Reviewerly/exHarmony huggingface dataset:
The effects of tempo and loudness variations during warm-up with music on perceived exertion, physical enjoyment and specific performances in male and female taekwondo athletes

The ergogenic effect of pre-selected warm-up music with the tempo and loudness variations on the performance of combat sports' athletes as well as the difference between sexes is not well established. The present study aimed to assess the effects of listening to music with different tempos and loudness during warm-up on perceived exertion, physical enjoyment and physical performances in young taekwondo athletes. In a randomized study design, 20 taekwondo athletes (10 males, mean ± SD: age: 17.5 ± 0.7 years, taekwondo experience: ≥ 6 year) performed the taekwondo specific agility test (TSAT) and the 10s and multiple frequency speed of kick test (FSKT-10s and FSKT-mult) after warming-up with or without music. The music was played at high (140 beats·min-1) or very high (200 beats·min-1) tempo combined with low (60 dB) or high (80 dB) loudness, resulting in four experimental and control conditions. The ratings of perceived exertion (RPE) and physical activity enjoyment scale (PACES) were assessed after each condition. After normality, homogeneity and sphericity checks, two-way (or multivariate) analysis of variance and Bonferrroni (or Friedman's and Wilcoxon's test) post-hoc test were operated when necessary. For TSAT, 140 beats·min-1+80 dB induced better performance compared with 200 beats·min-1+80 dB, 200 beats·min-1+60 dB, control and the 140 beats·min-1+60 dB conditions. For FSKT-10s, 140 beats·min-1+80 dB condition induced higher performance compared with 200 beats·min-1+60 dB, 200 beats·min-1+80 dB, 140 beats·min-1+60 dB and the control conditions. For FSKT-mult, 140 beats·min-1+80 dB induced higher number of techniques compared with 200 beats·min-1+60 dB, 140 beats·min-1+60 dB, control and the 200 beats·min-1+80 dB conditions. Moreover, 140 beats·min-1+80 dB induced lower decrement index (DI) compared with the other conditions and lower DI in 140 beats·min-1+60 dB compared with 200 beats·min-1+80 dB and control conditions. Moreover, 140 beats·min-1+80 dB resulted in greater PACES scores compared with 200 beats·min-1+80 dB and control conditions. Better performance was found for males compared with females in TSAT, FSKT-10s and FSKT-mult (i.e., techniques' number), as well as lower DI and higher RPE post-FSKT-10s. Pre-selected warm-up music played at 140 beats·min-1 and 80 dB is an efficient strategy to enhance physical activity enjoyment and specific performances in taekwondo.
Complete Active Space Methods for NISQ Devices: The Importance of Canonical Orbital Optimization for Accuracy and Noise Resilience

To avoid the scaling of the number of qubits with the size of the basis set, one can divide the molecular space into active and inactive regions, which is also known as complete active space methods. However, selecting the active space alone is not enough to accurately describe quantum mechanical effects such as correlation. This study emphasizes the importance of optimizing the active space orbitals to describe correlation and improve the basis-dependent Hartree-Fock energies. We will explore classical and quantum computation methods for orbital optimization and compare the chemically inspired ansatz, UCCSD, with the classical full CI approach for describing the active space in both weakly and strongly correlated molecules. Finally, we will investigate the practical implementation of a quantum CASSCF, where hardware-efficient circuits must be used and noise can interfere with accuracy and convergence. Additionally, we will examine the impact of using canonical and noncanonical active orbitals on the convergence of the quantum CASSCF routine in the presence of noise.
A stain color normalization with robust dictionary learning for breast cancer histological images processing

Microscopic analyses of tissue samples are crucial for confirming the diagnosis of breast cancer. The digitization of these samples has led to the development of computational systems that can assist pathologists. However, these systems may face limitations owing to color variations in the images. Normalization studies have been widely conducted to address these issues, but there is still a need for new proposals that take into account the biological properties of dyes and tissues. This study presents a novel method for normalizing hematoxylin and eosin-stained histological images by estimating the color appearance matrices and density maps of the stain. The proposed method offers contributions in terms of pixel selection and weight definition to improve the color estimation of histological images. Besides, to the best of our knowledge, no previous studies have evaluated normalized images considering both handcrafted and learning features. Breast cancer images with significant color variations were used to evaluate this approach and the results demonstrated its effectiveness and efficiency. The average values of FSIM, NIQE, and QSSIM were up to 0.9866, 3.4298, and 0.9655, respectively. Compared with other normalization techniques, the proposed method showed an increase of up to 5.9261, with the largest difference observed in the amount of noise added, as indicated by the NIQE metric. To determine the impact of normalization on feature extraction, the evaluations included an analysis of both color and deep-learned features. These experiments showed that all evaluated methods harmed the separation of breast cancer samples by color features. In contrast, the deep-learned features resulted in less complex classification problems, especially with the proposed normalization. This technique also reached one of the lowest processing times, nearly 6 s with the largest image from the databases.
Improving the number of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>T</mml:mi></mml:math> gates and their spread in integer multipliers on quantum computing

Quantum circuits performing arithmetic operations are critical in quantum computing because of the need for such operations in proven quantum algorithms. Although quantum computers are becoming increasingly resourceful, the number of qubits currently available is still limited. Furthermore, these qubits are heavily affected by internal and external noise. It has been proven that quantum circuits built using Clifford $+T$ gates can be made fault tolerant. However, the use of the $T$ gates comes at a very high cost. If the number of $T$ gates used in a circuit is not optimized, the cost of the circuit will be increased excessively. As a consequence, it is essential to optimize the circuits so that they are as resource efficient as possible and also noise tolerant. This paper presents the design of a circuit to perform the multiplication of two integers. The circuit is built using only Clifford $+T$ gates for compatibility with error detection and correction codes. It outperforms the circuits in the state of the art in terms of $T$ count and $T$ depth.
New AIR for the Archaeological Process? The Use of 3D Web Semantic for Publishing Archaeological Reports

The management of archaeological excavation data has been the subject of scientific debate in the last decades: critical elements have been identified, such as maintaining analytical data and the derived knowledge entangled, and other relevant aspects, like data curation, accessibility, and long-term preservation, have emerged. This study describes, illustrates, and evaluates the use of the Archaeological Interactive Report (AIR), a cutting-edge information system designed to manage excavation data that is oriented toward the 3D web semantics. AIR is a web platform for recording archaeological investigations live, an online archive that incorporates the complete dataset of the investigations, and a multimedia visualization system providing a 3D environment for data analysis and assemblages, testing interpretation hypotheses, and publishing dynamic editorialization outputs. AIR is applied and evaluated within the case study of Västra Vång (southeastern Sweden), demonstrating that it is possible to use a flexible ontological data model tailored to the archaeologists’ needs.
Rotating behind security: an enhanced authentication protocol for IoT-enabled devices in distributed cloud computing architecture

Abstract With the continuous progress of the Internet of Things (IoT) technology, IoT devices have gradually penetrated all aspects of life. However, due to rapid data growth, IoT devices with limited memory resources cannot store massive data. Cloud computing is an Internet-centric network that can provide security services and data storage. The combination of IoT devices with cloud computing considerably promotes resource sharing, facilitates users’ access to information at any time, and provides users with corresponding cloud computing services on time. Because the information transmitted through public channels is easily intercepted, tampered with, and eavesdropped on by malicious attackers. As a result, users’ and servers’ private information are disclosed. Numerous scholars have proposed different authentication protocols in this environment to protect the communications between users and servers. Amin et al. devised a smart card based authentication protocol. Unfortunately, Kang et al. demonstrated that their protocol was insecure. Huang et al. discovered Kang et al.’s improvement also has security flaws and then designed a protocol to enhance security. In this paper, we first show that Huang et al.’s protocol cannot resist privileged insider and temporary value disclosure attacks. Afterward, we propose an enhanced protocol based on their protocol. Finally, we use formal/informal security analysis to demonstrate the security of the improved protocol. The comparison results are indicated that our protocol has lower computational and communication costs under the same security level.
Enhancing Data Security in IoT Networks with Blockchain-Based Management and Adaptive Clustering Techniques

The rapid proliferation of smart devices in Internet of Things (IoT) networks has amplified the security challenges associated with device communications. To address these challenges in 5G-enabled IoT networks, this paper proposes a multi-level blockchain security architecture that simplifies implementation while bolstering network security. The architecture leverages an adaptive clustering approach based on Evolutionary Adaptive Swarm Intelligent Sparrow Search (EASISS) for efficient organization of heterogeneous IoT networks. Cluster heads (CH) are selected to manage local authentication and permissions, reducing overhead and latency by minimizing communication distances between CHs and IoT devices. To implement network changes such as node addition, relocation, and deletion, the Network Efficient Whale Optimization (NEWO) algorithm is employed. A localized private blockchain structure facilitates communication between CHs and base stations, providing an authentication mechanism that enhances security and trustworthiness. Simulation results demonstrate the effectiveness of the proposed clustering algorithm compared to existing methodologies. Overall, the lightweight blockchain approach presented in this study strikes a superior balance between network latency and throughput when compared to conventional global blockchain systems. Further analysis of system under test (SUT) behavior was accomplished by running many benchmark rounds at varying transaction sending speeds. Maximum, median, and lowest transaction delays and throughput were measured by generating 1000 transactions for each benchmark. Transactions per second (TPS) rates varied between 20 and 500. Maximum delay rose when throughput reached 100 TPS, while minimum latency maintained a value below 1 s.
Students Digital Maturity and Its Implications for Sustainable Behavior

The COVID-19 pandemic has accelerated the transition to remote and hybrid teaching and learning, highlighting the importance of digital maturity among university staff and students. Digital maturity includes technological proficiency and skills necessary to navigate and use digital tools for personal learning and development, as well as responsible and ethical use of technology, digital citizenship, and critical thinking. Developing digital maturity among students is critical to promoting sustainable practices and success in a digitally connected world. This article examines the impact of students’ digital maturity on online learning engagement and explores the relationship between digital maturity, acceptance of universities’ digital transformation, online education satisfaction, student engagement, and sustainable behavior. The study randomly selected 358 students from three Polish universities who completed an online survey (CAWI). The results indicate that digital competences positively affect the students’ acceptance of the digital transformation of the university. Personal innovation and motivation for formal digital learning also influence acceptance. Accepting the digital transformation has a positive impact on online learning satisfaction. Engagement in online learning has minimal impact on informal digital learning. The positive moderating effects of commitment to sustainable development on satisfaction and commitment to distance learning and informal digital learning were insignificant. The study suggests that universities need to promote digital maturity among all stakeholders, and students need to improve their digital competences to take full advantage of the educational offer of universities.
Stability-Level Evaluation of the Construction Site above the Goaf Based on Combination Weighting and Cloud Model

Mineral resource-based cities have formed a large number of goafs due to the long-term mining of coal. It is of great significance to make full use of the abandoned land resources above the goaf to promote the transformation and development of resource-based cities. In order to avoid the threat of surface residual deformation to the proposed construction project, it is an urgent problem to obtain the stability results of the construction site accurately. First of all, based on the principles of relevance, hierarchy, representativeness and feasibility of index selection, 10 indexes are selected to construct the stability evaluation index system. Then the subjective weight and objective weight of evaluation indexes are determined based on improved AHP, rough set and CRITIC methods, which improves the accuracy of the determination of the index weights. In addition, the membership degree of each index is determined using the cloud model. Finally, the stability grade can be obtained according to the maximum membership degree theory. The above researches are applied to evaluate the stability of the Mianluan expressway construction site, and the results show that the stability level of the study area is not uniform and that there are two states: stable and basically stable. Finally, a sensitivity analysis of the subjective weight of each index is carried out, the index stopping time has the highest sensitivity to weight (12.44%), which is far lower than the corresponding weight change rate of 100%, indicating that the determination of weight is scientific and reasonable. These things considered, the reliability of the evaluation result is indirectly verified according to the field leveling. This research can provide a reference for the effective utilization of land resources above an old goaf.
Computational Intelligence in Cancer Diagnostics: A Contemporary Review of Smart Phone Apps, Current Problems, and Future Research Potentials

Cancer is a dangerous and sometimes life-threatening disease that can have several negative consequences for the body, is a leading cause of mortality, and is becoming increasingly difficult to detect. Each form of cancer has its own set of traits, symptoms, and therapies, and early identification and management are important for a positive prognosis. Doctors utilize a variety of approaches to detect cancer, depending on the kind and location of the tumor. Imaging tests such as X-rays, Computed Tomography scans, Magnetic Resonance Imaging scans, and Positron Emission Tomography (PET) scans, which may provide precise pictures of the body's interior structures to spot any abnormalities, are some of the tools that doctors use to diagnose cancer. This article evaluates computational-intelligence approaches and provides a means to impact future work by focusing on the relevance of machine learning and deep learning models such as K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Deep Neural Network, Deep Boltzmann machine, and so on. It evaluates information from 114 studies using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). This article explores the advantages and disadvantages of each model and provides an outline of how they are used in cancer diagnosis. In conclusion, artificial intelligence shows significant potential to enhance cancer imaging and diagnosis, despite the fact that there are a number of clinical issues that need to be addressed.
Community preserving adaptive graph convolutional networks for link prediction in attributed networks

Link prediction in attributed networks has attracted increasing attention recently due to its valuable real-world applications. Various related methods have been proposed, but most of them cannot effectively utilize community structure, neither can they well fuse attribute information and link information to improve the performance. Inspired by our empirical observations on how community structure affects the generation of links, we propose a novel Community Preserving Adaptive Graph Convolutional Networks (CPAGCN) method to tackle the link prediction task in attributed networks. Specifically, CPAGCN is composed of two core modules: network embedding and link prediction. Network embedding module utilizes AGCN to seamlessly fuse link information and attribute information to obtain node representations, which are simultaneously driven to preserve community structure via an appropriate community detection model. Taking these node representations as the input, link prediction module applies multilayer perception (MLP) to directly learn the prediction scores for potential links. Through combining the graph reconstruction loss with the prediction loss to train AGCN and MLP jointly, CPAGCN can learn node representations that are more beneficial to predicting links. To verify the effectiveness of CPAGCN, we conduct extensive experiments on six real-world attributed networks. The results demonstrate that CPAGCN performs better than several strong competitors in link prediction. The source code is available at https://github.com/GDM-SCNU/CPAGCN.
Multi-scene image enhancement based on multi-channel illumination estimation

The current mainstream algorithms cannot effectively take into account different luminance areas for image enhancement under different lighting conditions. Although there are many excellent algorithms for low-light image enhancement, the results are less satisfactory when those algorithms are applied to different exposure scenes of images. To solve the above problems, this paper proposes an image enhancement algorithm that can adapt to different exposure conditions. First, we introduce two forward and reverse dual-channels as initial illumination maps, which can effectively take into account the different exposure areas in an image. For the lack of details in enhanced images, we use a guided filter to pre-process the illumination map for retaining more detailed information. Second, the illumination map estimated by an optimization function will cause some distortion in fused images, so we propose an improved adaptive gamma correction method. At last, these images are fused by multi-scale exposure to obtain high-quality images. The exposure fusion allows our algorithm to cope with more scenes (shadows, overexposure, etc.). Experiments show that the proposed algorithm is not only effective in processing low-light images but can also be adapted for images in different exposure environments without deliberate adjustment of parameters. The proposed algorithm has a good performance in low-light, shadow, and overexposure scenes. Compared with other algorithms, the enhanced images are subjectively more realistic, natural, and maintain more complete details. Besides, the proposed algorithm outperforms other algorithms in comparison to several objective evaluation indicators.
Break Through the Border Restriction of Horizontal Bounding Box for Arbitrary-Oriented Ship Detection in SAR Images

Substantial progress has been made in detecting ships of arbitrary orientation in synthetic aperture radar (SAR) images. However, the mainstream method is still limited by the horizontal bounding box (HBB) boundary, which cannot provide scaling information for the length and width of the oriented bounding box (OBB) in an intuitive way. In this study, we propose a novel encode representation to describe the OBB by breaking through the border restriction of the HBB. Specifically, we derive an inclination factor from two left-top point offsets (LTPO), which enables us to directly infer the coordinates of the four OBB vertices and obtain an oriented rectangular proposal. To obtain high-quality oriented semantic features, we utilize a feature adaptive module (FAM) to learn the shape and orientation implied by arbitrary-oriented ships through spatial transformation. Our comparative experiments demonstrate that our proposed method achieves superior performance and detection accuracy on two commonly-used benchmark datasets for oriented SAR ship detection, namely SSDD and HRSID.
Reversible Data Hiding in Encrypted Images With Secret Sharing and Hybrid Coding

Reversible data hiding in encrypted images (RD-HEI) is an essential data security technique.Most RDHEI methods cannot perform well in embedding capacity and security.To address this issue, we propose a new RDHEI method using Chinese remainder theorem-based secret sharing (CRTSS) and hybrid coding.Specifically, a hybrid coding is first proposed for RDH to achieve high embedding capacity.At the content owner side, a novel iterative encryption is designed to conduct block based encryption for perfectly preserving the spatial correlation of original blocks in their encrypted blocks.Then, the CRTSS with the constraints is exploited to generate multiple encrypted image shares, in which spatial correlations of the encrypted blocks are also preserved.Meanwhile, the CRTSS provides good security properties for the proposed method.Since there are strong spatial correlations in the blocks of each share, the datahider can exploit the proposed hybrid coding to perform data embedding for improving capacity.On the receiver side, even if some shares are corrupted/missing, the original image can be losslessly recovered as long as enough uncorrupted marked shares are obtained.Experiment results show that the proposed RDHEI method outperforms some state-of-the-art methods, including some secret sharing (SS) based methods in terms of embedding capacity.