Reviewerly/exHarmony huggingface dataset:
A Systematic Review of Privacy-Preserving Blockchain in e-Medicine

Blockchains provide a decentralized, permanent, and verifiable ledger that can record transactions having digital properties, leading to a fundamental shift in various revolutionary scenarios, such as smart cities, eHealth, or eGovernment. Blockchain has a wide variety of applications in healthcare that can enhance mobile health applications, tracking devices, exchanging, and storing electronic medical records, clinical trial data, and insurance information storage. The survey covers privacy strategies in public and unauthorized blockchains, e.g., Bitcoin and Ethereum, and privacy-preserving research ideas and solutions in both public and private blockchains. We also take into account various blockchain scenarios such as privacy-preserving identity management systems and platforms.
The Lightning Network: Turning Bitcoin into money

The Lightning Network (LN) is a means of netting Bitcoin payments outside the blockchain. We find a significant association between LN adoption and reduced blockchain congestion, suggesting that the LN has helped improve the efficiency of Bitcoin as a means of payment. This improvement cannot be explained by other factors, such as changes in demand or the adoption of SegWit. We find mixed evidence on whether increased centralization in the Lightning Network has improved its efficiency. Our findings have implications for the future of cryptocurrencies as a means of payment and their environmental footprint.
Modulation leakage-free continuous-variable quantum key distribution

Abstract Distributing cryptographic keys over public channels in a way that can provide information-theoretic security is the holy grail for secure communication. This can be achieved by exploiting quantum mechanical principles in so-called quantum key distribution (QKD). Continuous-variable (CV) QKD based on coherent states, in particular, is an attractive scheme for secure communication since it requires only standard telecommunication technology that can operate at room temperature. However, a recently discovered side-channel created in the process of state preparation leads to a leakage of information about the transmitted quantum state, opening a security loophole for eavesdroppers to compromise the security of the CVQKD system. Here, we present a CVQKD system without this modulation leakage vulnerability. Our implementation is based on a baseband modulation approach and uses an in-phase and quadrature (IQ) modulator for state preparation and radio frequency heterodyne detection together with carefully designed digital signal processing for state measurement. We consider practical aspects in the implementation and demonstrate the generation of a composable secret key secure against collective attacks. This work is a step towards protecting CVQKD systems against practical imperfections of physical devices and operational limitations without performance degradation.
Bias reduction in representation of histopathology images using deep feature selection

Abstract Appearing traces of bias in deep networks is a serious reliability issue which can play a significant role in ethics and generalization related concerns. Recent studies report that the deep features extracted from the histopathology images of The Cancer Genome Atlas (TCGA), the largest publicly available archive, are surprisingly able to accurately classify the whole slide images (WSIs) based on their acquisition site while these features are extracted to primarily discriminate cancer types. This is clear evidence that the utilized Deep Neural Networks (DNNs) unexpectedly detect the specific patterns of the source site, i.e, the hospital of origin, rather than histomorphologic patterns, a biased behavior resulting in degraded trust and generalization. This observation motivated us to propose a method to alleviate the destructive impact of hospital bias through a novel feature selection process. To this effect, we have proposed an evolutionary strategy to select a small set of optimal features to not only accurately represent the histological patterns of tissue samples but also to eliminate the features contributing to internal bias toward the institution. The defined objective function for an optimal subset selection of features is to minimize the accuracy of the model to classify the source institutions which is basically defined as a bias indicator. By the conducted experiments, the selected features extracted by the state-of-the-art network trained on TCGA images (i.e., the KimiaNet), considerably decreased the institutional bias, while improving the quality of features to discriminate the cancer types. In addition, the selected features could significantly improve the results of external validation compared to the entire set of features which has been negatively affected by bias. The proposed scheme is a model-independent approach which can be employed when it is possible to define a bias indicator as a participating objective in a feature selection process; even with unknown bias sources.
Eye movements as a predictor of preference for progressive power lenses

The purpose of this study is to determine if there is any correlation between the characteristics of the user’s eye movements (EMs) and the preference of the user when wearing different Progressive power lenses (PPLs) distributions. An eye-tracker system with a sample rate of 120Hz and temporal resolution of 8.3ms (Tobii-X3-120) was used to register EMs of 38 PPL users when reading in a computer screen with 2 types of PPLs (PPL-soft and PPL-hard). Number of fixations, complete fixation time, fixation duration mean, saccade duration mean, saccade distance mean, and number of regressions were analyzed for 6 different regions of the computer screen. A statistically significant difference was observed between the characteristics of the user’s EMs and the user’s PPL subjective preference (p&lt;0.05*). Subjects that preferred the PPL-hard presented significantly lower complete fixation time, lower fixation duration mean and lower number of regressions than those subjects indicating a preference for the PPL-soft. Results of this study suggest that eye-tracking systems can be used as PPL design recommendation systems according to the user EMs performance.
Joint Learning of Salient Object Detection, Depth Estimation and Contour Extraction

Benefiting from color independence, illumination invariance and location discrimination attributed by the depth map, it can provide important supplemental information for extracting salient objects in complex environments. However, high-quality depth sensors are expensive and can not be widely applied. While general depth sensors produce the noisy and sparse depth information, which brings the depth-based networks with irreversible interference. In this paper, we propose a novel multi-task and multi-modal filtered transformer (MMFT) network for RGB-D salient object detection (SOD). Specifically, we unify three complementary tasks: depth estimation, salient object detection and contour estimation. The multi-task mechanism promotes the model to learn the task-aware features from the auxiliary tasks. In this way, the depth information can be completed and purified. Moreover, we introduce a multi-modal filtered transformer (MFT) module, which equips with three modality-specific filters to generate the transformer-enhanced feature for each modality. The proposed model works in a depth-free style during the testing phase. Experiments show that it not only significantly surpasses the depth-based RGB-D SOD methods on multiple datasets, but also precisely predicts a high-quality depth map and salient contour at the same time. And, the resulted depth map can help existing RGB-D SOD methods obtain significant performance gain. The source code will be publicly available at https://github.com/Xiaoqi-Zhao-DLUT/MMFT.
A Layered Reference Model for Penetration Testing with Reinforcement Learning and Attack Graphs

This paper considers key challenges to using re-inforcement learning (RL) with attack graphs to automate penetration testing in real-world applications from a systems perspective. RL approaches to automated penetration testing are actively being developed, but there is no consensus view on the representation of computer networks with which RL should be interacting. Moreover, there are significant open challenges to how those representations can be grounded to the real networks where RL solution methods are applied. This paper elaborates on representation and grounding using topic challenges of interacting with real networks in real-time, emulating realistic adversary behavior, and handling unstable, evolving networks. These challenges are both practical and mathematical, and they directly concern the reliability and dependability of penetration testing systems. This paper proposes a layered reference model to help organize related research and engineering efforts. The presented layered reference model contrasts traditional models of attack graph workflows because it is not scoped to a sequential, feed-forward generation and analysis process, but to broader aspects of lifecycle and continuous deployment. Researchers and practitioners can use the presented layered reference model as a first-principles outline to help orient the systems engineering of their penetration testing systems.
On the importance of color in mass spectrometry imaging

Mass spectrometry imaging (MSI) data visualization relies on heatmaps to show the spatial distribution and measured abundances of molecules within a sample. Nonuniform color gradients such as jet are still commonly used to visualize MSI data, increasing the probability of data misinterpretation and false conclusions. Also, the use of nonuniform color gradients and the combination of hues used in common colormaps make it challenging for people with color vision deficiencies (CVDs) to visualize and accurately interpret data. Here we present best practices for choosing a colormap to accurately display MSI data, improve readability, and accommodate all CVDs. We also provide other resources on the misuse of color in the scientific field and resources on scientifically derived colormaps presented herein.
The National Geochemical Survey of Australia: review and impact

The National Geochemical Survey of Australia (NGSA) is Australia's first national-scale geochemical survey. It was delivered to the public on 30 June 2011, after almost five years of stakeholder engagement, strategic planning, sample collection, preparation and analysis, quality assurance/quality control, and preliminary data analytics. The project was comprehensively documented in seven initial open-file reports and six data and map sets, followed over the next decade by more than 70 well-cited scientific publications. This review compiles the body of work and knowledge that emanated from the project to-date as an indication of the impact the NGSA had over the decade 2011–21. The geochemical fabric of Australia as never seen before has been revealed by the NGSA. This has spurred further research and stimulated the mineral exploration industry. This paper also critically looks at operational decisions taken at project time (2007–11) that were good and perhaps – with the benefit of hindsight – not so good, with the intention of providing experiential advice for any future large-scale geochemical survey of Australia or elsewhere. We conclude that the NGSA was a low-cost, high-benefit project that delivered scientific excellence, promoted stakeholder engagement, demonstrated national leadership, and contributed to enhanced national resource wealth. Thematic collection: This article is part of the Continental-scale geochemical mapping collection available at: https://www.lyellcollection.org/topic/collections/continental-scale-geochemical-mapping
MAENet: A novel multi-head association attention enhancement network for completing intra-modal interaction in image captioning

Image captioning attracts much attention as it bridges computer vision and natural language processing. Recent works show that transformer-based models with the multi-head self-attention can explore intra-modal interactions for generating high-quality image captions. However, the subspace of each attention head is operated independently in these multi-head attention methods, which ignores the association between attention heads and makes the learning of intra-modal interaction incomplete. In this paper, we propose a Multi-head Association Attention Enhancement Network (MAENet) for image captioning, which leverages a novel Multi-head Association Attention Enhancement (MAE) block for completing intra-modal interaction learning. The proposed MAE block contains Multi-head Association Attention (MAA) and Attention Enhancement (AE) module.The MAA calculates the contributive weight of different attention heads, and captures the associated information from adjacent attention subspaces via learned associative parameters. The AE module follows with the MAA to further enhance the association attention results through an additional spatial and channel-wise attention aggregation. It's worth noting that the MAE block is a plug-and-play module that can be cascaded with other multi-head attention mechanisms. Extensive experiments on MS COCO show that our model achieves a quite competitive performance, especially for the model of MAE block cascaded with X-linear attention obtains the best-reported SPICE performance of 23.5% on the Karpathy test split. This clearly demonstrates that the proposed model can better model the interactive information and result in superior captions.
Are Retail Customers Ready for Service Robot Assistants?

The use of service robots is rapidly expanding in many industries, including the retail sector. In some Asian countries retail companies already deploy in-store humanoid robot assistants that interact with customers in a personalized and dynamic way. However, little is known about customers' perceptions of these robot-assisted interactions. Therefore, we compared customers' perceived service quality of a human-robot interaction with a human-human interaction in a retail store, using an experiment with hypothetical scenarios (N = 425). Participants' general attitudes towards robots, their age, gender, and educational level were included as moderators in the analyses (a mixed-design ANOVA model). Results showed that participants valued service quality higher in the human-human interaction scenario. Additionally, findings from the moderation analyses indicated that perceived service quality of the human- robot interaction and the human-human interaction did not statistically significantly differ for participants with relatively high positive attitudes towards robots. Participants with relatively low positive attitudes towards robots, however, held statistically significantly lower perceptions of service quality for the human-robot interaction. Neither age, gender nor educational level statistically significantly influenced participants' perceived service quality scores for both service encounters.
Quantum Speed Limit for a Moving Qubit inside a Leaky Cavity

The quantum speed limit (QSL) is a theoretical lower bound of the time required for a quantum system to evolve from an arbitrary initial state to its orthogonal counterpart. This figure can be used to characterize the dynamics of open quantum systems, including non-Markovian maps. In this paper, we investigate the QSL time for a model that consists of a single qubit moving inside a leaky cavity. Notably, we show that for both weak and strong coupling regimes, the QSL time increases while we boost the velocity of the qubit inside the leaky cavity. Moreover, it is observed that by increasing the qubit velocity, the speed of the evolution tends to a constant value, and the system becomes more stable. The results provide a better understanding of the dynamics of atom-photon couplings and can be used to enhance the controllability of quantum systems.
Blockchain-based privacy preservation framework for healthcare data in cloud environment

ABSTRACTABSTRACTThe storage of Electronic Health Records (EHRs) on mobile cloud environments has undergone a paradigm shift in recent years, with mobile devices integrating with cloud computing to improve medical data transfers between patients and healthcare providers. This sophisticated paradigm allows for minimal operational costs, significant flexibility, and the use of electronic health records (EHRs). However, for e-health systems, this new paradigm poses concerns regarding data privacy and network security. It's a challenging problem to exchange EHRs consistently among mobile users while maintaining high-security levels in the mobile cloud. It's a challenging problem to exchange EHRs consistently among mobile users while maintaining high-security levels in the mobile cloud. Here, this paper intends to introduce a novel blockchain technology for secure health data in the cloud, which aids in ensuring authentication and offers integrity to medical records. Here, blockchain with optimal encryption is deployed via an improved blowfish model that also guarantees authentication features. Further, the optimal key generation is carried out by a new approach termed as Elephant Herding Optimization with Opposition-based Learning (EHO-OBL). Thus, the data integrity is maintained by the developed approach, and at last, the supremacy of the presented approach is proved concerning various measures. Accordingly, the key generation time of the proposed method has attained less value, and it was 51.04%, 91.48%, 92.64%, 91.48%, 89.99%, 91.06% and 91.48% better than traditional Blowfish, Rivest–Shamir–Adleman (RSA), Advanced Encryption Standard (AES), Elliptic-Curve Cryptography (ECC), Elephant Herding Optimization (EHO), Moth-Flame Optimization (MFO) and Whale Optimization (WOA) models, for file size of 10 kb.KEYWORDS: Securityprivacy preservationblockchaindata integrityEHO-OBL approach Disclosure statementNo potential conflict of interest was reported by the author(s).Nomenclature AbbreviationDescriptionACLAccess Control ListAESAdvanced Encryption StandardEHRsElectronic Health RecordsEHOElephant Herding OptimizationECCElliptic-Curve CryptographyHGDHealthcare Data GatewayIoMTInternet Of Medical ThingsIPFSInterplanetary File SystemICSIndicator-Centric SchemaKSIBCKeyless Signature InfrastructureMFOMoth-Flame OptimizationMCCMobile Cloud ComputingOBLOpposition Based LearningRSARivest – Shamir–AdlemanSEHRTBSecure and Efficient Health Record Transaction Utilizing Block ChainWOAWhale OptimizationIoTInternet of thingsTP-EHRTamper-Proofing-Electronic Health Records
Full counting statistics of the photocurrent through a double quantum dot embedded in a driven microwave resonator

Detection of single, itinerant microwave photons is an important functionality for emerging quantum technology applications as well as of fundamental interest in quantum thermodynamics experiments on heat transport. In a recent experiment [W. Khan et al., Nat. Commun. 12, 5130 (2021)], it was demonstrated that a double quantum dot (DQD) coupled to a microwave resonator can act as an efficient and continuous photodetector by converting an incoming stream of photons to an electrical photocurrent. In the experiment, average photon and electron flows were analyzed. Here we theoretically investigate, in the same system, the fluctuations of the photocurrent through the DQD for a coherent microwave drive of the resonator. We consider both the low frequency full counting statistics as well as the finite-frequency noise (FFN) of the photocurrent. Numerical results and analytical expressions in limiting cases are complemented by a mean-field approach neglecting dot-resonator correlations, providing a compelling and physically transparent picture of the photocurrent statistics. We find that for ideal, unity efficiency detection, the fluctuations of the charge current reproduce the Poisson statistics of the incoming photons, while the statistics for non-ideal detection is sub-Poissonian. Moreover, the FFN provides information of the system parameter dependence of detector short-time properties. Our results give novel insight into microwave photon-electron interactions in hybrid dot-resonator systems and provide guidance for further experiments on continuous detection of single microwave photons.
Prior depth-based multi-view stereo network for online 3D model reconstruction

This study addresses the online multi-view stereo (MVS) problem when reconstructing precise 3D models in real time. To solve this problem, most previous studies adopted a motion stereo approach that sequentially estimates depth maps from multiple localized images captured in a local time window. To compute the depth maps quickly, the motion stereo methods process down-sampled images or use a simplified algorithm for cost volume regularization; therefore, they generally produce reconstructed 3D models that are inaccurate. In this paper, we propose a novel online MVS method that accurately reconstructs high-resolution 3D models. This method infers prior depth information based on sequentially estimated depths and leverages it to estimate depth maps more precisely. The method constructs a cost volume by using the prior-depth-based visibility information and then fuses the prior depths into the cost volume. This approach significantly improves the stereo matching performance and completeness of the estimated depths. Extensive experiments showed that the proposed method outperforms other state-of-the-art MVS and motion stereo methods. In particular, it significantly improves the completeness of 3D models.
Monthly streamflow prediction using hybrid extreme learning machine optimized by bat algorithm: a case study of Cheliff watershed, Algeria

In the present paper, we propose a new approach for monthly streamflow prediction based on the extreme learning machine (ELM) and the metaheuristic bat algorithm (Bat-ELM). The performance of the Bat-ELM was compared to that of ELM, support vector regression (SVR), Gaussian process regression (GPR), multilayer perceptron neural network (MLPNN), and generalized regression neural network (GRNN). The proposed models were applied using data from three hydrometric stations located in the Cheliff Basin, Algeria. The results showed that the Bat-ELM was more satisfactory than the standalone models. The Bat-ELM achieved the highest numerical performance with correlation coefficient and Nash-Sutcliffe efficiency ranging from 0.927 to 0.973 and from 0.846 to 0.944, respectively, much higher than the respective values obtained using the MLPNN, GRNN, SVR, GPR and ELM approaches. The obtained results demonstrate that the Bat-ELM is an interesting alternative algorithm for predicting high and extreme streamflow.
Features requirement elicitation process for designing a chatbot application

Abstract Today, chatbots are being employed across a wide range of businesses. The researchers will present the outcome of the literature exploration on attributes of chatbots that later become a reference to build a new chatbot for banking use. The goal is to use human natural language as an interaction medium to aid humans in doing any prescribed activity. It is important to think about how people engage with chatbots in order to create a viable and high‐quality chatbot solution. The authors aim to help the chatbot community by determining what qualities a chatbot should have and how to build a chatbot for a bank. For the purpose of compiling useful chatbot characteristics, the researchers searched through a group of linked papers. The survey was done on a small group of chatbot end users in order to get a sense of what functionalities are most important to the end users. On the basis of a summary of the literature research and data from a survey, a set of chatbot functional features is proposed. This research intended to provide a quality feature criterion for creating a chatbot application.