Reviewerly/exHarmony huggingface dataset:
Deep learning-based approach for Arabic open domain question answering

Open-domain question answering (OpenQA) is one of the most challenging yet widely investigated problems in natural language processing. It aims at building a system that can answer any given question from large-scale unstructured text or structured knowledge-base. To solve this problem, researchers traditionally use information retrieval methods to retrieve the most relevant documents and then use answer extractions techniques to extract the answer or passage from the candidate documents. In recent years, deep learning techniques have shown great success in OpenQA by using dense representation for document retrieval and reading comprehension for answer extraction. However, despite the advancement in the English language OpenQA, other languages such as Arabic have received less attention and are often addressed using traditional methods. In this paper, we use deep learning methods for Arabic OpenQA. The model consists of document retrieval to retrieve passages relevant to a question from large-scale free text resources such as Wikipedia and an answer reader to extract the precise answer to the given question. The model implements dense passage retriever for the passage retrieval task and the AraELECTRA for the reading comprehension task. The result was compared to traditional Arabic OpenQA approaches and deep learning methods in the English OpenQA. The results show that the dense passage retriever outperforms the traditional Term Frequency-Inverse Document Frequency (TF-IDF) information retriever in terms of the top-20 passage retrieval accuracy and improves our end-to-end question answering system in two Arabic question-answering benchmark datasets.
Gradient and Multi Scale Feature Inspired Deep Blind Gaussian Denoiser

In this paper, a novel deep blind Gaussian denoising network is proposed utilizing the concepts of gradient information, multi-scale feature information and feature denoising for removing additive white Gaussian noise (AWGN) from images. The proposed network consists of two modules where in the first module generates an intermediate image whose gradient information is concatenated with the features of second module to generate the final residual image. Subtracting this residual image with the noisy image gives the desired denoised image. The feature denoising block used in the middle of the first module enhances the feature information of the intermediate image. The usage of gradient information of this intermediate denoised image, together with the multi scale feature information block, in the second module further contributes to the quality of the final denoised image. Experimental results show superior denoising performance of the proposed method in comparison to several state of the art classical and learning based blind denoising methods like EPLL, BM3D, WNNM, DnCNN, MemNet, BUIFD, Self2Self and ComplexNet by a decent margin (an improvement of up to 2.4dB in PSNR, 0.07 in SSIM and 0.03 in FOM index with the second best performing model) when experimented over BSD68, Set5, Set14, SunHays80 and Manga109 image databases.
The Regulation and Influence of Physical Exercise on Human Body's Neutrosophic Set, Respiratory System and Nervous System

The goal of this paper studying the influence of physical exercise (PE) on the human body. Physical exercise plays a vital role in the nervous system and respiratory due it has significant importance. The PE present a benefit in all functions in the body human. But if PE is not regular, it presents a high risk in humans, and humans are not safe when practices it. The impact of PE has many criteria and sub-criteria, which is complex and conflict criteria. So, the multi-criteria decision-making method is present for overcoming this problem. This problem contains incomplete and vague information. So, the neutrosophic sets are used for overcoming uncertainty. The Decision-making trial and evaluation laboratory (DEMATEL) method is a powerful tool for the present the importance and influence criteria on others. So DEMATEL integrated with neutrosophic sets for analyzing and influence regular PE in body human-like nervous systems. An illustrative example was conducted to show the outcome of this method
Backdoor-resistant identity-based proxy re-encryption for cloud-assisted wireless body area networks

The wireless body area network (WBAN) provides users with real-time medical services. Meanwhile, the cloud technology provides greater storage space and computing power for medical data. Both of them have contribute to the development of telemedicine. In a cloud-assisted WBAN, the open network environment and the semi-trust cloud service providers expose the user’s private medical data to backdoor adversaries who can make exfiltration attacks, such as the algorithm substitution attack (ASA) through the process of data sharing. Therefore, it is necessary to find a secure and efficient medical data sharing scheme for the huge amount of medical data. In this paper, we first design an identity-based proxy re-encryption scheme with cryptographic reverse firewall (IBPRE-CRF), then show the application in a multiple-access telemedicine data sharing scenario. Security analysis shows that the IBPRE-CRF scheme provides chosen plaintext attack security and resists exfiltration attacks. Performance analysis shows that the IBPRE-CRF scheme has a significant communication and computational cost advantage while being resistant to exfiltration attacks in clouds. Therefore, our IBPRE-CRF scheme is suitable for telemedicine data sharing in a cloud-assisted WBAN.
Partial Multiple Imputation With Variational Autoencoders: Tackling Not at Randomness in Healthcare Data

Missing data can pose severe consequences in critical contexts, such as clinical research based on routinely collected healthcare data. This issue is usually handled with imputation strategies, but these tend to produce poor and biased results under the Missing Not At Random (MNAR) mechanism. A recent trend that has been showing promising results for MNAR is the use of generative models, particularly Variational Autoencoders. However, they have a limitation: the imputed values are the result of a single sample, which can be biased. To tackle it, an extension to the Variational Autoencoder that uses a partial multiple imputation procedure is introduced in this work. The proposed method was compared to 8 state-of-the-art imputation strategies, in an experimental setup with 34 datasets from the medical context, injected with the MNAR mechanism (10% to 80% rates). The results were evaluated through the Mean Absolute Error, with the new method being the overall best in 71% of the datasets, significantly outperforming the remaining ones, particularly for high missing rates. Finally, a case study of a classification task with heart failure data was also conducted, where this method induced improvements in 50% of the classifiers.
Smart and real-time image dehazing on mobile devices

Haze is one of the common factors that degrades the visual quality of the images and videos. This diminishes contrast and reduces visual efficiency. The ALS (Atmospheric light scattering) model which has two unknowns to be estimated from the scene: atmospheric light and transmission map, is commonly used for dehazing. The process of modelling the atmospheric light scattering is complex and estimation of scattering is time consuming. This condition makes dehazing in real-time difficult. In this work, a new approach is employed for dehazing in real time which reads the orientation sensor of mobile device and compares the amount of rotation with a pre-specified threshold. The system decides whether to recalculate the atmospheric light or not. When the amount of rotation is little means there are only subtle changes to the scene, it uses the pre-estimated atmospheric light. Therefore, the system does not need to recalculate it at each time instant and this approach accelerates the overall dehazing process. 0.07 s fps (frame per second) per frame processing time (~ 15 fps) is handled for 360p imagery. Frame processing time results show that our approach is superior to the state-of-the-art real-time dehazing implementations on mobile operating systems.
An Interview with the Physicist that Her Head in the Universe and Both Feet on the Earth

Put simply, Lisa Randall’s job is to figure out how the universe works, and what it’s made of. Her contributions to theoretical particle physics include two models of space-time that bear her name. The first Randall–Sundrum model addressed a problem with the Standard Model of the universe, and the second concerned the possibility of a warped additional dimension of space. In this work, we caught up with Randall to talk about why she chose a career in physics, where she finds inspiration, and what advice she’d offer budding physicists. This article has been edited for clarity. My favourite quote in this interview is, “Figure out what you enjoy, what your talents are, and what you’re most curious to learn about.” If you insterest in her work, you can contact her on Twitter @lirarandall.
Degradation

Throughout history obscenity has not really been about sex but about degradation. Sexual depictions have been suppressed when they were seen as lowering the status of humans, furthering our distance from the gods or God and moving us toward the animals. In the current era, when we recognize ourselves and both humans and animals, sexual depiction has lost some of its sting. Its degrading role has been replaced by hate speech that distances groups, whether based on race, ethnicity, gender, or sexual orientation, not only from God but from humanity to a subhuman level. This book traces the legal trajectory of degradation as it moved from sexual depiction to hateful speech. Looking closely at hate speech in several arenas, including racist, homophobic, and sexist speech in the workplace, classroom, and other real-life scenarios, the book posits that if hate speech is today's conceptual equivalent of obscenity, then the body of law that dictated obscenity might shed some much-needed light on what may or may not qualify as punishable hate speech.
Designing A Software for Processing Pencil-Based Instrument of Learning Assessment

This work presented my step by step in designing software for processing pencil-based instrument of learning assessment. To design a software, I using office excell worksheet, because it also can be used to analysis validity and reliability of instrument with a simple step. Then, I try my design to process all data from my class, in details: 25 multiple choices type from 4 classes; 15 multiple choices type from 4 classes; and 10 multiple choices type from 9 classes. At the same period, I ask validation from my peers-in-teaching class. No matter the lesson is, the results reported here is useful and potential for processing pencil-based assessment in large participants’ test. It work, however, still leave a weakness, that was only implemented for multiple choices type. Further investigation is in progress to shorten this time.
Human Detection in Moving Fisheye Camera using an Improved YOLOv3 Framework

Pedestrian detection has large relevance to the understanding of static and moving scenes of video sequences. The increasing demand for safety and security of people has resulted in more research on intelligent visual surveillance in a wide range of applications, such as moving human detection. With the great success of deep learning methods, researchers decided to switch from traditional methods based hand-crafted feature extractors to recent deep learning-based techniques in order to detect and track people. In this work, the topic of person detection with a Top-view moving fisheye camera is addressed. Although the fisheye camera is a useful tool for video monitoring, most of object detection techniques, with (or without) deep learning, concern classical perspective cameras. However, due to the distortions of fisheye images, we are expected to have higher requirements and challenges on the pedestrian detection using this device. In this paper, we propose an end-to-end learning people detection method based on YOLOv3 detector that detects people using oriented bounding boxes. The proposed model customizes the traditional YOLOv3 for the detection of oriented bounding boxes, by regressing the angle of each bounding box using a periodic loss function. With rotation bounding box prediction, our approach is efficient, reaching 98,1% of true detection. The proposed method is evaluated on a new available dataset where rotated bounding boxes represent annotations from several fisheye videos: https://partage.imt.fr/index.php/s/nytmFqiq8jaztkX
Estimating the Role of Blockchain, Deep Learning and Cryptography algorithms in Cloud Security

Cloud network has become very popular in recent days due to its accessibility merits. The data stored in the cloud environment are accessible by the clients from any location. A reliable shielding approach will protect the data stored in the cloud from the hackers and malwares. Blockchain is one of the recent technologies implemented to the cloud network for storing the location of the saved data in an encrypted ledger format. This saves the stored data location without exploring it to the hacker’s algorithm. Hence the hacking algorithm fails by not knowing the location to be targeted. Deep learning is an advanced technique developed to act like that of the human neurological analysis on several problems. Implementation of deep learning algorithm to the cloud security module identifies the movement of malware and spywares in the cloud storage. Similarly the cryptography is an old technique structured to hide the information with a cover data or cover image. It allows the hacking algorithm to extract only the useless data. This paper reviews the recent advancements in the cloud security with blockchain, deep learning and cryptographic models.
Automatic optimal camera exposure time control for digital image correlation

Abstract We present a method that can automatically determine the optimal camera exposure time for high-quality deformation measurement with digital image correlation (DIC) techniques. The proposed method needs to capture a series of surface images of a test sample at its reference state with different camera exposure times. The relationship between the mean intensity gradients (MIGs) and average grayscales of these images reveals that the best quality (i.e. maximum MIG) of a speckled sample surface always corresponds to a certain average grayscale. Thus, the proposed method can serve two purposes in DIC practice. First, at the initial state, the camera exposure time can be adjusted automatically to obtain a reference image with the best speckle pattern quality. Second, by adjusting the camera exposure time to make the average grayscale of an image close to the predetermined optimal value, the proposed method can adaptively output high-quality deformed images with an almost constant speckle pattern quality, regardless of serious ambient light variations. Experimental results demonstrated that the proposed method can automatically obtain stable and high-quality speckle pattern images, thus delivering better DIC measurement compared with regular DIC techniques using a fixed camera exposure time. Because the present automatic camera exposure time control method allows a nonprofessional operator to consistently obtain high-quality speckle pattern images that warrant high-accuracy DIC measurements, it is therefore suggested that the present method should be used as a routine practice in practical DIC applications.
Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer

Click-Through Rate (CTR) prediction, which aims to estimate the probability that a user will click an item, is an essential component of online advertising. Existing methods mainly attempt to mine user interests from users' historical behaviours, which contain users' directly interacted items. Although these methods have made great progress, they are often limited by the recommender system's direct exposure and inactive interactions, and thus fail to mine all potential user interests. To tackle these problems, we propose Neighbor-Interaction based CTR prediction (NI-CTR), which considers this task under a Heterogeneous Information Network (HIN) setting. In short, Neighbor-Interaction based CTR prediction involves the local neighborhood of the target user-item pair in the HIN to predict their linkage. In order to guide the representation learning of the local neighbourhood, we further consider different kinds of interactions among the local neighborhood nodes from both explicit and implicit perspective, and propose a novel Graph-Masked Transformer (GMT) to effectively incorporates these kinds of interactions to produce highly representative embeddings for the target user-item pair. Moreover, in order to improve model robustness against neighbour sampling, we enforce a consistency regularization loss over the neighbourhood embedding. We conduct extensive experiments on two real-world datasets with millions of instances and the experimental results show that our proposed method outperforms state-of-the-art CTR models significantly. Meanwhile, the comprehensive ablation studies verify the effectiveness of every component of our model. Furthermore, we have deployed this framework on the WeChat Official Account Platform with billions of users. The online A/B tests demonstrate an average CTR improvement of 21.9% against all online baselines.
Federated Learning based Anomaly Detection as an Enabler for Securing Network and Service Management Automation in Beyond 5G Networks

Network automation is a necessity in order to meet the unprecedented demand in the future networks and zero touch network architecture is proposed to cater such requirements. Closed-loop and artificial intelligence are key enablers in this proposed architecture in critical elements such as security. Apart from the arising privacy concerns, machine learning models can also face resource limitations. Federated learning is a machine learning-based technique that addresses both privacy and communication efficiency issues. Therefore, we propose a federated learning-based model incorporating the ZSM architecture for network automation. The paper also contains the simulations and results of the proposed multi-stage federated learning model that uses the UNSW-NB15 dataset.
Research on an aerial object detection algorithm based on improved YOLOv5

Aerial target detection is an important direction in target detection. Due to the limitations of UAV’s volume and computing power, it is necessary to balance the time complexity and accuracy of the algorithm. This paper selects YOLOv5s as the benchmark method of the article. By introducing the feature extraction structure of shufflenetv2, the backbone of YOLOv5s is improved to reduce the amount of calculation of the network, and then the attention mechanism CBMA module is added to the network. Finally, the detection comparison experiment is carried out on visdrone data set. Experimental results show that flops is reduced from 15.7G to 3.7G. After accelerated by tensorRT framework, the improved algorithm can run 53fps on Jetson nano, which can meet the needs of real-time UAV aerial target detection.
Learning intra-domain style-invariant representation for unsupervised domain adaptation of semantic segmentation

In this paper, we aim to tackle the problem of unsupervised domain adaptation (UDA) of semantic segmentation and improve the UDA performance with a novel conception of learning intra-domain style-invariant representation. Previous UDA methods focused on reducing the inter-domain inconsistency between the source domain and the target domain. However, due to the different data distributions of the two domains, reducing the inter-domain inconsistency cannot ensure the generalization ability of the trained model in the target domain. Therefore, to improve the UDA performance, we take into consideration the intra-domain diversity of the target domain for the first time in studies on UDA and aim to train the model to generalize well to the diverse intra-domain styles. To achieve this, we propose a self-ensembling method to learn the intra-domain style-invariant representation and we introduce a semantic-aware multimodal image-to-image translation model to obtain images with diversified intra-domain styles. Our method achieves state-of-the-art performance on two synthetic-to-real adaptation benchmarks, and we demonstrate the effectiveness of our method by conducting extensive experiments.
Corona-warn-app

On June 16, 2020, Germany launched an open-source smartphone contact tracing app ("Corona-Warn-App") to help tracing SARS-CoV-2 (coronavirus) infection chains. It uses a decentralized, privacy-preserving design based on the Exposure Notification APIs in which a centralized server is only used to distribute a list of keys of SARS-CoV-2 infected users that is fetched by the app once per day. Its success, however, depends on its adoption. In this poster, we characterize the early adoption of the app using Netflow traces captured directly at its hosting infrastructure. We show that the app generated traffic from allover Germany---already on the first day. We further observe that local COVID-19 outbreaks do not result in noticeable traffic increases.
TransSurv: Transformer-Based Survival Analysis Model Integrating Histopathological Images and Genomic Data for Colorectal Cancer

Survival analysis is a significant study in cancer prognosis, and the multi-modal data, including histopathological images, genomic data, and clinical information, provides unprecedented opportunities for its development. However, because of the high dimensionality and the heterogeneity of histopathological images and genomic data, acquiring effective predictive characters from these multi-modal data has always been a challenge for survival analysis. In this article, we propose a transformer-based survival analysis model (TransSurv) for colorectal cancer that can effectively integrate intra-modality and inter-modality features of histopathological images, genomic data, and clinical information. Specifically, to integrate the intra-modality relationship of image patches, we develop a multi-scale histopathological features fusion transformer (MS-Trans). Furthermore, we provide a cross-modal fusion transformer based on cross attention for multi-scale pathological representation and multi-omics representation, which includes RNA-seq expression and copy number alteration (CNA). At the output layer of the TransSurv, we adopt the Cox layer to integrate multi-modal fusion representation with clinical information for end-to-end survival analysis. The experimental results on the Cancer Genome Atlas (TCGA) colorectal cancer cohort demonstrate that the proposed TransSurv outperforms the existing methods and improves the prognosis prediction of colorectal cancer.