Real-time Object Detection: YOLOv1 Re-Implementation in PyTorch

Real-time object detection is a crucial problem to solve when in comes to computer vision systems that needs to make appropriate decision based on detection in a timely manner. I have chosen the YOLO v1 architecture to implement it using PyTorch framework, with goal to familiarize with entire object detection pipeline I attempted different techniques to modify the original architecture to improve the results. Finally, I compare the metrics of my implementation to the original

Lighting and Rotation Invariant Real-time Vehicle Wheel Detector based on YOLOv5

Creating an object detector, in computer vision, has some common challenges when initially developed based on Convolutional Neural Network (CNN) architecture. These challenges are more apparent when creating model that needs to adapt to images captured by various camera orientations, lighting conditions, and environmental changes. The availability of the initial training samples to cover all these conditions can be an enormous challenge with a time and cost burden. While the problem can exist when creating any type of object detection, some types are less common and have no pre-labeled image datasets that exists publicly. Sometime public datasets are not reliable nor comprehensive for a rare object type. Vehicle wheel is one of those example that been chosen to demonstrate the approach of creating a lighting and rotation invariant real-time detector based on YOLOv5 architecture. The objective is to provide a simple approach that could be used as a reference for developing other types of real-time object detectors.Comment: Paper is written back in December 202

DiffuGen: Adaptable Approach for Generating Labeled Image Datasets using Stable Diffusion Models

Generating high-quality labeled image datasets is crucial for training accurate and robust machine learning models in the field of computer vision. However, the process of manually labeling real images is often time-consuming and costly. To address these challenges associated with dataset generation, we introduce "DiffuGen," a simple and adaptable approach that harnesses the power of stable diffusion models to create labeled image datasets efficiently. By leveraging stable diffusion models, our approach not only ensures the quality of generated datasets but also provides a versatile solution for label generation. In this paper, we present the methodology behind DiffuGen, which combines the capabilities of diffusion models with two distinct labeling techniques: unsupervised and supervised. Distinctively, DiffuGen employs prompt templating for adaptable image generation and textual inversion to enhance diffusion model capabilities

SCALABLE PROCESSING OF MULTIPLE AGGREGATE CONTINUOUS QUERIES

Data Stream Management Systems (DSMSs) were developed to be at the heart of every monitor- ing application. Monitoring applications typically register hundreds of Continuous Queries (CQs) in DSMSs in order to continuously process unbounded data streams to detect events of interest. DSMSs must be designed to efficiently handle unbounded streams with large volumes of data and large numbers of CQs, i.e., exhibit scalability. This need for scalability means that the underlying processing techniques a DSMS adopts should be optimized for high throughput (i.e., tuple output rate). Towards this, two main approaches were proposed in the literature: (1) Multiple Query Opti- mization (MQO) and (2) Scheduling. In this dissertation we focus on optimizing the processing of multiple Aggregate Continuous Queries (ACQs), given their high processing cost and popularity in all monitoring applications. Specifically, in this dissertation, we explore shared processing of ACQs and introduce the con- cept of ’Weaveability’ as an indicator of the potential gains of sharing the processing of ACQs. We develop Weave Share, a multiple ACQs optimizer that considers the different uncorrelated factors of the processing cost, such as the input rate and ACQs’ specifications. In order to fully reap the benefits of the new weave-based optimization techniques, we conceptualize a new underlying ag- gregate operator implementation and realize it in the TriOps framework. TriOps enables adaptive sharing of multiple ACQs that have different window specification, predicates and group-by at- tributes. The properties of the proposed techniques are studied analytically and their performance advantages are experimentally evaluated using simulation and in the context of the AQSIOS DSMS prototype

Prevalence and impact of X-ray screening for atlantoaxial instability in children with Down syndrome

Introduction: Atlantoaxial instability (AAI) is defined as excessive movement between the first and second cervical vertebra. This can lead to spinal cord compression, resulting in myelopathic and radicular symptoms. These symptoms occur in 1-2% of the down syndrome (DS) population. DS athletes are often required to undergo pre-participation X-ray screening for AAI to help identify those at risk. However, the evidence for plain cervical spine X-ray as a form of screening is poor. Aim: This project aims to explore the use, sensitivity and specificity of X-ray screening for AAI in the Irish DS population, to investigate the prevalence of symptomatic AAI, to identify the rate of sport exclusion based on an abnormal X-ray and to investigate whether neuromuscular conditions, type of schooling or activity level are significantly correlated to an abnormal Xray. Method: This is a nation-wide cross-sectional online survey. It was rolled out via email by Down Syndrome Ireland to 1511 registered families. The survey asked parents if their child has had to undergo x-ray screening, the result of the x-ray, the impact of the result and if they developed symptoms. The survey also assessed parental knowledge of symptoms of AAI. Results: Out of 240 responders, 7 responders had symptomatic AAI and 5 of these had normal X-rays (29% sensitivity). Chi-Squared testing showed no variables significantly correlated with having an abnormal X-ray. Of the total group of 146 who underwent X-ray pre-participation screening, 20 had abnormal results and were excluded from playing their desired sports (specificity 86.7 %). Conclusion: X-ray screening for AAI in Ireland is very common and can result in the exclusion of many from participating in sports. Plain X-ray has low sensitivity. Therefore, it is not an optimal screening tool for AAI in asymptomatic children. More should be done to improve parental knowledge of this condition

“The cone of shame”: welfare implications of Elizabethan collar use on dogs and cats as reported by their owners

Elizabethan collars are used in companion animals primarily to prevent self-trauma and associated negative welfare states in animals. However, they have been anecdotally associated with negative impacts on animal health and welfare including distress, abraded/ulcerated skin and misadventure. This study aimed to characterise the welfare impacts of Elizabethan collar use on companion dogs and cats, as reported by owners. Owners of pets who wore an Elizabethan collar during the past 12 months were surveyed about the impacts that the use of Elizabethan collars had on animal activities, in particular sleep, eating, drinking, exercise, interactions with other animals, as well as overall quality of life (QOL). The majority of 434 respondents (77.4%) reported a worse QOL score when their companion animal was wearing the collar, significantly so when the Elizabethan collar irritated their pet or impacted on their ability to drink or play. While other factors are likely to impact animal welfare during veterinary treatment that necessitates the use of Elizabethan collars, this study suggests that Elizabethan collars themselves might have negative welfare impacts in a range of domains including nutrition, environment, health, behaviour and mental state. We recommend that animal owners are informed about potential negative impacts of Elizabethan collars and harm minimisation strategies. Where possible, alternative methods of preventing self-trauma should be explored

2020 MLK Keynote Address: Michelle Alexander Presentation

2020 MLK Series Keynote Michelle Alexander brings audiences profoundly necessary and meaningful insights on the practice of mass incarceration that plagues the US justice system, as well as eye-opening conversation on how we can end racial caste in America. Lecture Wednesday, January 22, 2020 at 5:30pm, RISD Auditorium, 17 Canal Walk, Providence, RI. In her acclaimed bestseller The New Jim Crow: Mass Incarceration in the Age of Colorblindness, Alexander peels back the curtain on systemic racism in the US prison system in a work that the New York Review of Books describes as striking in the intelligence of her ideas, her powers of summary and the force of her writing. With equal force and candor on stage, she breaks the silence about racial injustice in the modern legal system to reveal how mass incarceration has come to replace segregation. A legal scholar, social justice advocate, columnist at The New York Times and visiting professor at Union Theological Seminary, Alexander explores the myths surrounding our criminal justice system from a racial and ethical standpoint and offers solutions for combating this epidemic. Delivering an emphatic wakeup call from the colorblind slumber that our country has fallen under, she leaves audiences with a new perspective on the challenges facing the civil rights community and a call to action for a multiracial, multi-ethnic human rights movement for justice in America

BronzeGate: Real-time Transactional Data Obfuscation for GoldenGate

ABSTRACT Data privacy laws have appeared recently, such as the HIPAA laws for protecting medical records, and the PCI guidelines for protecting Credit Card information. Data privacy can be defined as maintaining the privacy of Personal Identifiable Information (PII) from unauthorized accessing. . PII includes any piece of data that can be used alone, or in conjunction with additional information, to uniquely identify an individual. Examples of such information include national identification numbers, credit card numbers, as well as financial and medical records. Access control methods and data encryption provide a level of data protection from unauthorized access, however, it is not enough; it does not prohibit identity thefts. It was reported that 70% of the data privacy breaches are internal breaches that involve an employee from the enterprise who has access to some training or testing database replica, which contains all the PII. In addition to access control, we need techniques to obfuscate (i.e., mask or dim) the datasets used for training, testing and analysis purposes. A good data obfuscation technique would, among other features, preserve the data usability while protecting its privacy. This challenge is further complicated when real time requirements are added. In this paper we present BronzeGate: Obfuscated GoldenGate, the GoldenGate's real-time solution for transactional data privacy while maintaining data usability. BronzeGate utilizes different obfuscation functions for different data types to securely obfuscate the data, on real-time, while maintaining its statistical characteristics

Prevalence of gastrointestinal symptoms in patients with cannabis abuse presenting to the emergency room

Introduction: Cannabis is a common recreational drug among young adults that is used to induce euphoria, promote relaxation and enhance appetite. There is a recognized correlation between cannabis abuse and cyclic episodes of nausea, vomiting, and abdominal pain (cannabinoid hyperemesis syndrome). However, the contribution of such symptoms to emergency room visits and hospital admissions is not known. Methods: A one-year, retrospective study was conducted on patients who presented to the emergency department and were later admitted at Abington Hospital-Jefferson Health with confirmed cannabis use (positive urine drug screen). Aim/Goal: Our primary outcome was to understand the prevalence of gastrointestinal symptoms in this patient population. We also studied other indices including other recreational drug use, the level of care, and comorbid psychological illnesses. Results: A total of 100 patients were included in the study, 60% were male, 30.3% were admitted with a primary diagnosis of gastrointestinal symptoms (e.g., nausea, vomiting and abdominal pain or cannabinoid hyperemesis), 26.3% with neurological symptoms (i.e., altered mental status, loss of consciousness (LOC) and/or seizures), 23.2% with trauma (i.e., fall or car accident), and 6.1% with cardiac issues. 34% of our patients had no past medical history. Over 50% had at least one psychiatric illness and 38.7% were taking at least one or more antipsychotic medications. Subgroup analysis of our population admitted with GI symptoms (n=30) revealed 47% males, 36% active smokers (vs 34% in total population), 33% admit to alcohol use (vs 40% in total), 10% positive for opiates (vs 18% in total), 6.7% positive for cocaine (vs 8% in total), and 16% are on prescribed narcotics (vs 20.2% total). 27 % of these patients were placed on GMF with tele or higher level of care such as MICU, SICU, or PCU (vs 51% total) and 13.3% received echocardiogram (vs 21.2% total). Interesting results were observed when stratifying patients based on age and type of controlled substance abuse. Marijuana, opiates, and benzodiazepines or barbiturates users were more prevalent in patients 27-36 yrs. of age, with 30%, 33%, and 50%, respectively. While, cocaine, hallucinogens, and amphetamines or methamphetamines users were more prevalent in those aged 26 yrs. or younger, with 37.5%, 100%, and 66%, respectively. Conclusion: GI symptoms represent an important presenting feature in patients with cannabis abuse likely more than any other presenting features. However, patients with predominant GI symptoms might require lower level of care and less cardiac workup. Additional studies are required to elucidate the significance of GI symptoms in this population of patients further