Reviewerly/exHarmony huggingface dataset:
Multi-site cross-organ calibrated deep learning (MuSClD): Automated diagnosis of non-melanoma skin cancer

Although deep learning (DL) has demonstrated impressive diagnostic performance for a variety of computational pathology tasks, this performance often markedly deteriorates on whole slide images (WSI) generated at external test sites. This phenomenon is due in part to domain shift, wherein differences in test-site pre-analytical variables (e.g., slide scanner, staining procedure) result in WSI with notably different visual presentations compared to training data. To ameliorate pre-analytic variances, approaches such as CycleGAN can be used to calibrate visual properties of images between sites, with the intent of improving DL classifier generalizability. In this work, we present a new approach termed Multi-Site Cross-Organ Calibration based Deep Learning (MuSClD) that employs WSIs of an off-target organ for calibration created at the same site as the on-target organ, based off the assumption that cross-organ slides are subjected to a common set of pre-analytical sources of variance. We demonstrate that by using an off-target organ from the test site to calibrate training data, the domain shift between training and testing data can be mitigated. Importantly, this strategy uniquely guards against potential data leakage introduced during calibration, wherein information only available in the testing data is imparted on the training data. We evaluate MuSClD in the context of the automated diagnosis of non-melanoma skin cancer (NMSC). Specifically, we evaluated MuSClD for identifying and distinguishing (a) basal cell carcinoma (BCC), (b) in-situ squamous cell carcinomas (SCC-In Situ), and (c) invasive squamous cell carcinomas (SCC-Invasive), using an Australian (training, n = 85) and a Swiss (held-out testing, n = 352) cohort. Our experiments reveal that MuSCID reduces the Wasserstein distances between sites in terms of color, contrast, and brightness metrics, without imparting noticeable artifacts to training data. The NMSC-subtyping performance is statistically improved as a result of MuSCID in terms of one-vs. rest AUC: BCC (0.92 vs 0.87, p = 0.01), SCC-In Situ (0.87 vs 0.73, p = 0.15) and SCC-Invasive (0.92 vs 0.82, p = 1e-5). Compared to baseline NMSC-subtyping with no calibration, the internal validation results of MuSClD (BCC (0.98), SCC-In Situ (0.92), and SCC-Invasive (0.97)) suggest that while domain shift indeed degrades classification performance, our on-target calibration using off-target tissue can safely compensate for pre-analytical variabilities, while improving the robustness of the model.
What about the usability in low-code platforms? A systematic literature review

Low-code development is a concept whose presence has grown both in academia and the software industry and is discussed alongside others, such as model-driven engineering and domain-specific languages. Usability is an important concept in low-code contexts since users of these tools often lack a background in programming. Grey literature articles have also stated that low-code tools have high usability. This paper examines the current literature about low-code and no-code to discover more about them and their relationship with usability, particularly its quality, which factors are the most relevant, and how users view these tools. This focus on usability aims to provide a different point of view from other works on low-code. We performed a systematic literature review based on a formal protocol for this study. The search protocol returned a total of 207 peer-review articles across five databases, which was supplemented with a snowballing process. These were filtered using inclusion and exclusion criteria, resulting in 38 relevant articles that were analysed, synthesised and reported. Despite growing interest and a strong enterprise presence in academia, we did not find a formal definition of low-code, although common characteristics have been specified. We found that users have a heightened awareness of usability regarding low-code tools, with some authors performing feasibility studies on their implementations or listing factors that influence the user experience in a given tool. Researchers are considering usability factors unconsciously, and the low-code field would grow if research on usability increased. This paper also suggests a definition for low-code development.
Software as storytelling: A systematic literature review

Storytelling has always been a crucial, perhaps constitutive part of our lives. All communities have told stories. In recent years, software development is becoming increasingly recognized as a creative process that has a lot in common with the process of writing or telling a story. Aim: The objectives of this paper are: (a) to review and aptly classify current principles and approaches that describe software development as a form of storytelling; (b) to describe and understand the heuristic function of storytelling in software development; and (c) to discuss and single out the principles of storytelling that may play a role, hence constitute significant improvements to the practices of software developers. Method: To achieve these goals and objectives we conducted a systematic literature review of relevant scientific papers and subsequently analyzed them by means of a textual narrative synthesis. Results: More specifically, we retrieved, screened and examined 51 relevant publications. The synthesis we conducted allowed us to understand and better visualize the many interesting correspondences and analogies between those two seemingly different processes, namely storytelling and software development. In particular, in our work, we focused on describing and analyzing how certain principles underlying storytelling can be adapted and applied in current practices of software engineering. Conclusion: This paper presents and re-elaborates in a critical fashion and from a different angle a substantial body of knowledge and research recently carried out in the software development literature.
Blockchain-Enhanced Federated Learning Market With Social Internet of Things

The machine learning performance usually could be improved by training with massive data. However, requesters can only select a subset of devices with limited training data to execute federated learning (FL) tasks as a result of their limited budgets in today’s IoT scenario. To resolve this pressing issue, we devise a blockchain-enhanced FL market (BFL) to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(i)$ </tex-math></inline-formula> make data in computationally bounded devices available for training with social Internet of things, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(ii)$ </tex-math></inline-formula> maximize the amount of training data with given budgets for an FL task, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(iii)$ </tex-math></inline-formula> decentralize the FL market with blockchain. To achieve these goals, we firstly propose a trust-enhanced collaborative learning strategy (TCL) and a quality-oriented task allocation algorithm (QTA), where TCL enables training data sharing among trusted devices with social Internet of things, and QTA allocates suitable devices to execute FL tasks while maximizing the training quality with fixed budgets. Then, we devise an encrypted model training scheme (EMT) based on a simple but countervailable differential privacy methodology to prevent attacks from malicious devices. In addition, we also propose a contribution-driven delegated proof of stake (DPoS) consensus mechanism to guarantee the fairness of reward distribution in the block generation process. Finally, extensive evaluations are conducted to verify the proposed BFL could improve the total utility of requesters and average accuracy of FL models significantly.
Parallel Multiobjective Multiverse Optimizer for Path Planning of Unmanned Aerial Vehicles in a Dynamic Environment with Moving Obstacles

Path planning with collision avoidance for unmanned aerial vehicles (UAVs) in environments with moving obstacles is a complex process of navigation, often considered a hard optimization problem. Ordinary resolution algorithms may fail to provide flyable and collision-free paths under the time-consumption constraints required by the dynamic 3D environment. In this paper, a new parallel multiobjective multiverse optimizer (PMOMVO) is proposed and successfully applied to deal with the increased computation time of the UAV path planning problem in dynamic 3D environments. Collision constraints with moving obstacles and narrow pass zones were established based on a mathematical characterization of any intersection with lines connecting two consecutive drones’ positions. For the implementation, a multicore central processing unit (CPU) architecture was proposed according to the concept of master–slave processing parallelization. Each subswarm of the entire PMOMVO population was granted to a corresponding slave, and representative solutions were selected and shared with the master core. Slaves sent their local Pareto fronts to the CPU core representing the master that merged the received set of nondominated solutions and built a global Pareto front. Demonstrative results and nonparametric ANOVA statistical analyses were carried out to show the effectiveness and superiority of the proposed PMOMVO algorithm compared to other homologous, multiobjective metaheuristics.
Towards Higher-Order Zeroing Neural Network Dynamics for Solving Time-Varying Algebraic Riccati Equations

One of the most often used approaches for approximating various matrix equation problems is the hyperpower family of iterative methods with arbitrary convergence order, whereas the zeroing neural network (ZNN) is a type of neural dynamics intended for handling time-varying problems. A family of ZNN models that correlate with the hyperpower iterative methods is defined on the basis of the analogy that was discovered. These models, known as higher-order ZNN models (HOZNN), can be used to find real symmetric solutions of time-varying algebraic Riccati equations. Furthermore, a noise-handling HOZNN (NHOZNN) class of dynamical systems is introduced. The traditional ZNN and HOZNN dynamic flows are compared theoretically and numerically.
Training Variational Quantum Circuits with CoVaR: Covariance Root Finding with Classical Shadows

Exploiting near-term quantum computers and achieving practical value is a considerable and exciting challenge. Most prominent candidates as variational algorithms typically aim to find the ground state of a Hamiltonian by minimizing a single classical (energy) surface which is sampled from by a quantum computer. Here, we introduce a method we call CoVaR, an alternative means to exploit the power of variational circuits: We find eigenstates by finding joint roots of a polynomially growing number of properties of the quantum state as covariance functions between the Hamiltonian and an operator pool of our choice. The most remarkable feature of our CoVaR approach is that it allows us to fully exploit the extremely powerful classical shadow techniques; i.e., we simultaneously estimate a very large number >104–107 of covariances. We randomly select covariances and estimate analytical derivatives at each iteration applying a stochastic Levenberg-Marquardt step via a large but tractable linear system of equations that we solve with a classical computer. We prove that the cost in quantum resources per iteration is comparable to a standard gradient estimation; however, we observe in numerical simulations a very significant improvement by many orders of magnitude in convergence speed. CoVaR is directly analogous to stochastic gradient-based optimizations of paramount importance to classical machine learning while we also offload significant but tractable work onto the classical processor. As we demonstrate numerically, the approach shares features with phase-estimation protocols that prepare eigenstates with a dominant initial fidelity contribution.8 MoreReceived 3 May 2022Revised 2 September 2022Accepted 7 October 2022DOI:https://doi.org/10.1103/PhysRevX.12.041022Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithmsQuantum computationQuantum information processingQuantum Information
Automated breast cancer detection by reconstruction independent component analysis (RICA) based hybrid features using machine learning paradigms

Breast cancer (BC) masses and microcalcification are nonlinear with complex dynamics due to which radiologists fail to properly diagnose breast cancer. In this paper, we used a hybrid features extracting approach based on texture, morphological, Scale Invariant Feature Transform (SIFT), Gray Level Co-occurrence Matrix (GLCM), entropy, Elliptic Fourier Descriptors (EFDs), RICA, and sparse filtering methods. Various machine learning techniques have been employed to detect breast cancer, viz. Support Vector Machines (SVM), Decision Trees (DT), k-Nearest Neighbour, and Naïve Bayes classifiers. The RICA-based feature set using SVM RBF has resulted in total accuracy of (94.88%), and ROC AUC = 0.9914. The hybrid features using RICA have been computed with other combinatorial logics. Moreover, the highest performance to detect BC based on the fusion of features was obtained with RICA with Textural features using SVM Gaussian kernel and yielded a total accuracy of (97.55%), and ROC AUC = 0.9976. The hybrid features with RICA were found to yield the highest detection performance. It is revealed that the new feature-extracting approach can be useful for the early detection of breast cancer by physicians to decrease the overall mortality rate. The methods will be very useful for treatment modification to achieve better clinical outcomes.
Incipient fault diagnosis of analog circuit with ensemble HKELM based on fused multi-channel and multi-scale features

As an essential part in electronics-rich system, the failure of analog circuits will severely affect the system reliability and security. Incipient fault of analog circuit refers to the early stage of degradation fault where the fault characteristics are generally weak and almost indistinguishable. In order to enhance the reliability of electronic systems, it is necessary to diagnose incipient faults of analog circuits promptly and effectively. Existing approaches generally capture fault characteristics only from single signal, ignoring the valuable information inherent in different domains and scales. To address this problem, a novel diagnostic strategy based on multi-scale feature extraction and multi-channel feature fusion is designed to guarantee the completeness and richness of fault information. In this study, a deep extreme learning machine denoising auto-encoder (DELM-DAE) based method is proposed to conduct unsupervised multi-scale and multi-channel feature fusion to extract distinguishable features for incipient faults. The proposed method has higher learning efficiency and overcomes the common problem of low efficiency in deep learning model training. Meanwhile, in order to improve the ability to distinguish high-resolution features, an ensemble hybrid kernel extreme learning machine with novel roulette selection and weighted voting scheme is proposed to enhance the recognition performance and stability. In the verification experiment, the diagnosis accuracy on four typical circuits all reaches above 98%, which demonstrates that the proposed incipient fault diagnosis method for analog circuits has more conspicuous performance than other state-of-the-art methods.
Quick Quantum Steering: Overcoming Loss and Noise with Qudits

A primary requirement for a robust and unconditionally secure quantum network is the establishment of quantum nonlocal correlations over a realistic channel. While loophole-free tests of Bell nonlocality allow for entanglement certification in such a device-independent setting, they are extremely sensitive to loss and noise, which naturally arise in any practical communication scenario. Quantum steering relaxes the strict technological constraints of Bell nonlocality by re-framing it in an asymmetric manner, thus providing the basis for one-sided device-independent quantum networks that can operate under realistic conditions. Here we introduce a noise-robust and loss-tolerant test of quantum steering designed for single detector measurements that harnesses the advantages of high-dimensional entanglement. We showcase the improvements over qubit-based systems by experimentally demonstrating detection loophole-free quantum steering in 53 dimensions through simultaneous loss and noise conditions corresponding to 14.2 dB loss equivalent to 79 km of telecommunication fibre, and 36% of white noise. We go on to show how the use of high dimensions counter-intuitively leads to a dramatic reduction in total measurement time, enabling a quantum steering violation almost two orders of magnitude faster obtained by simply doubling the Hilbert space dimension. By surpassing the constraints imposed upon the device-independent distribution of entanglement, our loss-tolerant, noise-robust, and resource-efficient demonstration of quantum steering proves itself a critical ingredient for making device-independent quantum communication over long distances a reality.
The fast-food employees' usage intention of robots: A cross-cultural study

Robots are adopted in numerous tourism and hospitality segments, including restaurants. This study aims to empirically investigate the service robots' (SRs) acceptance among fast-food employees across two cultures – Egypt and Malaysia - and examines the moderating role of culture on usage intention. Extending the UTAUT model, Hofstede's culture dimensions, and relevant literature, a survey was developed for data collection from a convenient sample of employees in two international fast-food chains. Structural equation modelling was used for hypotheses-testing purposes. The results revealed that four UTAUT main constructs explain 62% and 44% of fast-food employees' behavioural intention to adopt robots in Egypt and Malaysia, respectively. The findings revealed that easiness-of-use, recommendations of trusted people to use robots, and knowledge and skills required to use robots affect the intention of usage by fast-food employees. This study extends the research of robotics' adoption in tourism and hospitality and consequently supports the planning for the post-Covid-19 resume. It provides several theoretical and operational implications for future research.
$\mathcal {X}$-Metric: An N-Dimensional Information-Theoretic Framework for Groupwise Registration and Deep Combined Computing

This paper presents a generic probabilistic framework for estimating the statistical dependency and finding the anatomical correspondences among an arbitrary number of medical images. The method builds on a novel formulation of the $N$-dimensional joint intensity distribution by representing the common anatomy as latent variables and estimating the appearance model with nonparametric estimators. Through connection to maximum likelihood and the expectation-maximization algorithm, an information\hyp{}theoretic metric called $\mathcal{X}$-metric and a co-registration algorithm named $\mathcal{X}$-CoReg are induced, allowing groupwise registration of the $N$ observed images with computational complexity of $\mathcal{O}(N)$. Moreover, the method naturally extends for a weakly-supervised scenario where anatomical labels of certain images are provided. This leads to a combined\hyp{}computing framework implemented with deep learning, which performs registration and segmentation simultaneously and collaboratively in an end-to-end fashion. Extensive experiments were conducted to demonstrate the versatility and applicability of our model, including multimodal groupwise registration, motion correction for dynamic contrast enhanced magnetic resonance images, and deep combined computing for multimodal medical images. Results show the superiority of our method in various applications in terms of both accuracy and efficiency, highlighting the advantage of the proposed representation of the imaging process.
Pyramid Transformer for Traffic Sign Detection

Automatic detection and classification of traffic signs have become an essential asset in the visual system of autonomous vehicles and self-driving cars. Recently, vision transformers have achieved remarkable performance on various benchmarks of visual tasks. We observe that prior ViTs could not provide satisfactory gain in traffic sign detection because the dataset size of this task is very small, and the class distribution of traffic signs is highly unbalanced. To solve the problems, we propose a novel Pyramid Transformer with hybrid architecture in this paper. Specifically, Pyramid Transformer follows a hierarchical architecture to build a feature pyramid with local and global information by using atrous convolutions. Furthermore, it inherits an intrinsic inductive bias and aims to learn multi-scale feature representation for objects of varying sizes, thereby enhancing the network robustness against the size discrepancy of traffic signs. We conduct experiments on the German Traffic Sign Detection Benchmark (GTSDB), which results demonstrate the superiority of the proposed model in the traffic sign detection task. More specifically, Pyramid Transformer achieves 77.8% mAP on GTSDB when applied to the Cascade RCNN as the backbone, which surpasses all the state-of-the-art methods.
Big data-driven investigation into the maturity of library research data services (RDS)

Research data management (RDM) poses a significant challenge for academic organizations. The creation of library research data services (RDS) requires assessment of their maturity, i.e., the primary objective of this study. Its authors have set out to probe the nationwide level of library RDS maturity, based on the RDS maturity model, as proposed by Cox et al. (2019), while making use of natural language processing (NLP) tools, typical for big data analysis. The secondary objective consisted in determining the actual suitability of the above-referenced tools for this particular type of assessment. Web scraping, based on 72 keywords, and completed twice, allowed the authors to select from the list of 320 libraries that run RDS, i.e., 38 (2021) and 42 (2022), respectively. The content of the websites run by the academic libraries offering a scope of RDM services was then appraised in some depth. The findings allowed the authors to identify the geographical distribution of RDS (academic centers of various sizes), a scope of activities undertaken in the area of research data (divided into three clusters, i.e., compliance, stewardship, and transformation), and overall potential for their prospective enhancement. Although the present study was carried within a single country only (Poland), its protocol may easily be adapted for use in any other countries, with a view to making a viable comparison of pertinent findings.