Reviewerly/exHarmony huggingface dataset:
A Reversible Data Hiding Scheme in Encrypted Domain for Secret Image Sharing Based on Chinese Remainder Theorem

Schemes of reversible data hiding in encrypted domain (RDH-ED) based on symmetric or public key encryption are mainly applied in the scenarios of end-to-end communication. To provide security guarantees for the multi-party scenarios, a RDH-ED scheme for secret image sharing based on Chinese remainder theorem (CRT) is presented. In the application of (<inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>) secret image sharing, an image is first shared into <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> different shares of ciphertext. Only when not less than <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> shares obtained, can the image be reconstructed. In our scheme, additional data could be embedded into the image shares. To realize data extraction from the image shares and the reconstructed image separably, two data hiding methods are proposed: one is homomorphic difference expansion in encrypted domain (HDE-ED) that supports data extraction from the reconstructed image by utilizing the addition homomorphism of CRT secret sharing; the other is difference expansion in image shares (DE-IS) that supports the data extraction from the marked shares before image reconstruction. Experimental results demonstrate that the proposed scheme could not only maintain the security and the threshold function of secret sharing system, but also obtain a better reversibility and efficiency compared with most existing RDH-ED algorithms. The maximum embedding rate of HDE-ED could reach 0.500 bits per pixel and the average embedding rate of DE-IS could reach 0.4652 bits per pixel.
Named Entity Recognition of Traditional Chinese Medicine Patents Based on BiLSTM-CRF

With the growing popularity of traditional Chinese medicine (TCM) in the world and the increasing awareness of intellectual property protection, the number of TCM patent application is growing year by year. TCM patents contain rich medical, legal, and economic information. Effective text mining of TCM patents is of great theoretical and practical significance (e.g., the R&amp;D of new medicines, patent infringement litigation, and patent acquisition). Named entity recognition (NER) is a fundamental task in natural language processing and a crucial step before indepth analysis of TCM patent. In this paper, a method combining Bidirectional Long Short-Term Memory neural network with Conditional Random Field (BiLSTM-CRF) is proposed to automatically recognize entities of interest (i.e., herb names, disease names, symptoms, and therapeutic effects) from the abstract texts of TCM patents. By virtue of the capabilities of deep learning methods, the semantic information in the context can be learned without feature engineering. Experiments show that the BiLSTM-CRF-based method provides superior performance in comparison with various baseline methods.
Learning by Fixing: Solving Math Word Problems with Weak Supervision

Previous neural solvers of math word problems (MWPs) are learned with full supervision and fail to generate diverse solutions. In this paper, we address this issue by introducing a weakly-supervised paradigm for learning MWPs. Our method only requires the annotations of the final answers and can generate various solutions for a single problem. To boost weakly-supervised learning, we propose a novel learning-by-fixing (LBF) framework, which corrects the misperceptions of the neural network via symbolic reasoning. Specifically, for an incorrect solution tree generated by the neural network, the fixing mechanism propagates the error from the root node to the leaf nodes and infers the most probable fix that can be executed to get the desired answer. To generate more diverse solutions, tree regularization is applied to guide the efficient shrinkage and exploration of the solution space, and a memory buffer is designed to track and save the discovered various fixes for each problem. Experimental results on the Math23K dataset show the proposed LBF framework significantly outperforms reinforcement learning baselines in weakly-supervised learning. Furthermore, it achieves comparable top-1 and much better top-3/5 answer accuracies than fully-supervised methods, demonstrating its strength in producing diverse solutions.
Fully Distributed Verifiable Random Functions and their Application to Decentralised Random Beacons

We provide a systematic analysis of two related multiparty protocols, namely (Non-Interactive Fully) Distributed Verifiable Random Functions (DVRFs) and Decentralised Random Beacons (DRBs), including their syntax and definition of robustness and privacy properties. These two protocols are run by multiple network nodes where each node contributes with a partial evaluation and the collection of these partial values is used to evaluate a pseudorandom function. We refine current pseudorandomness definitions for distributed functions and show that the privacy provided by strong pseudorandomness, where an adversary is allowed to make partial function evaluation queries on the challenge value, is strictly better than that provided by standard pseudorandomness, where such adversarial queries are disallowed. We provide two new DVRF instantiations, named DDH-DVRF and GLOW-DVRF, that meet strong pseudorandomness under widely accepted cryptographic assumptions. We show the usefulness of our DRB formalism in two different ways. Firstly, we give a rigorous treatment of a folklore generic construction that builds a Decentralized Random Beacon from any DVRF instance and prove that it satisfies robustness and pseudorandomness provided that the original DVRF protocol is secure. Secondly, we capture several existing DRB protocols from academia and industry within our framework, which serves as an evidence of its wider applicability. Finally, we report on experimental evaluations of our newly introduced DVRFs with implementations under different cryptographic libraries, and we also report preliminary benchmark results on two of the DRBs obtained from the generic DVRF-to-DRB transformation. Our benchmarks can be independently verified as we provide an open source C++ reference implementation of the new DVRFs. Finally, we conclude that our new DRB instantiations are the most efficient instantiations currently available while enjoying strong and formally proven security properties.
POSEFusion: Pose-guided Selective Fusion for Single-view Human Volumetric Capture

We propose POseguided SElective Fusion (POSEFu-sion), a single-view human volumetric capture method that leverages tracking-based methods and tracking-free inference to achieve high-fidelity and dynamic 3D reconstruction. By contributing a novel reconstruction framework which contains pose-guided keyframe selection and robust implicit surface fusion, our method fully utilizes the advantages of both tracking-based methods and tracking-free inference methods, and finally enables the high-fidelity recon-struction of dynamic surface details even in the invisible regions. We formulate the keyframe selection as a dynamic programming problem to guarantee the temporal continuity of the reconstructed sequence. Moreover, the novel robust implicit surface fusion involves an adaptive blending weight to preserve high-fidelity surface details and an automatic collision handling method to deal with the potential self-collisions. Overall, our method enables high-fidelity and dynamic capture in both visible and invisible regions from a single RGBD camera, and the results and experiments show that our method outperforms state-of-the-art methods.
Towards histopathological stain invariance by Unsupervised Domain Augmentation using generative adversarial networks

The application of supervised deep learning methods in digital pathology is limited due to their sensitivity to domain shift. Digital Pathology is an area prone to high variability due to many sources, including the common practice of evaluating several consecutive tissue sections stained with different staining protocols. Obtaining labels for each stain is very expensive and time consuming as it requires a high level of domain knowledge. In this article, we propose an unsupervised augmentation approach based on adversarial image-to-image translation, which facilitates the training of stain invariant supervised convolutional neural networks. By training the network on one commonly used staining modality and applying it to images that include corresponding, but differently stained, tissue structures, the presented method demonstrates significant improvements over other approaches. These benefits are illustrated in the problem of glomeruli segmentation in seven different staining modalities (PAS, Jones H&E, CD68, Sirius Red, CD34, H&E and CD3) and analysis of the learned representations demonstrate their stain invariance.
An Image Encryption Scheme Based on IAVL Permutation Scheme and DNA Operations

In this paper, a novel image encryption algorithm based on a new permutation scheme and DNA operations are introduced.In our algorithm, SHA 256 and DNA hamming distance participate in the generation of the initial conditions of the 4D chaotic system, which can associate the encryption system with the original image.In the permutation process, based on the adjustment process of the IAVL (improved balanced binary tree), a new scrambling algorithm is constructed.Then the dynamic block coding rules are designed, in which different image blocks have different coding rules.In the diffusion process, a new diffusion algorithm with intra-block and inter-block is proposed to perform DNA operations on the intermediate encryption result and the key matrix.In the security analysis, the key space of the encryption system is 2 933 and the information entropy is about 7.9973.In addition, the NPCR and UACI in the differential attack test are close to the ideal values of 99.6094% and 33.4653%.To further prove the security of the encryption algorithm, the Irregular deviation, Maximum deviation, Energy, Contrast, and Homogeneity tests are introduced into the analysis.Experimental results illustrate that the encryption scheme can against multiple illegal attacks like statistical, brute-force and differential attacks.
Legal aspects of data cleansing in medical AI

Data quality is of paramount importance for the smooth functioning of modern data-driven AI applications with machine learning as a core technology. This is also true for medical AI, where malfunctions due to "dirty data" can have particularly dramatic harmful implications. Consequently, data cleansing is an important part in improving the usability of (Big) Data for medical AI systems. However, it should not be overlooked that data cleansing can also have negative effects on data quality if not performed carefully. This paper takes an interdisciplinary look at some of the technical and legal challenges of data cleansing against the background of European medical device law, with the key message that technical and legal aspects must always be considered together in such a sensitive context.
A Long Short-Term Memory-Based Solar Irradiance Prediction Scheme Using Meteorological Data

Solar irradiance prediction is an indispensable area of the photovoltaic (PV) power management system. However, PV management may be subject to severe penalties due to the unsteadiness pattern of PV output power that depends on solar radiation. A high-precision long short-term memory (LSTM)-based neural network model named SIPNet to predict solar irradiance in a short time interval is proposed to overcome this problem. Solar radiation depends on the environmental sensing of meteorological information such as temperature, pressure, humidity, wind speed, and direction, which are different dimensions in measurement. LSTM neural network can concurrently learn the spatiotemporal of multivariate input features via various logistic gates. Moreover, SIPNet can estimate the future solar irradiance given the historical observation of the meteorological information and the radiation data. The SIPNet model is simulated and compared with the actual and predicted data series and evaluated by the mean absolute error (MAE), mean square error (MSE), and root MSE. The empirical results show that the value of MAE, MSE, and root mean square error of SIPNet is 0.0413, 0.0033, and 0.057, respectively, which demonstrate the effectiveness of SIPNet and outperforms other existing models.
The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection

Low-cost monocular 3D object detection plays a fundamental role in autonomous driving, whereas its accuracy is still far from satisfactory. In this paper, we dig into the 3D object detection task and reformulate it as the sub-tasks of object localization and appearance perception, which benefits to a deep excavation of reciprocal information underlying the entire task. We introduce a Dynamic Feature Reflecting Network, named DFR-Net, which contains two novel standalone modules: (i) the Appearance-Localization Feature Reflecting module (ALFR) that first separates task-specific features and then self-mutually reflects the reciprocal features; (ii) the Dynamic Intra-Trading module (DIT) that adaptively realigns the training processes of various sub-tasks via a self-learning manner. Extensive experiments on the challenging KITTI dataset demonstrate the effectiveness and generalization of DFR-Net. We rank 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> among all the monocular 3D object detectors in the KITTI test set (till March 16th, 2021). The proposed method is also easy to be plug-and-play in many cutting-edge 3D detection frameworks at negligible cost to boost performance. The code will be made publicly available.
Contrastive learning improves critical event prediction in COVID-19 patients

Deep learning (DL) models typically require large-scale, balanced training data to be robust, generalizable, and effective in the context of healthcare. This has been a major issue for developing DL models for the coronavirus disease 2019 (COVID-19) pandemic, where data are highly class imbalanced. Conventional approaches in DL use cross-entropy loss (CEL), which often suffers from poor margin classification. We show that contrastive loss (CL) improves the performance of CEL, especially in imbalanced electronic health records (EHR) data for COVID-19 analyses. We use a diverse EHR dataset to predict three outcomes: mortality, intubation, and intensive care unit (ICU) transfer in hospitalized COVID-19 patients over multiple time windows. To compare the performance of CEL and CL, models are tested on the full dataset and a restricted dataset. CL models consistently outperform CEL models, with differences ranging from 0.04 to 0.15 for area under the precision and recall curve (AUPRC) and 0.05 to 0.1 for area under the receiver-operating characteristic curve (AUROC).
Digital Learning Is an Educational Format towards Sustainable Education

The year 2020, due to the pandemic, was a milestone in the history of digital technology in the education sector, allowing a sustainable education although the world was facing a pandemic crisis without precedents. Therefore, in a few days occur a transformation from traditional classroom teaching to online teaching and consequently forced to use digital learning. Nevertheless, more researches are needed to know how was this experience and if there is the intention to maintain the online format. The main goal of this article is to study how digital learning can be an educational format focused on sustainable education. This paper presents a systematic literature review on digital learning through PRISMA methodology, based on a literature search and field research aimed to analyze the significant predictors related to the digital learning experience on the likelihood of choosing to “keep” the online format in the next academic year. An online survey was conducted with 173 university students. The results obtained showed that the significant predictors were factor 1-”Characteristics of online classes; factor 2-”Support from the School and Professors; factor 3-”Online classes vs. face-to-face classes” and gender. The probability of choosing to keep online classes increases exponentially with the characteristics of online classes, with Support from school and teachers; Online classes vs. Face-to-face classes, and keeping factors 1, 2, and 3 constant the probability if a man chooses the online format compared to a woman is higher. This online format thus acquires central importance in the contemporary sustainability debate. The kind of life, education, and society we will have in the future will depend on the quality, depth, and extent of the learning processes we can create and exercise individually and socially. Education, and educators in particular, who concentrate on the tasks of designing and implementing social teaching and learning models, have a unique responsibility in this process. Although the reduced sample size the present work can provide strategic information for university staff, contributing to designing and implementation a sustainable education.
SVBE: searchable and verifiable blockchain-based electronic medical records system

Abstract Central management of electronic medical systems faces a major challenge because it requires trust in a single entity that cannot effectively protect files from unauthorized access or attacks. This challenge makes it difficult to provide some services in central electronic medical systems, such as file search and verification, although they are needed. This gap motivated us to develop a system based on blockchain that has several characteristics: decentralization, security, anonymity, immutability, and tamper-proof. The proposed system provides several services: storage, verification, and search. The system consists of a smart contract that connects to a decentralized user application through which users can transact with the system. In addition, the system uses an interplanetary file system (IPFS) and cloud computing to store patients’ data and files. Experimental results and system security analysis show that the system performs search and verification tasks securely and quickly through the network.
Virtual reality as a tool to promote wellbeing in the workplace

NHS staff are disproportionately impacted by workplace stress, threatening not only quality of service delivery, but the sustainability, of this vital healthcare system. There is an urgent need for accessible and cost-effective interventions that promote the wellbeing of this integral workforce.The aim of this study was to explore the potential propensity of a short virtual reality (VR) nature experience, delivered during the workday, to induce positive mood states NHS clinicians.NHS clinicians working in a fast-paced trauma service were offered the opportunity to take part in a short virtual reality (VR) relaxation session, during their working day, in which they were able to explore the "Green Meadows" experience available within the Nature Treks application. An indication of physiological arousal (heart rate) was obtained, and subjective measures of emotional state were employed to assess the effect of the intervention on participants' mood. Further, feedback was gathered to provide an initial indication of the acceptability of the experience.Analysis of data from 39 clinicians revealed that, following the VR experience, participants reported significantly increased feelings of happiness (p < 0.001) and relaxation (p < 0.001), and significantly decreased feelings of sadness (p = 0.003), anger (p < 0.001) and anxiety (p < 0.001). No significant differences in reported levels of surprise (p = 0.603) or vigour (p = 0.566) were found. Further, the experience was associated with a significant reduction in heart-rate (p = 0.025), and had high acceptability ratings, despite participants' varied experience with computers and VR technology.The findings of this study indicate that the administration of VR for the promotion of NHS staff wellbeing in the workplace is a potentially fruitful avenue of exploration that warrants further investigation.
AI-Infused Semantic Model to Enrich and Expand Programming Question Generation

Creating practice questions for programming learning is not easy. It requires the instructor to diligently organize heterogeneous learning resources, i.e., conceptual programming concepts and procedural programming rules. Today’s programming question generation (PQG) is still largely replying on the demanding creation task performed by the instructors without advanced technological support. In this work, we propose a semantic PQG model that aims to help the instructor generate new programming questions and expand the assessment items. The PQG model is designed to transform conceptual and procedural programming knowledge from textbooks into a semantic network by the Local Knowledge Graph (LKG) and Abstract Syntax Tree (AST). For any given question, the model queries the established network to find related code examples and generates a set of questions by the associated LKG/AST semantic structures. We conduct analysis to compare instructor-made questions from 9 undergraduate introductory programming courses and textbook questions. The results show that the instructor-made questions had much simpler complexity than the textbook ones. The disparity of topic distribution intrigued us to further research the breadth and depth of question quality and also to investigate the complexity of the questions in relations to the student performances. Finally, we report an user study results on the proposed AI-infused semantic PQG model in examining the machine-generated questions quality.