Reviewerly/exHarmony huggingface dataset:
A Cross-Attention Mechanism Based on Regional-Level Semantic Features of Images for Cross-Modal Text-Image Retrieval in Remote Sensing

With the rapid development of remote sensing (RS) observation technology over recent years, the high-level semantic association-based cross-modal retrieval of RS images has drawn some attention. However, few existing studies on cross-modal retrieval of RS images have addressed the issue of mutual interference between semantic features of images caused by “multi-scene semantics”. Therefore, we proposed a novel cross-attention (CA) model, called CABIR, based on regional-level semantic features of RS images for cross-modal text-image retrieval. This technique utilizes the CA mechanism to implement cross-modal information interaction and guides the network with textual semantics to allocate weights and filter redundant features for image regions, reducing the effect of irrelevant scene semantics on retrieval. Furthermore, we proposed BERT plus Bi-GRU, a new approach to generating statement-level textual features, and designed an effective temperature control function to steer the CA network toward smooth running. Our experiment suggested that CABIR not only outperforms other state-of-the-art cross-modal image retrieval methods but also demonstrates high generalization ability and stability, with an average recall rate of up to 18.12%, 48.30%, and 55.53% over the datasets RSICD, UCM, and Sydney, respectively. The model proposed in this paper will be able to provide a possible solution to the problem of mutual interference of RS images with “multi-scene semantics” due to complex terrain objects.
Employing Neural Style Transfer for Generating Deep Dream Images

In recent years, deep dream and neural style transfer emerged as hot topics in deep learning. Hence, mixing those two techniques support the art and enhance the images that simulate hallucinations among psychiatric patients and drug addicts. In this study, our model combines deep dream and neural style transfer (NST) to produce a new image that combines the two technologies. VGG-19 and Inception v3 pre-trained networks are used for NST and deep dream, respectively. Gram matrix is a vital process for style transfer. The loss is minimized in style transfer while maximized in a deep dream using gradient descent for the first case and gradient ascent for the second. We found that different image produces different loss values depending on the degree of clarity of that images. Distorted images have higher loss values in NST and lower loss values with deep dreams. The opposite happened for the clear images that did not contain mixed lines, circles, colors, or other shapes.
Entanglement Dynamics of Coupled Quantum Oscillators in Independent NonMarkovian Baths

This work strives to better understand how the entanglement in an open quantum system, here represented by two coupled Brownian oscillators, is affected by a nonMarkovian environment (with memories), here represented by two independent baths each oscillator separately interacts with. We consider two settings, a `symmetric' configuration wherein the parameters of both oscillators and their baths are identical, and an `asymmetric' configuration wherein they are different, in particular, a `hybrid' configuration, where one of the two coupled oscillators interacts with a nonMarkovian bath and the other with a Markovian bath. We ask two groups of questions: Q1) Which time regime does the bath's nonMarkovianity benefit the system's entanglement most? The answers we get from detailed numerical studies suggest that A1) For an initially entangled pair of oscillators, we see that in the intermediate time range, the duration of entanglement is proportional to the memory time, and it lasts a fraction of the relaxation time, but at late times when the dynamics reaches a steady state, the value of the symplectic eigenvalue of the partially transposed covariance matrix barely benefit from the bath nonMarkovianity. For the second group of questions: Q2)Can the memory of one nonMarkovian bath be passed on to another Markovian bath? And if so, does this memory transfer help to sustain the system's entanglement dynamics? Our results from numerical studies of the asymmetric hybrid configuration indicate that A2) A system with a short memory time can acquire improvement when it is coupled to another system with a long memory time, but, at a cost of the latter. The sustainability of the bipartite entanglement is determined by the party which breaks off entanglement most easily.
A Masked Pure-Hardware Implementation of Kyber Cryptographic Algorithm

Quantum computing-specifically Shor's algorithm [1]-presents an existential threat to some standard cryptographic algorithms. In preparation, post-quantum cryptography (PQC) algorithms have been in development and are nearing mathematical and cryptanalytic maturity. Standardization efforts through the National Institute of Standards and Technology (NIST) PQC standardization process have chosen one PKE/KEM algorithm (i.e., CRYSTALS-Kyber) and three digital signature algorithms (i.e., CRYSTALS-Dilithium, Falcon, and SPHINCS+). CRYSTALS-Kyber is a lattice-based, IND-CCA2-secure, key-encapsulation mechanism (KEM) based on the learning-with-errors problem over module lattices. This paper presents a masked hardware implementation of Kyber that is demonstrably secure against side-channel power analysis methods.
Topic Taxonomy and Metadata to Support Renewable Energy Digitalisation

Research and innovation in renewable energy, such as wind and solar, have been supporting the green transformation of energy systems, the backbone of a low-carbon climate-resilient society. The major challenge is to manage the complexity of the grid transformation to allow for higher shares of highly variable renewables while securing the safety of the stability of the grid and a stable energy supply. A great help comes from the ongoing digital transformation where digitisation of infrastructures and assets in research and industry generates multi-dimensional and multi-disciplinary digital data. However, a data user needs help to find the correct data to exploit. This has two significant facets: first, missing data management, i.e., datasets are neither findable because of missing community standard metadata and taxonomies, nor interoperable, i.e., missing standards for data formats; second, data owners having a negative perception of sharing data. To make data ready for data science exploitation, one of the necessary steps to map the existing data and their availability to facilitate their access is to create a taxonomy for the field’s topics. For this, a group of experts in different renewable technologies such as photovoltaics, wind and concentrated solar power and in transversal fields such as life cycle assessment and the EU taxonomy for sustainable activities have been gathered to propose a coherent and detailed taxonomy for renewable energy-related data. The result is a coherent classification of relevant data sources, considering both the general aspects applicable to electricity generation from selected renewable energy technologies and the specific aspects of each of them. It is based on previous relevant work and can be easily extended to other renewable resources not considered in this work and conventional energy technology.
Risk-aware survival time prediction from whole slide pathological images

Abstract Deep-learning-based survival prediction can assist doctors by providing additional information for diagnosis by estimating the risk or time of death. The former focuses on ranking deaths among patients based on the Cox model, whereas the latter directly predicts the survival time of each patient. However, it is observed that survival time prediction for the patients, particularly with close observation times, possibly has incorrect orders, leading to low prediction accuracy. Therefore, in this paper, we present a whole slide image (WSI)-based survival time prediction method that takes advantage of both the risk as well as time prediction. Specifically, we propose to combine these two approaches by extracting the risk prediction features and using them as guides for the survival time prediction. Considering the high resolution of WSIs, we extract tumor patches from WSIs using a pre-trained tumor classifier and apply the graph convolutional network to aggregate information across these patches effectively. Extensive experiments demonstrate that the proposed method significantly improves the time prediction accuracy when compared with direct prediction of the survival times without guidance and outperforms existing methods.
Characterizing Error Mitigation by Symmetry Verification in QAOA

Hardware errors are a major obstacle to demonstrating quantum advantage with the quantum approximate optimization algorithm (QAOA). Recently, symmetry verification has been proposed and empirically demonstrated to boost the quantum state fidelity, the expected solution quality, and the success probability of QAOA on a superconducting quantum processor. Symmetry verification uses parity checks that leverage the symmetries of the objective function to be optimized. We develop a theoretical framework for analyzing this approach under local noise and derive explicit formulas for fidelity improvements on problems with global ℤ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> symmetry. We numerically investigate the symmetry verification on the MaxCut problem and identify the error regimes in which this approach improves the QAOA objective. We observe that these regimes correspond to the error rates present in near-term hardware. We further demonstrate the efficacy of symmetry verification on an IonQ trapped ion quantum processor where an improvement in the QAOA objective of up to 19.2% is observed.
Fall Detection Algorithm Based on Inertial Sensor and Hierarchical Decision

With the aging of the human body and the reduction in its physiological capacities, falls have become a huge threat to individuals’ physical and mental health, leading to serious bodily damage to the elderly and financial pressure on their families. As a result, it is vital to design a fall detection algorithm that monitors the state of human activity. This work designs a human fall detection algorithm based on hierarchical decision making. First, this work proposes a dimensionality reduction approach based on feature importance analysis (FIA), which optimizes the feature space via feature importance. This procedure reduces the dimension of features greatly and reduces the time spent by the model in the training phase. Second, this work proposes a hierarchical decision-making algorithm with an XGBoost model. The algorithm is divided into three levels. The first level uses the threshold approach to make a preliminary assessment of the data and only transfers the fall type data to the next level. The second level is an XGBoost-based classification algorithm to analyze again the type of data which remained from the first level. The third level employs a comparison method to determine the direction of the falling. Finally, the fall detection algorithm proposed in this paper has an accuracy of 98.19%, a sensitivity of 97.50%, and a specificity of 98.63%. The classification accuracy of the fall direction reaches 93.44%, and the algorithm can efficiently determine the fall direction.
The impact of XR applications’ user experience-based design innovativeness on loyalty

Web-based extended reality (XR) is an umbrella term for applications such as augmented reality and virtual reality. However, prior studies have only focused on the technological advancement of XR applications. Thus, this research explored the impact of the user experience-based design innovativeness (UXBDI) of XR applications, which refers to the novelties in the design of an existing application created to satisfy user loyalty. This research proposed and examined how XR applications’ UXBDI affect user engagement and loyalty and used structural equation modeling to test the impact of UXBDI on web-based XR application loyalty. A total of 332 users of XR applications participated in the online questionnaire. The findings revealed that attractiveness and interaction, as sub-dimensions of UXBDI, increased XR application engagement. Moreover, an indirect effect of usability on loyalty indicated that engagement acts as a mediator. This study demonstrates that XR applications’ UXBDI is a key component for the user-XR application relationship. These results are meaningful in guiding the management of the XR application cycle.
The impact of ignorance and bias on information security protection motivation: a case of e-waste handling

Purpose Protection motivation theory (PMT) explains that the intention to cope with information security risks is based on informed threat and coping appraisals. However, people cannot always make appropriate assessments due to possible ignorance and cognitive biases. This study proposes a research model that introduces four antecedent factors from ignorance and bias perspectives into the PMT model and empirically tests this model with data from a survey of electronic waste (e-waste) handling. Design/methodology/approach The data collected from 356 Chinese samples are analyzed via structural equation modeling (SEM). Findings The results revealed that for threat appraisal, optimistic bias leads to a lower perception of risks. However, factual ignorance (lack of knowledge of risks) does not significantly affect the perceived threat. For coping appraisal, practical ignorance (lack of knowledge of coping with risks) leads to low response efficacy and self-efficacy and high perceptions of coping cost, but the illusion of control overestimates response efficacy and self-efficacy. Originality/value First, this study addresses a new type of information security problem in e-waste handling. Second, this study extends the PMT model by exploring the roles of ignorance and bias as antecedents. Finally, the authors reinvestigate the basic constructs of PMT to identify how rational threat and coping assessments affect user intentions to cope with data security risks.
A Blockchain-Enabled Framework for Enhancing Scalability and Security in IIoT

Industrial Internet of Things (IIoT) technology is widely used in modern industrial fields like transportation, but data security remains a major challenge. The blockchain-based access control mechanism can address the data security issue by preventing unauthorized devices from accessing limited IIoT resources. However, most existing blockchain-based access control mechanism for IIoT still has scalability and privacy issues. To deal with the above-mentioned problems, we propose a new scalable and secure strategy for the blockchain-based access control framework for IIoT via sharding, which consists of two components. First, the network sharding scheme based on the access frequency set (N2SAF) is designed to 1) improve the scalability of our proposed strategy by transaction sharding to reduce the storage pressure on nodes, and 2) increase the transaction processing speed on a three-layer architecture based on cloud-edge-device in IIoT. Second, the privacy protection scheme based on a Bloom filter (P2BF) is designed to deal with the privacy leakage problem caused by anonymous address clustering. Simulation results show that our proposed strategy improves the scalability of the system compared to existing methods while ensuring the security of the shard network, especially in large-scale IIoT environments.
Research on the integration and optimisation of MOOC teaching resources based on deep reinforcement learning

There are some problems in the merging of English MOOC learning information, such as high packet loss rate and high repetition rate. This paper designs a new method of resource data merging with the help of deep reinforcement learning. The operation mode of English MOOC platform was analysed, and teaching resources for the initial application of MOOC under this platform were collected. The pre-processing of the initial collection of resources is completed through translation, Chinese word segmentation, semantic annotation of word segmentation results and other steps. The features of English MOOC teaching resources are extracted, and the deep enhancement learning algorithm is adopted to optimise. Through comparative experiments, it is found that the packet loss rate of the combined learning information is only 0.28% and the integrated resource repetition rate is only 0.89%.
Service Robots in a Bakery Shop: A Field Study

In this paper, we report on a field study in which we employed two service robots in a bakery store as a sales promotion. Previous studies have explored public applications of service robots public such as shopping malls. However, more evidence is needed that service robots can contribute to sales in real stores. Moreover, the behaviors of customers and service robots in the context of sales promotions have not been examined well. Hence, the types of robot behavior that can be considered effective and the customers' responses to these robots remain unclear. To address these issues, we installed two tele-operated service robots in a bakery store for nearly 2 weeks, one at the entrance as a greeter and the other one inside the store to recommend products. The results show a dramatic increase in sales during the days when the robots were applied. Furthermore, we annotated the video recordings of both the robots' and customers' behavior. We found that although the robot placed at the entrance successfully attracted the interest of the passersby, no apparent increase in the number of customers visiting the store was observed. However, we confirmed that the recommendations of the robot operating inside the store did have a positive impact. We discuss our findings in detail and provide both theoretical and practical recommendations for future research and applications.
D2Net: Deep Denoising Network in Frequency Domain for Hyperspectral Image

Dear Editor, Since the existing hyperspectral image denoising methods suffer from excessive or incomplete denoising, leading to information distortion and loss, this letter proposes a deep denoising network in the frequency domain, termed D2Net. Our motivation stems from the observation that images from different hyperspectral image (HSI) bands share the same structural and contextual features while the reflectance variations in the spectra are mainly fallen on the details and textures. We design the D2Net in three steps: 1) spatial decomposition, 2) spatial-spectral denoising, and 3) refined reconstruction. It achieves multi-scale feature learning without information loss by adopting the rigorous symmetric discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT). In particular, the specific design for different frequency components ensures complete noise removal and preservation of fine details. Experiment results demonstrate that our D2Net can attain a promising denoising performance.
Two decades of studies on learning management system in higher education: A bibliometric analysis with Scopus database 2000-2020

Over the past twenty years, using learning management systems in higher education has attracted increasing interest from researchers around the globe. In this context, the current study aimed to explore the volume, growth trajectory, and geographic distribution of learning management systems in higher education literature, along with identifying impactful authors, sources, and publications, and highlight emerging research issues. The authors conducted bibliometric analysis on 1334 documents, related to the use of learning management systems in the context of higher education, extracted from Scopus database. The findings show a rapidly growing knowledge base on learning management systems in higher education, especially intensely in the years 2015-2020 and primarily from research in developed societies. This flourishing is consistent with the development trend of international education and the strong development of technology. In addition, the core literature was identified based on the volume of publications and citations. The results also reveal the emerging intellectual structure of the field and provide points of reference for scholars studying the discipline. This paper offers a knowledge map for future research assessments of learning management systems in higher education.
Dress Code: High-Resolution Multi-category Virtual Try-On

Image-based virtual try-on strives to transfer the appearance of a clothing item onto the image of a target person. Prior work focuses mainly on upper-body clothes (e.g. t-shirts, shirts, and tops) and neglects full-body or lower-body items. This shortcoming arises from a main factor: current publicly available datasets for image-based virtual try-on do not account for this variety, thus limiting progress in the field. To address this deficiency, we introduce Dress Code, which contains images of multi-category clothes. Dress Code is more than $$3\times $$ larger than publicly available datasets for image-based virtual try-on and features high-resolution paired images ( $$1024 \times 768$$ ) with front-view, full-body reference models. To generate HD try-on images with high visual quality and rich in details, we propose to learn fine-grained discriminating features. Specifically, we leverage a semantic-aware discriminator that makes predictions at pixel-level instead of image- or patch-level. Extensive experimental evaluation demonstrates that the proposed approach surpasses the baselines and state-of-the-art competitors in terms of visual quality and quantitative results. The Dress Code dataset is publicly available at https://github.com/aimagelab/dress-code .
BADPASS: Bots Taking ADvantage of Proxy as a Service

Web scraping bots are now using so-called Residential ip Proxy (resip) services to defeat state-of-the-art commercial bot countermeasures. resip providers promise their customers to give them access to tens of millions of residential ip addresses, which belong to legitimate users. They dramatically complicate the task of the existing anti-bot solutions and give the upper hand to the malicious actors. New specific detection methods are needed to identify and stop scrapers from taking advantage of these parties. This work, thanks to a 4 months-long experiment, validates the feasibility, soundness, and practicality of a detection method based on network measurements. This technique enables contacted servers to identify whether an incoming request comes directly from a client device or if it has been proxied through another device.
IGFormer: Interaction Graph Transformer for Skeleton-Based Human Interaction Recognition

Human interaction recognition is very important in many applications. One crucial cue in recognizing an interaction is the interactive body parts. In this work, we propose a novel Interaction Graph Transformer (IGFormer) network for skeleton-based interaction recognition via modeling the interactive body parts as graphs. More specifically, the proposed IGFormer constructs interaction graphs according to the semantic and distance correlations between the interactive body parts, and enhances the representation of each person by aggregating the information of the interactive body parts based on the learned graphs. Furthermore, we propose a Semantic Partition Module to transform each human skeleton sequence into a Body-Part-Time sequence to better capture the spatial and temporal information of the skeleton sequence for learning the graphs. Extensive experiments on three benchmark datasets demonstrate that our model outperforms the state-of-the-art with a significant margin.