Reviewerly/exHarmony huggingface dataset:
Privacy preserving data sharing method for social media platforms

Digital security as a service is a crucial aspect as it deals with user privacy provision and secure content delivery to legitimate users. Most social media platforms utilize end-to-end encryption as a significant security feature. However, multimedia data transmission in group communication is not encrypted. One of the most important objectives for a service provider is to send the desired multimedia data/service to only legitimate subscriber. Broadcast encryption is the most appropriate cryptographic primitive solution for this problem. Therefore, this study devised a construction called anonymous revocable identity-based broadcast encryption that preserves the privacy of messages broadcasted and the identity of legitimate users, where even revoked users cannot extract information about the user's identity and sent data. The update key is broadcast periodically to non-revoked users, who can obtain the message using the update and decryption keys. A third-party can also revoke the users. It is proven that the proposed construction is semantically secure against IND-ID-CPA attacks and efficient in terms of computational cost and communication bandwidth.
A Bionic Dynamic Path Planning Algorithm of the Micro UAV Based on the Fusion of Deep Neural Network Optimization/Filtering and Hawk-Eye Vision

A micro unmanned aerial vehicle (UAV) only equipped with a monocular camera is hard to accomplish a flying task with obstacles avoidance and target tracking simultaneously. In this article, a bionic dynamic path planning algorithm was developed for cooperation of obstacles avoidance and target tracking. An improved bat algorithm (BA) optimized transfer learning convolutional neural network (CNN) and bio-inspired optical flow balance algorithm was combined for obstacles avoidance. The Hawk-eye algorithm with line of sight (LOS) tracking rules is aimed at UAV dynamic tracking with obstacles avoidance. All of perception information, including avoidance and tracking were fused in UAV motion decision phase. The experiments include “obstacles avoidance” and “obstacles avoidance + target tracking” parts. Comparing with manual control and other algorithms, the bionic dynamic path planning algorithm in this article showed certain advantages in success rate, less obstacles collisions, and less major accidents.
Rate-Distortion in Image Coding for Machines

In recent years, there has been a sharp increase in transmission of images to remote servers specifically for the purpose of computer vision. In many applications, such as surveillance, images are mostly transmitted for automated analysis, and rarely seen by humans. Using traditional compression for this scenario has been shown to be inefficient in terms of bit-rate, likely due to the focus on human based distortion metrics. Thus, it is important to create specific image coding methods for joint use by humans and machines. One way to create the machine side of such a codec is to perform feature matching of some intermediate layer in a Deep Neural Network performing the machine task. In this work, we explore the effects of the layer choice used in training a learnable codec for humans and machines. We prove, using the data processing inequality, that matching features from deeper layers is preferable in the sense of rate-distortion. Next, we confirm our findings empirically by re-training an existing model for scalable human-machine coding. In our experiments we show the trade-off between the human and machine sides of such a scalable model, and discuss the benefit of using deeper layers for training in that regard.
On Benefits and Challenges of Conditional Interframe Video Coding in Light of Information Theory

The rise of variational autoencoders for image and video compression has opened the door to many elaborate coding techniques. One example here is the possibility of conditional interframe coding. Here, instead of transmitting the residual between the original frame and the predicted frame (often obtained by motion compensation), the current frame is transmitted under the condition of knowing the prediction signal. In practice, conditional coding can be straightforwardly implemented using a conditional autoencoder, which has also shown good results in recent works. In this paper, we provide an information theoretical analysis of conditional coding for inter frames and show in which cases gains compared to traditional residual coding can be expected. We also show the effect of information bottlenecks which can occur in practical video coders in the prediction signal path due to the network structure, as a consequence of the data-processing theorem or due to quantization. We demonstrate that conditional coding has theoretical benefits over residual coding but that there are cases in which the benefits are quickly canceled by small information bottlenecks of the prediction signal.
AI-app development for Yolov5-based face mask wearing detection

Corona is one of the most destructive viruses that has ever produced a pandemic in human life, not only in terms of direct victims but also in terms of the socio-economic consequences of the virus' transmission. The 2nd anniversary of the global coronavirus pandemic passed away in 2021. However, it's still impossible to say how long the epidemic will last. After reviewing a study by the World Health Organization on COVID-19, the country's national government urged residents to use facemask in order to reduce the incidence of COVID-19 transmission. As a result of COVID-19, there are presently no facemask detection app that are in great demand for ensuring safety in public area. In the context of the outbreak of COVID-19, A facemask detection model based on deep learning approach of state-of-the-art YOLOv5 may be useful in real-time applications. In this paper, we propose a web app for detecting if the people wears facemask or not in real-time via webcam or public camera. In the app, we deployed and persisted many different YOLOv5-based models that the users can switch between them to guarantee the performance and timing trade-off. Furthermore, our system is able to detect if an individual person captured by surveillance cameras is wearing facemask in acceptable counting time at staging level. In our opinion, this kind of system is extremely efficient for use in airports, train stations, offices, and other public areas, as well as in military.
Breast tumor detection in ultrasound images using artificial intelligence

Leveraging artificial intelligence (AI) for categorizing breast tumors as malignant or benign from breast ultrasound images can provide an effective and relatively low-cost method for the diagnosis of breast cancer. Presently, many machine learning (ML) and deep learning (DL) algorithms have been used for early-stage breast cancer detection. AI algorithms have shown promising results in breast cancer detection tasks. The use of deep convolutional neural network approaches has provided solutions for the efficient analysis of breast ultrasound images. Convolutional neural network (CNN) models analyze the image data in multiple layers and extract features which helps in better feature extractions and better performance in comparison to the conventional ML algorithms. Apart from conventional learning algorithms, we use the transfer learning technique which uses knowledge from its previous training in another related problem set. In this chapter, we have demonstrated the use of DL models through transfer learning, deep feature extraction, machine learning models, and comparison of their performances.
Breast cancer detection from mammograms using artificial intelligence

Breast cancer is one of the fastest-growing forms of cancer in the world today. Breast cancer is primarily found in women, and its frequency has been gaining significantly in the last few years. The key to tackle the rising cases of breast cancer is early detection. Many studies have shown that early detection significantly reduces the mortality rate of those affected. Machine learning and deep learning techniques have been adopted in the present scenario to help detect breast cancer in an early stage. Deep learning models such as the convolutional neural networks (CNNs) are suited explicitly to image data and overcome the drawbacks of machine learning models. To improve upon conventional approaches, we apply deep CNNs for automatic feature extraction and classifier building. In this chapter, we have demonstrated thoroughly the use of deep learning models through transfer learning, deep feature extraction, and machine learning models. Computer-aided detection or diagnosis systems have recently been developed to help health-care professionals increase diagnosis accuracy. This chapter presents early breast cancer detection from mammograms using artificial intelligence (AI). Various models have been presented along with an in-depth comparative analysis of the different state-of-the-art architectures, custom CNN networks, and classifiers trained on features extracted from pretrained networks. Our findings have indicated that deep learning models can achieve training accuracies of up to 99%, while both validation and test accuracies up to 96%. We conclude by suggesting various improvements that could be made to existing architectures and how AI techniques could help further improve and help in the early detection of breast cancer.
An efficient pattern-based approach for insider threat classification using the image-based feature representation

Insider threats are expensive, difficult to detect, and sadly, on the rise. Despite significant research efforts, existing approaches are inadequate in accuracy and precision. They also suffer from a high false-positive rate in detecting insider attacks due to the heterogeneous nature of available insider threat data. Researchers have attempted an image-based approach for detecting insider threats to overcome the challenges from existing techniques. Most existing image-based approaches utilized Convolutional neural networks (CNN) to detect insider threats. Moreover, the CNN-based model lost the important user behavioral features due to the pooling operation. Also, they are often unsuitable for predictive modeling with features that lack spatial correlations. Aiming to address this issue, the Wavelet convolutional neural network (WCNN) is proposed. The WCNN model takes advantage of spectral and spatial analysis to classify insider threats using image-based feature representations. The proposed approach combines the scenario-specific single-day features from the user activity logs into a one-dimensional feature vector. It is then represented as images that reveal visual patterns effectively to identify malicious insiders using WCNN. In addition, the proposed approach adopts the SMOTEENN sampling technique to solve the class imbalance problem. The performance of the proposed approach is evaluated on the benchmark dataset. Experimental results show the improvement of the proposed approach over the current state-of-the-art techniques in terms of classification accuracy (97.19%), AUC (97.30%), and low false positives to identify malicious insiders.
Multihierarchy Gaussian Process Models for Probabilistic Aerodynamic Databases using Uncertain Nominal and Off-Nominal Configuration Data

Probabilistic aerodynamic databases are a crucial component of the development lifecycle for aerospace vehicles. A key challenge when building aerodynamic databases is that most data used to construct them represent various simplifications of the real flight vehicle. For example, wind tunnel models often simplify the vehicle geometry and surface roughness characteristics, while CFD computations often make simplifications to the physics being modeled, such as fully laminar or turbulent calculations. Standard multifidelity data fusion models rely on a user being able to define a hierarchy of fidelity levels anchored to some "truth" data. This approach is unsatisfactory when no data can be considered to accurately reflect real flight conditions. In this work, we provide an alternative approach by presenting a consistent mathematical framework for building probabilistic aerodynamic databases in the form of a conditional probability distribution described by an ensemble of multifidelity Gaussian Processes. Instead of relying on a single hierarchy of data fidelity levels, the presented framework identifies a "nominal" configuration and potential corrections to the nominal which represent specific physical phenomena not represented in the nominal data. The nominal and correction functions themselves are constructed as multifidelity Gaussian Processes and linearly combined to form an ensemble model which fuses the uncertainties associated nominal and correction models. Results obtained using the proposed framework on a simplified Orion Crew Module wind tunnel dataset demonstrate the predictive capability of the multihierarchy framework. We further demonstrate the benefits of such a probabilistic aerodynamic database approach through function sampling and computing the conditional distributions of derived quantities, such as the trim angle of attack and aerodynamic coefficients at trim.
Physics-integrated Segmented Gaussian Process (SegGP) learning for cost-efficient training of diesel engine control system with low cetane numbers

Control model training is an essential step towards the development of an engine controls system. A robust controls strategy is required for engines to perform reliably and optimally under challenging conditions, such as using low cetane number fuels (vital to achieving a single fuel concept). Developing such a control strategy through physical experiments, however, can be very costly due to issues such as unexpected engine failure and manufacturing delays. One approach is to rely solely on CFD simulations for control model training, which can be accurate but places significant burden on computing resources to explore the desired control design space. Another approach is via purely data-driven machine learning models, but the training data needed to achieve desirable accuracy can also be prohibitively expensive to generate. To address this, we develop a novel physics-integrated Segmented Gaussian Process (SegGP) model, which integrates fundamental physics on the pressure curve within a flexible probabilistic learning framework. This integration of physics allows for accurate predictive modeling of pressure, heat release and thus control using limited training data, which greatly reduces computational burden. We demonstrate the effectiveness of this approach for quickening the control development training of diesel engines.
Development of Intelligent Control Strategy for an Unconventional UAV: A Novel Approach

Use of intelligent systems have marked a major breakthrough in the domain of aerial plat- forms. Under the ambit of Machine Learning (ML), Reinforcement Learning (RL) has started to address the inherent limitations of the conventional controllers and emerges as the most ac- tive, conceptually prudent and best suited machine learning category for autonomous control. In this paper paper, we present two innovating RL based control strategies namely ‘Reward Selective Dynamic Programming (RSDP)’ and ’Optimal Deep Deterministic Policy Gradient (O-DDPG)’ to control the dynamics of an unconventional UAV. Both the framework have been developed specifically to accommodate the continuous state and action domains of the aerial platform. Both the RL algorithms turned out to have satisfactory computational performance, and the agent was effectively trained for entire state and action space. The effectiveness of both the proposed strategies were verified through extensive 6-DOF simulations. Results indicate that both outperforms the classical control architecture primarily by eliminating the explicit requirement of gain scheduling for various equilibria during the trajectory. A perspective analysis was also carried out to perform a comparative analysis between both the proposed RL strategies.
Sandwiched Image Compression: Increasing the resolution and dynamic range of standard codecs

Given a standard image codec, we compress images that may have higher resolution and/or higher bit depth than allowed in the codec's specifications, by sandwiching the standard codec between a neural pre-processor (before the standard encoder) and a neural post-processor (after the standard decoder). Using a differentiable proxy for the the standard codec, we design the neural pre-and post-processors to transport the high resolution (super-resolution, SR) or high bit depth (high dynamic range, HDR) images as lower resolution and lower bit depth images. The neural processors accomplish this with spatially coded modulation, which acts as watermarks to preserve the important image detail during compression. Experiments show that compared to conventional methods of transmitting high resolution or high bit depth through lower resolution or lower bit depth codecs, our sandwich architecture gains ~9dB for SR images and $\sim$3dB for HDR images at the same rate over large test sets. We also observe significant gains in visual quality.
Deep Learning and Kinect Skeleton-based Approach for Fall Prediction of Elderly Physically Disabled

Falls are considered one of the most severe health problems, especially among older people with physical disabili-ties. To ensure the security of the elderly, it is necessary to predict fall before it happens. This paper proposes a computer vision method for fall prediction among physically disabled elderly. Within our approach, we propose a novel implementation of Encoder-Decoder ConvLSTM (Convolutional Long Short-Term Memory). This work includes three parts: Data Acquisition, Data Preprocessing, and Data Analysis. Starting by acquiring skeleton streams using the Kinect camera. Then, applying preprocessing techniques: extracting skeleton features and selecting key frames. Finally, the analysis step consists of predicting the next frames and classifying them. In case of a predicted fall, an alert will be launched. To evaluate our approach, we use the FallFree dataset that covers all fall types and scenarios of people using canes and others without any mobility aid. Our method achieves an accuracy of 99.64%.
Welcome Wagons: A Block Chain based Web Application for Car Booking

Blockchain applications are decentralized applications that provides security assurance for any online transactions. Online car reservation system is nowadays popular that makes the travel easier than the traditional methods. Welcome Wagons is a decentralized web application (DApp) for car booking that allows passengers to hail a ride and drivers to charge fares and get paid. This web application is built with an advanced tools such as React js, Next js. Tailwind CSS is used for styling. Firebase, Blockchain web3.0 is used as backend. This new web application provides secure payment based car booking serive.
Multidefect Detection Tool for Large-Scale PV Plants: Segmentation and Classification

Unmanned aerial vehicles (UAVs) with high-resolution optical and infrared ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IR</i> ) imaging have been introduced in recent years to perform inexpensive and fast inspections in operation and maintenance activities of solar power plants, reducing the labor needed, while lowering the on-site inspection time. Even though UAVs can acquire images extremely quickly, the analysis of those images is still a time-consuming procedure that should be performed by a trained professional. Therefore, a computer vision approach may be used to accelerate image analysis. In this work, a dataset of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IR</i> images was created from a 10-MW solar power plant and a comparative analysis between mask R- convolutional neural network (CNN) and U-Net was performed for two experiments. Concerning the defective module segmentation, the mask R-CNN algorithm achieved a mean average precision at intersection over union (IoU) = 0.50 of 0.96, using augmentation data. Regarding the segmentation and classification of failure type, the algorithm reached a value of 0.88 considering the same evaluation metric and data augmentation. When compared to the U-Net in terms of IoU, the mask R-CNN outperformed it with 0.87 and 0.83 for the first and second experiments, respectively.
From Zero to 100

Demystifying zero trust and its implications on enterprise people, process, and technology.
Intention recognition for multiple agents

Discovering common intentions of multiple agents is one of the important ways to detect the tendency of their collaborative behaviours. Existing work mainly focuses on intention recognition in a single-agent setting and uses a descriptive model, e.g. Bayesian networks, in the recognition process. In this article, we develop a new approach of identifying intentions for multiple agents through analysing their behaviours over time. We first define a prescriptive, behavioural model for a single agent that represents the agent’s behaviours where their intentions are hidden in the plan execution. We introduce landmarks into the behavioural model therefore enhancing informative features to identify common intentions for multiple agents. Subsequently, we refine the model by focusing only on action sequences in their plans and provide a light model for identifying and comparing their intentions. The new model provides a simple approach of grouping agents’ common intentions upon partial plans observed in agents’ interactions. After that, we transform the intention recognition into an un-supervised learning problem and adapt a clustering algorithm to group intentions of multiple agents through comparing their behavioural models. We conduct the clustering process through measuring similarity of probability distributions over potential landmarks in the behavioural models so as to discover agents’ common intentions. Finally, we examine the new intention recognition approaches in two problem domains. We demonstrate importance of recognising common intentions of multiple agents in achieving their goals and provide experimental results to show performance of the new approaches.
Weakly-supervised butterfly detection based on saliency map

Given the actual needs for detecting multiple features of butterflies in natural ecosystems, this paper proposes a model of weakly-supervised butterfly detection based on a saliency map (WBD-SM) to enhance the accuracy of butterfly detection in the ecological environment as well as to overcome the difficulty of fine annotation. Our proposed model first extracts the features of different scales using the VGG16 without the fully connected layers as the backbone network. Next, the saliency maps of butterfly images are extracted using the deep supervision network with shortcut connections (DSS) used for the butterfly target location. The class activation maps of butterfly images are derived via the adversarial complementary learning (ACoL) network for butterfly target recognition. Then, the saliency and class activation maps are post-processed with conditional random fields, thereby obtaining the refined saliency maps of butterfly objects. Finally, the locations of the butterflies are acquired based on the saliency maps. Experimental results on the 20 categories of butterfly dataset collected in this paper indicate that the WBD-SM achieves a higher recognition accuracy than that of the VGG16 under different division ratios. At the same time, when the training set and test set are 8:2, our WBD-SM attains a 95.67% localization accuracy, which is 9.37% and 11.87% higher than the results of the DSS and ACoL, respectively. Compared with three state-of-the-art fully-supervised object detection networks, RefineDet, YOLOv3 and single-shot detection (SSD), the detection performance of our WBD-SM is better than RefineDet, and YOLOv3, and is almost the same as SSD.