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- Journal Publications (6)
- Image transmission (3)
- Peer-to-peer networks (3)
- Scalable coding (3)
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- Peer assignment (2)
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- Biologically inspired robotics and micro-biorobotics - Biologically inspired locomotion (1)
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- Building permit (1)
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Articles 1 - 30 of 51
Full-Text Articles in Engineering
Tutorial: Are You My Neighbor?: Bringing Order To Neighbor Computing Problems, David Anastasiu, Huzefa Rangwala, Andrea Tagarelli
Tutorial: Are You My Neighbor?: Bringing Order To Neighbor Computing Problems, David Anastasiu, Huzefa Rangwala, Andrea Tagarelli
Faculty Publications
Finding nearest neighbors is an important topic that has attracted much attention over the years and has applications in many fields, such as market basket analysis, plagiarism and anomaly detection, community detection, ligand-based virtual screening, etc. As data are easier and easier to collect, finding neighbors has become a potential bottleneck in analysis pipelines. Performing pairwise comparisons given the massive datasets of today is no longer feasible. The high computational complexity of the task has led researchers to develop approximate methods, which find many but not all of the nearest neighbors. Yet, for some types of data, efficient exact solutions …
Influence Propagation For Social Graph-Based Recommendations, Avni Gulati, Magdalini Eirinaki
Influence Propagation For Social Graph-Based Recommendations, Avni Gulati, Magdalini Eirinaki
Faculty Publications
Social networking is an inevitable behavior of humans living in a society. In recent years, and with the rise of online social networks, personalized recommendations that leverage the social aspect have become a very intriguing domain for researchers. In this work, we explore how influence propagation and the decay in the cascading effect of influence from influential users can be leveraged to generate social graph-based recommendations. Understanding how influence propagates within a social network is itself a challenging problem. Few researchers have considered influence propagation and even fewer have considered decay in the cascading effect of influence in a social …
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 …
A Neuromorphic Quadratic, Integrate, And Fire Silicon Neuron With Adaptive Gain, David Parent, Eric Basham
A Neuromorphic Quadratic, Integrate, And Fire Silicon Neuron With Adaptive Gain, David Parent, Eric Basham
Faculty Publications
An integrated circuit implementation of a silicon neuron was designed, manufactured, and tested. The circuit was designed using the Quadratic, Integrate, and Fire (QIF) neuron model in 0.5 µm silicon technology. The neuron implementation was optimized for low current consumption, drawing only 1.56 mA per QIF circuit and utilized hysteretic reset, non-inverting integrator, and voltage-squarer circuits. The final area of each circuit in silicon was 268 µm height × 400 µm width. This design is the first IC of its kind for this neuron model and is successfully able to output true spiking that follows the behaviors of bistability, monotonic, …
Enabling Autonomous Navigation For Affordable Scooters, Kaikai Liu, Rajathswaroop Mulky
Enabling Autonomous Navigation For Affordable Scooters, Kaikai Liu, Rajathswaroop Mulky
Faculty Publications
Despite the technical success of existing assistive technologies, for example, electric wheelchairs and scooters, they are still far from effective enough in helping those in need navigate to their destinations in a hassle-free manner. In this paper, we propose to improve the safety and autonomy of navigation by designing a cutting-edge autonomous scooter, thus allowing people with mobility challenges to ambulate independently and safely in possibly unfamiliar surroundings. We focus on indoor navigation scenarios for the autonomous scooter where the current location, maps, and nearby obstacles are unknown. To achieve semi-LiDAR functionality, we leverage the gyros-based pose data to compensate …
The 2018 Nvidia Ai City Challenge, Milind Naphade, Ming-Ching Chang, Anuj Sharma, David Anastasiu, Vamsi Jagarlamudi, Pranamesh Chakraborty, Tingting Huang, Shuo Wang, Ming-Yu Liu, Rama Chellappa, Jenq-Neng Hwang, Siwei Lyu
The 2018 Nvidia Ai City Challenge, Milind Naphade, Ming-Ching Chang, Anuj Sharma, David Anastasiu, Vamsi Jagarlamudi, Pranamesh Chakraborty, Tingting Huang, Shuo Wang, Ming-Yu Liu, Rama Chellappa, Jenq-Neng Hwang, Siwei Lyu
Faculty Publications
The NVIDIA AI City Challenge has been created to accelerate intelligent video analysis that helps make cities smarter and safer. With millions of traffic video cameras acting as sensors around the world, there is a significant opportunity for real-time and batch analysis of these videos to provide actionable insights. These insights will benefit a wide variety of agencies, from traffic control to public safety. The second edition of the NVIDIA AI City Challenge, being organized as a CVPR workshop, provided a forum to more than 70 academic and industrial research teams to compete and solve real-world problems using traffic camera …
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 …
Patient Friendly Kidney Function Screening, Ragwa El Sayed, Rathna Ramesh, Alessandro Bellofiore, David Anastasiu, Melinda Simon
Patient Friendly Kidney Function Screening, Ragwa El Sayed, Rathna Ramesh, Alessandro Bellofiore, David Anastasiu, Melinda Simon
Faculty Publications
No abstract provided.
Teaching With Jupyter In-Class Activities: Lessons Learned And Next Steps, David Anastasiu
Teaching With Jupyter In-Class Activities: Lessons Learned And Next Steps, David Anastasiu
Faculty Publications
No abstract provided.
A Data-Driven Approach For Detecting Autism Spectrum Disorders, Manika Kapoor, David Anastasiu
A Data-Driven Approach For Detecting Autism Spectrum Disorders, Manika Kapoor, David Anastasiu
Faculty Publications
No abstract provided.
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 …
Parallel Cosine Nearest Neighbor Graph Construction, David Anastasiu, George Karypis
Parallel Cosine Nearest Neighbor Graph Construction, David Anastasiu, George Karypis
Faculty Publications
The nearest neighbor graph is an important structure in many data mining methods for clustering, advertising, recommender systems, and outlier detection. Constructing the graph requires computing up to n2 similarities for a set of n objects. This high complexity has led researchers to seek approximate methods, which find many but not all of the nearest neighbors. In contrast, we leverage shared memory parallelism and recent advances in similarity joins to solve the problem exactly. Our method considers all pairs of potential neighbors but quickly filters pairs that could not be a part of the nearest neighbor graph, based on similarity …
Document Clustering, David Anastasiu, Andrea Tagarelli
Document Clustering, David Anastasiu, Andrea Tagarelli
Faculty Publications
In a world flooded with information, document clustering is an important tool that can help categorize and extract insight from text collections. It works by grouping similar documents, while simultaneously discriminating between groups. In this article, we provide a brief overview of the principal techniques used to cluster documents, and introduce a series of novel deep-learning based methods recently designed for the document clustering task. In our overview, we point the reader to salient works that can provide a deeper understanding of the topics discussed.
Efficient Identification Of Tanimoto Nearest Neighbors; All Pairs Similarity Search Using The Extended Jaccard Coefficient, David Anastasiu, George Karypis
Efficient Identification Of Tanimoto Nearest Neighbors; All Pairs Similarity Search Using The Extended Jaccard Coefficient, David Anastasiu, George Karypis
Faculty Publications
Tanimoto, or extended Jaccard, is an important similarity measure which has seen prominent use in fields such as data mining and chemoinformatics. Many of the existing state-of-the-art methods for market basket analysis, plagiarism and anomaly detection, compound database search, and ligand-based virtual screening rely heavily on identifying Tanimoto nearest neighbors. Given the rapidly increasing size of data that must be analyzed, new algorithms are needed that can speed up nearest neighbor search, while at the same time providing reliable results. While many search algorithms address the complexity of the task by retrieving only some of the nearest neighbors, we propose …
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 …
Robust Classification Of City Roadway Objects For Traffic Related Applications, Niveditha Bhandary, Charles Mackay, Alex Richards, Ji Tong, David Anastasiu
Robust Classification Of City Roadway Objects For Traffic Related Applications, Niveditha Bhandary, Charles Mackay, Alex Richards, Ji Tong, David Anastasiu
Faculty Publications
The increasing prevalence of video data, particularly from traffic and surveillance cameras, is accompanied by a growing need for improved object detection, tracking, and classification techniques. In order to encourage development in this area, the AI City Challenge, sponsored by IEEE Smart World and NVIDIA, cultivated a competitive environment in which teams from all over the world sought to demonstrate the effectiveness of their models after training and testing on a common dataset of 114,766 unique traffic camera keyframes. Models were constructed for two distinct purposes; track 1 designs addressed object detection, localization and classification, while track 2 designs aimed …
The Nvidia Ai City Challenge, Milind Naphade, David Anastasiu, Anuj Sharma, Vamsi Jagrlamudi, Hyeran Jeon, Kaikai Liu, Ming-Ching Chang, Siwei Lyu, Zeyu Gao
The Nvidia Ai City Challenge, Milind Naphade, David Anastasiu, Anuj Sharma, Vamsi Jagrlamudi, Hyeran Jeon, Kaikai Liu, Ming-Ching Chang, Siwei Lyu, Zeyu Gao
Faculty Publications
Web image analysis has witnessed an AI renaissance. The ILSVRC benchmark has been instrumental in providing a corpus and standardized evaluation. The NVIDIA AI City Challenge is envisioned to provide similar impetus to the analysis of image and video data that helps make cities smarter and safer. In its first year, this Challenge has focused on traffic video data. While millions of traffic video cameras around the world capture data, albeit low-quality, very little automated analysis and value creation results. Lack of labeled data, and trained models that can be deployed at the edge of the city fabric, ensure that …
Optimal Constrained Wireless Emergency Network Antenna Placement, Swapnil Gaikwad, David Anastasiu
Optimal Constrained Wireless Emergency Network Antenna Placement, Swapnil Gaikwad, David Anastasiu
Faculty Publications
Communication is paramount, especially during a natural disaster or other emergency. Even when traditional lines of communication become unavailable, emergency response teams must be able to communicate with each other and the outside world. To facilitate this need, major cities across the United States are deploying wireless emergency networks (WENs) that serve as a secure communication channel between emergency response points (police stations, shelters, food banks, hospitals, etc.) and the outside world. An important question when designing such networks is identifying the locations within the city where access points (APs) should be placed to construct a reliable WEN. We propose …
Efficient Neighborhood Graph Construction For Sparse High Dimensional Data, David Anastasiu
Efficient Neighborhood Graph Construction For Sparse High Dimensional Data, David Anastasiu
Faculty Publications
No abstract provided.
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 …
A Human-Centered Credit-Banking System For Convenient, Fair And Secure Carpooling Among Members Of An Association, H.-S. Jacob Tsao, Magdalini Eirinaki
A Human-Centered Credit-Banking System For Convenient, Fair And Secure Carpooling Among Members Of An Association, H.-S. Jacob Tsao, Magdalini Eirinaki
Faculty Publications
This paper proposes an unconventional carpool-matching system concept that is different from existing systems with four innovative operational features: (F1) The proposed matching system will be used by members of an association and sponsored by the association, e.g., the employees of a company, members of a homeowner association, employees of a shopping center. This expands the scope beyond commute trips. Such associations can also voluntarily form alliances to increase the number of possible carpool partners and geographical reach. (F2) Service provided by a driver or received by a rider incurs credit or debt to a bank centrally and fairly managed …
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.
On Optimal Media/Video Distribution In Closed P2p-Based Iptv Networks, Hao Cui, Xiao Su, Weijia Shang
On Optimal Media/Video Distribution In Closed P2p-Based Iptv Networks, Hao Cui, Xiao Su, Weijia Shang
Faculty Publications
Video distribution over the Internet has become a popular service because of technological advances in internet (e.g., higher network bandwidth) and video coding (e.g., H.264/SVC). In this and other similar media distribution applications, a server or distribution center sends a media/video to a group peers with different bandwidth resources and display capacities. In one of the approaches, the peer-to-peer approach, the server sends only one copy of the media over Internet, and each peer receives one segment of the media and exchanges his/her segment with other peers to receive the complete media. A key design issue in this approach is …