MyVote - An Effective Online Voting System that can be Trusted

In a country where leaders are elected by the people, election, and the process of electing play a crucial role. Every citizen of a country has the right to vote. There are different ways of casting a vote and electing an individual. With such a large population, the country needs its own effective and secure voting system. The voting system has made drastic changes from traditional paper ballot voting to current electronic voting and now the online voting system. Advancements in the new system eliminate the drawbacks of the previous system. This paper proposes a new online voting system that provides every individual to cast a vote securely and effectively irrespective of the location

A Little Fog for a Large Turn

Small, carefully crafted perturbations called adversarial perturbations can easily fool neural networks. However, these perturbations are largely additive and not naturally found. We turn our attention to the field of Autonomous navigation wherein adverse weather conditions such as fog have a drastic effect on the predictions of these systems. These weather conditions are capable of acting like natural adversaries that can help in testing models. To this end, we introduce a general notion of adversarial perturbations, which can be created using generative models and provide a methodology inspired by Cycle-Consistent Generative Adversarial Networks to generate adversarial weather conditions for a given image. Our formulation and results show that these images provide a suitable testbed for steering models used in Autonomous navigation models. Our work also presents a more natural and general definition of Adversarial perturbations based on Perceptual Similarity.Comment: Accepted to WACV 202

Recognition and Detection of Vehicle License Plates Using Convolutional Neural Networks

The rise in toll road usage has sparked a lot of interest in the newest, most effective, and most innovative intelligent transportation system (ITS), such as the Vehicle License Plate Recognition (VLPR) approach. This research uses Convolutional Neural Networks to deliver effective deep learning principally based on Automatic License Plate Recognition (ALPR) for detection and recognition of numerous License Plates (LPs) (CNN). Two fully convolutional one-stage object detectors are utilized in ALPRNet to concurrently identify and categorize LPs and characters, followed by an assembly module that outputs the LP strings. Object detectors are typically employed in CNN-based approaches such as You Only Look Once (YOLO), Faster Region-based Convolutional Neural Network (Faster R-CNN), and Mask Region-based Convolutional Neural Network (Mask R-CNN) to locate LPs. The VLPR model is used here to detect license plates using You Only Look Once (YOLO) and to recognize characters in license plates using Optical Character Recognition (OCR). Unlike existing methods, which treat license plate detection and recognition as two independent problems to be solved one at a time, the proposed method accomplishes both goals using a single network. Matlab R2020a was used as a tool

Recognition and Detection of Vehicle License Plates Using Convolutional Neural Networks

The rise in toll road usage has sparked a lot of interest in the newest, most effective, and most innovative intelligent transportation system (ITS), such as the Vehicle License Plate Recognition (VLPR) approach. This research uses Convolutional Neural Networks to deliver effective deep learning principally based on Automatic License Plate Recognition (ALPR) for detection and recognition of numerous License Plates (LPs) (CNN). Two fully convolutional one-stage object detectors are utilized in ALPRNet to concurrently identify and categorize LPs and characters, followed by an assembly module that outputs the LP strings. Object detectors are typically employed in CNN-based approaches such as You Only Look Once (YOLO), Faster Region-based Convolutional Neural Network (Faster R-CNN), and Mask Region-based Convolutional Neural Network (Mask R-CNN) to locate LPs. The VLPR model is used here to detect license plates using You Only Look Once (YOLO) and to recognize characters in license plates using Optical Character Recognition (OCR). Unlike existing methods, which treat license plate detection and recognition as two independent problems to be solved one at a time, the proposed method accomplishes both goals using a single network. Matlab R2020a was used as a tool

On Gosper's Pi(q) and Lambert series identities

In an interesting article entitled `Experiments and discoveries in q-trigonometry'', R. W. Gosper conjectured few beautiful Pi(q) and Lambert series identities. Many people have attempted confirming some of those identities in the Gosper's list, mainly by using Gosper's q-trigonometric identities. In this paper we either prove or disprove all the Pi(q) and Lambert series identities in the Gosper's list by mainly using S. Ramanujan's theta function identities and W. N. Bailey's summation formula. In the process, we obtain three new Gosper kind of identities

The Coronavirus Pandemic: Associations of College Students\u27 Financial Situations and Optimism with Mental & Physical Health

The coronavirus pandemic has led to a turbulent environment, putting college students and their families in unprecedented situations. The rise in unemployment and concerns about the overall economy may be impacting student finances. Increased depression and anxiety are common responses to such stressful situations. However, certain psychosocial factors, such as optimism, may be a valuable resource for coping with stress. Individuals who are more versus less optimistic tend to show less distress and have better physical functioning. Thus, the purpose of this study was to examine how college students’ financial situation during the coronavirus pandemic is related to mental and physical health, as well as how optimism moderates this relationship. We hypothesized that worse financial situations would be associated with higher levels of depression, anxiety, and physical symptoms, but that optimism would buffer against worse outcomes. To investigate these hypotheses, students at a private university in Southern California were recruited through their university email addresses to complete an online questionnaire in the spring of 2020. Nearly 300 students self-reported their financial situation, depression, anxiety, physical symptoms (e.g., nausea, headaches), and optimism. Linear regression models tested associations. Results indicated that, as expected, a worsening financial situation and an increase in worry about paying for school were significantly associated with higher levels of depression, anxiety, and physical symptoms (ps \u3c 0.05). By contrast, greater optimism was associated with lower levels of depression, anxiety, and physical symptoms (ps \u3c 0.05). However, the effect of financial situation on students’ mental and physical health did not depend on optimism (ps \u3e 0.05). This may be because students in this study had lower optimism scores relative to pre-pandemic cohorts, suggesting they struggled to be optimistic during the pandemic. Further investigation on how financial situations and optimism relate to mental and physical health is crucial to not only improve the quality of life for college students, but to also help in creating and implementing effective mental and physical health interventions

Child Postoperative Pain: Impact of Child Temperament and Parent Mood on Pain After Surgery

Around 80% of children who undergo surgery experience moderate to severe postoperative pain. Various psychosocial factors contribute to exacerbations of this pain. A child’s inborn personality traits and style of interaction with the environment are known as temperament. Children who are less sociable and more distress-prone (e.g., those who cry, throw tantrums) are more likely to have an anxious temperament. This anxiety before and after surgery may lead to an increase in postoperative pain levels as well. Parent pain ratings do not always reflect true child pain. Overtime, a parent’s emotional state and mood may change how they perceive child pain. Parents who have more negative moods or are more distressed tend to report their child’s pain as worse and have a negative impact on a child’s pain. Therefore, parents play a crucial role in treatment after surgery. Given this, the purpose of this study was to see how child temperament factors are associated with postoperative pain and how parent mood moderates this relationship. These specific factors are analyzed in a sample of children ages 2-13 who underwent elective surgery at the Children’s Hospital of Orange County (N = 112). Prior to the surgery, parents completed online surveys assessing child temperament and parent mood. Postoperative pain measures were reported by both children and parents after surgery on days 1, 3, and 7. Child temperament factors did not interact with parent mood to predict postoperative pain. Emotionality and sociability were not significantly associated with parent pain measures or child pain after surgery (ps \u3e 0.05). However, other studies have shown that child temperament does affect pain in a hospital setting more than they do at home. Interestingly, pain in children was low at home after surgery (less than 3 on a scale of 0 to 10). It is possible that the impact of temperament and mood on pain were less potent once the child is at home following the surgery and pain is lower. Further investigation on the influence of temperament and mood on child pain is important to obtain more clarity and discover optimal methods of treating pain in children. Future work may benefit from investigating different surgical procedures that might lead to a greater diversity of pain once children are home