Reviewerly/exHarmony huggingface dataset:
Re-evaluating the hedge and safe-haven properties of Islamic indexes, gold and Bitcoin: evidence from DCC–GARCH and quantile models

Purpose This paper aims to investigate the hedge, safe-haven and diversifier properties of Islamic indexes, Bitcoin and gold for ten of the most affected countries by the coronavirus, which are the USA, Brazil, the UK, Italy, Spain, Germany, France, Russia, China and Malaysia. Design/methodology/approach This research uses the Ratner and Chiu (2013) methodology based on the dynamic conditional correlation models to improve Baur and McDermott (2010). The authors adopt a careful investigation of the features of a diversifier, hedge and safe haven using the dynamic conditional correlation–GARCH and quantile regression models. Findings Empirical results indicate that Islamic indexes are not considered as hedge assets for the conventional market for all studied countries during the COVID-19 pandemic crisis period. However, gold works as a strong hedge in all countries, except for Brazil and Malaysia. Bitcoin is a strong hedge in the USA and a strong hedge and safe haven in China. Practical implications International investors in China and the US stock markets should replace Islamic ‎indexes with Bitcoin in their conventional portfolio of securities during the pandemic. Originality/value To the best of the authors’ knowledge, this is the first paper that re-evaluates the hedge, safe-haven and diversifier properties of Islamic indexes, Bitcoin and gold for ten of the most affected countries by the coronavirus.
Optimal feature selection using novel flamingo search algorithm for classification of COVID-19 patients from clinical text

Though several AI-based models have been established for COVID-19 diagnosis, the machine-based diagnostic gap is still ongoing, making further efforts to combat this epidemic imperative. So, we tried to create a new feature selection (FS) method because of the persistent need for a reliable system to choose features and to develop a model to predict the COVID-19 virus from clinical texts. This study employs a newly developed methodology inspired by the flamingo's behavior to find a near-ideal feature subset for accurate diagnosis of COVID-19 patients. The best features are selected using a two-stage. In the first stage, we implemented a term weighting technique, which that is RTF-C-IEF, to quantify the significance of the features extracted. The second stage involves using a newly developed feature selection approach called the improved binary flamingo search algorithm (IBFSA), which chooses the most important and relevant features for COVID-19 patients. The proposed multi-strategy improvement process is at the heart of this study to improve the search algorithm. The primary objective is to broaden the algorithm's capabilities by increasing diversity and support exploring the algorithm search space. Additionally, a binary mechanism was used to improve the performance of traditional FSA to make it appropriate for binary FS issues. Two datasets, totaling 3053 and 1446 cases, were used to evaluate the suggested model based on the Support Vector Machine (SVM) and other classifiers. The results showed that IBFSA has the best performance compared to numerous previous swarm algorithms. It was noted, that the number of feature subsets that were chosen was also drastically reduced by 88% and obtained the best global optimal features.
Multiple color image cryptosystem based on coupled-logistic-map-biometric keys, QR decomposition with column pivoting and optical Fresnel transform

A novel multiple color image cryptosystem based on coupled-logistic-map-biometric keys (CBK), QR decomposition with column pivoting and optical Fresnel transform (FT), is proposed. In this system, the four sub-bands of each primary color image are produced by the discrete wavelet transform (DWT). The LL sub-bands of four images are fused. The fused image is divided into R, G, and B channels. The two sets of channels (R and B) and (G and B) are independently encoded into complex images, scrambled with generalized Arnold transform (GAT) and then decomposed by QRD with column pivoting. A novel concept of single-channel image constructed by adding of Q1.R2 and Q2.R1 modulated with the R channel of CBK is introduced because the parameters of the coupled logistic map and uniqueness of iris image protect against unauthorized access. The single-channel image is modulated by G channel of CBK and then Fresnel transformed. The phase and amplitude truncation operations are applied to the resultant image to obtain the first cipher image and the first phase key, respectively. The first cipher image is modulated by B channel of CBK and then Fresnel transformed. Finally, the phase and amplitude truncation operations are performed to the transformed image to get the final cipher image and the second phase key, respectively. Two decryption keys produced by QR decomposition with column pivoting and two-phase keys are asymmetric keys to resist potential attacks. Moreover, the parameters of the coupled logistic map (in R, G, and B channels of CBK), GAT and FT supply a large key space of the cryptosystem. A hybrid opto-electronic system can be used to perform the proposed approach. Numerical simulations demonstrate the validity and viability of the proposed scheme.
COVID-19 pandemic-induced organisational cultural shifts and employee information security compliance behaviour: a South African case study

Purpose The purpose of this preliminary empirical research study is to understand how environmental disruption such as brought on by the COVID-19 pandemic induces shifts in organisational culture, information security culture and subsequently employee information security compliance behaviour. Design/methodology/approach A single-organisation case study was used to develop understanding from direct experiences of organisational life. Both quantitative and qualitative data were collected using a sequential mixed methods approach, with the qualitative phase following the quantitative to achieve complementarity and completeness in analysis. For the quantitative phase, 48 useful responses were received after a questionnaire was sent to all 150–200 employees. For the qualitative phase, eight semi-structured interviews were conducted. Statistical software was used to analyse the quantitative data and NVivo software was used to analyse the qualitative data. Findings The pandemic-induced environmental disruption manifested as a sudden shift to work-from-home for employees, and relatedly an increase in cybercrime. The organisational response to this gave rise to shifts in both organisational and information security culture towards greater control (rule and goal orientations) and greater flexibility (support and innovation orientations), most significantly with information security culture flexibility. The net effect was an increase in employee information security compliance. Originality/value The vast literature on organisational culture and information security culture was drawn on to theoretically anchor and develop parsimonious measures of information security culture. Environmental disruptions such as those caused by the pandemic are unpredictable and their effects uncertain, hence, the study provides insight into the consequences of such disruption on information security in organisations.
Blockchain-enabled K-harmonic framework for industrial IoT-based systems

Industrial Internet of Things (IIoT)-based systems have become an important part of industry consortium systems because of their rapid growth and wide-ranging application. Various physical objects that are interconnected in the IIoT network communicate with each other and simplify the process of decision-making by observing and analyzing the surrounding environment. While making such intelligent decisions, devices need to transfer and communicate data with each other. However, as devices involved in IIoT networks grow and the methods of connections diversify, the traditional security frameworks face many shortcomings, including vulnerabilities to attack, lags in data, sharing data, and lack of proper authentication. Blockchain technology has the potential to empower safe data distribution of big data generated by the IIoT. Prevailing data-sharing methods in blockchain only concentrate on the data interchanging among parties, not on the efficiency in sharing, and storing. Hence an element-based K-harmonic means clustering algorithm (CA) is proposed for the effective sharing of data among the entities along with an algorithm named underweight data block (UDB) for overcoming the obstacle of storage space. The performance metrics considered for the evaluation of the proposed framework are the sum of squared error (SSE), time complexity with respect to different m values, and storage complexity with CPU utilization. The results have experimented with MATLAB 2018a simulation environment. The proposed model has better sharing, and storing based on blockchain technology, which is appropriate IIoT.
The effect of immersive experience, user engagement and perceived authenticity on place satisfaction in the context of augmented reality

Purpose This study aims to measure the effect of augmented reality (AR) on immersive experiences, place satisfaction, user engagement and perceived authenticity. Design/methodology/approach The population of the research consists of museum visitors visiting the Sakip Sabanci Museum in Turkey, which provides an interactive experience to the audience using AR technology via iPads. The data were collected both online and offline. The research was carried out with 397 questionnaire forms. The hypotheses were tested through smart partial least squares (PLS) 3. Findings Immersive experience positively affects place satisfaction, user engagement and perceived authenticity. It was also concluded that user engagement and perceived authenticity mediated the relationship between immersive experience and place satisfaction. Research limitations/implications The entire universe could not be reached, as the study had limitations in terms of time, cost, accessibility and control difficulties. Therefore, the study was carried out with 397 visitors who visited the Sakip Sabanci Museum (SSM) in Turkey and used AR technology. Practical implications The study emphasized the importance of AR in cultural tourism. The interactions of museum visitors with AR can be greatly enhanced by immersive experiences. Expanding AR to provide different experiences can increase the number of visitors. Originality/value Although there are recent studies on AR, it seems insufficient to investigate and empirically examine this subject in the context of museums. No studies conducted in Turkey have investigated the current topic of this study. Conducting this research in Turkey with the proposed research model will make the topic easier to understand, proving the study's originality.
Out-of-Vocabulary Challenge Report

This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge. The OOV contest introduces an important aspect that is not commonly studied by Optical Character Recognition (OCR) models, namely, the recognition of unseen scene text instances at training time. The competition compiles a collection of public scene text datasets comprising of 326,385 images with 4,864,405 scene text instances, thus covering a wide range of data distributions. A new and independent validation and test set is formed with scene text instances that are out of vocabulary at training time. The competition was structured in two tasks, end-to-end and cropped scene text recognition respectively. A thorough analysis of results from baselines and different participants is presented. Interestingly, current state-of-the-art models show a significant performance gap under the newly studied setting. We conclude that the OOV dataset proposed in this challenge will be an essential area to be explored in order to develop scene text models that achieve more robust and generalized predictions.
Confidence-ranked reconstruction of census microdata from published statistics

A reconstruction attack on a private dataset D takes as input some publicly accessible information about the dataset and produces a list of candidate elements of D . We introduce a class of data reconstruction attacks based on randomized methods for nonconvex optimization. We empirically demonstrate that our attacks can not only reconstruct full rows of D from aggregate query statistics Q ( D )∈ℝ m but can do so in a way that reliably ranks reconstructed rows by their odds of appearing in the private data, providing a signature that could be used for prioritizing reconstructed rows for further actions such as identity theft or hate crime. We also design a sequence of baselines for evaluating reconstruction attacks. Our attacks significantly outperform those that are based only on access to a public distribution or population from which the private dataset D was sampled, demonstrating that they are exploiting information in the aggregate statistics Q ( D ) and not simply the overall structure of the distribution. In other words, the queries Q ( D ) are permitting reconstruction of elements of this dataset, not the distribution from which D was drawn. These findings are established both on 2010 US decennial Census data and queries and Census-derived American Community Survey datasets. Taken together, our methods and experiments illustrate the risks in releasing numerically precise aggregate statistics of a large dataset and provide further motivation for the careful application of provably private techniques such as differential privacy.
Prostate Ultrasound Image Segmentation Based on DSU-Net

In recent years, the incidence of prostate cancer in the male population has been increasing year by year. Transrectal ultrasound (TRUS) is an important means of prostate cancer diagnosis. The accurate segmentation of the prostate in TRUS images can assist doctors in needle biopsy and surgery and is also the basis for the accurate identification of prostate cancer. Due to the asymmetric shape and blurred boundary line of the prostate in TRUS images, it is difficult to obtain accurate segmentation results with existing segmentation methods. Therefore, a prostate segmentation method called DSU-Net is proposed in this paper. This proposed method replaces the basic convolution in the U-Net model with the improved convolution combining shear transformation and deformable convolution, making the network more sensitive to border features and more suitable for prostate segmentation tasks. Experiments show that DSU-Net has higher accuracy than other existing traditional segmentation methods.
An anonymization-based privacy-preserving data collection protocol for digital health data

Digital health data collection is vital for healthcare and medical research. But it contains sensitive information about patients, which makes it challenging. To collect health data without privacy breaches, it must be secured between the data owner and the collector. Existing data collection research studies have too stringent assumptions such as using a third-party anonymizer or a private channel amid the data owner and the collector. These studies are more susceptible to privacy attacks due to third-party involvement, which makes them less applicable for privacy-preserving healthcare data collection. This article proposes a novel privacy-preserving data collection protocol that anonymizes healthcare data without using a third-party anonymizer or a private channel for data transmission. A clustering-based k-anonymity model was adopted to efficiently prevent identity disclosure attacks, and the communication between the data owner and the collector is restricted to some elected representatives of each equivalent group of data owners. We also identified a privacy attack, known as "leader collusion", in which the elected representatives may collaborate to violate an individual's privacy. We propose solutions for such collisions and sensitive attribute protection. A greedy heuristic method is devised to efficiently handle the data owners who join or depart the anonymization process dynamically. Furthermore, we present the potential privacy attacks on the proposed protocol and theoretical analysis. Extensive experiments are conducted in real-world datasets, and the results suggest that our solution outperforms the state-of-the-art techniques in terms of privacy protection and computational complexity.
Assessment of solar energy potential in China using an ensemble of photovoltaic power models

Development of solar energy is one of the key solutions towards carbon neutrality in China. The output of solar energy is dependent on weather conditions and shows distinct spatiotemporal characteristics. Previous studies have explored the photovoltaic (PV) power potential in China but with single models and low-resolution radiation data. Here, we estimated the PV power potential in China for 2016–2019 using an ensemble of 11 PV models based on hourly solar radiation at the resolution of 5 km retrieved by the Himawari-8 geostationary satellite. On the national scale, the ensemble method revealed an annual average PV power potential of 242.79 kWh m−2 with the maximum in the west (especially the Tibetan Plateau) and the minimum in the southeast (especially the Sichuan Basin). The multi-model approach shows inter-model spreads of 6 %–7 % distributed uniformly in China, suggesting a robust spatial pattern predicted by these models. The seasonal variation in general shows the largest PV power generation in summer months except for Tibetan Plateau, where the peak value appears in spring because the high cloud coverage dampens the regional solar radiation in summer. On the national scale, the deseasonalized PV power potential shows a high correlation with cloud coverage (R2 = 0.71, p < 0.01) but a low correlation with aerosol optical depth (R2 = 0.08, p < 0.05). Sensitivity experiments show that national PV power potential increases by 0.55 % per 1 W m−2 increase of radiation and 0.79 % per 1 m s−1 increase of wind speed, but decreases by 0.46 % per 1 °C increase of air temperature. These sensitivities provide a solid foundation for the future projection of PV power potential in China under climate change.
Toward Understanding the Design of Intertwined Human–Computer Integrations

Human–computer integration is an HCI trend in which computational machines can have agency, i.e., take control. Our work focuses on a particular form of integration in which the user and the computational machine share agency over the user's body, that is, can simultaneously (in contrast to a traditional turn-taking approach) control the user's body. The result is a user experience where the agency of the user and the computational machine is so intertwined that it is often no more discernable who contributed what to what extent; we call this “intertwined integration”. Due to the recency of advanced technologies enabling intertwined integration systems, we find that little understanding and documented design knowledge exist. To begin constructing such an understanding, we use three case studies to propose two key dimensions (“awareness of machine's agency” and “alignment of machine's agency”) to articulate a design space for intertwined integration systems. We differentiate four roles that computational machines can assume in this design space (angel, butler, influencer, and adversary). Based on our craft knowledge gained through designing such intertwined integration systems, we discuss strategies to help designers create future systems. Ultimately, we aim at advancing the HCI field's emerging understanding of sharing agency.
A photonic entanglement filter with Rydberg atoms

Devices capable of deterministically manipulating photonic entanglement are of paramount importance, because photons are ideal messengers for quantum information. However, due to the non-interacting nature of photons, many photonic quantum operations have only been demonstrated using probabilistic linear-optical approaches, leading to an overwhelming resource overhead and poor scalability. Here we report a novel entanglement filter that transmits the desired photonic entangled state and blocks unwanted ones. In contrast to previous probabilistic approaches, our experiment exploits the strong and controllable photon–photon interaction enabled by Rydberg atoms, so the filtering of undesired states succeeds in every experimental trial. Photonic entanglement with near-unity fidelity can be extracted from an input state with an arbitrarily low initial fidelity. The protocol is inherently robust, and succeeds both in the Rydberg blockade regime and in the interaction-induced dissipation regime. Such an entanglement filter opens new routes towards achieving scalable photonic quantum information processing with multiple ensembles of Rydberg atoms. An entanglement filter based on Rydberg atoms is demonstrated. It transmits a desired photonic entangled state and blocks unwanted ones. Near-perfect photonic entanglement can be extracted from a noisy input with arbitrarily low initial fidelity.
Distinguishing artificial spin ice states using magnetoresistance effect for neuromorphic computing

Artificial spin ice (ASI) consisting patterned array of nano-magnets with frustrated dipolar interactions offers an excellent platform to study frustrated physics using direct imaging methods. Moreover, ASI often hosts a large number of nearly degenerated and non-volatile spin states that can be used for multi-bit data storage and neuromorphic computing. The realization of the device potential of ASI, however, critically relies on the capability of transport characterization of ASI, which has not been demonstrated so far. Using a tri-axial ASI system as the model system, we demonstrate that transport measurements can be used to distinguish the different spin states of the ASI system. Specifically, by fabricating a tri-layer structure consisting a permalloy base layer, a Cu spacer layer and the tri-axial ASI layer, we clearly resolve different spin states in the tri-axial ASI system using lateral transport measurements. We have further demonstrated that the tri-axial ASI system has all necessary required properties for reservoir computing, including rich spin configurations to store input signals, nonlinear response to input signals, and fading memory effect. The successful transport characterization of ASI opens up the prospect for novel device applications of ASI in multi-bit data storage and neuromorphic computing.
ShadowFormer: Global Context Helps Shadow Removal

Recent deep learning methods have achieved promising results in image shadow removal. However, most of the existing approaches focus on working locally within shadow and non-shadow regions, resulting in severe artifacts around the shadow boundaries as well as inconsistent illumination between shadow and non-shadow regions. It is still challenging for the deep shadow removal model to exploit the global contextual correlation between shadow and non-shadow regions. In this work, we first propose a Retinex-based shadow model, from which we derive a novel transformer-based network, dubbed ShandowFormer, to exploit non-shadow regions to help shadow region restoration. A multi-scale channel attention framework is employed to hierarchically capture the global information. Based on that, we propose a Shadow-Interaction Module (SIM) with Shadow-Interaction Attention (SIA) in the bottleneck stage to effectively model the context correlation between shadow and non-shadow regions. We conduct extensive experiments on three popular public datasets, including ISTD, ISTD+, and SRD, to evaluate the proposed method. Our method achieves state-of-the-art performance by using up to 150X fewer model parameters.