Reviewerly/exHarmony huggingface dataset:
Multiparty Generation of an RSA Modulus

We present a new multiparty protocol for the distributed generation of biprime RSA moduli, with security against any subset of maliciously colluding parties assuming oblivious transfer and the hardness of factoring. Our protocol is highly modular, and its uppermost layer can be viewed as a template that generalizes the structure of prior works and leads to a simpler security proof. We introduce a combined sampling-and-sieving technique that eliminates both the inherent leakage in the approach of Frederiksen et al. (Crypto’18), and the dependence upon additively homomorphic encryption in the approach of Hazay et al. (JCrypt’19). We combine this technique with an efficient, privacy-free check to detect malicious behavior retroactively when a sampled candidate is not a biprime, and thereby overcome covert rejection-sampling attacks and achieve both asymptotic and concrete efficiency improvements over the previous state of the art.
Secure Storage Auditing With Efficient Key Updates for Cognitive Industrial IoT Environment

Cognitive computing over big data brings more development opportunities for enterprises and organizations in industrial informatics, and can make better decisions for them when they face data security challenges. To satisfy the requirement of real-time data storage in industrial Internet of Things (IoT), the remote unconstrained storage cloud is usually used to store the generated big data. However, the characteristic of semitrust of the cloud service provider determines that the data owners will worry about whether the data stored in cloud computing has been corrupted. In this article, a secure storage auditing is proposed, which supports efficient key updates and can be well used in cognitive industrial IoT environment. Moreover, the proposed basic auditing can be extended to support batch auditing that is suitable for multiple end devices to audit their data blocks simultaneously in practice. In addition, a hybrid data dynamics method is proposed, which employs a hash table to store the data blocks and uses a linked list to locate the operated data block. Compared with previous methods, the data block location time in the proposed data dynamics can be reduced by 40%. The security analysis results demonstrate that the proposed scheme can be proved to be correct, and is secure under computational differ-hellman (CDH) and discrete logarithm (DL) assumptions.
Multi-scenes Image Stitching Based on Autonomous Driving

Most of the previous image stitching methods only perform well on standard datasets. These methods assume that the images are collected under ideal conditions such as sufficient light and low noise. However, these methods can't be applied well to more complex practical scenarios. This paper proposes a multi-scenes image stitching method for autonomous driving, which uses autoencoder networks to extract feature points. The feature point extraction network includes a dimensionality-reduced feature extraction path and a precisely located symmetrical decoding path. The proposed method can provide a stable wide field of view for autonomous vehicles under different weather and different lighting conditions. Multiple experimental data show the effectiveness of the method.
Extraction of an Explanatory Graph to Interpret a CNN

This paper introduces an explanatory graph representation to reveal object parts encoded inside convolutional layers of a CNN. Given a pre-trained CNN, each filter <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> in a conv-layer usually represents a mixture of object parts. We develop a simple yet effective method to learn an explanatory graph, which automatically disentangles object parts from each filter without any part annotations. Specifically, given the feature map of a filter, we mine neural activations from the feature map, which correspond to different object parts. The explanatory graph is constructed to organize each mined part as a graph node. Each edge connects two nodes, whose corresponding object parts usually co-activate and keep a stable spatial relationship. Experiments show that each graph node consistently represented the same object part through different images, which boosted the transferability of CNN features. The explanatory graph transferred features of object parts to the task of part localization, and our method significantly outperformed other approaches.
Photovoltaic system modeling: A validation study at high latitudes with implementation of a novel DNI quality control method

As the share of photovoltaics (PV) in electricity production increases, accurate modeling and forecasting of its output becomes critical. PV output is however affected by multiple factors, making accurate modeling of these systems a non-trivial task. This study provides new insight on the performance of a popular parametric PV output model in a Nordic context. The model and its subcomponents – including estimations on plane-of-array radiation, PV module temperature, and PV output – are here evaluated against dedicated measurements at two Finnish sites. In addition, a novel Quality Control (QC) approach is introduced for handling calculated direct normal irradiance (DNI) values. All reviewed methods show good agreement with the references. The proposed QC efficiently filters unrealistic calculated DNI data, providing a potential approach for DNI QC implementation. In snow-free conditions, the selected PV module temperature scheme has a mean absolute error (MAE) around 2 °C, while the bias is below 1 °C. The plane-of-array radiation model has a MAE of 10–15 W/m2 with a bias of smaller than ±10 W/m2, depending on the site location and available input data. Altogether, the PV model is shown to provide relevant PV system performance information while demonstrating precise performance in snow-free conditions. By implementing site-specific parameter optimization, MAE was reduced from 19 to 14 W/kWp and bias lowered from 15 to 7 W/kWp. For the locations studied, the estimated PV output losses due to snow cover for the winter period 2017–2018 are estimated to be up to 1.5 months of summer production.
Classification of galaxy color images using quaternion polar complex exponential transform and binary Stochastic Fractal Search

Galaxies’ studies play an important role in the astronomic. Accurate classification of these galaxies enables scientists to understand the formation and evolution of the Universe. During the last decades, there have been several methods applied to classify the galaxy images. However, these methods encounter three big challenges. First, most existing methods converted the color images of galaxies into gray images which result in losing the essential color information. Second, the utilized feature selection methods, that used to remove the irrelevant features, may be stuck at the attractive local point. Third, using an irrelevant classifier could lead to decrease the classification accuracy. In this paper, a new algorithm is proposed to classify color images of galaxies. In this algorithm, highly accurate non-redundant color features are extracted from the color images of galaxies by using the quaternion polar complex exponential transform moments (QPCET). The quaternion representation deals with a color image in a holistic way which keeps the correlation between components and then successfully represents the color images. The QPCET moments are highly accurate, noise resistant, and numerically stable. Moreover, these moments are invariants with respect to rotation, scaling and translation (RST). These characteristics assure the excellency of the extracted color features. The Stochastic Fractal Search (SFS) has a very high ability to avoid the stuck at local point. Its binary version is utilized to select the most appropriate features which improve the classification process. The Extreme Machine learning (EML) is used to classify the color images of galaxies using the selected color features. Experiments are performed with the well-known datasets of galaxies (EFIGI catalog), where the proposed algorithm achieved high classification rate. The obtained results clearly show that the proposed method outperformed all existing galaxies classification methods.
Analysis of the SEO visibility of university libraries and how they impact the web visibility of their universities

This article comparatively analyzes the web visibility of 20 libraries corresponding to the top 10 universities in the Times Higher Education World University Rankings and the 10 largest Spanish universities, and explores whether a correlation exists between the web visibility of libraries and of their respective universities. To study web visibility, a search engine optimization (SEO) tool called Sistrix Toolbox was used. It analyzes a large amount of data, the most notable of which is the visibility index, which combines different data indicators to analyze web visibility. The results are checked with Xovi, another SEO tool that offers its own visibility index. Both tools allow us to observe similar trends in the visibility of library websites. The results show that university library visibility is generally low and that there is no direct correlation between the visibility index of libraries and that of their universities. Some revealing exceptions were identified, in which libraries have made significant contributions to the web visibility of their universities. The results would suggest that higher education institutions need to implement SEO strategies in order to increase their visibility more effectively.
Speech Emotion Recognition Based on Robust Discriminative Sparse Regression

Speech emotion recognition has recently attracted much interest due to the widespread of multimedia data. It generally involves two basic problems: 1) feature extraction and 2) emotion classification. Most previous algorithms just focus on solving one of these two problems. In this article, we aim to deal with these two problems in a joint learning framework, and present a novel regression algorithm, namely, robust discriminative sparse regression (RDSR). In RDSR, we propose a sparse regression algorithm to make our model robust to outliers and noises, and introduce a feature selection regularization constraint simultaneously to select the most discriminative and relevant features. In addition, to well predict the labels, we exploit the local and global consistency over labels, and incorporate it into the proposed framework. To solve the objective function of RDSR, we design an efficient alternative optimization algorithm. Finally, experimental results on several public emotion data sets verify the effectiveness and the superiority of our proposed method.
Trust Networking for Beyond 5G and 6G

Trust in a network context is about expected outcomes of decisions to communicate with a remote party, to click on a link or to believe in what an email says. The possible outcomes are either the positive value of the communication or being hacked or cheated in some way. Trust spans all protocol layers from the IP layer to applications and content. ITU - T is working on the framework architecture for trust networking. The White Paper after 1st IEEE 6G Summit advocated embedding trust into the 6G network in its networking chapter. We have worked on a concept we call Customer Edge Switching or cooperative firewalling for several years. In this paper, we analyze the proposed frameworks for the context of 6G and point out directions of future work. We offer several use cases where the framework could be first used and what are the regulatory issues in using the technology for Internet Access under the “open Internet” regulation.
Topological wormholes: Nonlocal defects on the toric code

Locality plays a fundamental role in quantum computation but also severely restricts our ability to store and process quantum information. We argue that this restriction may be unwarranted and re-examine quantum error correcting codes. We proceed to introduce new defects on the surface code called wormholes. These novel defects entangle two spatially separated sectors of the lattice. When anyonic excitations enter the mouth of a wormhole, they emerge through the other mouth. Wormholes thus serve to connect two spatially separated sectors of a flat, $2$D lattice. We show that these defects are capable of encoding logical qubits and can be used to perform all gates in the Clifford group.
Identity Enhanced Residual Image Denoising

We propose to learn a fully-convolutional network model that consists of a Chain of Identity Mapping Modules and residual on the residual architecture for image denoising. Our network structure possesses three distinctive features that are important for the noise removal task. Firstly, each unit employs identity mappings as the skip connections and receives pre-activated input to preserve the gradient magnitude propagated in both the forward and backward directions. Secondly, by utilizing dilated kernels for the convolution layers in the residual branch, each neuron in the last convolution layer of each module can observe the full receptive field of the first layer. Lastly, we employ the residual on the residual architecture to ease the propagation of the high-level information. Contrary to current state-of-the-art real denoising networks, we also present a straightforward and single-stage network for real image denoising.The proposed network produces remarkably higher numerical accuracy and better visual image quality than the classical state-of-the-art and CNN algorithms when being evaluated on the three conventional benchmark and three real-world datasets.
Oblivious coopetitive analytics using hardware enclaves

Coopetitive analytics refers to cooperation among competing parties to run queries over their joint data. Regulatory, business, and liability concerns prevent these organizations from sharing their sensitive data in plaintext.
Breast lesion classification based on ultrasonic radio-frequency signals using convolutional neural networks

We propose a novel approach to breast mass classification based on deep learning models that utilize raw radio-frequency (RF) ultrasound (US) signals. US images, typically displayed by US scanners and used to develop computer-aided diagnosis systems, are reconstructed using raw RF data. However, information related to physical properties of tissues present in RF signals is partially lost due to the irreversible compression necessary to make raw data readable to the human eye. To utilize the information present in raw US data, we develop deep learning models that can automatically process small 2D patches of RF signals and their amplitude samples. We compare our approach with classification method based on the Nakagami parameter, a widely used quantitative US technique utilizing RF data amplitude samples. Our better performing deep learning model, trained using RF signals and their envelope samples, achieved good classification performance, with the area under the receiver attaining operating characteristic curve (AUC) and balanced accuracy of 0.772 and 0.710, respectively. The proposed method significantly outperformed the Nakagami parameter-based classifier, which achieved AUC and accuracy of 0.64 and 0.611, respectively. The developed deep learning models were used to generate parametric maps illustrating the level of mass malignancy. Our study presents the feasibility of using RF data for the development of deep learning breast mass classification models.
3D Pose Estimation Based on Reinforce Learning for 2D Image-Based 3D Model Retrieval

In this paper, we propose a novel characteristic view selection model (CVSM) to address the 2D image-based 3D object retrieval problem. This work includes two key contributions: 1) we propose a novel reinforcement learning model to estimate the 3D pose based on a 2D image; and 2) we render the pose-specific model to generate a representative angle view for retrieval applications. First, we define state, policy, action and reward functions to train an agent with the reinforcement learning framework, by which the agent can effectively reduce the computational cost of the characteristic view selection and directly obtain the 3D model pose. Second, to resolve the problem of computing similarity in the cross-domain between the virtual 3D model view and the real query image, we project them into the skeleton domain, and the skeleton information can effectively bridge the gap between the image and 3D model view for cross-media retrieval. To demonstrate the performance of our approach, we compare with some classic 3D pose estimation methods using the popular Pascal3D dataset. To demonstrate the performance of our approach in model retrieval, we collect a new dataset that includes pairs of 2D images and 3D objects, where 3D objects are based on the ModelNet40 dataset and 2D images are based on the ImageNet dataset, and we experiment with our method using the SHREC 2018 and SHREC 2019 databases. The experimental results demonstrate the superiority of our method.
Correlation-Aware Deep Generative Model for Unsupervised Anomaly Detection

Unsupervised anomaly detection aims to identify anomalous samples from highly complex and unstructured data, which is pervasive in both fundamental research and industrial applications. However, most existing methods neglect the complex correlation among data samples, which is important for capturing normal patterns from which the abnormal ones deviate. In this paper, we propose a method of Correlation aware unsupervised Anomaly detection via Deep Gaussian Mixture Model (CADGMM), which captures the complex correlation among data points for high-quality low-dimensional representation learning. More specifically, the relations among data samples are correlated firstly in forms of a graph structure, in which, the node denotes the sample and the edge denotes the correlation between two samples from the feature space. Then, a dual-encoder that consists of a graph encoder and a feature encoder, is employed to encode both the feature and correlation information of samples into the low-dimensional latent space jointly, followed by a decoder for data reconstruction. Finally, a separate estimation network as a Gaussian Mixture Model is utilized to estimate the density of the learned latent vector, and the anomalies can be detected by measuring the energy of the samples. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed method.
Improved region growing segmentation for breast cancer detection: progression of optimized fuzzy classifier

Purpose Breast cancer is one of the most common malignant tumors in women, which badly have an effect on women's physical and psychological health and even danger to life. Nowadays, mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer. Though, due to the intricate formation of mammogram images, it is reasonably hard for practitioners to spot breast cancer features. Design/methodology/approach Breast cancer is one of the most common malignant tumors in women, which badly have an effect on women's physical and psychological health and even danger to life. Nowadays, mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer. Though, due to the intricate formation of mammogram images, it is reasonably hard for practitioners to spot breast cancer features. Findings The performance analysis was done for both segmentation and classification. From the analysis, the accuracy of the proposed IAP-CSA-based fuzzy was 41.9% improved than the fuzzy classifier, 2.80% improved than PSO, WOA, and CSA, and 2.32% improved than GWO-based fuzzy classifiers. Additionally, the accuracy of the developed IAP-CSA-fuzzy was 9.54% better than NN, 35.8% better than SVM, and 41.9% better than the existing fuzzy classifier. Hence, it is concluded that the implemented breast cancer detection model was efficient in determining the normal, benign and malignant images. Originality/value This paper adopts the latest Improved Awareness Probability-based Crow Search Algorithm (IAP-CSA)-based Region growing and fuzzy classifier for enhancing the breast cancer detection of mammogram images, and this is the first work that utilizes this method.
Cited text span identification for scientific summarisation using pre-trained encoders

Abstract We present our approach for the identification of cited text spans in scientific literature, using pre-trained encoders (BERT) in combination with different neural networks. We further experiment to assess the impact of using these cited text spans as input in BERT-based extractive summarisation methods. Inspired and motivated by the CL-SciSumm shared tasks, we explore different methods to adapt pre-trained models which are tuned for generic domain to scientific literature. For the identification of cited text spans, we assess the impact of different configurations in terms of learning from augmented data and using different features and network architectures (BERT, XLNET, CNN, and BiMPM) for training. We show that identifying and fine-tuning the language models on unlabelled or augmented domain specific data can improve the performance of cited text span identification models. For the scientific summarisation we implement an extractive summarisation model adapted from BERT. With respect to the input sentences taken from the cited paper, we explore two different scenarios: (1) consider all the sentences (full-text) of the referenced article as input and (2) consider only the text spans that have been identified to be cited by other publications. We observe that in certain experiments, by using only the cited text-spans we can achieve better performance, while minimising the input size needed.
A comparative analysis of illumination estimation based Image Enhancement techniques

Computer vision has gathered the attention of several researchers due to the incremental use of it in almost every field. The performance of computer vision systems is many times limited by input image quality. Thus, image enhancement becomes an essential step in these systems. Retinex based algorithms have proven performance in enhancement of low light images. Many Retinex-based algorithms focus on illumination estimation to perform image enhancement. This paper presents a comparative analysis of state-of-the-art image enhancement algorithms based on illumination estimation. In Retinex based approaches, computation of illumination and reflectance is a challenging task. In early approaches, a smooth image is considered as the illumination. However, in the last decade, various methods for estimation of illumination and reflectance have evolved up to a great extent. In this work, we analyze these image enhancement techniques based on illumination estimation. We perform extensive experimentation on a large set of images with varying illumination. The performance is analyzed both quantitatively and qualitatively. This analysis aims to assist researchers and ignite research to develop new efficient algorithms in this field.