Reviewerly/exHarmony huggingface dataset:
Multi-objective dynamic distribution adaptation with instance reweighting for transfer feature learning

In knowledge adaptation, the source and target knowledge are transferred into the same mapping space by simultaneously reducing the difference between the marginal and conditional distributions; however, it is difficult to precisely balance the two distributions at each transformation. To address this problem, a novel multi-objective dynamic distribution adaptation (MODDA) with instance reweighting is proposed to reduce discrepancies between the two distributions. In addition, a customised non-dominated sorting genetic algorithm-II (NSGA2) optimisation method is presented for searching the optimal cumulative weight path, and four genetic operator combinations are compared to determine which one is optimal for MODDA. Moreover, kernel mean matching is proposed for the first time for dynamic compensation based on an individual’s relevance in instance reweighting. The experimental results confirm that MODDA outperforms other state-of-the-art algorithms in terms of the classification accuracy for 16 well-known cross-domain tasks.
MSRANet: Learning discriminative embeddings for speaker verification via channel and spatial attention mechanism in alterable scenarios

Speaker embeddings have become the most popular feature representation in speaker verification. Improving the robustness of speaker embedding extraction systems is a crucial problem. A multi-scale residual aggregation network (MSRANet), which is a simple but efficient network with triplet input and triplet loss, is proposed in this paper. Two different aggregation strategies are utilized in frame-level feature extractors to capture long-term variations in speaker characteristics. Attention mechanism is employed to filter a large number of parameters in temporal and frequency dimensions, which can effectively focus on the significant information and neglect the redundancy feature. Extensive experiments on the VoxCeleb1 and VoxCeleb2 wild datasets are also conducted to evaluate the performance of the proposed method. In comparison with four baselines experiments, obtained results demonstrate that the proposed MSRANet achieves a state-of-the-art performance of an equal error rate of 3.84% and an accuracy rate of 98.76%. Furthermore, the proposed method is proven to be effective in cross-scenarios adaptability through training performance on the LibriSpeech dataset. The proposed MSRANet has an equal error rate of 2.64% and an accuracy rate of 99.20% on LibriSpeech.
3D modeling of detachment faults in the Jiaodong gold province, eastern China: A Bayesian inference perspective and its exploration implications

The world-class Jiaodong gold province, which is located in the Jiaodong Peninsula, eastern North China Craton, hosts > 5,000 t of gold resources. The major gold mineralization in this area is mainly controlled by NE-NNE faults, such as the Zhaoping, Jiaojia, and Sanshandao faults. While intensive efforts have been devoted to the investigation of the architecture of the three detachment faults, the fault geometry at depth remains highly uncertain and hotly controversial due to the scarcity of observations, non-uniqueness of the geophysical inversion, and underutilization of the geological knowledge. Along these lines, the deep three-dimensional (3D) geometry of detachment faults from a Bayesian inference perspective is reconstructed in this work. In the Bayesian framework, the multi-source information and the prior geological knowledge are combined into an integrated probabilistic model and the posterior probability distribution of detachment faults is inferred. The resulting posterior distribution permits an estimation of the uncertainty (information entropy) of the 3D fault geometry and the expected shape undulations compared with a prior reference model. More specifically, it demonstrates that specific and detailed 3D models of faults could be inferred by tracking surfaces with maximum information entropy of the geological units. The reconstructed models exhibit detailed 3D geometry of the fault surfaces and their reliability is validated by a recent drill hole at ∼−2,300 m depth. In terms of the reconstructed 3D models, the dip variations are analyzed and the possible stress directions at deep zones of the three faults are discussed. The shape features of the reconstructed faults are also quantified, which indicates that the fault sections with a steep-to-gentle transition in dip angle are closely associated with the gold mineralization. The 3D gold prospectivity mapping with the spatial features as predictor variables, highlights seven deep prospecting targets, which are generally consistent with the gently dipping zones and beneath the known orebodies.
Multi-UAV Cooperative Trajectory Planning Based on FDS-ADEA in Complex Environments

Multi-UAV cooperative trajectory planning (MUCTP) refers to the planning of multiple flyable trajectories based on the location of each UAV and mission point in a complex environment. In the planning process, the complex 3D space structure increases the difficulty of solving the trajectory points, and the mutual constraints of the UAV cooperative constraints can degrade the performance of the planning system. Therefore, to improve the efficiency of MUCTP, this study proposes MUCTP based on feasible domain space and adaptive differential evolution algorithm (FDS-ADEA). The method first constructs a three-dimensional feasible domain space to reduce the complexity of the search space structure; then, the constraints of heterogeneous UAVs are linearly weighted and transformed into a new objective function, and the information of the fitness value is shared in accordance with the adaptive method and the code correction method to improve the search efficiency of the algorithm; finally, the trajectories are smoothed to ensure the flyability of the UAVs performing the mission by combining the cubic B-spline curves. Experiments 1, 2, 3, and 4 validate the proposed algorithm. Simulation results verify that FDS-ADEA has fast convergence, high cooperative capability, and more reasonable planned trajectory sets when processing MUCTP.
Decline in reported measles cases in Italy in the COVID-19 era, January 2020 – July 2022: The need to prevent a resurgence upon lifting non-pharmaceutical pandemic measures

From January 2020 to July 2022, 120 measles cases were reported to the Italian national surveillance system, of which 105 had symptom onset in 2020, nine in 2021 and six in the first seven months of 2022. This represents a sharp decline compared to the time period immediately preceding the COVID-19 pandemic, most likely due to the non-pharmaceutical interventions implemented to prevent SARS-CoV2 transmission. Of 105 cases reported in 2020, 103 acquired the infection before a national lockdown was instituted on 9 March 2020. Overall, one quarter of cases reported at least one complication. As non-pharmaceutical pandemic measures are being eased worldwide, and considering measles seasonality, infectiousness, and its potential severity, it is important that countries ensure high vaccination coverage and close immunity gaps, to avoid risk of future outbreaks.
Lightweight Separable Convolution Network for Breast Cancer Histopathological Identification

Breast cancer is one of the leading causes of death among women worldwide. Histopathological images have proven to be a reliable way to find out if someone has breast cancer over time, however, it could be time consuming and require much resources when observed physically. In order to lessen the burden on the pathologists and save lives, there is need for an automated system to effectively analysis and predict the disease diagnostic. In this paper, a lightweight separable convolution network (LWSC) is proposed to automatically learn and classify breast cancer from histopathological images. The proposed architecture aims to treat the problem of low quality by extracting the visual trainable features of the histopathological image using a contrast enhancement algorithm. LWSC model implements separable convolution layers stacked in parallel with multiple filters of different sizes in order to obtain wider receptive fields. Additionally, the factorization and the utilization of bottleneck convolution layers to reduce model dimension were introduced. These methods reduce the number of trainable parameters as well as the computational cost sufficiently with greater non-linear expressive capacity than plain convolutional networks. The evaluation results depict that the proposed LWSC model performs optimally, obtaining 97.23% accuracy, 97.71% sensitivity, and 97.93% specificity on multi-class categories. Compared with other models, the proposed LWSC obtains comparable performance.
Recognizing geometric primitives in 3D point clouds of mechanical CAD objects

The problem faced in this paper concerns the recognition of simple and complex geometric primitives in point clouds resulting from scans of mechanical CAD objects. A large number of points, the presence of noise, outliers, missing or redundant parts and uneven distribution are the main problems to be addressed to meet this need. In this article we propose a solution, based on the Hough transform, that can recognize simple and complex geometric primitives and is robust to noise, outliers, and missing parts. Additionally, we can extract a series of geometric descriptors that uniquely characterize a primitive and, based on them, aggregate the output into maximal or compound primitives, thus reducing oversegmentation. The results presented in the paper demonstrate the robustness of the method and its competitiveness with respect to other solutions proposed in the literature.
A Testset-Based Method to Analyse the Negation-Detection Performance of Lexicon-Based Sentiment Analysis Tools

The correct detection of negations is essential to the performance of sentiment analysis tools. The evaluation of such tools is currently conducted through the use of corpora as an opportunistic approach. In this paper, we advocate using a different evaluation approach based on a set of intentionally built sentences that include negations, which aim to highlight those tools’ vulnerabilities. To demonstrate the effectiveness of this approach, we propose a basic testset of such sentences. We employ that testset to evaluate six popular sentiment analysis tools (with eight lexicons) available as packages in the R language distribution. By adopting a supervised classification approach, we show that the performance of most of these tools is largely unsatisfactory.
Structure-embedded ghosting artifact suppression network for high dynamic range image reconstruction

In high-dynamic-range (HDR) image reconstruction, the background offset among multiple multi-exposure low-dynamic-range (LDR) images, wide-range movement of targets, and missing edge structure information in the over-/under-exposure region cause both ghosting and blurring artifacts. This study proposed a structure-embedded ghosting artifact suppression network (SGARN) to achieve detailed preservation and ghosting artifact suppression to address this issue. According to the different image feature maps’ correlation in channels, a channel and multi-head joint attention network (CMAN) was designed to highlight the features conducive to high-quality HDR image reconstruction. A dense multi-scale information transfer network (DMITN) was designed to integrate the characteristics of different combinations of convolution kernels with different receptive fields. In addition, a structure-embedded network was designed to predict the edge structure to be compensated from the reference image. The predicted edge was integrated into the reconstructed HDR image. Compared with state-of-the-art methods, the proposed method can achieve better visual performance and higher objective evaluation results on three public datasets. The source codes of the proposed method are available at https://github.com/lhf12278/SGARN.
Evolutionary competitive multitasking optimization via improved adaptive differential evolution

Competitive multitasking optimization (CMTO) is a special multitasking optimization paradigm that has been recently proposed. In the CMTO problems, the objective values of all tasks are competitive, and the purpose of CMTO is to find an optimal solution for multiple tasks. However, existing algorithms designed for the CMTO problems perform poorly since their resource allocation strategies are prone to incorrect task selection. To remedy this drawback, this paper proposes an improved multitasking adaptive differential evolution, which can be featured as: (i) a success-history based resource allocation strategy is proposed; (ii) an adaptive random mating probability control strategy is devised to adapt to different task combinations; (iii) an adaptive multitasking differential evolution operator is designed to enhance the searchability. To evaluate the performance of the proposed method, three CMTO benchmark test suites and two real-world optimization problems are chosen. Compared with other related methods, the results show that the proposed method achieved better performance empirically.
Never-Ending Tourism: Tourism Experience Scenarios for 2030

Abstract The advent of recent technological advancements, and particularly, the metaverse, has brought unprecedented opportunities for tourism. The integration of multiple realities extends tourism beyond the traditional chronological classification of pre-travel, during-travel, and post-travel stages. The novel concept of never-ending tourism captures a holistic multi-dimensional experience in which technological solutions are at the foundation to elevate tourism to an on-going, never-ending activity that permeates every day and travel life. This study adopted a futures methods approach through qualitative in-depth interviews and a scenario development supported by a storytelling technique. The findings present three distinct scenarios of never-ending tourism on a spectrum of digital companion, digital escape and digital connection. The scenarios suggest a growing importance of the pre-travel and post-travel phases, while technologies continue support visitors to enjoy the irreplaceable multisensory experiences on site. Towards 2030, the metaverse will enable visitors to mentally teleport themselves to destinations, regardless of time, financial resources and physical abilities. Individuals will seamlessly move between integrated digital and physical realities, where the engagement with travel becomes a part of daily life. The study is novel and original in that it empirically explores the emerging concept of never-ending tourism and contributes to the wider metaverse discourse in the tourism context. Several practical suggestions and future research directions are given to unlock the potential of never-ending tourism in relation to integrated experience design, new business models and long-term customer engagement.
Protecting your business against ransomware attacks? Explaining the motivations of entrepreneurs to take future protective measures against cybercrimes using an extended protection motivation theory model

Entrepreneurs are likely to be victims of ransomware. Previous studies have found that entrepreneurs tend to adopt few preventive measures, thereby increasing their chances of victimization. Due to a lack of research, however, not much is known about why entrepreneurs lack self-protective behaviors and how they can be encouraged to change said behaviors. Therefore, the purpose of this study is to explain, by means of an extended model of the Protection Motivation Theory (PMT), the motivation for entrepreneurs using protective measures against ransomware in the future. The data for our study were collected thanks to a questionnaire that was answered by 1,020 Dutch entrepreneurs with up to 250 employees. Our Structural Equation Modelling (SEM) analysis revealed that entrepreneurs are more likely to take preventive measures against ransomware if they perceive the risk of ransomware as severe (perceived severity), if they perceive their company as being vulnerable (perceived vulnerability), if they are concerned about the risks (affective response), and if they think that the people and companies around them expect them to apply preventive measures (subjective norms). However, if entrepreneurs think that they are capable of handling the risk (self-efficacy) and are convinced that their adopted preventive measures are effective (response efficacy), they are less likely to take preventive measures. Furthermore, for entrepreneurs that outsource IT security, the significant effect of perceived vulnerability and subjective norms disappears. The likelihood of entrepreneurs protecting their business against ransomware is thus influenced by a complex interplay of various motivational factors and is partly dependent on the business' characteristics. Based on these findings, we will discuss security professionals' prospects for increasing the cyber resilience of entrepreneurs, thus preventing cybercrime victimization.
Prototype UX Design: Mobile Augmented Reality Application based on Gamification for Cultural Heritage Tourism

This study aims to identify the processes of UX designing a mobile application, Augmented Reality based on Gamification for Cultural Heritage Tourism, and to provide the prototype design and the testing result; this research employs the Design Thinking Method to design the prototype and uses the System Usability Scale (SUS) to identify and validate the testing result; the author establishes two research questions for this study, then explains the answers through a clear description of the stages of making a prototype which consists of the empathize, define, ideate, prototype, and testing stages, then to find out the testing results, the author uses the SUS method, scoring 88.5 in the excellent and acceptable categories; the originality of the research, this study provides the Design Thinking method to investigate the process of UX designing and the SUS Score method to calculate the resting result, helps build a complete understanding of developing the prototype and validation through testing comprehensively.
Metaverse as A Future of Education: A Systematic Review

Learning has become the most important activity in our life. There are many ways to learn, but only some are interesting. However, by using technology, learning has become more exciting and attractive. One of the most promising technologies in education fields are Virtual Reality and Augmented Reality, which combines the real world with the virtual world. This virtual world is called "METAVERSE". With metaverse, we can interact with people in a virtual environment with life-like participants’ avatars. Some works of literature have used metaverse technology as a learning tool, making learning more attractive than seeing people on a screen. Through this research, we use systematic literature reviews to discover the possibilities, effectiveness, and advantages and disadvantages of learning using the metaverse. Based on our study, we can conclude that metaverse is proven to be an effective learning tool. Because learning in the metaverse gives many advantages, such as it helps to visualize materials and make learning more interactive, easier, and interesting for students, and also help teachers in the learning process. However, there are also some limitations, such as the cost of the device needed to support the metaverse. Therefore, there are more advantages to using the metaverse in education as a learning tool. Future development is needed to maximize the metaverse’s advantages and handle the limitations of the metaverse as a learning platform.
Evolution of symmetry index in minerals

Abstract Crystal structures of minerals are defined by a specific atomic arrangement within the unit‐cell, which follows the laws of symmetry specific to each crystal system. The causes for a mineral to crystallize in a given crystal system have been the subject of many studies showing their dependency on different formation conditions, such as the presence of aqueous fluids, biotic activity and many others. Different attempts have been made to quantify and interpret the information that we can gather from studying crystal symmetry and its distribution in the mineral kingdom. However, these methods are mostly outdated or at least not compatible for use on large datasets available today. Therefore, a revision of symmetry index calculation has been made in accordance with the growing understanding of mineral species and their characteristics. In the gathered data, we observe a gradual but significant decrease in crystal symmetry through the stages of mineral evolution, from the formation of the solar system to modern day. However, this decrease is neither uniform nor linear, which provides further implications for mineral evolution from the viewpoint of crystal symmetry. The temporal distribution of minerals based on the number of essential elements in their chemical formulae and their symmetry index has been calculated and compared to explore their behaviour. Minerals with four to eight essential elements have the lowest average symmetry index, while being the most abundant throughout all stages of mineral evolution. There are many open questions, including those pertaining to whether or not biological activity on Earth has influenced the observed decrease in mineral symmetry through time and whether or not the trajectory of planetary evolution of a geologically active body is one of decreasing mineral symmetry/increasing complexity.
What really changes when developers intend to improve their source code: a commit-level study of static metric value and static analysis warning changes

Abstract Many software metrics are designed to measure aspects that are believed to be related to software quality. Static software metrics, e.g., size, complexity and coupling are used in defect prediction research as well as software quality models to evaluate software quality. Static analysis tools also include boundary values for complexity and size that generate warnings for developers. While this indicates a relationship between quality and software metrics, the extent of it is not well understood. Moreover, recent studies found that complexity metrics may be unreliable indicators for understandability of the source code. To explore this relationship, we leverage the intent of developers about what constitutes a quality improvement in their own code base. We manually classify a randomized sample of 2,533 commits from 54 Java open source projects as quality improving depending on the intent of the developer by inspecting the commit message. We distinguish between perfective and corrective maintenance via predefined guidelines and use this data as ground truth for the fine-tuning of a state-of-the art deep learning model for natural language processing. The benchmark we provide with our ground truth indicates that the deep learning model can be confidently used for commit intent classification. We use the model to increase our data set to 125,482 commits. Based on the resulting data set, we investigate the differences in size and 14 static source code metrics between changes that increase quality, as indicated by the developer, and changes unrelated to quality. In addition, we investigate which files are targets of quality improvements. We find that quality improving commits are smaller than non-quality improving commits. Perfective changes have a positive impact on static source code metrics while corrective changes do tend to add complexity. Furthermore, we find that files which are the target of perfective maintenance already have a lower median complexity than files which are the target of non-pervective changes. Our study results provide empirical evidence for which static source code metrics capture quality improvement from the developers point of view. This has implications for program understanding as well as code smell detection and recommender systems.