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Object-Detection From Multi-View Remote Sensing Images: A Case Study Of Fruit And Flower Detection And Counting On A Central Florida Strawberry Farm, Caiwang Zheng, Tao Liu, Amr Abd-Elrahman, Vance M. Whitaker, Benjamin Wilkinson
Object-Detection From Multi-View Remote Sensing Images: A Case Study Of Fruit And Flower Detection And Counting On A Central Florida Strawberry Farm, Caiwang Zheng, Tao Liu, Amr Abd-Elrahman, Vance M. Whitaker, Benjamin Wilkinson
Michigan Tech Publications, Part 2
Object detection in remote sensing images is one of the most critical computer vision tasks for various earth observation applications. Previous studies applied object detection models to orthomosaic images generated from the SfM (Structure-from-Motion) analysis to perform object detection and counting. However, some small objects that are occluded from the vertical view but observable in raw images from the oblique views cannot be detected in the orthomosaic image, leading to an occlusion issue that cannot be resolved with the traditional orthophoto-based approach. Taking strawberry detection as a case study, the objective of this study is to detect small objects directly …
Harnessing Large Language Models For Simulink Toolchain Testing And Developing Diverse Open-Source Corpora Of Simulink Models For Metric And Evolution Analysis, Sohil Lal Shrestha
Harnessing Large Language Models For Simulink Toolchain Testing And Developing Diverse Open-Source Corpora Of Simulink Models For Metric And Evolution Analysis, Sohil Lal Shrestha
Association of Computing Machinery Open Access Agreement Publications
MATLAB/Simulink is a de-facto standard tool in several safety-critical industries such as automotive, aerospace, healthcare, and industrial automation for system modeling and analysis, compiling models to code, and deploying code to embedded hardware. On one hand, testing cyber-physical system (CPS) development tools such as MathWorks’ Simulink is important as a bug in the toolchain may propagate to the artifacts they produce. On the other hand, it is equally important to understand modeling practices and model evolution to support engineers and scientists as they are widely used in design, simulation, and verification of CPS models. Existing work in this area is …
Chatbots, Generative Ai, And Scholarly Manuscripts: Wame Recommendations On Chatbots And Generative Artificial Intelligence In Relation To Scholarly Publications, Chris Zielinski, Margaret A. Winker, Rakesh Aggarwal, Lorraine E. Ferris, Markus Heinemann, Jose Florencio Lapeña, Sanjay A. Pai, Edsel Ing, Leslie Citrome, Murad Alam, Michael Voight, Farrokh Habibzadeh
Chatbots, Generative Ai, And Scholarly Manuscripts: Wame Recommendations On Chatbots And Generative Artificial Intelligence In Relation To Scholarly Publications, Chris Zielinski, Margaret A. Winker, Rakesh Aggarwal, Lorraine E. Ferris, Markus Heinemann, Jose Florencio Lapeña, Sanjay A. Pai, Edsel Ing, Leslie Citrome, Murad Alam, Michael Voight, Farrokh Habibzadeh
NYMC Faculty Publications
This statement revises our earlier "WAME Recommendations on ChatGPT and Chatbots in Relation to Scholarly Publications" (January 20, 2023). The revision reflects the proliferation of chatbots and their expanding use in scholarly publishing over the last few months, as well as emerging concerns regarding lack of authenticity of content when using chatbots. These recommendations are intended to inform editors and help them develop policies for the use of chatbots in papers published in their journals. They aim to help authors and reviewers understand how best to attribute the use of chatbots in their work and to address the need for …
Psdoodle: Fast App Screen Search Via Partial Screen Doodle, Soumik Mohian, Christoph Csallner
Psdoodle: Fast App Screen Search Via Partial Screen Doodle, Soumik Mohian, Christoph Csallner
Association of Computing Machinery Open Access Agreement Publications
Searching through existing repositories for a specific mobile app screen design is currently either slow or tedious. Such searches are either limited to basic keyword searches (Google Image Search) or require as input a complete query screen image (SWIRE). A promising alternative is interactive partial sketching, which is more structured than keyword search and faster than complete-screen queries. PSDoodle is the first system to allow interactive search of screens via interactive sketching. PSDoodle is built on top of a combination of the Rico repository of some 58k Android app screens, the Google QuickDraw dataset of icon-level doodles, and DoodleUINet, a …
Psdoodle: Searching For App Screens Via Interactive Sketching, Soumik Mohian, Christoph Csallner
Psdoodle: Searching For App Screens Via Interactive Sketching, Soumik Mohian, Christoph Csallner
Association of Computing Machinery Open Access Agreement Publications
Keyword-based mobile screen search does not account for screen content and fails to operate as a universal tool for all levels of users. Visual searching (e.g., image, sketch) is structured and easy to adopt. Current visual search approaches count on a complete screen and are therefore slow and tedious. PSDoodle employs a deep neural network to recognize partial screen element drawings instantly on a digital drawing interface and shows results in real-time. PSDoodle is the first tool that utilizes partial sketches and searches for screens in an interactive iterative way. PSDoodle supports different drawing styles and retrieves search results that …
Light-Weight Seated Posture Guidance System With Machine Learning And Computer Vision, Rithik Kapoor, Ashish Jaiswal, Fillia Makedon
Light-Weight Seated Posture Guidance System With Machine Learning And Computer Vision, Rithik Kapoor, Ashish Jaiswal, Fillia Makedon
Association of Computing Machinery Open Access Agreement Publications
In today’s world, the increased time people spend in front of their computers has been one of the main causes for neck and back pains. Especially, since the pandemic, it has been quite evident that slouching at home for long hours on hand-held devices and computers has led many people towards spinal pains and injuries. Backed with scientific research, it has been proven that these pains can be prevented with proper monitoring of the seated posture and taking breaks in between. This paper focuses on building a light-weight end-to-end system that monitors the user’s posture and provides feedback whenever it …
Large-Scale Self-Supervised Human Activity Recognition, Mohammad Zaki Zadeh, Ashish Jaiswal, Hamza Reza Pavel, Aref Hebri, Rithik Kapoor, Fillia Makedon
Large-Scale Self-Supervised Human Activity Recognition, Mohammad Zaki Zadeh, Ashish Jaiswal, Hamza Reza Pavel, Aref Hebri, Rithik Kapoor, Fillia Makedon
Association of Computing Machinery Open Access Agreement Publications
In this paper, a self-supervised approach is used to obtain an effective human activity representation using a limited set of annotated data. This research is aimed on acquiring human activity representation in order to improve the accuracy of classifying videos of human activities in the NTU RGB+D 120 dataset. The effectiveness of various self-supervised approaches, as well as a supervised method, is studied. The results reveal that when the training set gets smaller, the performance of supervised learning approaches diminishes, whereas self-supervised methods maintain their performance by utilizing unlabeled data.
Detecting Road Intersections Automatically From Satellite Images Using A Deep Learning Approach, Fatmaelzahraa Eltaher, Luis Miralles-Pechuán, Jane Courtney, Susan Mckeever
Detecting Road Intersections Automatically From Satellite Images Using A Deep Learning Approach, Fatmaelzahraa Eltaher, Luis Miralles-Pechuán, Jane Courtney, Susan Mckeever
Datasets
Automatic detection of road intersections is an important task in various domains such as navigation, route planning, traffic prediction, and road network extraction. Road intersections range from simple three-way T-junctions (degree 3) to complex large-scale junctions with many branches. The location of intersections and their complexity is an important consideration in route planning, such as the requirement to avoid complex intersections on pedestrian journeys. This is relevant to vulnerable road users such as People with Blindness or Visually Impairment (PBVI) or children. Route planning applications, however, do not give information about the location or complexity of intersections as this information …
Efficient, Low-Cost Bridge Cracking Detection And Quantification Using Deep-Learning And Uav Images, Chao Sun, Xiangyu Meng, Joshua O. Ogbebor, Shaopan Guo
Efficient, Low-Cost Bridge Cracking Detection And Quantification Using Deep-Learning And Uav Images, Chao Sun, Xiangyu Meng, Joshua O. Ogbebor, Shaopan Guo
Data
Many bridges in the State of Louisiana and the United States are working under serious degradation conditions where cracks on bridges threaten structural integrity and public security. To ensure structural integrity and public security, it is required that bridges in the US be inspected and rated every two years. Currently, this biannual assessment is largely implemented using manual visual inspection methods, which is slow and costly. In addition, it is challenging for workers to detect cracks in regions that are hard to reach, e.g., the top part of the bridge tower, cables, mid-span of the bridge girders, and decks. This …
Automated System To Measure Tandem Gait To Assess Executive Functions In Children, Mohammad Zaki Zahed, Ramesh Babu Ashwin, Ashish Jaiswal, Maria Kyrarini, Morris Bell, Fillia Makedon
Automated System To Measure Tandem Gait To Assess Executive Functions In Children, Mohammad Zaki Zahed, Ramesh Babu Ashwin, Ashish Jaiswal, Maria Kyrarini, Morris Bell, Fillia Makedon
Association of Computing Machinery Open Access Agreement Publications
As mobile technologies have become ubiquitous in recent years, computer-based cognitive tests have become more popular and efficient. In this work, we focus on assessing motor function in children by analyzing their gait movements. Although there has been a lot of research on designing automated assessment systems for gait analysis, most of these efforts use obtrusive wearable sensors for measuring body movements. We have devised a computer visionbased assessment system that only requires a camera which makes it easier to employ in school or home environments. A dataset has been created with 27 children performing the test. Furthermore, in order …
Attacking Audio Event Detection Deep Learning Classifiers With White Noise, Rodrigo Dos Santos, Ashwitha Kassetty, Shirin Nilizadeh
Attacking Audio Event Detection Deep Learning Classifiers With White Noise, Rodrigo Dos Santos, Ashwitha Kassetty, Shirin Nilizadeh
Association of Computing Machinery Open Access Agreement Publications
We develop deep learning-based classifiers for Audio Event Detection (AED), attacking them next with some white noise disturbances. We show that an attacker can use such simple disturbances to potentially fully avoid detection by AED systems. Prior work has shown that attackers can mislead image classification tasks, however this work focuses on attacks against AED systems, by tampering the audio and not image. This work brings awareness to the designers and manufacturers of AED systems and devices, as these solutions are becoming more ubiquitous by the day.
Self-Supervised Human Activity Recognition By Augmenting Generative Adversarial Networks, Mohammad Zaki Zahed, Ashish Jaiswal, Ramesh Babu Ashwin, Maria Kyrarini, Fillia Makedon
Self-Supervised Human Activity Recognition By Augmenting Generative Adversarial Networks, Mohammad Zaki Zahed, Ashish Jaiswal, Ramesh Babu Ashwin, Maria Kyrarini, Fillia Makedon
Association of Computing Machinery Open Access Agreement Publications
This article proposes a novel approach for augmenting generative adversarial network (GAN) with a self-supervised task in order to improve its ability for encoding video representations that are useful in downstream tasks such as human activity recognition. In the proposed method, input video frames are randomly transformed by different spatial transformations, such as rotation, translation and shearing or temporal transformations such as shuffling temporal order of frames. Then discriminator is encouraged to predict the applied transformation by introducing an auxiliary loss. Subsequently, results prove superiority of the proposed method over baseline methods for providing a useful representation of videos used …
Slgpt: Using Transfer Learning To Directly Generate Simulink Model Files And Find Bugs In The Simulink Toolchain, Lal Shrestha Sohil, Christoph Csallner
Slgpt: Using Transfer Learning To Directly Generate Simulink Model Files And Find Bugs In The Simulink Toolchain, Lal Shrestha Sohil, Christoph Csallner
Association of Computing Machinery Open Access Agreement Publications
Finding bugs in a commercial cyber-physical system (CPS) development tool such as Simulink is hard as its codebase contains millions of lines of code and complete formal language specifications are not available. While deep learning techniques promise to learn such language specifications from sample models, deep learning needs a large number of training data to work well. SLGPT addresses this problem by using transfer learning to leverage the powerful Generative Pre-trained Transformer 2 (GPT-2) model, which has been pre-trained on a large set of training data. SLGPT adapts GPT-2 to Simulink with both randomly generated models and models mined from …
Recognition And Repetition Counting For Local Muscular Endurance Exercises In Exercise-Based Rehabilitation: A Comparative Study Using Artificial Intelligence Models, Ghanashyama Prabhu, Noel E. O’Connor, Kieran Moran
Recognition And Repetition Counting For Local Muscular Endurance Exercises In Exercise-Based Rehabilitation: A Comparative Study Using Artificial Intelligence Models, Ghanashyama Prabhu, Noel E. O’Connor, Kieran Moran
Open Access archive
Exercise-based cardiac rehabilitation requires patients to perform a set of certain prescribed exercises a specific number of times. Local muscular endurance exercises are an important part of the rehabilitation program. Automatic exercise recognition and repetition counting, from wearable sensor data, is an important technology to enable patients to perform exercises independently in remote settings, e.g., their own home. In this paper, we first report on a comparison of traditional approaches to exercise recognition and repetition counting (supervised ML and peak detection) with Convolutional Neural Networks (CNNs). We investigated CNN models based on the AlexNet architecture and found that the performance …
Hybrid Transfer Learning For Classification Of Uterine Cervix Images For Cervical Cancer Screening, Vidya Kudva, Keerthana Prasad, Shyamala Guruvare
Hybrid Transfer Learning For Classification Of Uterine Cervix Images For Cervical Cancer Screening, Vidya Kudva, Keerthana Prasad, Shyamala Guruvare
Open Access archive
Transfer learning using deep pre-trained convolutional neural networks is increasingly used to solve a large number of problems in the medical field. In spite of being trained using images with entirely different domain, these networks are flexible to adapt to solve a problem in a different domain too. Transfer learning involves fine-tuning a pre-trained network with optimal values of hyperparameters such as learning rate, batch size, and number of training epochs. The process of training the network identifies the relevant features for solving a specific problem. Adapting the pre-trained network to solve a different problem requires fine-tuning until relevant features …
Compact Recurrent Neural Networks For Acoustic Event Detection On Low-Energy Low-Complexity Platforms, Gianmarco Cerutti, Rahul Prasad, Alessio Brutti, Elisabetta Farella
Compact Recurrent Neural Networks For Acoustic Event Detection On Low-Energy Low-Complexity Platforms, Gianmarco Cerutti, Rahul Prasad, Alessio Brutti, Elisabetta Farella
Open Access archive
Outdoor acoustic event detection is an exciting research field but challenged by the need for complex algorithms and deep learning techniques, typically requiring many computational, memory, and energy resources. These challenges discourage IoT implementations, where an efficient use of resources is required. However, current embedded technologies and microcontrollers have increased their capabilities without penalizing energy efficiency. This paper addresses the application of sound event detection at the very edge, by optimizing deep learning techniques on resource-constrained embedded platforms for the IoT. The contribution is two-fold: firstly, a two-stage student-teacher approach is presented to make state-of-the-art neural networks for sound event …
Improving The Performance Of Convolutional Neural Network For The Segmentation Of Optic Disc In Fundus Images Using Attention Gates And Conditional Random Fields, Bhargav J. Bhatkalkar, Dheeraj R. Reddy, Srikanth Prabhu, Sulatha V. Bhandary
Improving The Performance Of Convolutional Neural Network For The Segmentation Of Optic Disc In Fundus Images Using Attention Gates And Conditional Random Fields, Bhargav J. Bhatkalkar, Dheeraj R. Reddy, Srikanth Prabhu, Sulatha V. Bhandary
Open Access archive
The localization and segmentation of optic disc (OD) in fundus images is a crucial step in the pipeline for detecting the early onset of retinal diseases, such as macular degeneration, diabetic retinopathy, glaucoma, etc. In this paper, we are proposing a novel convolutional neural network architecture for the precise segmentation of the OD in fundus images. We modify the basic architectures of DeepLab v3+ and U-Net models by integrating a novel attention module between the encoder and decoder to attain the finest accuracy. We also use fully-connected conditional random fields to further boost the performance of these architectures. We compare …
Artificial Intelligence (Ai) In Urology-Current Use And Future Directions: An Itrue Study, Milap Shah, Nithesh Naik, Bhaskar K. Somani, B. M.Zeeshan Hameed
Artificial Intelligence (Ai) In Urology-Current Use And Future Directions: An Itrue Study, Milap Shah, Nithesh Naik, Bhaskar K. Somani, B. M.Zeeshan Hameed
Open Access archive
Objective: Artificial intelligence (AI) is used in various urological conditions such as urolithiasis, pediatric urology, urogynecology, benign prostate hyperplasia (BPH), renal transplant, and uro-oncology. The various models of AI and its application in urology subspecialties are reviewed and discussed. Material and methods: Search strategy was adapted to identify and review the literature pertaining to the application of AI in urology using the keywords “urology,” “artificial intelligence,” “machine learning,” “deep learning,” “artificial neural networks,” “computer vision,” and “natural language processing” were included and categorized. Review articles, editorial comments, and non-urologic studies were excluded. Results: The article reviewed 47 articles that reported …