Reviewerly/exHarmony huggingface dataset:
Real-time Detection of Distracted Driving using Dual Cameras

Distracted driving is one of the main contributors to traffic accidents. This paper proposes a deep learning approach to detecting multiple distracted driving behaviors. In order to obtain more accurate detection results, a synchronized image recognition system based on two cameras is designed, by which the body movements and face of the driver are monitored respectively. The images captured from driver's body and face areas are fed to two Convolutional Neural Networks (CNNs) simultaneously to ensure the performance of classification. The data collection and validation processes of the proposed distraction detection approach were conducted on a laboratory-based assisted driving testbed to provide near-realistic driving experiences. Our dataset includes distracted and safe driving images of the drivers. Furthermore, we developed a meaningful and practical application of a voice-alert system that alerts the distracted driver to focus on the driving task. We evaluated VGG-16, ResNet, and MobileNet-v2 networks for the proposed approach. Experimental results show that by using two cameras and VGG-16 networks, we can achieve a recognition accuracy of 96.7% with a computation speed of 8 fps.
Low-Light Image Restoration With Short- and Long-Exposure Raw Pairs

Low-light imaging with handheld mobile devices is a challenging issue. Limited by the existing models and training data, most existing methods cannot be effectively applied in real scenarios. In this paper, we propose a new low-light image restoration method by using the complementary information of short- and long-exposure images. We first propose a novel data generation method to synthesize realistic short- and long-exposure raw images by simulating the imaging pipeline in low-light environment. Then, we design a new long-short-exposure fusion network (LSFNet) to deal with the problems of low-light image fusion, including high noise, motion blur, color distortion and misalignment. The proposed LSFNet takes pairs of short- and long-exposure raw images as input, and outputs a clear RGB image. Using our data generation method and the proposed LSFNet, we can recover the details and color of the original scene, and improve the low-light image quality effectively. Experiments demonstrate that our method can outperform the state-of-the-art methods.
CALYPSO

Distributed ledgers provide high availability and integrity , making them a key enabler for practical and secure computation of distributed workloads among mutually distrustful parties. Many practical applications also require strong confidentiality , however. This work enhances permissioned and permissionless blockchains with the ability to manage confidential data without forfeiting availability or decentralization. The proposed Calypso architecture addresses two orthogonal challenges confronting modern distributed ledgers: (a) enabling the auditable management of secrets and (b) protecting distributed computations against arbitrage attacks when their results depend on the ordering and secrecy of inputs. Calypso introduces on-chain secrets, a novel abstraction that enforces atomic deposition of an auditable trace whenever users access confidential data. Calypso provides user-controlled consent management that ensures revocation atomicity and accountable anonymity. To enable permissionless deployment, we introduce an incentive scheme and provide users with the option to select their preferred trustees. We evaluated our Calypso prototype with a confidential document-sharing application and a decentralized lottery. Our benchmarks show that transaction-processing latency increases linearly in terms of security (number of trustees) and is in the range of 0.2 to 8 seconds for 16 to 128 trustees.
Overview on Quantum Computing and its Applications in Artificial Intelligence

This paper will review the basic building blocks of quantum computing and discuss the main applications in artificial intelligence that can be addressed more efficiently using the quantum computers of today. Artificial intelligence and quantum computing have many features in common. Quantum computing can provide artificial intelligence and machine learning algorithms with speed of training and computational power in less price. Artificial intelligence on the other hand can provide quantum computers with the necessary error correction algorithms. Some of the algorithms in AI that have been successfully implemented on a quantum computer, that we will present in this paper, are both unsupervised learning algorithms (clustering and Principal component analysis) and also supervised learning classification, such as support vector machines.
LC-GAN: Image-to-image Translation Based on Generative Adversarial Network for Endoscopic Images

Intelligent vision is appealing in computer-assisted and robotic surgeries. Vision-based analysis with deep learning usually requires large labeled datasets, but manual data labeling is expensive and time-consuming in medical problems. We investigate a novel cross-domain strategy to reduce the need for manual data labeling by proposing an image-to-image translation model live-cadaver GAN (LC-GAN) based on generative adversarial networks (GANs). We consider a situation when a labeled cadaveric surgery dataset is available while the task is instrument segmentation on an unlabeled live surgery dataset. We train LC-GAN to learn the mappings between the cadaveric and live images. For live image segmentation, we first translate the live images to fake-cadaveric images with LC-GAN and then perform segmentation on the fake-cadaveric images with models trained on the real cadaveric dataset. The proposed method fully makes use of the labeled cadaveric dataset for live image segmentation without the need to label the live dataset. LC-GAN has two generators with different architectures that leverage the deep feature representation learned from the cadaveric image based segmentation task. Moreover, we propose the structural similarity loss and segmentation consistency loss to improve the semantic consistency during translation. Our model achieves better image-to-image translation and leads to improved segmentation performance in the proposed cross-domain segmentation task.
Separable Structure Modeling for Semi-Supervised Video Object Segmentation

In this paper, we propose a separable structure modeling approach for semi-supervised video object segmentation. Unlike most existing methods which preclude the semantically structural information of target objects, our method not only captures pixel-level similarity relationships between the reference and target frames but also reveals the separable structure of the specified objects in target frames. Specifically, we first compute a pixel-wise similarity matrix by using representations of reference and target pixels and then select top rank reference pixels for target pixel classification. According to the prior knowledge from these top-rank reference pixels, we further appoint the representative target pixels for object structure modeling. Particularly, in the structure modeling branch, we extract the shared and individual features that can well represent the whole object and its components, respectively. Moreover, the proposed method is a fast algorithm without online fine-tuning and any post-processing. We conduct extensive experiments and ablation studies on the DAVIS-16, DAVIS-17, and YouTube-VOS datasets, and experimental results on three widely-used datasets demonstrate that our method achieves a superior performance, compared with state-of-the-art semi-supervised video object segmentation approaches in terms of speed and accuracy.
Efficient Volumetric Video Streaming Through Super Resolution

Volumetric videos allow viewers to exercise 6-DoF (degrees of freedom) movement when consuming fully 3D content (e.g., point clouds). Due to their truly immersive nature, streaming volumetric videos is highly bandwidth-demanding. In this work, we present to our knowledge a first volumetric video streaming system that leverages 3D super resolution (SR) of point clouds to boost the video quality on commodity devices, and to facilitate the distribution of volumetric content over bandwidth-constrained wireless networks. However, directly applying off-the-shelf 3D SR models leads to unacceptably low performance (~ 0.1 FPS even on a powerful GPU). To overcome this limitation, we propose a series of optimizations to make SR efficient. Our preliminary results indicate that for an edge-assisted (standalone mobile) setup, a small subset of our proposed optimizations can already drastically improve the FPS by a factor of 131× (53×) and reduce GPU memory usage by 83% (76%), while maintaining the same or even better SR inference accuracy, compared to using an off-the-shelf SR model.
Blockchain-Enabled Identity Verification for Safe Ridesharing Leveraging Zero-Knowledge Proof

The on-demand mobility market, including ridesharing, is becoming increasingly important with e-hailing fares growing at a rate of approximately 130% per annum since 2013. By increasing utilization of existing vehicles and empty seats, ridesharing can provide many benefits including reduced traffic congestion and environmental impact from vehicle usage and production. However, the safety of riders and drivers has become of paramount concern and a method for privacy-preserving identity verification between untrusted parties is essential for protecting users. To this end, we propose a novel privacy-preserving identity verification system, extending zero-knowledge proof (ZKP) and blockchain for use in ridesharing applications. We design a permissioned blockchain network to perform the ZKP verification of a driver's identity, which also acts as an immutable ledger to store ride logs and ZKP records. For the ZKP module, we design a protocol to facilitate user verification without requiring the exchange of any private information. We prototype the proposed system on the Hyperledger Fabric platform, with the Hyperledger Ursa cryptography library, and conduct extensive experimentation. To measure the prototype's performance, we utilize the Hyperledger Caliper benchmark tool to perform extensive analysis and the results show that our system is suitable for use in real-world ridesharing applications.
Intelligent handwritten recognition using hybrid CNN architectures based-SVM classifier with dropout

Text recognition in Arabic handwritten scripts is an active research field. These recognition systems face numerous challenges, including enormous open data-bases, infinite variation in people’s handwriting, and freestyle. In this manuscript, Authors model deep learning architecture which can efficiently be utilized to recognizing Arabic handwritten scripts. This work explored a new model for both single font and multi-font type which concentrate on two common classifiers which are: Support Vector Machine (SVM) along with Convolutional Neural Network (CNN). Furthermore, authors protected the proposed model against the issue of over-fitting because of the strong performance of dropout technique. Both classification and feature extraction are done automatically. In the light of the error backpropagation method analysis, authors also have been proposed an innovative depth neural network training rule for maximum interval minimum classification error. In the meantime, max-margin minimum classification error (M3CE) and cross entropy are analyzed and hybridized to obtain better outcomes. Authors tested the proposed model on AHDB, AHCD, HACDB, and IFN/ENIT databases. The proposed model performance is compared with the accuracies of text recognition gained from state-of-the-art Arabic text recognition. The proposed model delivers favorable results.
Extracting the Tailings Ponds from High Spatial Resolution Remote Sensing Images by Integrating a Deep Learning-Based Model

Due to a lack of data and practical models, few studies have extracted tailings pond margins in large areas. In addition, there is no public dataset of tailings ponds available for relevant research. This study proposed a new deep learning-based framework for extracting tailings pond margins from high spatial resolution (HSR) remote sensing images by combining You Only Look Once (YOLO) v4 and the random forest algorithm. At the same time, we created an open source tailings pond dataset based on HSR remote sensing images. Taking Tongling city as the study area, the proposed model can detect tailings pond locations with high accuracy and efficiency from a large HSR remote sensing image (precision = 99.6%, recall = 89.9%, mean average precision = 89.7%). An optimal random forest model and morphological processing were utilized to further extract accurate tailings pond margins from the target areas. The final map of the entire study area was obtained with high accuracy. Compared with the random forest algorithm, the total extraction time was reduced by nearly 99%. This study can be beneficial to mine monitoring and ecological environmental governance.
Unsupervised News Topic Modelling with Doc2Vec and Spherical Clustering

In the digital and Internet era, companies are racing to profile their target users based on their online activities. One of the reliable sources is the news articles they read that can represent their interests. However, extracting latent information from the news articles is not an easy task for a human. In this paper, we introduced a practical model to automatically extract latent information from news articles with pre-determined topics. Our proposed model used unsupervised learning, thus alleviating the need for humans to label news items manually. Doc2vec was used to generate word vectors for each article. Afterward, a spectral clustering algorithm was applied to group the data based on the similarity. A supervised Long Short Term Memory (LSTM) model was built to compare the clustering performance. The best 1, best 3, and best 5 scores were used to evaluate our model. The result showed that our model could not outperformed LSTM model for the best 1 score. However, the best 5 score result indicated that our model was sufficiently robust to cluster the articles based on topic similarity. Additionally, the proposed unsupervised model was implemented in both an on-premise server, and a cloud server. Surprisingly, our proposed method could run faster in the cloud server despite its less number of CPU cores.
Single Camera Masked Face Identification

In light of the novel Covid-19 pandemic, wearing masks has been declared mandatory in several institutions and public places for its widespread prevention and public health safety. Under given circumstances, person identification for security purposes including smart-phones face unlock has been a challenging task since the previous practices including both the human authentication by a person as well as by face recognition systems have heavily relied on complete facial features. However, the emergence of large datasets of masked images led to the rapid development of occluded face detection techniques. This paper focuses on single camera masked face detection and identification via the following two approaches: (i) single-step pre-trained YOLO-face/trained YOLOv3 model on the set of known individuals; and (ii) two-step process having pre-trained one stage feature pyramid detector network RetinaFace for localizing masked faces and VGGFace2 that generates facial feature vectors for efficient mask face verification. The dataset employed consists of real-world video examples comprising of 7 individuals with various orientations, illuminations, and occlusions. Experimental results show that RetinaFace and VGGFace2 achieve state-of-the-art results of 92.7% on overall performance, 98.1% face detection, and 94.5% face verification accuracy respectively in 1:1 face mask verification on our custom dataset.
Internet of Things (IoT) based Assistive Devices

In the 21st century, all electronic devices have the capability to communicate and exchange data and resources through the technology known as the Internet of Things (IoT). With the number of IoT-enabled devices growing at an exponential rate, it has limitless prospects to augment the standard of living for the differently-abled as it has great potential to significantly enhance the quality of all kinds of assistive devices. In this article, a few of the many applications of IoT in assistive devices were analyzed and discussed to examine the existing prototypes that have been developed for each of them. The applications discussed here are in the fields of navigation, communication, healthcare, and education for disabled people, and the future scope of the same is also examined.
Robust Secure Color Image Watermarking Using 4D Hyperchaotic System, DWT, HbD, and SVD Based on Improved FOA Algorithm

In the recent past, a different set of algorithms for watermarking and securing the color images have been developed by using transformation, decomposition, and optimization techniques for watermark embedding and extraction. In this paper, we propose an optimized and robust watermarking algorithm coupled with a 4D hyperchaotic system, and its performance is analyzed by extending and differentiating the existing work. Our contribution in the presented work is watermarking and securing the color images by an optimized algorithm that uses transformation technique such as Discrete Wavelet Transformation (DWT) and decomposition techniques such as Hessenberg decomposition (HbD) and singular value decomposition (SVD) coupled with the 4D hyperchaotic system, while the optimization is carried out by improved evolution fruit fly optimization algorithm (IEFOA). The experimental results based on different types of attacks (filter attacks, noise attacks, cropping attack, JPEG compression, motion blur, sharpening, and rotation), key sensitivity, normalized correlation, peak signal-to-noise ratio, and structural similarity index measure are done for measuring the algorithm’s performance regarding invisibility and robustness. The experimental results show that the proposed scheme has excellent invisibility and keeps a good trade-off between invisibility and robustness. The experiment results show that the proposed approach outperforms the previous approaches.
Secure deduplication with efficient user revocation in cloud storage

Secure deduplication is a promising solution to greatly reduce the storage space of the cloud. However, the encryption key is deterministically derived from the plaintext, such that who owns the plaintext has the derived key to decrypt the ciphertext. Therefore, how to revoke a user in deduplication schemes is a critical challenge. In the existing work, when updating the data authority, the data owner has to download the data from the cloud, decrypt, re-encrypt and finally upload them to the cloud. This process increases the communication and computation overheads. In this paper, we first propose a multi-user updatable encryption scheme. Specifically, the data owner can update the remote ciphertext under a new group key by sending an update token to the cloud. Then we adopt this technique to propose a new secure deduplication scheme supporting efficiently revoking an unauthorized user. In our scheme, the data owner just needs to send a token to the cloud to update the data authority, which saves the communication and computation costs. The security and efficiency analysis demonstrate that our proposed deduplication scheme can achieve the desired security properties with high efficiency.
Blockchain technology in the Middle East and North Africa region

Blockchain Technology has disrupted our social, business, and technical domains and will continue to do so in the next coming years. As technology is still in its early stages, with several countries investigating ways of utilizing it and incorporating their systems and frameworks' capabilities. The Blockchain community is now recognizing some significant limitations, such as the absence of regulatory frameworks, collaborative leadership, a relatively small number of use cases, the challenge of cross country collaboration, and the limited availability of a skilled, educated workforce. The need for awareness and increased adoption in both developed and developing countries is of paramount importance, and this paper aims to contribute to this area. The paper seeks to shade light regarding the use of blockchain technology in the United Arab Emirates (UAE) and the broader Middle East and North Africa (MENA) region.
Wearable devices as a valid support for diagnostic excellence: lessons from a pandemic going forward

Today, the use of wearable devices is continuously increasing with many different application fields. Their low-cost and wide availability make these devices proper instruments for long-term monitoring, potentially useful to detect physiological changes related to influenza or other viruses. The relevance of this aspect and the impact of such technology have become evident particularly in the last year, during COVID-19 emergency; (big) data from wearable devices (already worn by many citizens) together with artificial intelligence techniques gave birth to specific studies dedicated to quickly identify patterns discriminating between healthy and infected people. These evaluations are made on the basis of parameters measured by these devices, among which heart rate, physical activity, and sleep seem to play a dominant role. This could be extremely significant in terms of early detection and limit of contagion risk. However, there is still a lot of research to be conducted in terms of measurement accuracy, data management (privacy and security issues), and results exploitation, in order to reach an accurate and reliable solution helping the whole healthcare system particularly in epidemic events, such as the SARS-CoV-2 pandemic.
Image encryption using a novel quintic jerk circuit with adjustable symmetry

Summary An electronic implementation of a novel chaotic oscillator with quintic nonlinearity is proposed herein. Dynamical behaviors of the system are investigated using well‐known numerical simulations and analyses such as phase portraits, Lyapunov exponents, bifurcation diagrams, and basins of attraction. The chaotic circuit presents an inversion‐symmetry, and we show that it can exhibit some nonlinear phenomena specific to symmetric systems, such as symmetric bifurcations, symmetric attractors, coexisting symmetric bubbles, and coexisting symmetric attractors. Since symmetry is never perfect, some symmetry imperfections must be always assumed to be present. Thus, an external Direct Current (DC) voltage is introduced in order to highlight the influence of asymmetry on the dynamics of the chaotic oscillator. It is found that more complex nonlinear behaviors occur in the presence of symmetry breaking like asymmetric coexisting bifurcations, asymmetric attractors, coexisting asymmetric bubbles, and coexisting asymmetric attractors, to name a few. The control of multistability is also performed by using the so‐called linear augmentation scheme. Probe Simulation Program with Integrated Circuits Emphasis (Pspice) circuit simulations are carried out to verify the theoretical analyses. Furthermore, a chaos‐based image encryption is investigated using pseudorandom numbers generated by the proposed chaotic circuit and deoxyribonucleic acid (DNA) encoding technique.