Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification

Deep convolutional neural networks (CNNs) have shown a strong ability in mining discriminative object pose and parts information for image recognition. For fine-grained recognition, context-aware rich feature representation of object/scene plays a key role since it exhibits a significant variance in the same subcategory and subtle variance among different subcategories. Finding the subtle variance that fully characterizes the object/scene is not straightforward. To address this, we propose a novel context-aware attentional pooling (CAP) that effectively captures subtle changes via sub-pixel gradients, and learns to attend informative integral regions and their importance in discriminating different subcategories without requiring the bounding-box and/or distinguishable part annotations. We also introduce a novel feature encoding by considering the intrinsic consistency between the informativeness of the integral regions and their spatial structures to capture the semantic correlation among them. Our approach is simple yet extremely effective and can be easily applied on top of a standard classification backbone network. We evaluate our approach using six state-of-the-art (SotA) backbone networks and eight benchmark datasets. Our method significantly outperforms the SotA approaches on six datasets and is very competitive with the remaining two.Comment: Extended version of the accepted paper in 35th AAAI Conference on Artificial Intelligence 202

Evaluating the performance of a hybrid model for classification of bicycle crash severity and identification of associated risk factors

This study conducted an exploratory analysis of bicycle crash data from Great Britain with the aim of identifying the key variables that influence the classification of such incidents. It also analysed data on a range of factors that may contribute to bicycle crashes, including the age of the cyclist, lighting conditions, weather conditions, road types, road conditions, and speed limits. Results indicated that these variables are among the most significant predictors of bicycle crashes, with road conditions, time of day, and lighting conditions being particularly vital factors. In addition, the study sought to compare the efficacy of different machine learning and deep learning models in predicting the severity of such incidents. Results indicated that these models demonstrated poor performance in predicting the severity of bicycle crashes. As a result, a hybrid model that combines the K-Nearest Neighbor and eXtreme Gradient Boosting algorithms was developed to improve accuracy. The hybrid model outperformed all other models, achieving an accuracy rate of 83.56%. The study, additionally, has put forward several recommendations, including the mandatory use of reflective clothing and the installation of Intelligent Transportation Systems (ITS) to enhance the safety of cyclists

Secure web gateway on website in cloud

Developing interest for transfer work from home associations needs to permit numbers of specialists to get to confidential organizations on the business' nearby web. This leads to greater expenses to authorize the homeworker admittance to the association's assets secretly and safely through assigned gadgets and administration. The primary purpose of additionally supports the association execution with various administrations for telecommuters. SASE utilizes Zero Trust Engineering as its spine, without confiding in any gadget or client, yet validate and approve at each solicitation. The main purpose to apply designated spot capabilities such as Secure Web Passage and Cloud Access Security Representative to additionally uphold the security of the association's resources that may not be trusted in the cloud. Toward this paper's end, we will comprehend how those strategies referenced for doing the work on secure systems for work with associations' system associations and security. We conduct a systematic literature review of security challenges of cloud computing. In addition to security issues, the benefits of cloud computing security were also studied. Whenever a website is hacked by an attacker, businesses and organizations lose their valuable data. Thus, we choose the best platform to organize and secure the data online in a valuable way. The main point of attack in the website is the use of weak passwords and sharing authentication to other users

Secure web gateway on website in cloud

Developing interest for transfer work from home associations needs to permit numbers of specialists to get to confidential organizations on the business' nearby web. This leads to greater expenses to authorize the homeworker admittance to the association's assets secretly and safely through assigned gadgets and administration. The primary purpose of additionally supports the association execution with various administrations for telecommuters. SASE utilizes Zero Trust Engineering as its spine, without confiding in any gadget or client, yet validate and approve at each solicitation. The main purpose to apply designated spot capabilities such as Secure Web Passage and Cloud Access Security Representative to additionally uphold the security of the association's resources that may not be trusted in the cloud. Toward this paper's end, we will comprehend how those strategies referenced for doing the work on secure systems for work with associations' system associations and security. We conduct a systematic literature review of security challenges of cloud computing. In addition to security issues, the benefits of cloud computing security were also studied. Whenever a website is hacked by an attacker, businesses and organizations lose their valuable data. Thus, we choose the best platform to organize and secure the data online in a valuable way. The main point of attack in the website is the use of weak passwords and sharing authentication to other users

Demographic, Clinical Features and Outcome Determinants of Thoracic Trauma in Sri Lanka: A Multicentre Prospective Cohort Study

Prognostic determinants in thoracic trauma are of major public health interest. We intended to describe patterns of thoracic trauma, demographic factors, clinical course, and predictors of outcome in selected tertiary care hospitals in Sri Lanka. A multicentre prospective cohort study was conducted in five leading teaching hospitals from June to September 2017. Patients with thoracic trauma were followed up during the hospital stay. A logistic regression analysis was conducted using in-hospital morbidity as the dichotomous outcome variable. One hundred seventy-one patients were included in the study yielding 1450 (median = 8.5) person-days of observation. Of them, 71.9% (n = 123) were males. The mean age was 45.8 ± 17.9 years. Majority (39.2%, n = 67) were recruited from the National Hospital of Sri Lanka. Automobile accidents were the commonest (62.6%, n = 107), followed by falls (26.9%, n = 46), assaults (8.8%, n = 15), and animal attacks (1.8%, n = 3). The ratio of blunt to penetrating trauma was 5.6 : 1. Injury patterns were rib fractures (80.7%, n = 138), haemothorax (44.4%, n = 76), pneumothorax (44.4%, n = 76), lung contusion (22.8%, n = 39), flail segment (15.8%, n = 27), tracheobronchial trauma (7.0%, n = 12), diaphragmatic injury (2.3%, n = 4), vascular injury (2.3%, n = 4), cardiac contusions (1.1%, n = 2), and oesophageal injury (0.6%, n = 1). Ninety nine (57.9%) had extrathoracic injuries. Majority (63.2%, n = 108) underwent operative management including intercostal tube insertion (60.8%, n = 104), wound exploration (6.4%, n = 11), thoracotomy (4.1%, n = 7), rib reconstruction (4.1%, n = 7), and video-assisted thoracoscopic surgery (2.9%, n = 5). Pneumonia (10.5%, n = 8), bronchopleural fistulae (2.3%, n = 4), tracheaoesophageal fistulae (1.8%, n = 3), empyema (1.2%, n = 2), and myocardial infarction (1.2%, n = 2) were the commonest postoperative complications. The mean hospital stay was 15.6 ± 18.0 days. The in-hospital mortality was 11 (6.4%). The binary logistic regression analysis with five predictors (age, gender, mechanism of injury (automobile/fall/assault), type of trauma (blunt/penetrating), and the presence of extrathoracic injuries) was statistically significant to predict in-hospital morbidity (X2 (6, n = 168) = 13.1; p=0.041), explaining between 7.5% (Cox and Snell R2) and 14.5% (Nagelkerke R2) of variance. The automobile accidents (OR: 2.3, 95% CI = 0.2–26.2) and being males (OR: 2.3, 95% CI = 0.6–9.0) were the strongest predictors of morbidity