Reviewerly/exHarmony huggingface dataset:
Equivalence of approximate Gottesman-Kitaev-Preskill codes

The Gottesman-Kitaev-Preskill (GKP) quantum error correcting code attracts much attention in continuous variable (CV) quantum computation and CV quantum communication due to the simplicity of error correcting routines and the high tolerance against Gaussian errors. Since the GKP code state should be regarded as a limit of physically meaningful approximate ones, various approximations have been developed until today, but explicit relations among them are still unclear. In this paper, we rigorously prove the equivalence of these approximate GKP codes with an explicit correspondence of the parameters. We also propose a standard form of the approximate code states in the position representation, which enables us to derive closed-from expressions for the Wigner functions, the inner products, and the average photon numbers in terms of the theta functions. Our results serve as fundamental tools for further analyses of fault-tolerant quantum computation and channel coding using approximate GKP codes.
Multimodal interfaces and communication cues for remote collaboration

Remote collaboration has been studied for more than two decades and now there is the possibilities for new types of collaboration with the recent advances in immersive technologies such as Virtual, Augmented, Mixed Reality (VR/AR/MR). However, despite the increasing research interest in remote collaboration study with VR/AR/MR technologies, there is still a lack of academic venues specifically focusing on VR/AR/MR remote collaboration research. This special issue provides high-quality papers on the topic of remote collaboration research and increases visibility of this timely interesting and important research area. We particularly focus on three research aspects in remote collaboration: (1) use of multimodal communication cues, (2) awareness of the task space, and (3) human factors understanding. In this editorial, we first describe five essential factors for remote collaboration: task, local user, remote user, communication, and tool/interface, and then summarize a brief history of the research areas. We also cover the feature papers accepted in this issue, which introduce novel multimodal interfaces for remote collaboration and the effects on task performance and perceptual factors. Finally, we discuss some potential future research directions while concluding the editorial.
Condensed Silhouette: An Optimized Filtering Process for Cluster Selection in K-Means

In K-Means based clustering algorithms, different initial seeds can lead to different clustering results. Selecting the best result from different initial seeds is called the filtering process. The filtering process follows three steps, 1- performing several clustering trials, 2- scoring each trial, and 3- choosing the trial with the best score. A typical method to score the clustering results of K-Means is the within-cluster sum of squares (WCSS). There are more advanced methods that can be used to score the clustering trials. These methods usually provide a better score with the cost of being more computationally demanding. In this paper, we propose Condensed Silhouette, which is a very efficient version of the Silhouette algorithm. For this purpose, we replace the elements of Silhouette algorithm with similar elements of the K-Means algorithm. This helps us to maintain the accuracy of the Silhouette and at the same time, significantly reduce the computational requirements of the method. Our experiments on 14 real datasets show the effectiveness of the proposed method.
SLRL4D: Joint Restoration of Subspace Low-Rank Learning and Non-Local 4-D Transform Filtering for Hyperspectral Image

During the process of signal sampling and digital imaging, hyperspectral images (HSI) inevitably suffer from the contamination of mixed noises. The fidelity and efficiency of subsequent applications are considerably reduced along with this degradation. Recently, as a formidable implement for image processing, low-rank regularization has been widely extended to the restoration of HSI. Meanwhile, further exploration of the non-local self-similarity of low-rank images are proven useful in exploiting the spatial redundancy of HSI. Better preservation of spatial-spectral features is achieved under both low-rank and non-local regularizations. However, existing methods generally regularize the original space of HSI, the exploration of the intrinsic properties in subspace, which leads to better denoising performance, is relatively rare. To address these challenges, a joint method of subspace low-rank learning and non-local 4-d transform filtering, named SLRL4D, is put forward for HSI restoration. Technically, the original HSI is projected into a low-dimensional subspace. Then, both spectral and spatial correlations are explored simultaneously by imposing low-rank learning and non-local 4-d transform filtering on the subspace. The alternating direction method of multipliers-based algorithm is designed to solve the formulated convex signal-noise isolation problem. Finally, experiments on multiple datasets are conducted to illustrate the accuracy and efficiency of SLRL4D.
Perception is reality… How digital retail environments influence brand perceptions through presence

How and when should retailers use 360-virtual reality (VR) versus other media? What role does haptic sensory information play in VR and can consumers actually imagine touch? The aim of this research is to answer these questions by evaluating how presence induced by media (360-VR versus video) leads to heightened attitudes and purchase intentions and how this effect depends on consumer’s knowledge of the product category as well as haptic information. Specifically, 360-VR (versus low presence media) elicits more favorable evaluations (study 1). Yet, in-store, 360-VR results in less favorable responses (study 2). We show that when consumers have high product knowledge, 360-VR decreases consumer responses toward the brand. Alternatively, when consumers have low product knowledge 360-VR enhances consumer responses toward the brand (study 3). Introduction of haptic instructions attenuates the unilateral negative effect of product knowledge (study 4). Importantly, mental imagery underpins these relationships (studies 3 and 4).
Biomedical Named-Entity Recognition by Hierarchically Fusing BioBERT Representations and Deep Contextual-Level Word-Embedding

Text mining in the biomedical domain is increasingly important as the volume of biomedical documents increases. Thanks to advances in natural language processing (NLP), extracting valuable information from the biomedical literature is gaining popularity among researchers, and deep learning has enabled the development of effective biomedical text mining models. However, directly applying advancements in NLP to biomedical sources often yields unsatisfactory results, due to a word distribution drift from the general language domain corpora to specific biomedical corpora, and this drift introduces linguistic ambiguities. To overcome these challenges, this paper presents a novel method for biomedical named entity-recognition (BioNER) through hierarchically fusing representations from BioBERT, which is trained on biomedical corpora and Deep contextual-level word embeddings to handle the linguistic challenges within biomedical literature. Proposed text representation is then fed to attention-based Bi-directional Long Short Term Memory (BiLSTM) with Conditional random field (CRF) for the BioNER task. The experimental analysis shows that our proposed end-to-end methodology outperforms existing state-of-the-art methods for the BioNER task.
Quantum Collision Attacks on AES-Like Hashing with Low Quantum Random Access Memories

At EUROCRYPT 2020, Hosoyamada and Sasaki proposed the first dedicated quantum attack on hash functions—a quantum version of the rebound attack exploiting differentials whose probabilities are too low to be useful in the classical setting. This work opens up a new perspective toward the security of hash functions against quantum attacks. In particular, it tells us that the search for differentials should not stop at the classical birthday bound. Despite these interesting and promising implications, the concrete attacks described by Hosoyamada and Sasaki make use of large quantum random access memories (qRAMs), a resource whose availability in the foreseeable future is controversial even in the quantum computation community. Without large qRAMs, these attacks incur significant increases in time complexities. In this work, we reduce or even avoid the use of qRAMs by performing a quantum rebound attack based on differentials with non-full-active super S-boxes. Along the way, an MILP-based method is proposed to systematically explore the search space of useful truncated differentials with respect to rebound attacks. As a result, we obtain improved attacks on AES-MMO, AES-MP, and the first classical collision attacks on 4- and 5-round Grøstl-512. Interestingly, the use of non-full-active super S-box differentials in the analysis of AES-MMO gives rise to new difficulties in collecting enough starting points. To overcome this issue, we consider attacks involving two message blocks to gain more degrees of freedom, and we successfully compress the qRAM demand of the collision attacks on AES-MMO and AES-MP (EUROCRYPT 2020) from $$2^{48}$$ to a range from $$2^{16}$$ to 0, while still maintaining a comparable time complexity. To the best of our knowledge, these are the first dedicated quantum attacks on hash functions that slightly outperform Chailloux, Naya-Plasencia, and Schrottenloher's generic quantum collision attack (ASIACRYPT 2017) in a model where large qRAMs are not available. This work demonstrates again how a clever combination of classical cryptanalytic technique and quantum computation leads to improved attacks, and shows that the direction pointed out by Hosoyamada and Sasaki deserves further investigation.
Towards Domain Generalization In Underwater Object Detection

A General Underwater Object Detector (GUOD) should perform well on most of underwater circumstances. However, with limited underwater dataset, conventional object detection methods suffer from domain shift severely. This paper aims to build a GUOD using small underwater dataset with limited types of water quality. First, we propose a data augmentation method Water Quality Transfer (WQT) to increase domain diversity of the original small dataset. Second, for mining the semantic information from data generated by WQT, Domain Generalization YOLO (DG-YOLO) is proposed, which consists of three parts: YOLOv3, Domain Invariant Module and Invariant Risk Minimization penalty. Finally, experiments on original and synthetic URPC2019 dataset prove that WQT combined with DG-YOLO achieves promising performance of domain generalization in underwater object detection. The source code can be found at https://github.com/mousecpn/DG-YOLO.
Robust empirical wavelet fuzzy cognitive map for time series forecasting

Fuzzy cognitive maps have achieved significant success in time series modeling and forecasting. However, fuzzy cognitive maps still contain weakness to handle the nonstationarity and outliers. We propose a novel time series forecasting model based on fuzzy cognitive maps and empirical wavelet transformation in this paper. The empirical wavelet transformation is applied to decompose the original time series into different levels which capture information of different frequencies. Then, the high-order fuzzy cognitive map is trained to model the relationships among all the sub-series generated and original time series. To enhance the robustness of high-order fuzzy cognitive maps against outliers, a novel learning method based on support vector regression is designed. Finally, we divide the summation of each concept value of the high-order fuzzy cognitive map by two to obtain the numerical predictions. A comprehensive empirical study on eight public time series validates the superiority of proposed model compared with the popular baseline models from the literature.
Siamese networks with distractor-reduction method for long-term visual object tracking

Many trackers which divide the tracking process into two stages have recently been proposed to solve the problem of long-term tracking. Their outstanding performance makes them become one of the mainstream algorithms of long-term tracking. To further improve the performance of two-stage tracking algorithms, some improvements are proposed in this paper. (a) A hard negative mining method is proposed. It can optimize the training process of the verification network and bridge the gap between the two sub-networks. (b) The architecture of the verification network is designed as a Siamese structure; therefore, the semantic ambiguity in classification can be alleviated. Extensive experiments performed on benchmarks demonstrate that the proposed approach significantly outperforms the state-of-the-art methods, yielding 7% relative gain in the VOT2018-LT dataset and 14.2% relative gain in the OxUvA dataset.
Identification of 31 loci for mammographic density phenotypes and their associations with breast cancer risk

Mammographic density (MD) phenotypes are strongly associated with breast cancer risk and highly heritable. In this GWAS meta-analysis of 24,192 women, we identify 31 MD loci at P < 5 × 10-8, tripling the number known to 46. Seventeen identified MD loci also are associated with breast cancer risk in an independent meta-analysis (P < 0.05). Mendelian randomization analyses show that genetic estimates of dense area (DA), nondense area (NDA), and percent density (PD) are all significantly associated with breast cancer risk (P < 0.05). Pathway analyses reveal distinct biological processes involving DA, NDA and PD loci. These findings provide additional insights into the genetic basis of MD phenotypes and their associations with breast cancer risk.
Modular Tree Network for Source Code Representation Learning

Learning representation for source code is a foundation of many program analysis tasks. In recent years, neural networks have already shown success in this area, but most existing models did not make full use of the unique structural information of programs. Although abstract syntax tree (AST)-based neural models can handle the tree structure in the source code, they cannot capture the richness of different types of substructure in programs. In this article, we propose a modular tree network that dynamically composes different neural network units into tree structures based on the input AST. Different from previous tree-structural neural network models, a modular tree network can capture the semantic differences between types of AST substructures. We evaluate our model on two tasks: program classification and code clone detection. Our model achieves the best performance compared with state-of-the-art approaches in both tasks, showing the advantage of leveraging more elaborate structure information of the source code.
OptiSwap: Fast Optimistic Fair Exchange

Selling digital commodities securely over the Internet is a challenging task when Seller and Buyer do not trust each other. With the advent of cryptocurrencies, one prominent solution for digital exchange is to rely on a smart contract as a trusted arbiter that fairly resolves disputes when Seller and Buyer disagree. Such protocols have an optimistic mode, where the digital exchange between the parties can be completed with only minimal interaction with the smart contract. In this work we present OptiSwap, a new smart contract based fair exchange protocol that significantly improves the optimistic case of smart contract based fair exchange protocols. In particular, OptiSwap has almost no overhead in communication complexity, and improves on the computational overheads of the parties compared to prior solutions. An additional feature of OptiSwap is a protection mechanism against so-called grieving attacks, where an adversary attempts to violate the financial fairness of the protocol by forcing the honest party to pay fees. We analyze OptiSwap's security in the UC model and provide benchmark results over Ethereum.
Survey of machine learning algorithms for breast cancer detection using mammogram images

Breast cancer is the primary cause of death in most cancer affected women. Mammography is one of the most dependable strategies for early detection and diagnosis of breast cancer and reduces the death rate. Mammograms are radiographic images of the breast which are utilized to identify the early symptoms of breast cancer. These radiographic images reduce human errors in detecting cysts and reduce the diagnosing time and also increase the diagnosis accuracy. An overview of the machine learning techniques for breast cancer detection and classification has been presented in this paper, which can be divided into three main stages: pre-processing, extraction of features, and classification. This article discusses about the effects of several Machine learning techniques for automation of mammogram image classification are investigated. This investigation assembles agent works that show how Machine learning technique is applied to the result of different issues identified with various analytic science examinations. This study portrays the impacts of pre-taken care of mammogram images before entering the classifier, which brings about higher effective classification. The detection stage is trailed by segmentation of the tumor region in a mammogram image. This study is an attempt to gather and compare the various screening techniques, classifiers, and their performance in terms of sensitivity, specificity and exactness for breast cancer diagnosis.
A blockchain-based secure transaction model for distributed energy in Industrial Internet of Things

In the Industrial Internet of Things (IIoT), peer-to-peer (P2P) distributed energy (DE) transactions exist in various scenarios. This paper attempts to improve the intelligence, real-timeliness, and security of the direct transaction between DE generation companies (DEGCs) and users, and reduce the default frequency of distributed power (DP) transactions. For these purposes, a P2P DE transaction model for the IIoT was proposed based on blockchain. Firstly, a blockchain-based distributed energy peer-to-peer transaction framework is constructed, which is more suitable for generalized energy transactions based on typical transaction scenarios of the IIoT. Using credit value evaluation and smart contracts to ensure the transparency, openness, and non-tampering of credit scores. On this basis, the energy currency reward mechanism is used to promote the trustworthiness of transaction nodes and maintain transaction security. Finally, the P2P direct transaction based on credit value was designed to improve the transaction efficiency and security. Through case analysis, the DE transaction model for the IIoT, which is based on the credit value of the blockchain, supports fast and frequent energy transactions, as it overcomes the confirmation delays of energy blockchain transactions. The proposed model improves the efficiency of DE transactions in the IIoT, effectively suppresses default frequency, and maintains the order of DE market in the IIoT.
QualDash: Adaptable Generation of Visualisation Dashboards for Healthcare Quality Improvement

Adapting dashboard design to different contexts of use is an open question in visualisation research. Dashboard designers often seek to strike a balance between dashboard adaptability and ease-of-use, and in hospitals challenges arise from the vast diversity of key metrics, data models and users involved at different organizational levels. In this design study, we present QualDash, a dashboard generation engine that allows for the dynamic configuration and deployment of visualisation dashboards for healthcare quality improvement (QI). We present a rigorous task analysis based on interviews with healthcare professionals, a co-design workshop and a series of one-on-one meetings with front line analysts. From these activities we define a metric card metaphor as a unit of visual analysis in healthcare QI, using this concept as a building block for generating highly adaptable dashboards, and leading to the design of a Metric Specification Structure (MSS). Each MSS is a JSON structure which enables dashboard authors to concisely configure unit-specific variants of a metric card, while offloading common patterns that are shared across cards to be preset by the engine. We reflect on deploying and iterating the design of OualDash in cardiology wards and pediatric intensive care units of five NHS hospitals. Finally, we report evaluation results that demonstrate the adaptability, ease-of-use and usefulness of QualDash in a real-world scenario.
Digital Voting: A Blockchain-based E-Voting System using Biohash and Smart Contract

The voting method is the mechanism for implementing the people's view to better administer the system. Throughout recent years, conventional votes have pleased neither people nor government authorities. They are not entirely safe since ballots are simple to strike. It also challenges voter safety and transparency. Additionally, counting the votes takes too long. Modification of voting worldwide is an intriguing issue in current voting system. To solve these problems Digital technology is used in the voting phase for citizens in many nations. Digitalisation alone can not fix the issues fully. There are also many ways of manipulating or modifying digital technology and hindering voting. There should be fairness, independence and impartiality in the voting method. By analyzing the aforementioned problems, this research work combines the digitalisation with the blockchain technology to provide a voting mechanism. The main goals of our voting mechanism are to provide integrity, anonymity, privacy, and security of voters. With the use of markle tree and fingerprint hash, the data integrity and anonymity, privacy, security of the voters has been achieved in our proposed digital voting systems.
Integrative Data Augmentation with U-Net Segmentation Masks Improves Detection of Lymph Node Metastases in Breast Cancer Patients

Deep learning models have potential to improve performance of automated computer-assisted diagnosis tools in digital histopathology and reduce subjectivity. The main objective of this study was to further improve diagnostic potential of convolutional neural networks (CNNs) in detection of lymph node metastasis in breast cancer patients by integrative augmentation of input images with multiple segmentation channels. For this retrospective study, we used the PatchCamelyon dataset, consisting of 327,680 histopathology images of lymph node sections from breast cancer. Images had labels for the presence or absence of metastatic tissue. In addition, we used four separate histopathology datasets with annotations for nucleus, mitosis, tubule, and epithelium to train four instances of U-net. Then our baseline model was trained with and without additional segmentation channels and their performances were compared. Integrated gradient was used to visualize model attribution. The model trained with concatenation/integration of original input plus four additional segmentation channels, which we refer to as ConcatNet, was superior (AUC 0.924) compared to baseline with or without augmentations (AUC 0.854; 0.884). Baseline model trained with one additional segmentation channel showed intermediate performance (AUC 0.870-0.895). ConcatNet had sensitivity of 82.0% and specificity of 87.8%, which was an improvement in performance over the baseline (sensitivity of 74.6%; specificity of 80.4%). Integrated gradients showed that models trained with additional segmentation channels had improved focus on particular areas of the image containing aberrant cells. Augmenting images with additional segmentation channels improved baseline model performance as well as its ability to focus on discrete areas of the image.