Reviewerly/exHarmony huggingface dataset:
A Compressive Integrity Auditing Protocol for Secure Cloud Storage

With the widespread application of cloud storage, ensuring the integrity of user outsourced data catches more and more attention. To remotely check the integrity of cloud storage, plenty of protocols have been proposed, implemented by checking the equation constructed by the aggregated blocks, tags, and indices. However, the verifier only has the knowledge of the indices of the audited blocks and tags, which thus requires the cloud to store both data blocks and tags for integrity verification. In this article, we present a compressive secure cloud storage protocol inspired by Goldreich-Goldwasser-Halevi (GGH) cryptosystem. Since the aggregated blocks can be reconstructed from the aggregated tags without the help of data indices, the cloud can only store data tags for providing the verifiable integrity proof. In this way, communication and storage costs can be hugely reduced and user private information can be hidden from the cloud. Furthermore, the proposed protocol only contains a few basic algebraic operations, making it highly efficient. We also provide formal security proof of the proposed protocol regarding forge, replay and replace attacks. In addition, we explore a new technique to support data dynamics. Furthermore, we establish a generic framework of compressive secure cloud storage protocols. Finally, we provide the theoretical analysis and experimental results, which further validate the effectiveness of the proposed protocol.
Sentiment analysis of user feedback on the HSE’s Covid-19 contact tracing app

Digital Contact Tracing is seen as a key tool in reducing the propagation of Covid-19. But it requires high uptake and continued participation across the population to be effective. To achieve sufficient uptake/participation, health authorities should address, and thus be aware of, user concerns.This work manually analyzes user reviews of the Irish Heath Service Executive's (HSE) Contact Tracker app, to identify user concerns and to lay the foundations for subsequent, large-scale, automated analyses of reviews. While this might seem tightly scoped to the Irish context, the HSE app provides the basis for apps in many jurisdictions in the USA and Europe.Manual analysis of (1287) user reviews from the Google/Apple playstores was performed, to identify the aspects of the app that users focused on, and the positive/negative sentiment expressed.The findings suggest a largely positive sentiment towards the app, and that users thought it handled data protection and transparency aspects well. But feedback suggests that users would appreciate more targeted feedback on the incidence of the virus, and facilities for more proactive engagement, like notifications that prompt users to submit their health status daily. Finally, the analysis suggests that the "android battery" issue and the backward-compatibility issue with iPhones seriously impacted retention/uptake of the app respectively.The HSE have responded to the public's desire for targeted feedback in newer versions, but should consider increasing the app's proactive engagement. The results suggest they should also raise the backward compatibility issue, regarding older iPhones, with Apple.
Genozip: a universal extensible genomic data compressor

Abstract We present Genozip, a universal and fully featured compression software for genomic data. Genozip is designed to be a general-purpose software and a development framework for genomic compression by providing five core capabilities—universality (support for all common genomic file formats), high compression ratios, speed, feature-richness and extensibility. Genozip delivers high-performance compression for widelyused genomic data formats in genomics research, namely FASTQ, SAM/BAM/CRAM, VCF, GVF, FASTA, PHYLIP and 23andMe formats. Our test results show that Genozip is fast and achieves greatly improved compression ratios, even when the files are already compressed. Further, Genozip is architected with a separation of the Genozip Framework from file-format-specific Segmenters and data-type-specific Codecs. With this, we intend for Genozip to be a general-purpose compression platform where researchers can implement compression for additional file formats, as well as new codecs for data types or fields within files, in the future. We anticipate that this will ultimately increase the visibility and adoption of these algorithms by the user community, thereby accelerating further innovation in this space. Availability and implementation Genozip is written in C. The code is open-source and available on http://www.genozip.com. The package is free for non-commercial use. It is distributed through the Conda package manager, github, and as a Docker container on DockerHub. Genozip is tested on Linux, Mac and Windows. Supplementary information Supplementary data are available at Bioinformatics online.
DynamiChain: Development of Medical Blockchain Ecosystem Based on Dynamic Consent System

Although blockchain is acknowledged as one of the most important technologies to lead the fourth industrial revolution, major technical challenges regarding security breach and privacy issues remain. This issue is particularly sensitive in applied medical fields where personal health information is handled within the network. In addition, contemporary blockchain-converged solutions do not consider restricted medical data regulations that are still obstacles in many countries worldwide. This implies a crucial need for a system or solution that is suitable for the healthcare sector. Therefore, this article proposes the development of a dynamic consent medical blockchain system called DynamiChain, based on a ruleset management algorithm for handling health examination data. Moreover, medical blockchain-related studies were systematically reviewed to prove the novelty of DynamiChain. The proposed system was implemented in a scenario where the exercise management healthcare company provided health management services based on data obtained from the data provider’s hospital. The proposed research is envisioned to provide a widely compatible blockchain medical system that could be applied in future healthcare fields.
Modeling Dynamic Heterogeneous Network for Link Prediction Using Hierarchical Attention with Temporal RNN

Network embedding aims to learn low-dimensional representations of nodes while capturing structure information of networks. It has achieved great success on many tasks of network analysis such as link prediction and node classification. Most of existing network embedding algorithms focus on how to learn static homogeneous networks effectively. However, networks in the real world are more complex, e.g.., networks may consist of several types of nodes and edges (called heterogeneous information) and may vary over time in terms of dynamic nodes and edges (called evolutionary patterns). Limited work has been done for network embedding of dynamic heterogeneous networks as it is challenging to learn both evolutionary and heterogeneous information simultaneously. In this paper, we propose a novel dynamic heterogeneous network embedding method, termed as DyHATR, which uses hierarchical attention to learn heterogeneous information and incorporates recurrent neural networks with temporal attention to capture evolutionary patterns. We benchmark our method on four real-world datasets for the task of link prediction. Experimental results show that DyHATR significantly outperforms several state-of-the-art baselines.
Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models

In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets.
A parallel inertial S-iteration forward-backward algorithm for regression and classification problems

In this paper, a novel algorithm, called parallel inertial S-iteration forward-backward algorithm (PISFBA) isproposed for finding a common fixed point of a countable family of nonexpansive mappings and convergencebehavior of PISFBA is analyzed and discussed. As applications, we apply PISFBA to estimate the weight con-necting the hidden layer and output layer in a regularized extreme learning machine. Finally, the proposedlearning algorithm is applied to solve regression and data classification problems
Multi-objective search of robust neural architectures against multiple types of adversarial attacks

Many existing deep learning models are vulnerable to adversarial examples that are imperceptible to humans. To address this issue, various methods have been proposed to design network architectures that are robust to one particular type of adversarial attacks. It is practically impossible, however, to predict beforehand which type of attacks a machine learn model may suffer from. To address this challenge, we propose to search for deep neural architectures that are robust to five types of well-known adversarial attacks using a multi-objective evolutionary algorithm. To reduce the computational cost, a normalized error rate of a randomly chosen attack is calculated as the robustness for each newly generated neural architecture at each generation. All non-dominated network architectures obtained by the proposed method are then fully trained against randomly chosen adversarial attacks and tested on two widely used datasets. Our experimental results demonstrate the superiority of optimized neural architectures found by the proposed approach over state-of-the-art networks that are widely used in the literature in terms of the classification accuracy under different adversarial attacks.
3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images

Automated grading systems using deep convolution neural networks (DCNNs) have proven their capability and potential to distinguish between different breast cancer grades using digitized histopathological images. In digital breast pathology, it is vital to measure how confident a DCNN is in grading using a machine-confidence metric, especially with the presence of major computer vision challenging problems such as the high visual variability of the images. Such a quantitative metric can be employed not only to improve the robustness of automated systems, but also to assist medical professionals in identifying complex cases. In this paper, we propose Entropy-based Elastic Ensemble of DCNN models (3E-Net) for grading invasive breast carcinoma microscopy images which provides an initial stage of explainability (using an uncertainty-aware mechanism adopting entropy). Our proposed model has been designed in a way to (1) exclude images that are less sensitive and highly uncertain to our ensemble model and (2) dynamically grade the non-excluded images using the certain models in the ensemble architecture. We evaluated two variations of 3E-Net on an invasive breast carcinoma dataset and we achieved grading accuracy of 96.15% and 99.50%.
Deep graph learning for semi-supervised classification

Graph learning (GL) can dynamically capture the distribution structure (graph structure) of data based on graph convolutional networks (GCN), and the learning quality of the graph structure directly influences GCN for semi-supervised classification. Most existing methods combine the computational layer and the related losses into GCN for exploring the global graph (measuring graph structure from all data samples) or local graph (measuring graph structure from local data samples). The global graph emphasizes the whole structure description of the inter-class data, while the local graph tends to the neighborhood structure representation of the intra-class data. However, it is difficult to simultaneously balance these learning process graphs for semi-supervised classification because of the interdependence of these graphs. To simulate the interdependence, deep graph learning (DGL) is proposed to find a better graph representation for semi-supervised classification. DGL can not only learn the global structure by the previous layer metric computation updating, but also mine the local structure by next layer local weight reassignment. Furthermore, DGL can fuse the different structures by dynamically encoding the interdependence of these structures, and deeply mine the relationship of the different structures by hierarchical progressive learning to improve the performance of semi-supervised classification. Experiments demonstrate that the DGL outperforms state-of-the-art methods on three benchmark datasets (Citeseer, Cora, and Pubmed) for citation networks and two benchmark datasets (MNIST and Cifar10) for images.
Collaborative Complete Coverage and Path Planning for Multi-Robot Exploration

In mobile robotics research, the exploration of unknown environments has always been an important topic due to its practical uses in consumer and military applications. One specific interest of recent investigation is the field of complete coverage and path planning (CCPP) techniques for mobile robot navigation. In this paper, we present a collaborative CCPP algorithms for single robot and multi-robot systems. The incremental coverage from the robot movement is maximized by evaluating a new cost function. A goal selection function is then designed to facilitate the collaborative exploration for a multi-robot system. By considering the local gains from the individual robots as well as the global gain by the goal selection, the proposed method is able to optimize the overall coverage efficiency. In the experiments, our CCPP algorithms are carried out on various unknown and complex environment maps. The simulation results and performance evaluation demonstrate the effectiveness of the proposed collaborative CCPP technique.
BCNet: Searching for Network Width with Bilaterally Coupled Network

Searching for a more compact network width recently serves as an effective way of channel pruning for the deployment of convolutional neural networks (CNNs) under hardware constraints. To fulfill the searching, a one-shot supernet is usually leveraged to efficiently evaluate the performance w.r.t. different network widths. However, current methods mainly follow a unilaterally augmented (UA) principle for the evaluation of each width, which induces the training unfairness of channels in supernet. In this paper, we introduce a new supernet called Bilaterally Coupled Network (BCNet) to address this issue. In BCNet, each channel is fairly trained and responsible for the same amount of network widths, thus each network width can be evaluated more accurately. Besides, we leverage a stochastic complementary strategy for training the BCNet, and propose a prior initial population sampling method to boost the performance of the evolutionary search. Extensive experiments on benchmark CIFAR-10 and ImageNet datasets indicate that our method can achieve state-of-the-art or competing performance over other baseline methods. Moreover, our method turns out to further boost the performance of NAS models by refining their network widths. For example, with the same FLOPs budget, our obtained EfficientNet-B0 achieves 77.36% Top-1 accuracy on ImageNet dataset, surpassing the performance of original setting by 0.48%.
Assessment and multi-objective optimization of an off-grid solar based energy system for a Conex

In this study, an off-grid PV system is optimized to supply a Conex electricity demand in the top ten earthquake-prone cities using mixed-integer linear programming techniques. The stand-alone photovoltaic system is designed by a photovoltaic array, a cooling/heating system, battery banks, an inverter, and a charge controller. For determining the optimum size and specifications of the system components such as PV panel, HVAC coefficient of performance, by considering two objectives of the study, a mixed-integer linear programming method is used. These conflicting objectives are the probability of lack of power and total cost of the system. The weighted factor method is utilized, and final optimized systems are achieved using MATLAB 2019b. Using the weighted factor method, several optimum solutions, in which the importance of objectives are different from each other, are obtained for each case concerning objectives. The suggested model is optimized for ten earthquake-prone cities globally, while it can be utilized for any location. The Pareto frontiers are presented to show the trade-offs between two objective functions. The average cost of off-grid PV system supplied electrical power is from about 1000$ for lima (subtropical desert climate) to 4000$ for Tokyo and Osaka (humid subtropical). Analysis of obtained results demonstrates that the system is suitable for all of the considered cities. It can supply the load demand of an off-grid Conex with a loss of power supply probability as low as 1%.
Generating Two Continuous Entangled Microwave Beams Using a dc-Biased Josephson Junction

We show experimentally that a dc-biased Josephson junction in series with two microwave resonators emits entangled beams of microwaves leaking out of the resonators. In the absence of a stationary phase reference for characterizing the entanglement of the outgoing beams, we measure second-order coherence functions for proving entanglement up to an emission rate of 2.5 billion photon pairs per second. The experimental results are found in quantitative agreement with theory, proving that the low frequency noise of the dc bias is the main limitation for the coherence time of the entangled beams. This agreement allows us to evaluate the entropy of entanglement of the resonators, and to identify the improvements that could bring this device closer to a useful bright source of entangled microwaves for quantum-technological applications.
Full-Field Bridge Deflection Monitoring with Off-Axis Digital Image Correlation

Video deflectometer based on using off-axis digital image correlation (DIC) has emerged as a robust non-contact optical tool for deflection measurements of bridges. In practice, a video deflectometer often needs to measure the deflections at multiple positions of the bridge. The existing 2D-DIC-based measurement methods usually use a laser rangefinder to measure the distance from each point to the camera to obtain the scale factor for the point. It is only suitable for the deflection measurements of a few points since manually measuring distances for a large number of points is time consuming and impractical. In this paper, a novel method for full-field bridge deflection measurement based on off-axis DIC is proposed. Because the bridge is usually a slender structure and the region of interest on the bridge is often a narrow band, the new approach can determine the scale factors of all the points of interest with a spatial straight-line fitting scheme. Moreover, the proposed technique employs reliability-guided processing and a fast initial parameter estimation strategy for real-time and accurate image-matching analysis. An indoor cantilever beam experiment verified the accuracy of the proposed approach, and a field test of a high-speed railway bridge demonstrated the robustness and practicability of the technique.
NNrepair: Constraint-Based Repair of Neural Network Classifiers

Abstract We present NNrepair , a constraint-based technique for repairing neural network classifiers. The technique aims to fix the logic of the network at an intermediate layer or at the last layer . NNrepair first uses fault localization to find potentially faulty network parameters (such as the weights ) and then performs repair using constraint solving to apply small modifications to the parameters to remedy the defects. We present novel strategies to enable precise yet efficient repair such as inferring correctness specifications to act as oracles for intermediate layer repair, and generation of experts for each class. We demonstrate the technique in the context of three different scenarios: (1) Improving the overall accuracy of a model, (2) Fixing security vulnerabilities caused by poisoning of training data and (3) Improving the robustness of the network against adversarial attacks. Our evaluation on MNIST and CIFAR-10 models shows that NNrepair can improve the accuracy by 45.56% points on poisoned data and 10.40% points on adversarial data. NNrepair also provides small improvement in the overall accuracy of models, without requiring new data or re-training.
Advanced data integration in banking, financial, and insurance software in the age of <scp>COVID</scp>‐19

Abstract This study contributes to our understanding of how the emergence of the COVID‐19 pandemic changes the global Banking Financial Services and Insurance (BFSI) landscape. Before the COVID‐19 pandemic, BFSIs corporate strategy was solely aligned to the quest for operational efficiency. However, during the ongoing COVID‐19 pandemic, global BFSIs are forced to adopt digital transformation in their operations due to a rise in transaction volumes. The ongoing COVID‐19 pandemic already triggers holistic innovations concerning the global BFSI's product, process, concept, trend, or idea. Thus, the BFSI cannot survive without efficient and innovative system software for global operations. The study plots the hype cycle to identify relevant technologies to deal with real‐world business problems. The hype cycle indicates that the need for advanced data integration is growing and COVID‐19 pandemic has already triggered it. The study argues that the incorporation of data integration might be challenging initially for BFSIs but eventually it may result in an efficient model to handle these types of pandemic or unexpected circumstances.
Case Study of Solar Photovoltaic Power-Plant Site Selection for Infrastructure Planning Using a BIM-GIS-Based Approach

Evaluating the site-selection process for photovoltaic (PV) plants is essential for securing available areas for solar power plant installation in limited spaces. Although the vicinities of highway networks can be suitable for installing PV plants, in terms of economic feasibility, they have rarely been investigated because the impacts of various factors, including geographic or weather patterns, have not been analyzed. In this respect, this study conducts a case study on selecting the site for PV-panel installation in the vicinity of a highway (e.g., slopes) by integrating geographic information system (GIS) and building information model (BIM) techniques. Using location (e.g., highways, lakes, rivers), monthly solar power output, and orographic (e.g., slope) data, suitable regions are identified with the geo-spatial analysis; then, the amount of power that can be generated is evaluated in the regions. For the proposed sites, the surface conditions and potential PV systems are transformed and visualized in a BIM environment. In the results, the power output at optimal sites selected from the case area was computed at a total of 8227 MWh and was transformed into solar-panel families in three-dimensional environments. Thus, this study may help to identify optimal sites for PV plants in the unused areas near highways.