Reviewerly/exHarmony huggingface dataset:
A DIC-assisted fringe projection profilometry for high-speed 3D shape, displacement and deformation measurement of textured surfaces

High-speed three-dimensional (3D) shape measurement techniques based on fringe projection profilometry (FPP) have undergone huge advances over the past two decades. However, accurate 3D displacement mapping and deformation analysis of dynamic scenes using FPP remains an unsolved problem. Because fringe patterns are projected rather than attached on the tested surfaces, the full-field point-to-point correspondence cannot be accurately established between any two 3D shape results. To deal with this challenge, a DIC-assisted FPP for high-speed 3D shape, displacement and deformation measurement of textured surfaces is proposed. Firstly, a high-speed 3D shape measurement system is adopted using our recently proposed robust and efficient Gray-coded coding strategy, which can accurately reconstruct full-field shape of discontinuous surfaces with rich texture information. Then, the modulation-based method is proposed to retrieve high-quality texture map from three phase-shifting fringe patterns, which can eliminate the adverse influence of the nonuniform and time-varying ambient light. By matching the retrieved surface texture images at difference states using DIC, accurate point tracking between the measured 3D shape data can be fulfilled, leading to precise 3D displacement and deformation measurement. Experiments have verified that the proposed method can achieve 3D shape, displacement and deformation measurement of dynamic scenes at a frame rate of 542 fps without any extra expense of hardware or calibration for the FPP system. The presented method is reliable and promising for further 3D displacement mapping and deformation analysis of dynamic scenes using FPP.
Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images

Breast cancer (BC) is one of the most common types of cancer that affects females worldwide. It may lead to irreversible complications and even death due to late diagnosis and treatment. The pathological analysis is considered the gold standard for BC detection, but it is a challenging task. Automatic diagnosis of BC could reduce death rates, by creating a computer aided diagnosis (CADx) system capable of accurately identifying BC at an early stage and decreasing the time consumed by pathologists during examinations. This paper proposes a novel CADx system named Histo-CADx for the automatic diagnosis of BC. Most related studies were based on individual deep learning methods. Also, studies did not examine the influence of fusing features from multiple CNNs and handcrafted features. In addition, related studies did not investigate the best combination of fused features that influence the performance of the CADx. Therefore, Histo-CADx is based on two stages of fusion. The first fusion stage involves the investigation of the impact of fusing several deep learning (DL) techniques with handcrafted feature extraction methods using the auto-encoder DL method. This stage also examines and searches for a suitable set of fused features that could improve the performance of Histo-CADx. The second fusion stage constructs a multiple classifier system (MCS) for fusing outputs from three classifiers, to further improve the accuracy of the proposed Histo-CADx. The performance of Histo-CADx is evaluated using two public datasets; specifically, the BreakHis and the ICIAR 2018 datasets. The results from the analysis of both datasets verified that the two fusion stages of Histo-CADx successfully improved the accuracy of the CADx compared to CADx constructed with individual features. Furthermore, using the auto-encoder for the fusion process has reduced the computation cost of the system. Moreover, the results after the two fusion stages confirmed that Histo-CADx is reliable and has the capacity of classifying BC more accurately compared to other latest studies. Consequently, it can be used by pathologists to help them in the accurate diagnosis of BC. In addition, it can decrease the time and effort needed by medical experts during the examination.
A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers

Readers of academic research papers often read with the goal of answering specific questions.Question Answering systems that can answer those questions can make consumption of the content much more efficient.However, building such tools requires data that reflect the difficulty of the task arising from complex reasoning about claims made in multiple parts of a paper.In contrast, existing informationseeking question answering datasets usually contain questions about generic factoid-type information.We therefore present QASPER, a dataset of 5,049 questions over 1,585 Natural Language Processing papers.Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text.The questions are then answered by a separate set of NLP practitioners who also provide supporting evidence to answers.We find that existing models that do well on other QA tasks do not perform well on answering these questions, underperforming humans by at least 27 F 1 points when answering them from entire papers, motivating further research in document-grounded, information-seeking QA, which our dataset is designed to facilitate.
Development of the method for encoding service data in cryptocompression image representation systems

The demand for image confidentiality is constantly growing. At the same time, ensuring the confidentiality of video information must be organized subject to ensuring its reliability with a given time delay in processing and transmission. Methods of cryptocompression representation of images can be used to solve this problem. They are designed to simultaneously provide compression and protection of video information. The service component is used as the key of the cryptocompression transformation. However, it has a significant volume. It is 25 % of the original video data volume. A method for coding systems of service components in a differentiated basis on the second cascade of cryptocompression representation of images has been developed. The method is based on the developed scheme of data linearization from three-dimensional coordinates of representation in a two-dimensional matrix into a one-dimensional coordinate for one-to-one representation of this element in a vector. Linearization is organized horizontally line by line. On the basis of the developed method, a non-deterministic number of code values of information components is formed. They have non-deterministic lengths and are formed on a non-deterministic number of elements. The uncertainty of positioning of cryptocompression codograms in the general code stream is provided, which virtually eliminates the possibility of their unauthorized decryption. The method provides a reduction in the volume of the service component of the cryptocompression codogram. The service data volume is 6.25 % of the original video data volume. The method provides an additional reduction in the volume of cryptocompression representation of images without loss of information quality relative to the original video data on average from 1.08 to 1.54 times, depending on the degree of their saturation
UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks

Hypergraph, an expressive structure with flexibility to model the higher-order correlations among entities, has recently attracted increasing attention from various research domains. Despite the success of Graph Neural Networks (GNNs) for graph representation learning, how to adapt the powerful GNN-variants directly into hypergraphs remains a challenging problem. In this paper, we propose UniGNN, a unified framework for interpreting the message passing process in graph and hypergraph neural networks, which can generalize general GNN models into hypergraphs. In this framework, meticulously-designed architectures aiming to deepen GNNs can also be incorporated into hypergraphs with the least effort. Extensive experiments have been conducted to demonstrate the effectiveness of UniGNN on multiple real-world datasets, which outperform the state-of-the-art approaches with a large margin. Especially for the DBLP dataset, we increase the accuracy from 77.4% to 88.8% in the semi-supervised hypernode classification task. We further prove that the proposed message-passing based UniGNN models are at most as powerful as the 1-dimensional Generalized Weisfeiler-Leman (1-GWL) algorithm in terms of distinguishing non-isomorphic hypergraphs. Our code is available at https://github.com/OneForward/UniGNN.
Whole‐Device Entanglement in a 65‐Qubit Superconducting Quantum Computer

Abstract The ability to generate large‐scale entanglement is an important progenitor of quantum information processing capability in noisy intermediate‐scale quantum (NISQ) devices. In this paper, the extent to which entangled quantum states over large numbers of qubits can be prepared on current superconducting quantum devices is investigated. Native‐graph states on the IBM Quantum 65‐qubit ibmq_manhattan device and the 53‐qubit ibmq_rochester device are prepared and quantum readout‐error mitigation (QREM) is applied. Connected entanglement graphs spanning each of the full devices are detected, indicating bipartite entanglement over the whole of each device. The application of QREM is shown to increase the observed entanglement within all measurements, in particular, the detected number of entangled pairs of qubits found within ibmq_rochester increases from 31 to 56 of the total 58 connected pairs. The results of this work indicate full bipartite entanglement in two of the largest superconducting devices to date.
An End‐To‐End Earthquake Detection Method for Joint Phase Picking and Association Using Deep Learning

Abstract Earthquake monitoring by seismic networks typically involves a workflow consisting of phase detection/picking, association, and location tasks. In recent years, the accuracy of these individual stages has been improved through the use of machine learning techniques. In this study, we introduce a new end‐to‐end approach that improves overall earthquake detection accuracy by jointly optimizing each stage of the detection pipeline. We propose a neural network architecture for the task of multi‐station processing of seismic waveforms recorded over a seismic network. This end‐to‐end architecture consists of three sub‐networks: a backbone network that extracts features from raw waveforms, a phase picking sub‐network that picks P‐ and S‐wave arrivals based on these features, and an event detection sub‐network that aggregates the features from multiple stations to associate and detect earthquakes across a seismic network. We use these sub‐networks together with a shift‐and‐stack module based on back‐projection that introduces kinematic constraints on arrival times, allowing the neural network model to generalize to different velocity models and to variable station geometry in seismic networks. We evaluate our proposed method on the STanford EArthquake Dataset (STEAD) and on the 2019 Ridgecrest, CA earthquake sequence. The results demonstrate that our end‐to‐end approach can effectively pick P‐ and S‐wave arrivals and achieve earthquake detection accuracy rivaling that of other state‐of‐the‐art approaches. Because our approach preserves information across tasks in the detection pipeline, it has the potential to outperform approaches that do not.
Blockchain Application in Internet of Vehicles: Challenges, Contributions and Current Limitations

Blockchain technology is highly coupled with cryptocurrencies; however, it provides several other potential use cases, related to energy and sustainability, Internet of Things (IoT), smart cities, smart mobility and more. Blockchain can offer security for Electric Vehicle (EV) transactions in the Internet of Vehicles (IoV) concept, allowing electricity trading to be performed in a decentralized, transparent and secure way. Additionally, blockchain provides the necessary functionalities for IoV decentralized application development, such as data exchange, personal digital identity, sharing economy and optimized charging pattern. Moreover, blockchain technology has the potential to significantly increase energy efficiency, decrease management costs and guarantee the effective use of the energy recourses. Therefore, its application in the IoV concept provides secure, autonomous and automated energy trading between EVs. While several studies on blockchain technology in smart grids have been conducted, insufficient attention has been given to conducting a detailed review and state-of-the-art analysis of blockchain application in the IoV domain. To this end, this work provides a systematic literature review of blockchain-based applications in the IoV domain. The aim is to investigate the current challenges of IoV and to highlight how blockchain characteristics can contribute to this emerging paradigm. In addition, limitations and future research directions related to the integration of blockchain technology within the IoV are discussed. To this end, this study incorporates the theoretical foundations of several research articles published in scientific publications over the previous five years, as a method of simplifying our assessment and capturing the ever-expanding blockchain area. We present a comprehensive taxonomy of blockchain-enabled applications in the IoV domain, such as privacy and security, data protection and management, vehicle management, charging optimization and P2P energy trading, based on a structured, systematic review and content analysis of the discovered literature, and we identify key trends and emerging areas for research. The contribution of this article is two-fold: (a) we highlight the limitations presented in the relevant literature, particularly the barriers of blockchain technology and how they influence its integration into the IoV and (b) we present a number of research gaps and suggest future exploratory areas.
Language Models are Few-shot Multilingual Learners

General-purpose language models have demonstrated impressive capabilities, performing on par with state-of-the-art approaches on a range of downstream natural language processing (NLP) tasks and benchmarks when inferring instructions from very few examples.Here, we evaluate the multilingual skills of the GPT and T5 models in conducting multi-class classification on non-English languages without any parameter updates.We show that, given a few English examples as context, pre-trained language models can predict not only English test samples but also non-English ones.Finally, we find the in-context few-shot cross-lingual prediction results of language models are significantly better than random prediction, and they are competitive compared to the existing state-of-the-art cross-lingual models and translation models.
Cross-SRN: Structure-Preserving Super-Resolution Network With Cross Convolution

It is challenging to restore low-resolution (LR) images to super-resolution (SR) images with correct and clear details. Existing deep learning works almost neglect the inherent structural information of images, which acts as an important role for visual perception of SR results. In this paper, we design a hierarchical feature exploitation network to probe and preserve structural information in a multi-scale feature fusion manner. First, we propose a cross convolution upon traditional edge detectors to localize and represent edge features. Then, cross convolution blocks (CCBs) are designed with feature normalization and channel attention to consider the inherent correlations of features. Finally, we leverage multi-scale feature fusion group (MFFG) to embed the cross convolution blocks and develop the relations of structural features in different scales hierarchically, invoking a lightweight structure-preserving network named as Cross-SRN. Experimental results demonstrate the Cross-SRN achieves competitive or superior restoration performances against the state-of-the-art methods with accurate and clear structural details. Moreover, we set a criterion to select images with rich structural textures. The proposed Cross-SRN outperforms the state-of-the-art methods on the selected benchmark, which demonstrates that our network has a significant advantage in preserving edges.
Machine Learning Technologies for Big Data Analytics

Big data analytics is one high focus of data science and there is no doubt that big data is now quickly growing in all science and engineering fields [...]
LOEN: Lensless opto-electronic neural network empowered machine vision

Machine vision faces bottlenecks in computing power consumption and large amounts of data. Although opto-electronic hybrid neural networks can provide assistance, they usually have complex structures and are highly dependent on a coherent light source; therefore, they are not suitable for natural lighting environment applications. In this paper, we propose a novel lensless opto-electronic neural network architecture for machine vision applications. The architecture optimizes a passive optical mask by means of a task-oriented neural network design, performs the optical convolution calculation operation using the lensless architecture, and reduces the device size and amount of calculation required. We demonstrate the performance of handwritten digit classification tasks with a multiple-kernel mask in which accuracies of as much as 97.21% were achieved. Furthermore, we optimize a large-kernel mask to perform optical encryption for privacy-protecting face recognition, thereby obtaining the same recognition accuracy performance as no-encryption methods. Compared with the random MLS pattern, the recognition accuracy is improved by more than 6%.
Intelligent Satin Bowerbird Optimizer Based Compression Technique for Remote Sensing Images

Due to latest advancements in the field of remote sensing, it becomes easier to acquire high quality images by the use of various satellites along with the sensing components.But the massive quantity of data poses a challenging issue to store and effectively transmit the remote sensing images.Therefore, image compression techniques can be utilized to process remote sensing images.In this aspect, vector quantization (VQ) can be employed for image compression and the widely applied VQ approach is Linde-Buzo-Gray (LBG) which creates a local optimum codebook for image construction.The process of constructing the codebook can be treated as the optimization issue and the metaheuristic algorithms can be utilized for resolving it.With this motivation, this article presents an intelligent satin bowerbird optimizer based compression technique (ISBO-CT) for remote sensing images.The goal of the ISBO-CT technique is to proficiently compress the remote sensing images by the effective design of codebook.Besides, the ISBO-CT technique makes use of satin bowerbird optimizer (SBO) with LBG approach is employed.The design of SBO algorithm for remote sensing image compression depicts the novelty of the work.To showcase the enhanced efficiency of ISBO-CT approach, an extensive range of simulations were applied and the outcomes reported the optimum performance of ISBO-CT technique related to the recent state of art image compression approaches.
VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients

In modern anesthesia, multiple medical devices are used simultaneously to comprehensively monitor real-time vital signs to optimize patient care and improve surgical outcomes. However, interpreting the dynamic changes of time-series biosignals and their correlations is a difficult task even for experienced anesthesiologists. Recent advanced machine learning technologies have shown promising results in biosignal analysis, however, research and development in this area is relatively slow due to the lack of biosignal datasets for machine learning. The VitalDB (Vital Signs DataBase) is an open dataset created specifically to facilitate machine learning studies related to monitoring vital signs in surgical patients. This dataset contains high-resolution multi-parameter data from 6,388 cases, including 486,451 waveform and numeric data tracks of 196 intraoperative monitoring parameters, 73 perioperative clinical parameters, and 34 time-series laboratory result parameters. All data is stored in the public cloud after anonymization. The dataset can be freely accessed and analysed using application programming interfaces and Python library. The VitalDB public dataset is expected to be a valuable resource for biosignal research and development.
A knowledge graph empowered online learning framework for access control decision-making

Abstract Knowledge graph, as an extension of graph data structure, is being used in a wide range of areas as it can store interrelated data and reveal interlinked relationships between different objects within a large system. This paper proposes an algorithm to construct an access control knowledge graph from user and resource attributes. Furthermore, an online learning framework for access control decision-making is proposed based on the constructed knowledge graph. Within the framework, we extract topological features to represent high cardinality categorical user and resource attributes. Experimental results show that topological features extracted from knowledge graph can improve the access control performance in both offline learning and online learning scenarios with different degrees of class imbalance status.
An Explainable AI-Based Intrusion Detection System for DNS Over HTTPS (DoH) Attacks

Over the past few years, Domain Name Service (DNS) remained a prime target for hackers as it enables them to gain first entry into networks and gain access to data for exfiltration. Although the DNS over HTTPS (DoH) protocol has desirable properties for internet users such as privacy and security, it also causes a problem in that network administrators are prevented from detecting suspicious network traffic generated by malware and malicious tools. To support their efforts in maintaining a secure network, in this paper, we have implemented an explainable AI solution using a novel machine learning framework. We have used the publicly available CIRA-CIC-DoHBrw-2020 dataset for developing an accurate solution to detect and classify the DNS over HTTPS attacks. Our proposed balanced and stacked Random Forest achieved very high precision (99.91%), recall (99.92%) and F1 score (99.91%) for the classification task at hand. Using explainable AI methods, we have additionally highlighted the underlying feature contributions in an attempt to provide transparent and explainable results from the model.