Agile System Against Fault And Anomaly Traffic In Software Defined Networks, Mihui Kim, Younghee Park, Rohit Kotalwar
Faculty Publications
The main advantage of software defined networking (SDN) is that it allows intelligent control and management of networking though programmability in real time. It enables efficient utilization of network resources through traffic engineering, and offers potential attack defense methods when abnormalities arise. However, previous studies have only identified individual solutions for respective problems, instead of finding a more global solution in real time that is capable of addressing multiple situations in network status. To cover diverse network conditions, this paper presents a comprehensive reactive system for simultaneously monitoring failures, anomalies, and attacks for high availability and reliability. We design three …
Impact Of Reviewer Social Interaction On Online Consumer Review Fraud Detection, Kunal Goswami, Younghee Park, Chungsik Song
Impact Of Reviewer Social Interaction On Online Consumer Review Fraud Detection, Kunal Goswami, Younghee Park, Chungsik Song
Faculty Publications
Background Online consumer reviews have become a baseline for new consumers to try out a business or a new product. The reviews provide a quick look into the application and experience of the business/product and market it to new customers. However, some businesses or reviewers use these reviews to spread fake information about the business/product. The fake information can be used to promote a relatively average product/business or can be used to malign their competition. This activity is known as reviewer fraud or opinion spam. The paper proposes a feature set, capturing the user social interaction behavior to identify fraud. …
A Multi-Value Sequence Generated By Power Residue Symbol And Trace Function Over Odd Characteristic Field, Yasuyuki Nogami, Satoshi Uehara, Kazuyoshi Tsuchiya, Nasima Begum, Hiroto Ino, Robert Morelos-Zaragoza
A Multi-Value Sequence Generated By Power Residue Symbol And Trace Function Over Odd Characteristic Field, Yasuyuki Nogami, Satoshi Uehara, Kazuyoshi Tsuchiya, Nasima Begum, Hiroto Ino, Robert Morelos-Zaragoza
Faculty Publications
This paper proposes a new multi-value sequence generated by utilizing primitive element, trace, and power residue symbol over odd characteristic finite field. In detail, let p and k be an odd prime number as the characteristic and a prime factor of p-1, respectively. Our proposal generates k-value sequence T={ti | ti=fk(Tr(ωi)+A)}, where ω is a primitive element in the extension field $\F{p}{m}$, Tr(⋅) is the trace function that maps $\F{p}{m} \rightarrow \f{p}$, A is a non-zero scalar in the prime field $\f{p}$, and fk(⋅) is a certain mapping function based on k-th power residue symbol. Thus, the proposed sequence has …
Threshold-Bounded Influence Dominating Sets For Recommendations In Social Networks, Magdalini Eirinaki, Nuno Moniz, Katerina Potika
Threshold-Bounded Influence Dominating Sets For Recommendations In Social Networks, Magdalini Eirinaki, Nuno Moniz, Katerina Potika
Faculty Publications
The process of decision making in humans involves a combination of the genuine information held by the individual, and the external influence from their social network connections. This helps individuals to make decisions or adopt behaviors, opinions or products. In this work, we seek to investigate under which conditions and with what cost we can form neighborhoods of influence within a social network, in order to assist individuals with little or no prior genuine information through a two-phase recommendation process. Most of the existing approaches regard the problem of identifying influentials as a long-term, network diffusion process, where information cascading …
Time-Based Ensembles For Prediction Of Rare Events In News Streams, Nuno Moniz, Luís Torgo, Magdalini Eirinaki
Time-Based Ensembles For Prediction Of Rare Events In News Streams, Nuno Moniz, Luís Torgo, Magdalini Eirinaki
Faculty Publications
Thousands of news are published everyday reporting worldwide events. Most of these news obtain a low level of popularity and only a small set of events become highly popular in social media platforms. Predicting rare cases of highly popular news is not a trivial task due to shortcomings of standard learning approaches and evaluation metrics. So far, the standard task of predicting the popularity of news items has been tackled by either of two distinct strategies related to the publication time of news. The first strategy, a priori, is focused on predicting the popularity of news upon their publication when …
Development Of Cloud-Based Uav Monitoring And Management System, Mason Itkin, Mihui Kim, Younghee Park
Development Of Cloud-Based Uav Monitoring And Management System, Mason Itkin, Mihui Kim, Younghee Park
Faculty Publications
Unmanned aerial vehicles (UAVs) are an emerging technology with the potential to revolutionize commercial industries and the public domain outside of the military. UAVs would be able to speed up rescue and recovery operations from natural disasters and can be used for autonomous delivery systems (e.g., Amazon Prime Air). An increase in the number of active UAV systems in dense urban areas is attributed to an influx of UAV hobbyists and commercial multi-UAV systems. As airspace for UAV flight becomes more limited, it is important to monitor and manage many UAV systems using modern collision avoidance techniques. In this paper, …
A Cloud-Based Framework For Smart Permit System For Buildings, Magdalini Eirinaki, Subhankar Dhar, Shishir Mathur
A Cloud-Based Framework For Smart Permit System For Buildings, Magdalini Eirinaki, Subhankar Dhar, Shishir Mathur
Faculty Publications
In this paper we propose a novel cloud-based platform for building permit system that is efficient, user-friendly, transparent, and has quick turn-around time for homeowners. Compared to the existing permit systems, the proposed smart city permit framework provides a pre-permitting decision workflow, and incorporates a data analytics and mining module that enables the continuous improvement of a) the end user experience, by analyzing explicit and implicit user feedback, and b) the permitting and urban planning process, allowing a gleaning of key insights for real estate development and city planning purposes, by analyzing how users interact with the system depending on …
Pro-Fit: Exercise With Friends, Saumil Dharia, Vijesh Jain, Jvalant Patel, Jainikkumar Vora, Rizen Yamauchi, Magdalini Eirinaki, Iraklis Varlamis
Pro-Fit: Exercise With Friends, Saumil Dharia, Vijesh Jain, Jvalant Patel, Jainikkumar Vora, Rizen Yamauchi, Magdalini Eirinaki, Iraklis Varlamis
Faculty Publications
The advancements in wearable technology, where embedded accelerometers, gyroscopes and other sensors enable the users to actively monitor their activity have made it easier for individuals to pursue a healthy lifestyle. However, most of the existing applications expect continuous commitment from the end users, who need to proactively interact with the application in order to connect with friends and attain their goals. These applications fail to engage and motivate users who have busy schedules, or are not as committed and self-motivated. In this work, we present PRO-Fit, a personalized fitness assistant application that employs machine learning and recommendation algorithms in …
Querie: Collaborative Database Exploration, Magdalini Eirinaki, Suju Abraham, Neoklis Polyzotis, Naushin Shaikh
Querie: Collaborative Database Exploration, Magdalini Eirinaki, Suju Abraham, Neoklis Polyzotis, Naushin Shaikh
Faculty Publications
No abstract provided.
Sql Querie Recommendations: A Query Fragment-Based Approach, Jayad Akbarnejad, Magdalini Eirinaki, Suju Koshy, Duc On, Neoklis Polyzotis
Sql Querie Recommendations: A Query Fragment-Based Approach, Jayad Akbarnejad, Magdalini Eirinaki, Suju Koshy, Duc On, Neoklis Polyzotis
Faculty Publications
Relational database systems are becoming increasingly popular in the scientific community to support the interactive exploration of large volumes of data. In this scenario, users employ a query interface (typically, a web-based client) to issue a series of SQL queries that aim to analyze the data and mine it for interesting information. First-time users, however, may not have the necessary knowledge to know where to start their exploration. Other times, users may simply overlook queries that retrieve important information. In this work we describe a framework to assist non-expert users by providing personalized query recommendations. The querying behavior of the …
Personal Vs. Social, Magdalini Eirinaki
Personal Vs. Social, Magdalini Eirinaki
Faculty Publications
The last few years we witnessed an impressive growth in social networks and in applications that add value to their amassed information. At the same time, the continuing expansion of mobile platforms and applications (e.g. iPhone), combined with the overwhelming supply of information and services, makes effective personalization and context-awareness much required features. One may consider "personal" and "social" data management as comprising two distinct directions with conflicting characteristics. However, it can be argued that they complement each other and that in future applications they will ultimately converge. This "personal vs. social" predicament presents a number of interesting topics that …
Selfish Wavelength Assignment In Multifiber Optical Networks, Evangelos Bampas, Aris Pagourtzis, George Pierrakos, Katerina Potika
Selfish Wavelength Assignment In Multifiber Optical Networks, Evangelos Bampas, Aris Pagourtzis, George Pierrakos, Katerina Potika
Faculty Publications, Computer Science
No abstract provided.
Loss Aware Rate Allocations In H.263 Coded Video Transmissions, Xiao Su, Benjamin Wah
Loss Aware Rate Allocations In H.263 Coded Video Transmissions, Xiao Su, Benjamin Wah
Faculty Publications
For packet video, information loss and bandwidth limitation are two factors that affect video playback quality. Traditional rate allocation approaches have focused on optimizing video quality under bandwidth constraint alone. However, in the best-effort Internet, packets carrying video data are susceptible to losses, which need to be reconstructed at the receiver side. In this paper, we propose loss aware rate allocations in both group-of-block (GOB) level and macroblock level, given that certain packets are lost during transmissions and reconstructed using simple interpolation methods at the receiver side. Experimental results show that our proposed algorithms can produce videos of higher quality …
Ontologies In Web Personalization, Magdalini Eirinaki, Michalis Vazirgiannis
Ontologies In Web Personalization, Magdalini Eirinaki, Michalis Vazirgiannis
Faculty Publications
No abstract provided.
The Igipi Ontological Framework: Integrating Gene Interactions With Protein Interactions, Bill Andreopoulos, Aijun An, Xiangji Huang
The Igipi Ontological Framework: Integrating Gene Interactions With Protein Interactions, Bill Andreopoulos, Aijun An, Xiangji Huang
Faculty Publications, Computer Science
No abstract provided.