Reviewerly/exHarmony huggingface dataset:
Effects of decades of physical driving experience on pre-exposure postural precursors of motion sickness among virtual passengers

Previous research has shown that motion sickness associated with virtual vehicles is more common among passengers than among drivers. Separately, other studies have shown that postural precursors of motion sickness during virtual driving differ as a function of prior experience driving physical vehicles. We investigated the intersection of those prior effects: We asked whether decades of physical driving experience 1) would influence motion sickness among passengers in a virtual vehicle, and 2) would influence postural precursors of motion sickness among passengers in a virtual vehicle. In our study, middle-aged adults were exposed to a virtual vehicle as passengers. Some participants (Physical Drivers) had decades of experience driving physical automobiles, while others (Physical Non-Drivers) had rarely or never driven a physical vehicle. First, we measured head and torso movement as standing participants performed simple visual tasks. Then, each participant watched a recording of the motion of a virtual vehicle, which induced motion sickness in some participants. Afterward, neither the incidence nor the severity of motion sickness differed between Physical Drivers and Physical Non-Drivers. Our analysis of pre-exposure standing body sway revealed postural precursors of motion sickness in measures of the spatial magnitude and temporal dynamics of movement. In statistically significant interactions, these precursors (Well vs. Sick) differed as a function of physical driving experience (Physical Drivers vs. Physical Non-Drivers). Overall, our results indicate that, among virtual passengers, long-term real-world driving experience influenced the postural precursors of motion sickness, but not the incidence or severity of motion sickness. We discuss these results in terms of relationships between perception and motor control in theories of motion sickness etiology.
Data reusability for migration research: a use case from SoDaNet data repository

This study highlights the role of Research Data Repositories (RDRs) in the concept of data reuse by examining a use case on migration research, a domain that requires up-to-date and accurate data for research and policy purposes. The survey from which the data for the use case was derived aimed to investigate the alignment of humanitarian assistance and social protection in Greece during the post-2015 refugee crisis. Through our analysis, we try to formulate a new corpus of variables and information that can create a value chain for research and policy purposes related to migration research, as well as to draw useful conclusions from this use case study in relation to the concept of data reuse. We address several issues related to data reuse, such as its definition, the role of research data repositories and research infrastructures in data reuse, as well as the limitations and advantages of reuse. We also present some specific features of the SoDaNet RDR, which hosts the primary data. We argue that comprehensive documentation of data adds value to the data and, through reuse, this value can be recycled to the RDR and, therefore, to potential new reusers.
Associating UAS images through a graph‐based guiding strategy for boosting structure from motion

Abstract Structure from motion (SfM) using optical images has been an important prerequisite for reconstructing three‐dimensional (3D) landforms. Although various algorithms have been developed in the past, they suffer from many image pairs for feature matching and recursive searching for the most suitable image to add to SfM reconstruction. Thus, carrying out SfM is computationally costly. This research proposes a boosting SfM (B‐SfM) pipeline containing two phases, indexing graph network (IGN) and graph tracking, to accelerate SfM reconstruction. The IGN intends to form image pairs presenting desirable spatial correlation to reduce the time costs spent for feature matching. Building on the IGN, graph tracking integrates ant colony optimisation and greedy sorting algorithms to encode an optimum image sequence to boost SfM reconstruction. Compared to the results derived from other available means, the experimental results show that the proposed approach can accelerate the two phases, feature matching and 3D reconstruction, by up to 14 times faster. The quality of the camera poses recovered is retained or even slightly improved. As a result, the developed B‐SfM can efficiently achieve SfM reconstruction by suppressing the time cost in the fashion of image pair selection for feature matching and image order determination for more efficient SfM reconstruction.
Modelling Data Poisoning Attacks Against Convolutional Neural Networks

Cybersecurity has become a great concern in many real-world applications involving adversaries with Machine Learning (ML) algorithms being more widely used. This concern is more challenging in Internet of Things (IoT) platforms. As IoT-enabled applications are growing at a rapid pace in every sector there are growing security related incidents as well. ML algorithms are widely deployed to perform data analysis, reasoning and decision-making over the data emanating from IoT devices. Security of this data while collection, communication and computing is a major challenge. Various attackers are trying to find weaknesses in the ML algorithms and trying to deceive these ML algorithms to learn the wrong information from the data. Countermeasures need to be developed to evaluate the security of the ML models. To develop such countermeasures, one needs to understand all possible attacks on the ML models. Data poisoning attacks are a class of adversarial attacks on ML where an adversary has the power to alter a small fraction of the training data in order to make the trained classifier to satisfy certain objectives. In order to develop an attack-resistant ML model, one needs to know all possible attacks on these models. The recent data poisoning technique such as the Fast Gradient Sign Method (FGSM) is static and provides very micro control to the attacker on creating adversarial data. In this research, we develop a more robust data poisoning technique for deep neural networks using Generative Adversarial Networks (GANs) to create a data poisoning attack. We then evaluate the performance of the proposed algorithm and also compare the results obtained by FGSM.
Research on Network Teaching Collaboration Platform Using Flipped Classroom Teaching Mode

The quick development of intelligent information technology in modern society has accelerated education informatisation. Accordingly, the traditional modes of instruction and methods of instruction are no longer able to satisfy the educational needs of modern society, so the flipped classroom teaching method emerges at the historic moment. While injecting fresh vitality into education, this model can also reflect good teaching effects, which is of enormous practical relevance to the development of education. Additionally, flipped classroom teaching is made possible by the online teaching resources offered by colleges and institutions. In view of the unreasonable distribution of curriculum resources in the current university network teaching platform, this study proposes a university network platform combining flipped classroom teaching mode and improved Apriori algorithm. First of all, to achieve the acceptable allocation and suggestion of learning materials in accordance with students’ learning demands, the Apriori algorithm is introduced to address the university network teaching platform’s lack of adequate planning and organisation. After that, the flipped classroom teaching mode is introduced into the online teaching platform for empirical research. According to the trial findings, more than 90.22 % of the students had improved their study habits. The network teaching platform proposed by the research institute can stimulate college students’ learning interest and improve their learning ability to a certain extent.
Concentration‐number (C‐N) fractal models reveal the distribution pattern of the elements in ancient nephrite measured by portable X‐ray fluorescence: Based on nephrite objects excavated from different sites in Nanyang, Henan Province

Abstract Changes in the composition of various chemical elements in ancient nephrite artifacts due to prolonged burial are critical factors that should not be underestimated. However, the increasingly stringent heritage management has made many techniques impractical. Consequently, portable X‐ray fluorescence (pXRF) has become an indispensable nondestructive field analysis method. This paper aims to make use of the pXRF dataset to distinguish which elements tested are endogenous or exogenous, as well as to gauge which elements have been affected by stronger burial effects. More specifically, we carry out this work on the example of 103 pieces of nephrite excavated from different cemeteries in the Nanyang area. In addition to traditional statistical techniques, a new tool, the concentration‐number (C‐N) fractal method can shed new light on the analysis of the distribution patterns of different elements in excavated nephrite. The anomalous boundaries generated by the method have clear geochemical significance and can be delineated between background zone and disturbed regions. The degree of resistance of different elements to fluctuations in external factors was assessed, which has a direct relationship with the content of the buried soil. Considering the richness of the model, it has the potential to be used in archaeometrics.
Unsupervised novelty detection for time series using a deep learning approach

In the Smart Homes and IoT devices era, abundant available data offers immense potential for enhancing system intelligence. However, the need for effective anomaly detection models to identify and rectify unusual data and behaviors within Smart Home Systems (SHS) remains a critical challenge. This research delves into the relatively unexplored domain of novelty anomaly detection, particularly in the context of unlabeled datasets. Introducing the novel DeepMaly method, this approach provides a practical tool for SHS developers. Functioning seamlessly in an unsupervised manner, DeepMaly distinguishes between seasonal and actual anomalies through a unique process of training on unlabeled pristine features extracted from time series data. Leveraging a combination of Long Short-Term Memory (LSTM) and Deep Convolutional Neural Network (DCNN), the model is primed to detect anomalies in real-time. The research culminates in a comprehensive data prediction and classification process into normal and abnormal data based on specified anomaly thresholds and fraction percentages. Notably, this function operates seamlessly unsupervised, eliminating the need for labeled datasets. The study concludes with a complete data forecasting and sorting method that divides data into normal and abnormal categories based on defined anomaly thresholds and fraction percentages. Working in an unsupervised mode reduces the requirement for labeled datasets. The results highlight the model's prowess in new detection, which has been successfully applied to benchmark datasets. However, there is a restriction since deep learning algorithms can recognize noise as abnormalities. Finally, the investigation enhances SHS anomaly detection, providing a crucial tool for real-time anomaly identification in the ever-changing IoT and Smart Homes scene.
Few‐shot object detection based on global context and implicit knowledge decoupled head

Abstract The acquisition cycle of remote sensing images is slow, and the labelling process encounters challenges, which have become prominent with the rapid development of remote sensing image object detection research. Therefore, this article provides a way to make the model better capture the diversity and contextual relationships in the data, and solve this problem by more than just data augmentation. Specifically, this method is a few‐shot object detection method for remote sensing based on global context combined with implicit knowledge decoupled head (GC‐IKDH). This method first uses a segmentation strategy to convert high‐resolution images into low‐resolution images and expands the sample size through a generative model. Secondly, GC attention is introduced to generate a GC vector by weighting and averaging the information of each position in the input sequence, which helps the model better understand the semantics of the input sequence. Finally, an IKDH is added to improve the model head, which is used to learn specific features in the data so that the model can better handle the diversity in the data. Experimental results show that GC attention and IKDH boosting provide a good performance boost to the baseline model. Compared with other few‐shot samples, this method achieves state‐of‐the‐art performance under different shot settings and highly competitive results on two benchmark datasets (NWPU VHR‐10 and DIOR).
Experimental and simulation data of hole bearing type connections

This paper presents a comprehensive dataset comprising experimental test data and numerical simulations of hole bearing tests involving 32 single-bolt, 20 two-bolt, and 20 four-bolt specimens. The dataset encompasses load-deformation curves obtained from experimental tests and displacement data acquired via the Digital Image Correlation (DIC) system, which covers specific regions of the specimens. Additionally, the dataset incorporates force-deformation curves derived from corresponding numerical simulations. The numerical simulation procedure is outlined, involving a simplified model employing solid elements for the specimen and rigid shell elements for the bolts. A "hard-contact" is employed to define the normal behavior of surface-to-surface contact between the specimen and the bolts. Material behavior modeling utilizes true stress-strain curves obtained from experimental tensile tests, encompassing both material properties extracted from these tests and the ensuing input parameters for numerical simulations. Furthermore, the DIC-system measurements provide data on displacements and strain distributions across various regions of the specimens. These strain measurements are meticulously evaluated and presented. The validation of the numerical simulations against experimental results substantiates the robustness of the numerical methodology, instilling confidence in its application for simulating bearing-type connections. Moreover, this dataset serves as a valuable resource for comparative analysis, enhancing the comprehension of these connections, and providing reference points for further numerical simulations.
Leaving no one behind: Information (re)packaging for rural dwellers in South Africa

In most rural communities in South Africa, there is inadequate access to information due to a lack of public facilities and infrastructure, such as libraries and internet connections. This has a negative impact on the day-to-day activities of rural dwellers as it deprives them of the opportunity to access information, which is a fundamental right. This qualitative study used the Delphi technique, interviewing five experts in the Library and Information Services (LIS) fraternity in three rounds. The format in which information is accessed, as well as available information sources and resources, need (re)packaging in order to ensure free and equal access to information on a daily basis. This can be done through the provision of free Wi-Fi by rural municipalities in churches, gathering places, and royal houses to bridge the digital chasm. Provision of free Wi-Fi would give rural dwellers an opportunity to have access to online information without any hurdles as most rural dwellers are unemployed and lack money to buy data bundles for information access. With technological advancements, lot of information is shared online be it job advertisements, tertiary education applications, news, among others. The study provides a framework for addressing issues of information inaccessibility in remote rural areas. There is a need for increasing high-speed internet access for those in rural communities.
MIHIC: a multiplex IHC histopathological image classification dataset for lung cancer immune microenvironment quantification

Background Immunohistochemistry (IHC) is a widely used laboratory technique for cancer diagnosis, which selectively binds specific antibodies to target proteins in tissue samples and then makes the bound proteins visible through chemical staining. Deep learning approaches have the potential to be employed in quantifying tumor immune micro-environment (TIME) in digitized IHC histological slides. However, it lacks of publicly available IHC datasets explicitly collected for the in-depth TIME analysis. Method In this paper, a notable Multiplex IHC Histopathological Image Classification (MIHIC) dataset is created based on manual annotations by pathologists, which is publicly available for exploring deep learning models to quantify variables associated with the TIME in lung cancer. The MIHIC dataset comprises of totally 309,698 multiplex IHC stained histological image patches, encompassing seven distinct tissue types: Alveoli, Immune cells, Necrosis, Stroma, Tumor, Other and Background. By using the MIHIC dataset, we conduct a series of experiments that utilize both convolutional neural networks (CNNs) and transformer models to benchmark IHC stained histological image classifications. We finally quantify lung cancer immune microenvironment variables by using the top-performing model on tissue microarray (TMA) cores, which are subsequently used to predict patients’ survival outcomes. Result Experiments show that transformer models tend to provide slightly better performances than CNN models in histological image classifications, although both types of models provide the highest accuracy of 0.811 on the testing dataset in MIHIC. The automatically quantified TIME variables, which reflect proportions of immune cells over stroma and tumor over tissue core, show prognostic value for overall survival of lung cancer patients. Conclusion To the best of our knowledge, MIHIC is the first publicly available lung cancer IHC histopathological dataset that includes images with 12 different IHC stains, meticulously annotated by multiple pathologists across 7 distinct categories. This dataset holds significant potential for researchers to explore novel techniques for quantifying the TIME and advancing our understanding of the interactions between the immune system and tumors.
The importance of cybersecurity disclosures in customer relationships

Abstract The escalating use of digital technologies has spotlighted the crucial role of cybersecurity in safeguarding sensitive information within companies. This study explores the relationship between a firm's major customers and its cybersecurity awareness. Drawing on SEC‐mandated disclosures, we employ four proxies to measure changes in customer‐supplier relationships. Our findings reveal that customers increase their purchases from suppliers whose cybersecurity awareness scores improve. Additionally, we examine the interplay between customers and suppliers more susceptible to nonpublic adverse news, particularly during cyber events. The study emphasizes the importance of cybersecurity disclosure for regulators, supply chain partners, and corporate management. It also contributes to the literature on factors influencing the duration of customer‐supplier relationships and underscores the significance of supplier characteristics. “Understanding and disclosing cybersecurity risks are” paramount in an increasingly digital business landscape.
Convex Body Collision Detection using the Signed Distance Function

We present a new algorithm to compute the minimum distance and penetration depth between two convex bodies represented by their Signed Distance Function (SDF). First, we formulate the problem as an optimization problem suitable for arbitrary non-convex bodies, and then we propose the ellipsoid algorithm to solve the problem when the two bodies are convex. Finally, we benchmark the algorithm and compare the results in collision detection against the popular Gilbert–Johnson–Keerthi (GJK) and Minkowski Portal Refinement (MPR) algorithms, which represent bodies using the support function. Results show that our algorithm has similar performance to both, providing penetration depth like MPR and, with better robustness, minimum distance like GJK. Our algorithm provides accurate and fast collision detection between implicitly modeled convex rigid bodies and is able to substitute existing algorithms in previous applications whenever the support function is replaced with the SDF.
A survey on blockchain deployment for biometric systems

Abstract Blockchain technology has become an emerging area in recent years due to its capacity to improve the security, dependability, and resilience of distributed systems. Research based on this technique has impacted several firms, including banking, healthcare, data processing, remote sensing, and many others. The key characteristics of blockchain technology that make it appealing are data immutability, transparency, privacy, decentralization, and distributed ledgers. However, there is a chance of a privacy breach with sensitive biometric data. The purpose of this investigation is to examine blockchain‐based biometric applications research. It begins by determining the myriad ways that biometrics and blockchain may work together, including the storage and protection of biometric templates, identity management, and biometric authentication systems. Different biometric applications with respect to blockchain technology are also identified, along with the types of biometric data taken into account, features and capabilities of blockchain technology exploited, and blockchain technology frameworks employed. Finally, the authors seek to investigate blockchain concepts in the biometric domain by evaluating their pros and cons and summarizing the methods developed on blockchain for diverse biometric applications. Additionally, the applications of blockchain‐based biometric systems are highlighted before moving on to open research questions and potential future research areas.
Spectral telescope: Convergence rate bounds for random-scan Gibbs samplers based on a hierarchical structure

Random-scan Gibbs samplers possess a natural hierarchical structure. The structure connects Gibbs samplers targeting higher-dimensional distributions to those targeting lower-dimensional ones. This leads to a quasi-telescoping property of their spectral gaps. Based on this property, we derive three new bounds on the spectral gaps and convergence rates of Gibbs samplers on general domains. The three bounds relate a chain's spectral gap to, respectively, the correlation structure of the target distribution, a class of random walk chains, and a collection of influence matrices. Notably, one of our results generalizes the technique of spectral independence, which has received considerable attention for its success on finite domains, to general state spaces. We illustrate our methods through a sampler targeting the uniform distribution on a corner of an n-cube.