Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network

Drone systems have been deployed by various law enforcement agencies to monitor hostiles, spy on foreign drug cartels, conduct border control operations, etc. This paper introduces a real-time drone surveillance system to identify violent individuals in public areas. The system first uses the Feature Pyramid Network to detect humans from aerial images. The image region with the human is used by the proposed ScatterNet Hybrid Deep Learning (SHDL) network for human pose estimation. The orientations between the limbs of the estimated pose are next used to identify the violent individuals. The proposed deep network can learn meaningful representations quickly using ScatterNet and structural priors with relatively fewer labeled examples. The system detects the violent individuals in real-time by processing the drone images in the cloud. This research also introduces the aerial violent individual dataset used for training the deep network which hopefully may encourage researchers interested in using deep learning for aerial surveillance. The pose estimation and violent individuals identification performance is compared with the state-of-the-art techniques.Comment: To Appear in the Efficient Deep Learning for Computer Vision (ECV) workshop at IEEE Computer Vision and Pattern Recognition (CVPR) 2018. Youtube demo at this: https://www.youtube.com/watch?v=zYypJPJipY

Disguised Face Identification (DFI) with Facial KeyPoints using Spatial Fusion Convolutional Network

Disguised face identification (DFI) is an extremely challenging problem due to the numerous variations that can be introduced using different disguises. This paper introduces a deep learning framework to first detect 14 facial key-points which are then utilized to perform disguised face identification. Since the training of deep learning architectures relies on large annotated datasets, two annotated facial key-points datasets are introduced. The effectiveness of the facial keypoint detection framework is presented for each keypoint. The superiority of the key-point detection framework is also demonstrated by a comparison with other deep networks. The effectiveness of classification performance is also demonstrated by comparison with the state-of-the-art face disguise classification methods.Comment: To Appear in the IEEE International Conference on Computer Vision Workshops (ICCVW) 201

Document Automation Architectures: Updated Survey in Light of Large Language Models

This paper surveys the current state of the art in document automation (DA). The objective of DA is to reduce the manual effort during the generation of documents by automatically creating and integrating input from different sources and assembling documents conforming to defined templates. There have been reviews of commercial solutions of DA, particularly in the legal domain, but to date there has been no comprehensive review of the academic research on DA architectures and technologies. The current survey of DA reviews the academic literature and provides a clearer definition and characterization of DA and its features, identifies state-of-the-art DA architectures and technologies in academic research, and provides ideas that can lead to new research opportunities within the DA field in light of recent advances in generative AI and large language models.Comment: The current paper is the updated version of an earlier survey on document automation [Ahmadi Achachlouei et al. 2021]. Updates in the current paper are as follows: We shortened almost all sections to reduce the size of the main paper (without references) from 28 pages to 10 pages, added a review of selected papers on large language models, removed certain sections and most of diagrams. arXiv admin note: substantial text overlap with arXiv:2109.1160

Lyapunov-Based Dropout Deep Neural Network (Lb-DDNN) Controller

Deep neural network (DNN)-based adaptive controllers can be used to compensate for unstructured uncertainties in nonlinear dynamic systems. However, DNNs are also very susceptible to overfitting and co-adaptation. Dropout regularization is an approach where nodes are randomly dropped during training to alleviate issues such as overfitting and co-adaptation. In this paper, a dropout DNN-based adaptive controller is developed. The developed dropout technique allows the deactivation of weights that are stochastically selected for each individual layer within the DNN. Simultaneously, a Lyapunov-based real-time weight adaptation law is introduced to update the weights of all layers of the DNN for online unsupervised learning. A non-smooth Lyapunov-based stability analysis is performed to ensure asymptotic convergence of the tracking error. Simulation results of the developed dropout DNN-based adaptive controller indicate a 38.32% improvement in the tracking error, a 53.67% improvement in the function approximation error, and 50.44% lower control effort when compared to a baseline adaptive DNN-based controller without dropout regularization

Evaluation of Android anti-malware resistance against transformation attacks

Android being most popular and user-friendly is targeted by most of the malware authors. The malware authors use various transformation techniques to create different variants of malwares. Different transformation techniques such as obfuscation, repackaging, renaming are used mostly. Many anti-malwares are developed to secure the Android devices. Android does not offer file access permissions to all the applications installed. Thus anti-malwares may not provide complete security to the Android devices. In this paper, many such different techniques are presented that can be used to evaluate different anti-malwares

A Survey of Evaluation Techniques for Android Anti-Malware using Transformation Attacks

Android an open-source operating system mainly used for mobile phones have become increasingly popular. Studies suggest that mobile malware threats have recently become a real concern and the impact of malware is getting worse. 2014 saw an astounding 75 percent increase in the Android mobile malware. It is therefore imperative to evaluate the resistance and robustness of anti-malware products for android against various malware. To evaluate existing anti-malware, a systematic framework called DroidChameleon is developed with several common transformation techniques. This survey examines the effectiveness and robustness of popular antimalware tools and compare them against one another aiding in the decision making process involved with developing a secure system

RELATIONSHIP BETWEEN ANTHROPOMETRIC PARAMETERS AND INTELLIGENCE IN PRESCHOOL CHILDREN FROM RURAL KONKAN

Aim: To study association between anthropometric parameters and intelligence in preschool children from Rural KONKAN.Method: Children between 3 to7 years of age were examined for anthropometry, dietary recall and Intelligence (Intelligent Quotient-IQ) assessment from rural anganwadis. IQ test was performed by clinical psychologist using Binet-Kamat test of intelligence (version 4). Nutritional information was collected from 24- hour dietary recall and food diversity. Results: Results were interpreted using Prorated IQ We studied 159 (82 boys, 78 girls) out of which 15 (9.6%) had higher IQ. 25 (15.8%) were born LBW. Anthropometry classification showed that 61 (38.4%) were stunted and 25(15.7%) were wasted. According to IOTF, 72 (46%) were thin, 83(52%) were normal and 3 (2%) were overweight. we found that there is no significant difference in IQ with respect to anthropometric parameters, birth weight and nutritional status. Conclusion: We could not find any association of anthropometric parameters with IQ inspite of high prevalence of malnutrition. Brain is vital organ which can be protected by redistribution of blood flow at the cost of other organs like liver and abdominal viscera, which are the markers for future risk of diabetes. There is need to improve nutritional status. New update of IQ test is much needed as the current test is more than 50 years old and does not take into account the social, cultural transition over last 2 decades. &nbsp

RELATIONSHIP BETWEEN ANTHROPOMETRIC PARAMETERS AND INTELLIGENCE IN PRESCHOOL CHILDREN FROM RURAL KONKAN

Aim: To study association between anthropometric parameters and intelligence in preschool children from Rural KONKAN.Method: Children between 3 to7 years of age were examined for anthropometry, dietary recall and Intelligence (Intelligent Quotient-IQ) assessment from rural anganwadis. IQ test was performed by clinical psychologist using Binet-Kamat test of intelligence (version 4). Nutritional information was collected from 24- hour dietary recall and food diversity. Results: Results were interpreted using Prorated IQ We studied 159 (82 boys, 78 girls) out of which 15 (9.6%) had higher IQ. 25 (15.8%) were born LBW. Anthropometry classification showed that 61 (38.4%) were stunted and 25(15.7%) were wasted. According to IOTF, 72 (46%) were thin, 83(52%) were normal and 3 (2%) were overweight. we found that there is no significant difference in IQ with respect to anthropometric parameters, birth weight and nutritional status. Conclusion: We could not find any association of anthropometric parameters with IQ inspite of high prevalence of malnutrition. Brain is vital organ which can be protected by redistribution of blood flow at the cost of other organs like liver and abdominal viscera, which are the markers for future risk of diabetes. There is need to improve nutritional status. New update of IQ test is much needed as the current test is more than 50 years old and does not take into account the social, cultural transition over last 2 decades. &nbsp