Reviewerly/exHarmony huggingface dataset:
Enhanced on-chip phase measurement by inverse weak value amplification

Abstract Optical interferometry plays an essential role in precision metrology such as in gravitational wave detection, gyroscopes, and environmental sensing. Weak value amplification enables reaching the shot-noise-limit of sensitivity, which is difficult for most optical sensors, by amplifying the interferometric signal without amplifying certain technical noises. We implement a generalized form of weak value amplification on an integrated photonic platform with a multi-mode interferometer. Our results pave the way for a more sensitive, robust, and compact platform for measuring phase, which can be adapted to fields such as coherent communications and the quantum domain. In this work, we show a 7 dB signal enhancement in our weak value device over a standard Mach-Zehnder interferometer with equal detected optical power, as well as frequency measurements with 2 kHz sensitivity by adding a ring resonator.
Efficient Multi Port-Based Teleportation Schemes

In this manuscript we analyse generalised port-based teleportation (PBT) schemes, allowing for transmitting more than one unknown quantum state (or a composite quantum state) in one go, where the state ends up in several ports at Bob's side. We investigate the efficiency of our scheme discussing both deterministic and probabilistic case, where parties share maximally entangled states. It turns out that the new scheme gives better performance than various variants of the optimal PBT protocol used for the same task. All the results are presented in group-theoretic manner depending on such quantities like dimensions and multiplicities of irreducible representations in the Schur-Weyl duality. The presented analysis was possible by considering the algebra of permutation operators acting on n systems distorted by the action of partial transposition acting on more than one subsystem. Considering its action on the n-fold tensor product of the Hilbert space with finite dimension, we present construction of the respective irreducible matrix representations, which are in fact matrix irreducible representations of the Walled Brauer Algebra. I turns out that the introduced formalism, and symmetries beneath it, appears in many aspects of theoretical physics and mathematics - theory of anti ferromagnetism, aspects of gravity theory or in the problem of designing quantum circuits for special task like for example inverting an unknown unitary.
Counterfactual Shapley Additive Explanations

Feature attributions are a common paradigm for model explanations due to their simplicity in assigning a single numeric score for each input feature to a model. In the actionable recourse setting, wherein the goal of the explanations is to improve outcomes for model consumers, it is often unclear how feature attributions should be correctly used. With this work, we aim to strengthen and clarify the link between actionable recourse and feature attributions. Concretely, we propose a variant of SHAP, Counterfactual SHAP (CF-SHAP), that incorporates counterfactual information to produce a background dataset for use within the marginal (a.k.a. interventional) Shapley value framework. We motivate the need within the actionable recourse setting for careful consideration of background datasets when using Shapley values for feature attributions with numerous synthetic examples. Moreover, we demonstrate the efficacy of CF-SHAP by proposing and justifying a quantitative score for feature attributions, counterfactual-ability, showing that as measured by this metric, CF-SHAP is superior to existing methods when evaluated on public datasets using tree ensembles.
Proof-of-principle demonstration of semi-quantum key distribution based on the Mirror protocol

Abstract Semi-quantum key distribution (SQKD) is used to establish a string of shared secret keys between a quantum party and a classical party. Here, we report the first proof-of-principle experimental demonstration of SQKD based on the Mirror protocol, which is the most experimentally feasible SQKD protocol, and equipped with time-phase encoding scheme employing the method of selective modulation. The experiment was performed at a repetition frequency of 62.5 MHz and a high raw key rate arrived at 69.8 kbps, and the average quantum bit error rate was found to be 4.56% and 2.78% for the “SWAP-x-Z” ( $\mathrm{x}\in \{01,10\}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>x</mml:mi> <mml:mo>∈</mml:mo> <mml:mo>{</mml:mo> <mml:mn>01</mml:mn> <mml:mo>,</mml:mo> <mml:mn>10</mml:mn> <mml:mo>}</mml:mo> </mml:math> ) and the “CTRL-X”, respectively. The results demonstrate the feasibility of our system, and this study is helpful for future research on SQKD experiments.
Hierarchical qubit maps and hierarchically implemented quantum error correction

We consider hierarchically implemented quantum error correction (HI-QEC), in which the fidelities of logical qubits are differentially optimized to enhance the capabilities of quantum devices in scientific applications. By employing qubit representations that propagate hierarchies in simulated systems to those in logical qubit noise sensitivities, heterogeneity in the distribution of physical-to-logical qubits can be systematically structured. For concreteness, we estimate HI-QEC's impact on surface code resources in computing low-energy observables to fixed precision, finding up to approximately $60%$ reductions in qubit requirements plausible in early error-corrected simulations. Hierarchical qubit maps are also possible without error correction in qubit and qudit systems where fidelities are nonuniform, either unintentionally or by design. Hierarchical optimizations are another element in the codesign process of quantum computations, including quantum simulations for nuclear and particle physics.
Deep Neural Networks Hyperparameter Optimization Using Particle Swarm Optimization for Detecting Frauds Transactions

The recent explosions and developments of new technologies have changed our lives, and that was shown by the quantity of information shared, posted, and stocked in big companies like Facebook, Google, Amazon, and so forth. Millions of transactions are made by cardholders every year to buy online using credit card as a mobile wallet or for sample payment and that make credit card transaction more frequent today. The developments of communication technologies and E-commerce have made credit cards the most ordinary methods of payment for both online and regular purchases. As a result, millions of online transactions are subject to various types of fraud. So security in this system is required to prevent fraudulent transactions. In this direction, researchers to detect this fraud invent many approaches. Traditional techniques cannot detect sophisticated fraudulent. Furthermore, analysis of cardholder behaviors or static risk management rules of the frauds have never stopped the fraudsters to commit their crimes. However, artificial intelligence techniques such as deep learning and machine learning have been able to handle these issues. This paper proposes an approach to detect fraud transactions by optimizing Deep Neural Networks (DNNs) hyperparameters using Particle Swarm Optimization (PSO) as optimization methods and compare them with the grid search (GS) method. The results obtained in terms of precision, accuracy, recall, F1-score, Time, and Area under the Curve (AUC) have shown that the PSO can generate better solutions in a short time in comparison with the GS method.
Importance of Preprocessing in Histopathology Image Classification Using Deep Convolutional Neural Network

The aim of this study is to propose an alternative and hybrid solution method for diagnosing the disease from histopathology images taken from animals with paratuberculosis and intact intestine. In detail, the hybrid method is based on using both image processing and deep learning for better results. Reliable disease detection from histo-pathology images is known as an open problem in medical image processing and alternative solutions need to be developed. In this context, 520 histopathology images were collected in a joint study with Burdur Mehmet Akif Ersoy University, Faculty of Veterinary Medicine, and Department of Pathology. Manually detecting and interpreting these images requires expertise and a lot of processing time. For this reason, veterinarians, especially newly recruited physicians, have a great need for imaging and computer vision systems in the development of detection and treatment methods for this disease. The proposed solution method in this study is to use the CLAHE method and image processing together. After this preprocessing, the diagnosis is made by classifying a convolutional neural network sup-ported by the VGG-16 architecture. This method uses completely original dataset images. Two types of systems were applied for the evaluation parameters. While the F1 Score was 93% in the method classified without data preprocessing, it was 98% in the method that was preprocessed with the CLAHE method.
Individual differences in the experience of body ownership are related to cortical thickness

Abstract The widely used rubber hand illusion (RHI) paradigm provides insight into how the brain manages conflicting multisensory information regarding bodily self-consciousness. Previous functional neuroimaging studies have revealed that the feeling of body ownership is linked to activity in the premotor cortex, the intraparietal areas, the occipitotemporal cortex, and the insula. The current study investigated whether the individual differences in the sensation of body ownership over a rubber hand, as measured by subjective report and the proprioceptive drift, are associated with structural brain differences in terms of cortical thickness in 67 healthy young adults. We found that individual differences measured by the subjective report of body ownership are associated with the cortical thickness in the somatosensory regions, the temporo-parietal junction, the intraparietal areas, and the occipitotemporal cortex, while the proprioceptive drift is linked to the premotor area and the anterior cingulate cortex. These results are in line with functional neuroimaging studies indicating that these areas are indeed involved in processes such as cognitive-affective perspective taking, visual processing of the body, and the experience of body ownership and bodily awareness. Consequently, these individual differences in the sensation of body ownership are pronounced in both functional and structural differences.
A Blockchain-Based Consent Mechanism for Access to Fitness Data in the Healthcare Context

Wearable fitness devices are widely used to track an individual&#x2019;s health and physical activities to improve the quality of health services. These devices sense a considerable amount of sensitive data processed by a centralized third party. While many researchers have thoroughly evaluated privacy issues surrounding wearable fitness trackers, no study has addressed privacy issues in trackers by giving control of the data to the user. Blockchain is an emerging technology with outstanding advantages in resolving consent management privacy concerns. As there are no fully transparent, legally compliant solutions for sharing personal fitness data, this study introduces an architecture for a human-centric, legally compliant, decentralized and dynamic consent system based on blockchain and smart contracts. Algorithms and sequence diagrams of the proposed system&#x2019;s activities show consent-related data flow among various agents, which are used later to prove the system&#x2019;s trustworthiness by formalizing the security requirements. The security properties of the proposed system were evaluated using the formal security modeling framework SeMF, which demonstrates the feasibility of the solution at an abstract level based on formal language theory. As a result, we have shown that blockchain technology is suitable for mitigating the privacy issues of fitness providers by recording individuals&#x2019; consent using blockchain and smart contracts.
CSCCRA: A Novel Quantitative Risk Assessment Model for SaaS Cloud Service Providers

Security and privacy concerns represent a significant hindrance to the widespread adoption of cloud computing services. While cloud adoption mitigates some of the existing information technology (IT) risks, research shows that it introduces a new set of security risks linked to multi-tenancy, supply chain and system complexity. Assessing and managing cloud risks can be a challenge, even for cloud service providers (CSPs), due to the increased numbers of parties, devices and applications involved in cloud service delivery. The limited visibility of security controls down the supply chain, further exacerbates this risk assessment challenge. As such, we propose the Cloud Supply Chain Cyber Risk Assessment (CSCCRA) model, a quantitative risk assessment model which is supported by supplier security posture assessment and supply chain mapping. Using the CSCCRA model, we assess the risk of a SaaS application, mapping its supply chain, identifying weak links in the chain, evaluating its security risks and presenting the risk value in monetary terms (£), with this, promoting cost-effective risk mitigation and optimal risk prioritisation. We later apply the Core Unified Risk Framework (CURF) in comparing the CSCCRA model with already established methods, as part of evaluating its completeness.
Map Construction and Path Planning Method for a Mobile Robot Based on Multi-Sensor Information Fusion

In order to solve the path planning problem of an intelligent vehicle in an unknown environment, this paper proposes a map construction and path planning method for mobile robots based on multi-sensor information fusion. Firstly, the extended Kalman filter (EKF) is used to fuse the ambient information of LiDAR and a depth camera. The pose and acceleration information of the robot is obtained through the pose sensor. The SLAM algorithm based on a fusion of LiDAR, a depth camera, and the inertial measurement unit was built. Secondly, the improved ant colony algorithm was used to carry out global path planning. Meanwhile, the dynamic window method was used to realize local planning and local obstacle avoidance. Finally, experiments were carried out on a robot platform to verify the reliability of the proposed method. The experiment results showed that the map constructed by multi-sensor information fusion was closer to the real environment, and the accuracy and robustness of SLAM were effectively improved. The turning angle of the path was smoothed using the improved ant colony algorithm, and the real-time obstacle avoidance was able to be realized using the dynamic window method. The efficiency of path planning was improved, and the automatic feedback control of the intelligent vehicle was able to be realized.
Role of social sustainability for financial inclusion and stability among Asian countries

Purpose Asia is the largest and most densely inhabited region in the world. Despite exhibiting an extremely expeditious economic growth, the majority of the world population categorized as poor resides in Asia, with more than a billion people financially excluded. This study aims to assess how social sustainability (SS) may increase financial inclusion (FI) and maintain financial stability (FS) in Asia. Design/methodology/approach Established on the stakeholder theory, the study analyzed the association among SS, FI and FS in Asia, employing a generalized method of moment’s estimation. The mediation of FI was also investigated in the relationship between SS and FS. Moreover, this study has analyzed the alternative proxies for the variables of interest to ensure dynamic results. Findings The findings point toward a positive association among SS, FI and FS. Furthermore, FI is observed to be undertaking a partial mediating role between SS and FS. Practical implications This study emphasizes that both SS and FI have individual parts in the amelioration of FS in Asia, whereas previous studies implied that FI is a mere tool for stimulating SS. Hence, Asian policymakers must keep these outcomes in mind due to their simultaneous contribution to FS. Originality/value The relationship between SS, FI and FS has received little attention in the literature. No previous study has deduced that increasing SS may instigate an increase in FI and FS. Additionally, quite contrary to previous studies that relied on narrow indicators, this study develops a broad measurement of SS by considering a wide range of crucial indicators for a sustainable society.
Determinants of immersive technology acceptance in the construction industry: management perspective

Purpose The usefulness of technology for managing projects in the construction industry is indisputable. The potential utility of immersive technologies (ImTs), including virtual and augmented reality, has recently received significant attention. However, the construction industry, especially in developing countries, lags on the implementation of technology generally and ImTs specifically. Forecasting the potential successful ImTs acceptance at the individual level is essential to strategic planning. The study's objective was to develop and test a conceptual model of factors influencing ImTs acceptance at the individual level in the construction industry. Design/methodology/approach A survey of construction management-level professionals in South Africa was undertaken. The study extended two complementary models, the technology acceptance model (TAM) and the theory of planned behavior (TPB), to analyze behavior towards technology acceptance using structural equation modelling. Findings Results indicated that attitude significantly influenced the intention to use ImTs and perceived usefulness ( PU) positively and significantly predicted the intention to use and usage attitude ( UA) . Further, the effects of perceived enjoyment ( PEn) on UA , and social norms (SNs) and perceived behavioral control ( PBC) on intention to use were positive and significant. Perceived ease of use (PEU) had negative and non-significant effects on intention to use and UA . By explaining 82% of the variance, the study established that the proposed model successfully evaluates how management-level professionals in the construction industry accept ImTs. Practical implications The study provides valuable insight into the acceptance of ImTs from the perspective of management-level stakeholders in the South African construction industry. It offers fundamental direction to create a general theory on integrating ImTs in construction. Originality/value This study systematically surveyed the intention to accept ImTs in the South African construction industry using an extension of the TAM and TPB models.
Computer-aided diagnosis for breast cancer detection and classification using optimal region growing segmentation with MobileNet model

Globally, breast cancer is considered a major reason for women’s morality. Earlier and accurate identification of breast cancer is essential to increase survival rates. Therefore, computer-aided diagnosis (CAD) models are developed to help radiologists in the detection of mammographic lesions. Presently, machine-learning (ML) and deep-learning (DL) models are widely employed in the disease diagnostic process. In this view, this paper designs a novel CAD using optimal region growing segmentation with a MobileNet (CAD-ORGSMN) model for breast cancer identification and classification. The proposed CAD-ORGSMN model involves different stages of operations, namely, pre-processing, segmentation, feature extraction, and classification. Primarily, the proposed model uses a Weiner filtering (WF)–based pre-processing technique to remove the existence of noise in the mammogram images. The CAD-ORGSMN model involves a glowworm swarm optimization (GSO)–based region growing technique for image segmentation where the initial seed points and threshold values are optimally created by the GSO algorithm. Besides, a MobileNet-based feature extractor is used in which the hyperparameters of the MobileNet model are optimally selected using a swallow swarm optimization (SSO) algorithm. Lastly, variational autoencoder is applied as a classifier to determine the class labels for the input mammogram images. The utilization of the GSO algorithm for the region growing technique and the SSO algorithm for hyperparameter optimization helps to considerably improve the breast cancer detection performance of the CAD-ORGSMN model. The performance validation of the CAD-ORGSMN model takes place against the Mini-MIAS database, and the obtained results highlighted the promising performance of the CAD-ORGSMN model over the recent state-of-the-art methods in terms of different measures.
Hybrid Spatiotemporal Contrastive Representation Learning for Content-Based Surgical Video Retrieval

In the medical field, due to their economic and clinical benefits, there is a growing interest in minimally invasive surgeries and microscopic surgeries. These types of surgeries are often recorded during operations, and these recordings have become a key resource for education, patient disease analysis, surgical error analysis, and surgical skill assessment. However, manual searching in this collection of long-term surgical videos is an extremely labor-intensive and long-term task, requiring an effective content-based video analysis system. In this regard, previous methods for surgical video retrieval are based on handcrafted features which do not represent the video effectively. On the other hand, deep learning-based solutions were found to be effective in both surgical image and video analysis, where CNN-, LSTM- and CNN-LSTM-based methods were proposed in most surgical video analysis tasks. In this paper, we propose a hybrid spatiotemporal embedding method to enhance spatiotemporal representations using an adaptive fusion layer on top of the LSTM and temporal causal convolutional modules. To learn surgical video representations, we propose exploring the supervised contrastive learning approach to leverage label information in addition to augmented versions. By validating our approach to a video retrieval task on two datasets, Surgical Actions 160 and Cataract-101, we significantly improve on previous results in terms of mean average precision, 30.012 ± 1.778 vs. 22.54 ± 1.557 for Surgical Actions 160 and 81.134 ± 1.28 vs. 33.18 ± 1.311 for Cataract-101. We also validate the proposed method’s suitability for surgical phase recognition task using the benchmark Cholec80 surgical dataset, where our approach outperforms (with 90.2% accuracy) the state of the art.
Medical Image Prediction for Diagnosis of Breast Cancer Disease Comparing the Machine Learning Algorithms: SVM, KNN, Logistic Regression, Random Forest, and Decision Tree to Measure Accuracy

Machine learning using data mining techniques are used rapidly in medical research to predict the disease diagnosis. The aim of this study is to evaluate the performance of SVM, KNN, Logistic Regression, Random Forest, and Decision Tree. Materials and Methods: A Total of 569 samples are collected from the UCI Machine Learning Laboratory. The samples are divided into benign and malignant cells using groups like SVM, KNN, Decision Tree, Random Forest, and Logistic Regression to compare the performance of benign and malignant cells. The required samples for this analysis are done by G power calculation. Minimum power of analysis is fixed as 0.8 and maximum accepted error is fixed as 0.5. Results: Logistic Regression prediction appears to be better accuracy of 95% than SVM, KNN, Decision Tree, and Random Forest of 92%, 90%, 85%, 91%. Significance of this proposed system is likely to be 0.55. Conclusion: In this study, it is found that Logistic Regression appears to be better than the SVM, KNN, Decision Tree, and Random Forest in breast cancer detection using the Wisconsin dataset.