A5101741278- Human Mobility and Location-Based Analysis
- Traffic Prediction and Management Techniques
- Video Surveillance and Tracking Methods
- Advanced Image and Video Retrieval Techniques
- Computational and Text Analysis Methods
- Transportation Planning and Optimization
- Robotics and Sensor-Based Localization
- Time Series Analysis and Forecasting
- Anomaly Detection Techniques and Applications
- Natural Language Processing Techniques
- Data-Driven Disease Surveillance
- Advanced Algorithms and Applications
- Autonomous Vehicle Technology and Safety
- Multi-Agent Systems and Negotiation
- Software System Performance and Reliability
- Video Analysis and Summarization
- Scientific Computing and Data Management
- Topic Modeling
- Qualitative Research Methods and Applications
- Media Influence and Health
- AI-based Problem Solving and Planning
- Complex Network Analysis Techniques
- Remote-Sensing Image Classification
- Multimedia Communication and Technology
- Astronomical Observations and Instrumentation
Novateur Research Solutions (United States)
2021-2024
ObjectVideo (United States)
2007-2009
University of Central Florida
2002
Large Language Models (LLMs) have emerged as coding assistants, capable of generating source code from natural language prompts. With the increasing adoption LLMs in software development, academic research and industry based projects are developing various tools, benchmarks, metrics to evaluate effectiveness LLM-generated code. However, there is a lack solutions evaluated through empirically grounded methods that incorporate practitioners perspectives assess functionality, syntax, accuracy...
Collecting real-world mobility data is challenging. It often fraught with privacy concerns, logistical difficulties, and inherent biases. Moreover, accurately annotating anomalies in large-scale nearly impossible, as it demands meticulous effort to distinguish subtle complex patterns. These challenges significantly impede progress geospatial anomaly detection research by restricting access reliable complicating the rigorous evaluation, comparison, benchmarking of methodologies. To address...
In this paper, we present a method to remove commercials from talk and game show videos segment these into host guest shots. our approach, mainly rely on information contained in shot transitions, rather than analyzing the scene content of individual frames. We utilize inherent differences structure shows differentiate between them. Similarly, make use well-defined shows, which can be exploited classify shots as or The entire is first segmented camera based color histogram. Then, construct...
A need to understand and predict vehicles' behavior underlies both public private goals in the transportation domain, including urban planning management, ride-sharing services, intelligent systems. Individuals' preferences intended destinations vary throughout day, week, year: for example, bars are most popular evenings, beaches summer. Despite this principle, we note that recent studies on a benchmark dataset from Porto, Portugal have found, at best, only marginal improvements predictive...
Modern automated video analysis systems consist of large networks heterogeneous sensors. These must extract, integrate and present relevant information from the sensors in real-time. This paper addresses some major challenges such face: efficient processing for high-resolution sensors; data fusion across multiple modalities; robustness to changing environmental conditions errors; intuitive user interfaces visualization analysis. The discusses enabling technologies overcome these presents a...
The recent proliferation of real-world human mobility datasets has catalyzed geospatial and transportation research in trajectory prediction, demand forecasting, travel time estimation, anomaly detection. However, these also enable, more broadly, a descriptive analysis intricate systems mobility. We formally define patterns life as natural, explainable extension online unsupervised detection, where we not only monitor data stream for anomalies but explicitly extract normal over time. To...
Recent advancements in Large Language Models (LLMs) have enabled collaborative human-bot interactions Software Engineering (SE), similar to many other professions. However, the potential benefits and implications of incorporating LLMs into qualitative data analysis SE not been completely explored. For instance, conducting manually can be a time-consuming, effort-intensive, error-prone task for researchers. LLM-based solutions, such as generative AI models trained on massive datasets,...
Existing methods for anomaly detection often fall short due to their inability handle the complexity, heterogeneity, and high dimensionality inherent in real-world mobility data. In this paper, we propose DeepBayesic, a novel framework that integrates Bayesian principles with deep neural networks model underlying multivariate distributions from sparse complex datasets. Unlike traditional models, DeepBayesic is designed manage heterogeneous inputs, accommodating both continuous categorical...
Existing methods for anomaly detection often fall short due to their inability handle the complexity, heterogeneity, and high dimensionality inherent in real-world mobility data. In this paper, we propose DeepBayesic, a novel framework that integrates Bayesian principles with deep neural networks model underlying multivariate distributions from sparse complex datasets. Unlike traditional models, DeepBayesic is designed manage heterogeneous inputs, accommodating both continuous categorical...
Present-day software development faces three major challenges: complexity, time consumption, and high costs. Developing large systems often requires battalions of teams considerable for meetings, which end without any action, resulting in unproductive cycles, delayed progress, increased cost. What if, instead meetings with no immediate results, the product is completed by meeting? In response, we present a vision system called TimeLess, designed to reshape process enabling action during...
Human mobility research has significantly benefited from recent advances in machine learning, as have numerous other industries. Aided by the ever-increasing availability of geospatial and data, learning models enabled large-scale systems for simulating city-wide macro micro behaviors, urban planning, transportation management, disaster relief optimization. However, while many fields invested significant effort solving model transferability generalization problem, inability learning-based...
In this paper, we present JanusNet, an efficient CNN model that can perform online background subtraction and robustly detect moving targets using resource-constrained computational hardware on-board unmanned aerial vehicles (UAVs). Most of the existing work on sub-traction either assume camera is stationary or make limiting assumptions about motion camera, structure scene under observation, apparent in video. JanusNet does not have these limitations therefore, applicable to a variety UAV...
A need to understand and predict vehicles' behavior underlies both public private goals in the transportation domain, including urban planning management, ride-sharing services, intelligent systems. Individuals' preferences intended destinations vary throughout day, week, year: for example, bars are most popular evenings, beaches summer. Despite this principle, we note that recent studies on a benchmark dataset from Porto, Portugal have found, at best, only marginal improvements predictive...
The recent proliferation of real-world human mobility datasets has catalyzed geospatial and transportation research in trajectory prediction, demand forecasting, travel time estimation, anomaly detection. However, these also enable, more broadly, a descriptive analysis intricate systems mobility. We formally define patterns life as natural, explainable extension online unsupervised detection, where we not only monitor data stream for anomalies but explicitly extract normal over time. To...
This work introduces DIVINIA, a feature extractor and novel training objective for content-based image retrieval. DIVINIA combines semantic matching with ranking to produce that is able retrieve semantically relevant regions from large search corpus. It further ranks them appropriately according visual similarity. Furthermore, provides mechanism performing one-shot even zero-shot object localization without the need fine-tune extraction model or re-index corpus of features. We demonstrate...