Reviewerly/exHarmony huggingface dataset:
Sustained Flexible Photonic Transistor Memories Based on Fully Natural Floating Gate Electrets

Photonic transistor memory with high-speed communication and energy-saving capabilities has emerged as a new data storage technology. However, most floating-gate electrets are composed of quantum dots derived from petroleum or metals, which are either toxic or harmful to the environment. In this study, an environmentally friendly floating-gate electret made entirely from biomass-derived materials was designed for photonic memories. The results show that the photosensitive hemin and its derivative protoporphyrin IX (PPIX) were successfully embedded in a polylactic acid (PLA) matrix. Correspondingly, their disparate photochemistry and core structure strongly affected the photosensitivity and charge-trapping capacity of the prepared electrets. With an appropriate energy-level alignment, the interlayer exciton formed with the correct alignment of energy levels within the PPIX/PLA electret. In addition, the demetallized core offered a unique relaxation dynamic and additional trapping sites to consolidate the charges. Correspondingly, the as-prepared device exhibited a memory ratio of up to 2.5 × 107 with photo-writing-electrical-erasing characteristics. Conversely, hemin demonstrated self-charge transfer during relaxation, making it challenging for the device to store the charges and exhibit a photorecovery behavior. Furthermore, the effect of trapping site discreteness on memory performance was also investigated. The photoactive components were effectively distributed due to the high dipole-dipole interaction between the PLA matrix and PPIX, resulting in a sustained memory performance for at least 104 s after light removal. The photonic memory was also realized on a bio-derived dielectric flexible substrate. Accordingly, a reliable photorecording behavior was observed, wherein, even after 1000 cycles of bending under a 5 mm bending radius, the data was retained for more than 104 s. To our knowledge, it is the first time that a two-pronged approach has been used to improve the performance of photonic memories while addressing the issue of sustainability with a biodegradable electret made entirely from natural materials.
A Survey of Recent Cloud Computing Data Security and Privacy Disputes and Defending Strategies

"Cloud computing" means ability towards view and modifies information stored on distant servers from any platform that is attached to the Internet. A self-service Internet infrastructure known as cloud computing enables users in order to use computer resources from any place in globe. Cloud computing has changed the way that the world operates. A fresh type of computing offers an on-demand service reasonable price which is cloud computing. Cloud computing has contributed to the development of IT by expanding the size and adaptability of data storage, as well as by offering processing and computing that is scalable capability that meets the requirements for dynamic data. Data privacy and security are key problems in world of communication technology. Because data is stored in numerous locations, frequently all over the world, it intensifies serious in the context of cloud computing. Data privacy and security are main concerns, and users have got with cloud technology. This paper, we perform an exhaustive review of the writings on cloud storage system, data encryption methods, data security and privacy challenges, and related remedies. This works of literature study. The goal is to find out primary security challenges cloud computing well-being and their solutions and also potential study areas in the future. 35 papers were examined for the review, with 16 making it into the final result. Despite the fact that many security challenges and solutions have been found, it has become clear that much of the research being done is largely theoretical. As a result of this analysis, future research should place a stronger emphasis on practical applications, ramifications, and the safety of these vulnerabilities, despite the fact that many issues have been found.
Coupled quantum pendula as a possible model for Josephson-junction-based axion detection

The model of two coupled quantum pendula is studied and its suitability to describe Josephson junctions interacting with axions is analysed. It is shown that some physical features of one pendulum, not directly accessible, can be deduced by local measures on the other one, which is experimentally available. Such an effect can be exploited for the axion (the invisible pendulum) detection based on Josephson junctions (the accessible pendulum). The interaction between axion and Josephson junction can be enhanced at the resonance, if the axion and the junction frequencies match, and if the accessible system is prepared in the most convenient initial quantum state.
The effect of prototypicality on webpage aesthetics, usability, and trustworthiness

The user expects webpages of specific categories to have a look-and-feel specific to that category. For example, unlike university homepages, online-shop webpages typically feature relatively little text, a long grid-like structure listing products, and numerous functional elements for product search, filtering, and recommendation. Ensuring that a webpage meets user expectations enhances its prototypicality and has a positive impact on the user’s impression of the webpage. Despite the potential impact on users, the concept of webpage prototypicality has not been fully explored or extensively employed in Human-Computer Interaction (HCI). This paper addresses this gap by conducting a user study with 1530 participants to investigate webpage prototypicality. The study revealed a strong correlation between prototypicality and webpage visual aesthetics, perceived pre-use usability, and trustworthiness. Notably, the direct effect of prototypicality on trustworthiness outweighed the indirect effects through aesthetics and usability. Overall, prototypicality, aesthetics, and usability collectively accounted for 29% to 68% of the variance in trustworthiness, depending on the webpage category. These findings underscore the importance of embracing prototypicality within the field of HCI, encouraging its wider adoption.
Investigating the Factors Influencing the Adoption of Blockchain Technology across Different Countries and Industries: A Systematic Literature Review

Despite the reported disruptive nature of blockchain technology in the extant literature, its adoption is slower than its potential. This difference between the technology’s promises and its current adoption has sparked interest in understanding the factors impeding widespread adoption. This systematic literature review (SLR), drawn from 1786 studies published between 2008 and May 2023, seeks to address this gap. Specifically, our research explores the influence of factors and their differences and commonalities on blockchain adoption. The SLR, examining individual and organisational perspectives, identifies 152 unique factors influencing 25 industries across 21 countries. This review also highlights distinct commonalities and variations in these factors across industries and countries. For instance, while regulatory issues and costs were universal concerns, the importance of technical understanding diverged between industries. Furthermore, country-specific factors, including local regulations and cultural aspects, emerged as significantly influenced insights that provide a comprehensive perspective on the dynamics of blockchain adoption, offering valuable guidance to industry practitioners and researchers striving to navigate the complexities of blockchain integration.
Hybrid-Scale Hierarchical Transformer for Remote Sensing Image Super-Resolution

Super-resolution (SR) technology plays a crucial role in improving the spatial resolution of remote sensing images so as to overcome the physical limitations of spaceborne imaging systems. Although deep convolutional neural networks have achieved promising results, most of them overlook the advantage of self-similarity information across different scales and high-dimensional features after the upsampling layers. To address the problem, we propose a hybrid-scale hierarchical transformer network (HSTNet) to achieve faithful remote sensing image SR. Specifically, we propose a hybrid-scale feature exploitation module to leverage the internal recursive information in single and cross scales within the images. To fully leverage the high-dimensional features and enhance discrimination, we designed a cross-scale enhancement transformer to capture long-range dependencies and efficiently calculate the relevance between high-dimension and low-dimension features. The proposed HSTNet achieves the best result in PSNR and SSIM with the UCMecred dataset and AID dataset. Comparative experiments demonstrate the effectiveness of the proposed methods and prove that the HSTNet outperforms the state-of-the-art competitors both in quantitative and qualitative evaluations.
Hydrogeochemistry and Mercury Contamination of Surface Water in the Lom Gold Basin (East Cameroon): Water Quality Index, Multivariate Statistical Analysis and Spatial Interpolation

Artisanal and small-scale gold mining activities have an impact on natural resources and human health. This study addresses the assessment of surface water quality in the Lom gold basin. A combined approach of water quality index calculation, multivariate statistical analysis and spatial interpolation was used. Sampling was performed at 15 stations during low- and high-water periods in 2021 for classical physicochemical parameters and total mercury measurements. The results show that the physicochemical parameters were below drinking water standards for both periods, except for pH, total suspended solids and total mercury. These waters show a large cation deficit as well as an anion deficit. The water chemistry is controlled by precipitations and silicate dissolutions in rock that convert the water into the Ca-HCO3 and Ca-Mg-HCO3 types. The level of mercury contamination varied from acceptable to high due to high flow rates during high water that cause a dilution effect for the mercury concentration upstream and its accumulation downstream. The water quality varied from excellent to very poor with better quality during the high-water period. The Lom watershed is locally affected by physical and chemical pollution due to the abundance of suspended solids and mercury resulting from the different gold mining activities.
Enhanced Spatial Stream of Two-Stream Network Using Optical Flow for Human Action Recognition

Introduction: Convolutional neural networks (CNNs) have maintained their dominance in deep learning methods for human action recognition (HAR) and other computer vision tasks. However, the need for a large amount of training data always restricts the performance of CNNs. Method: This paper is inspired by the two-stream network, where a CNN is deployed to train the network by using the spatial and temporal aspects of an activity, thus exploiting the strengths of both networks to achieve better accuracy. Contributions: Our contribution is twofold: first, we deploy an enhanced spatial stream, and it is demonstrated that models pre-trained on a larger dataset, when used in the spatial stream, yield good performance instead of training the entire model from scratch. Second, a dataset augmentation technique is presented to minimize overfitting of CNNs, where we increase the dataset size by performing various transformations on the images such as rotation and flipping, etc. Results: UCF101 is a standard benchmark dataset for action videos, and our architecture has been trained and validated on it. Compared with the other two-stream networks, our results outperformed them in terms of accuracy.
Quantum simulation of battery materials using ionic pseudopotentials

Ionic pseudopotentials are widely used in classical simulations of materials to model the effective potential due to the nucleus and the core electrons. Modeling fewer electrons explicitly results in a reduction in the number of plane waves needed to accurately represent the states of a system. In this work, we introduce a quantum algorithm that uses pseudopotentials to reduce the cost of simulating periodic materials on a quantum computer. We use a qubitization-based quantum phase estimation algorithm that employs a first-quantization representation of the Hamiltonian in a plane-wave basis. We address the challenge of incorporating the complexity of pseudopotentials into quantum simulations by developing highly-optimized compilation strategies for the qubitization of the Hamiltonian. This includes a linear combination of unitaries decomposition that leverages the form of separable pseudopotentials. Our strategies make use of quantum read-only memory subroutines as a more efficient alternative to quantum arithmetic. We estimate the computational cost of applying our algorithm to simulating lithium-excess cathode materials for batteries, where more accurate simulations are needed to inform strategies for gaining reversible access to the excess capacity they offer. We estimate the number of qubits and Toffoli gates required to perform sufficiently accurate simulations with our algorithm for three materials: lithium manganese oxide, lithium nickel-manganese oxide, and lithium manganese oxyfluoride. Our optimized compilation strategies result in a pseudopotential-based quantum algorithm with a total Toffoli cost four orders of magnitude lower than the previous state of the art for a fixed target accuracy.
3D global optimization of calibration parameters of deflectometry system by using a spherical mirror

The calibration of a deflectometry system is an essential requirement for achieving accurate specular surface reconstruction. In this paper, we propose a 3D-based optimization method for the calibration of system parameters, which achieves the same level of accuracy as the 2D multi-pose optimization, but utilizes only a single pose. Our approach introduces a high-precision spherical mirror as the target mirror (TM), and the cost function is the residual of the reconstruction of the TM, as opposed to the 2D re-projection error in image space. Single-pose calibration optimization is achieved because the spherical surface can be regarded as a combination of multiple pose plane mirrors. The simulations and experiments demonstrate that TM with a smaller radius outperforms other methods, and the surface reconstruction error of a spherical mirror with a radius of 500 mm is 186.3 nm RMS, offering a fast and efficient system calibration scheme for in-situ measurement in practical application scenarios.
Ensemble-Based Gradient Inference for Particle Methods in Optimization and Sampling

.We propose an approach based on function evaluations and Bayesian inference to extract higher-order differential information of objective functions from a given ensemble of particles. Pointwise evaluation of some potential V in an ensemble contains implicit information about first- or higher-order derivatives, which can be made explicit with little computational effort (ensemble-based gradient inference). We suggest using this information for the improvement of established ensemble-based numerical methods for optimization and sampling such as consensus-based optimization and Langevin-based samplers. Numerical studies indicate that the augmented algorithms are often superior to their gradient-free variants; in particular, the augmented methods help the ensembles to escape their initial domain, to explore multimodal, non-Gaussian settings, and to speed up the collapse at the end of optimization dynamics. The code for the numerical examples in this manuscript can be found in the paper's Github repository.KeywordsoptimizationsamplingLangevin dynamicsensemble methodsMSC codes62F1565N7590C5690C2635Q8337N4060H10
Exploring Mixed Reality

Researchers recently became interested in mixed reality (MR) because of the availability of hardware that integrates the advantages of two worlds. As an immersive and innovative technological solution for consumers and brands, research has provided insights into the implementation of MR. This technology is promising to transform business models and consumer behavior. Studies have revealed how MR boosts consumer engagement and purchase intentions. In this chapter, the diverse concepts and techniques of recent technological breakthroughs are reviewed. This includes the differences between augmented reality (AR), virtual reality (VR), and MR. Also, MR will be explored to understand the fusion of reality and virtuality, challenges, and limitations in emerging trends. Thus, providing insights from the perspective of digital marketing that results in better interaction with consumers due to the immersive and interactive experiences it offers.
SqueezeJet-3: An HLS-based Accelerator for Edge CNN Applications on SoC FPGAs

Most FPGA-based Convolutional Neural Network (CNN) hardware accelerators target the datacenter rather than edge processing units. To further fill this gap, this work presents SqueezeJet-3 and the corresponding design flow of a novel FPGA-based embedded system, consisting of software and hardware for accelerating edge CNN inference. SqueezeJet-3 is optimized for accelerating small ImageNet class CNNs, such as SqueezeNet vl.l and ZynqNet, on low-end low-cost SoC FPGA devices. SqueezeJet-3 is evaluated against the DietChai accelerator, which is part of Xilinx's ChaiDNN v2 framework, in terms of performance, resource utilization, power, and accuracy; the results demonstrate that for the acceleration of SqueezeNet vl.l, SqueezeJet-3 is better than DietChai in all categories. Our evaluation results also show that, by using the presented design framework, a developer can implement FPGA accelerators for larger CNNs, such as the VGG16, with similar performance to the accelerators designed by Angel-Eye and fpgaConvNet frameworks which are optimized for VGG16-like CNN networks.
Survey and systematization of 3D object detection models and methods

Abstract Strong demand for autonomous vehicles and the wide availability of 3D sensors are continuously fueling the proposal of novel methods for 3D object detection. In this paper, we provide a comprehensive survey of recent developments from 2012–2021 in 3D object detection covering the full pipeline from input data, over data representation and feature extraction to the actual detection modules. We introduce fundamental concepts, focus on a broad range of different approaches that have emerged over the past decade, and propose a systematization that provides a practical framework for comparing these approaches with the goal of guiding future development, evaluation, and application activities. Specifically, our survey and systematization of 3D object detection models and methods can help researchers and practitioners to get a quick overview of the field by decomposing 3DOD solutions into more manageable pieces.
Automatically Detecting Incoherent Written Math Answers of Fourth-Graders

Arguing and communicating are basic skills in the mathematics curriculum. Making arguments in written form facilitates rigorous reasoning. It allows peers to review arguments, and to receive feedback about them. Even though it requires additional cognitive effort in the calculation process, it enhances long-term retention and facilitates deeper understanding. However, developing these competencies in elementary school classrooms is a great challenge. It requires at least two conditions: all students write and all receive immediate feedback. One solution is to use online platforms. However, this is very demanding for the teacher. The teacher must review 30 answers in real time. To facilitate the revision, it is necessary to automatize the detection of incoherent responses. Thus, the teacher can immediately seek to correct them. In this work, we analyzed 14,457 responses to open-ended questions written by 974 fourth graders on the ConectaIdeas online platform. A total of 13% of the answers were incoherent. Using natural language processing and machine learning algorithms, we built an automatic classifier. Then, we tested the classifier on an independent set of written responses to different open-ended questions. We found that the classifier achieved an F1-score = 79.15% for incoherent detection, which is better than baselines using different heuristics.
HKGCL: Hierarchical graph contrastive learning for multi-domain recommendation over knowledge graph

Multi-domain recommendation (MDR) aims to improve the recommendation performance in all target domains simultaneously by leveraging rich data from relevant domains. However, conventional approaches either only rely on sparse interactions in each separate scenario, or simply combine multiple shared recommendation solutions, resulting in MDR systems still suffering from severely low recommendation accuracy. To tackle the above issues, we propose a novel recommendation model called Hierarchical Graph Contrastive Learning for Multi-Domain Recommendation over Knowledge Graph (HKGCL). Different from previous MDR models, our HKGCL can treat each relevant domain as a hierarchy in the interaction network. Based on the LightGCN aggregation strategy, a hierarchical message passing mechanism over the knowledge graph is designed to aggregate hierarchical knowledge representations for users and items. Then, a hierarchical node dropping scheme is proposed as our data augmentation technique on different hierarchical views to obtain more self-supervised semantic signals. Subsequently, three graph contrastive learning tasks in multiple and separate domains are proposed to explore domain-shared and domain-specific preference features for target users, respectively. Additionally, a novel Multi-domain Bayesian Personalized Ranking (MBPR) approach is proposed to assist in training our multi-task learning framework. Extensive experiments conducted on two real-world datasets demonstrate the consistent superiority of our proposed HKGCL over various state-of-the-art baselines, and also verify that the HKGCL can achieve strong performance on sparse interaction scenarios.