Reviewerly/exHarmony huggingface dataset:
Speech encryption algorithm based on two newly designed chaotic maps

Two new integrated chaotic maps are designed. The sine and logistic maps are used to create a sine-logistic integrated map (SLIM). The sine and cubic maps are used to design a sine-cubic integrated map (SCIM). Their dynamical analyses are done using time series plots, 0–1 tests, bifurcations, sample entropy, and Lyapunov exponent diagrams. The newly designed chaotic maps exhibit their dynamical properties of a period-two cycle, period-four cycle, and chaos. The speech signal is compressed first, and encryption is performed based on the new integrated chaotic maps. The two keys, such as key-A and key-B, are generated using SLIM and SCIM, respectively. Key-A is used for the shifting operation, while the bitwise XOR operation is carried out between the compressed-shifted speech signal and key-B. After that, the encrypted speech is sent across a public channel, and the original speech is recovered by performing reverse operations on the receiving end. The degree of security is measured using various speech security and attack analysis tools. It is found that the proposed strategy is more secure than some existing methods described in the literature and can withstand several attacks.
Earlier stage, higher temperature, and deeper space facilitate indium precipitation in a skarn system, as exemplified by the Baoshan Pb-Zn polymetallic deposit, South China

Indium (In), one of the strategic critical metal elements, is preferred to be enriched in the skarn system, but the enrichment regularity in terms of temporal and spatial distribution in a specific skarn Pb-Zn deposit remains unclear. To address the problem, we conducted a systematic geological, mineralogical, and trace element geochemical investigation on the Baoshan In-rich Pb-Zn polymetallic deposit, Hunan Province. This deposit is situated in the central part of the famous Nanling W-Sn-Pb-Zn polymetallic metallogeny belt, South China. Three ore districts in the Baoshan deposit with distinct economic element associations are divided, including the Central District (dominated by Cu-Mo), West District (dominated by Pb-Zn), and North District (dominated by Pb-Zn). They comprise a typical skarn system, including the outcropped granodiorite porphyry, typical skarn mineral assemblages, and Pb-Zn-hosting Carboniferous limestone. We systematically sampled the representative Pb-Zn ores in two ore districts (West and North) and three underground levels (-270 m, −230 m, and −190 m). Four types of sphalerites with different mineral associations and mineralogical characteristics were identified, that is, Sp1a and Sp1b (formed in the early sulfide stage), as well as Sp2a and Sp2b (formed in the late sulfide stage). Our EPMA (Electron Probe Micro-Analyzer) and LA-ICP-MS (Laser Ablation Inductively Coupled Plasma Mass Spectrometer) analyses reveal the four types of sphalerite have distinct chemical compositions. Such as, the mean value of indium is 3524 ppm for Sp1a, 142 ppm for Sp1b, 48.03 ppm for Sp2a, and 87.98 ppm for Sp2b in a decreasing order, and a similar trend of calculated temperatures ranging from 336 to 135 °C using GGIMFis thermometers can be obtained. Further LA-ICP-MS trace elements mapping show that the core of Sp1a is relatively enriched in more In, Cu, Sn, and Ag contents. Spatially, the sphalerites in the deep contain a higher indium content (mean = 1581 ppm) than those in the shallow (107.20 ppm) in the vertical profile. In the planar, indium is more enriched in the West District (mean = 1090 ppm) than in the North District (60.92 ppm). The indium distribution regularity reflects that the metals-carrying magmatic hydrothermal fluids flow from Central District, through the West District, to the North District. Collectively, we conclude that indium prefers to be enriched in the earlier stages, higher temperature, and deeper space during the sphalerite crystallization in the Baoshan skarn system, and therefore highlight the deep space of Baoshan West District is a promising target for indium exploration. This new finding maybe shed light on the scientific understanding on indium enrichment and associated exploration strategies in the similar skarn Pb-Zn metallogenic systems.
AI-driven malaria diagnosis: developing a robust model for accurate detection and classification of malaria parasites

AI-driven malaria diagnosis: developing a robust model for accurate detection and classification of malaria parasites
Noise-Assisted Digital Quantum Simulation of Open Systems Using Partial Probabilistic Error Cancellation

Quantum systems are inherently open and susceptible to environmental noise, which can have both detrimental and beneficial effects on their dynamics. This phenomenon has been observed in biomolecular systems, where noise enables novel functionalities, making the simulation of their dynamics a crucial target for digital and analog quantum simulation. Nevertheless, the computational capabilities of current quantum devices are often limited due to their inherent noise. In this work, we present a novel approach that capitalizes on the intrinsic noise of quantum devices to reduce the computational resources required for simulating open quantum systems. Our approach combines quantum noise characterization methods with quantum error mitigation techniques, enabling us to manipulate and control the intrinsic noise in a quantum circuit. Specifically, we selectively enhance or reduce decoherence rates in the quantum circuit to achieve the desired simulation of open-system dynamics. We provide a detailed description of our methods and report on the results of noise characterization and quantum error mitigation experiments conducted on both real and emulated IBM Quantum computers. Additionally, we estimate the experimental resource requirements for our techniques. Our approach holds the potential to unlock new simulation techniques in noisy intermediate-scale quantum devices, harnessing their intrinsic noise to enhance quantum computations.5 MoreReceived 15 March 2023Accepted 10 October 2023DOI:https://doi.org/10.1103/PRXQuantum.4.040329Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasOpen quantum systems & decoherenceQuantum simulationQuantum Information, Science & Technology
Metaopera: A Cross-Metaverse Interoperability Protocol

Various metaverse applications have entered our daily life and show a promising trend that will occupy people's attention in the era of Web3. This makes interoperability across metaverses become one of the fundamental technologies in the context of multiple metaverse platforms. The aim of interoperability is to provide a seamless service for users when their requests interact with multiple metaverses. However, the development of cross-metaverse interoperability is still in its initial stage in both industry and academia. In this article, we review the state- of-the-art cross-metaverse interoperability solutions, which are designed for a dedicated purpose but do not apply to all metaverse platforms. To this end, we propose MetaOpera, a generalized cross-metaverse interoperability protocol. Connecting to MetaOpera by means of wireless communication, users and digital objects across different metaverses that rely on centralized servers or decentralized blockchains are capable of interacting with each other. We also implement a proof-of-concept mechanism for Meta- Opera, aiming at evaluating its performance with a state-of-the-art cross-metaverse solution based on the Sidechains technique. Simulation results demonstrate that the size of cross-metaverse proof and the average latency of cross-metaverse transactions using the proposed solution are about eight to three times smaller, respectively, than those of the Sidechains solution. This article also suggests a number of open issues and challenges faced by cross-metaverse interoperability that may inspire future research.
Lightweight Remote-Sensing Image Super-Resolution via Attention-Based Multilevel Feature Fusion Network

In recent years, advancements in remote-sensing image super-resolution have achieved remarkable performance. However, many methods demand significant computational resources. This is problematic for edge devices with limited computational capabilities. To alleviate this problem, we propose an attention-based multi-level feature fusion network (AMFFN) to enhance the resolution of remote-sensing images. This proposed network integrates three efficient design strategies to provide a lightweight solution. Initially, we design the partial shallow residual block (PSRB) to replace the redundant convolution operation. The PSRB optimizes feature extraction via partial convolution and capitalizes on information across channels using pointwise convolution. Subsequently, integrating the PSRB, our dynamic feature distillation block (DFDB) leverages an information distillation mechanism to distill and capture only the crucial features securing a robust feature depiction. Conclusively, for superior feature fusion, we conceptualized an attention-based multi-level feature fusion (AMFF) mechanism. The attention intrinsic to AMFF weighs the significance of features from varied branches, assuring that the resulting output is comprehensive and discerning. We conduct thorough experimental validation on two datasets of remote-sensing images and measure network complexity by evaluating network parameters and multi-adds operations. The results show that our method effectively balances computational complexity and performance. In addition, we have expanded the application of AMFFN to the field of natural image super-resolution. Experimental results on five benchmark test datasets further confirm the effectiveness of our method.
Construction, dynamic analysis and DSP implementation of a novel 3D discrete memristive hyperchaotic map

In this paper, a three-dimensional discrete memristive hyperchaotic map (3DMCHM) with a complex structure and rich, dynamic characteristics is designed by using a discrete charge-controlled memristor. By introducing the offset control, amplitude control, and coupling parameters, we have found that changing the parameters and initial values can lead to different outcomes. We have discovered that modifying the parameters and initial values yields significant results. The 3DMCHM exhibits several dynamic characteristics, including complex offset behavior, multi-vortex-like behavior, separate structure phenomenon, transient-like phenomenon, expansion and contraction phenomenon, and inversion symmetry phenomenon. The offset behavior appears with multi-state transition controlled by parameters. Additionally, we have discovered that by changing the related parameters, The 3DMCHM can evolve from period, period doubling, chaos, and hyperchaos. We have conducted tests to assess the complexity and randomness of the 3DMCHM and have verified its high complexity and good randomness. Finally, we have successfully implemented the 3DMCHM using the DSP hardware circuit, thereby confirming the feasibility and potential applications of the 3DMCHM.
Do We Perform Systematic Literature Review Right? A Scientific Mapping and Methodological Assessment

Background: Systematic literature review (SLR) is increasingly utilized to maximize the element of rigor and minimize the individual bias of research synthesis. An analysis of the Web of Science (WoS) database indicates that 90% of the literature review studies using SLR have been published between 2012 and 2022. However, this progressive agenda is impaired by the lack of methodological consistency and rigorousness. To fill this gap, this paper aims at mapping the theoretical comprehension and practices of SLR and providing a stepwise approach to employing such a framework. Methods: A comprehensive narrative review is used in this paper to analyze the studies concerning the literature review typology and the structural assessment of the SLR. Furthermore, the methodological approach of the literature review studies that adopted the SLR and were published in the Logistics journal is assessed across a set of vital criteria associated with conducting an SLR. Results: There is a concrete link between the purpose of a review, i.e., to describe, test, extend, or critique, and the literature review type. There are 17 distinct literature review types, e.g., a narrative review, a bibliometric analysis, etc., which must be justified meticulously regardless of the SLR. The ambiguity in conceiving the SLR either as a toolkit or a review type, the lack of justification regarding the review purpose and type, and vague conceptual distinguishment between the bibliometric analysis, as a distinct review type, and the SLR framework, are only a few of the shortcomings observed in the analyzed papers. Conclusions: Given the significant role of SLR in elevating the element of rigor within the literature review studies, it is deemed essential to employ this framework by paying attention to two holistic factors: (1) theoretical distinction between the literature review purpose, the literature review type, and the SLR; (2) strict adherence to the SLR procedure with a high degree of accuracy and explicitness.
The Quantum Alternating Operator Ansatz for Satisfiability Problems

We comparatively study, through large-scale numerical simulation, the performance across a large set of Quantum Alternating Operator Ansatz (QAOA) implementations for finding approximate and optimum solutions to unconstrained combinatorial optimization problems. Our survey includes over 100 different mixing unitaries, and we combine each mixer with both the standard phase separator unitary representing the objective function and a thresholded version. Our numerical tests for randomly chosen instances of the unconstrained optimization problems Max 2-SAT and Max 3-SAT reveal that the traditional transverse-field mixer with the standard phase separator performs best for problem sizes of 8 through 14 variables, while the recently introduced Grover mixer with thresholding wins at problems of size 6. This result (i) corrects earlier work suggesting that the Grover mixer is a superior mixer based only on results from problems of size 6, thus illustrating the need to push numerical simulation to larger problem sizes to more accurately predict performance; and (ii) it suggests that more complicated mixers and phase separators may not improve QAOA performance.
An Optimization-Driven Network With Knowledge Prior Injection for HSI Denoising

Due to the limitations of sensor hardware devices, the hyperspectral image (HSI) often suffers from various types of noise, such as Gaussian noise, impulse noise, stripe noise, and deadlines, which can significantly degrade their quality. Although many data-driven methods have been proposed to deal with complex noise, few of them consider the structural characteristics of noise. This not only leads to a lack of interpretability but also results in poor performance when dealing with structural noise in practical applications. To address this issue, this article proposes KPInet, a convolutional neural network (CNN) driven by the structural knowledge of noise for HSI denoising. First and foremost, the knowledge optimization-driven module (KODM) utilizes the deep unrolling method to unfold a total variation (TV) algorithm that considers the structural characteristics of noise. This approach improves the network’s interpretability and results in better performance on structural noise, while maintaining the effect of removing Gaussian noise. Second, the statistical feature injection module (SFIM) extracts more features by utilizing spectral gradients, medians, and means of the HSI. Third, the multiscale degradation guidance module (MDGM) utilizes a dual-stream decoder with a low-resolution upsampling guidance branch to better distinguish the real structure and noise structure in the HSI. Experimental results on simulated and real datasets indicate that the approach achieves favorable denoising performance, as evidenced by both quantitative evaluation metrics and visual results. Furthermore, it also demonstrates the robustness and generalization capacity of the proposed KPInet.
When ChatGPT Gives Incorrect Answers: The Impact of Inaccurate Information by Generative AI on Tourism Decision-Making

This study investigates how inaccurate information provided by ChatGPT impacts travelers’ acceptance of recommendations. Six experiments were conducted based on the accessibility-diagnosticity framework. These examined the moderating role of the prominence and type of incorrect information and their effects on decision-making. The results show that participants perceived more accuracy and trustworthiness, leading to stronger intentions to visit when incorrect information was absent. However, there was a decline in their intentions to visit when incorrect information was present and more prominent or in the same domain. This effect diminished when multiple domains were involved or when participants were focused on the initial task. The research highlights that both the prominence and type of incorrect information are boundary conditions and provides insights into AI applications in tourism. Furthermore, it offers practical implications for online travel agencies in terms of user interface and user experience design planning.
YOLO-SE: Improved YOLOv8 for Remote Sensing Object Detection and Recognition

Object detection remains a pivotal aspect of remote sensing image analysis, and recent strides in Earth observation technology coupled with convolutional neural networks (CNNs) have propelled the field forward. Despite advancements, challenges persist, especially in detecting objects across diverse scales and pinpointing small-sized targets. This paper introduces YOLO-SE, a novel YOLOv8-based network that innovatively addresses these challenges. First, the introduction of a lightweight convolution SEConv in lieu of standard convolutions reduces the network’s parameter count, thereby expediting the detection process. To tackle multi-scale object detection, the paper proposes the SEF module, an enhancement based on SEConv. Second, an ingenious Efficient Multi-Scale Attention (EMA) mechanism is integrated into the network, forming the SPPFE module. This addition augments the network’s feature extraction capabilities, adeptly handling challenges in multi-scale object detection. Furthermore, a dedicated prediction head for tiny object detection is incorporated, and the original detection head is replaced by a transformer prediction head. To address adverse gradients stemming from low-quality instances in the target detection training dataset, the paper introduces the Wise-IoU bounding box loss function. YOLO-SE showcases remarkable performance, achieving an average precision at IoU threshold 0.5 (AP50) of 86.5% on the optical remote sensing dataset SIMD. This represents a noteworthy 2.1% improvement over YOLOv8 and YOLO-SE outperforms the state-of-the-art model by 0.91%. In further validation, experiments on the NWPU VHR-10 dataset demonstrated YOLO-SE’s superiority with an accuracy of 94.9%, surpassing that of YOLOv8 by 2.6%. The proposed advancements position YOLO-SE as a compelling solution in the realm of deep learning-based remote sensing image object detection.
Generative artificial intelligence

Abstract Recent developments in the field of artificial intelligence (AI) have enabled new paradigms of machine processing, shifting from data-driven, discriminative AI tasks toward sophisticated, creative tasks through generative AI. Leveraging deep generative models, generative AI is capable of producing novel and realistic content across a broad spectrum (e.g., texts, images, or programming code) for various domains based on basic user prompts. In this article, we offer a comprehensive overview of the fundamentals of generative AI with its underpinning concepts and prospects. We provide a conceptual introduction to relevant terms and techniques, outline the inherent properties that constitute generative AI, and elaborate on the potentials and challenges. We underline the necessity for researchers and practitioners to comprehend the distinctive characteristics of generative artificial intelligence in order to harness its potential while mitigating its risks and to contribute to a principal understanding.
An introduction to the bootstrap: a versatile method to make inferences by using data-driven simulations.

The bootstrap is a versatile technique that relies on data-driven simulations to make statistical inferences. When combined with robust estimators, the bootstrap can afford much more powerful and flexible inferences than is possible with standard approaches such as T-tests on means. In this tutorial, we use detailed illustrations of bootstrap simulations to give readers an intuition of what the bootstrap does and how it can be applied to solve many practical problems, such as building confidence intervals for many aspects of the data. In particular, we illustrate how to build confidence intervals for measures of location, including measures of central tendency, in the one-sample case, for two independent and two dependent groups. We also demonstrate how to compare correlation coefficients using the bootstrap and to perform simulations to determine if the bootstrap is fit for purpose for a particular application. Our approach is to suggest and motivate what could be done in a situation, with an understanding that various options are valid, though they may help answer different questions about a dataset. The tutorial also addresses two widespread misconceptions about the bootstrap: that it makes no assumptions about the data, and that it leads to robust inferences on its own. The tutorial focuses on detailed graphical descriptions, with data and code available online to reproduce the figures and analyses in the article (OSF: https://osf.io/8b4t5/; GitHub: https://github.com/GRousselet/bootstrap).
GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Multi-query attention (MQA), which only uses a single key-value head, drastically speeds up decoder inference. However, MQA can lead to quality degradation, and moreover it may not be desirable to train a separate model just for faster inference. We (1) propose a recipe for uptraining existing multi-head language model checkpoints into models with MQA using 5% of original pre-training compute, and (2) introduce grouped-query attention (GQA), a generalization of multi-query attention which uses an intermediate (more than one, less than number of query heads) number of key-value heads. We show that uptrained GQA achieves quality close to multi-head attention with comparable speed to MQA.
Practically-exploitable Cryptographic Vulnerabilities in Matrix

We report several practically-exploitable cryptographic vulnerabilities in the Matrix standard for federated real-time communication and its flagship client and prototype implementation, Element. These, together, invalidate the confidentiality and authentication guarantees claimed by Matrix against a malicious server. This is despite Matrix’ cryptographic routines being constructed from well-known and -studied cryptographic building blocks. The vulnerabilities we exploit differ in their nature (insecure by design, protocol confusion, lack of domain separation, implementation bugs) and are distributed broadly across the different subprotocols and libraries that make up the cryptographic core of Matrix and Element. Together, these vulnerabilities highlight the need for a systematic and formal analysis of the cryptography in the Matrix standard.