Reviewerly/exHarmony huggingface dataset:
Virtual Reality for Railway Signaling System Training

The railway signaling system is an important part of the rail systems that is used for railway traffic control and safety. At present, most of railway signaling labs rely on physical models and devices which require large space and regular maintenance. Emerging of Virtual Reality (VR) offers more flexibility to many applications including education, training, and testing since its operations are based on software and visualization. Using VR allows installation, updating, adjustment and maintenance to be performed more easily and effectively with effective cost, time, and space requirement. This paper aims to develop a virtual reality lab for training in railway signaling and control. The VR lab of railway signaling simulation consists of two stations, trains, tracks, a virtual dashboard and signaling posts including point machines. All models used in this paper are created by Blender which is a free software tool for 3D modeling. The VR environment of the lab is created by Unity which is a software platform for VR development. The functions of all developed models are programed using C# language. The basic route setting and the usage of subsidiary signal e.g., call-on and shut signal are demonstrated in this paper. The test results ensure the capability of the developed VR lab for training the target groups including students, engineers, and train drivers.
Efficient fraud detection using deep boosting decision trees

Fraud detection is to identify, monitor, and prevent potentially fraudulent activities from complex data. The recent development and success in AI, especially machine learning, provides a new data-driven way to deal with fraud. From a methodological point of view, machine learning based fraud detection can be divided into two categories, i.e., conventional methods (decision tree, tree boosting methods, etc.) and deep learning, both of which have significant limitations in terms of the lack of representation learning ability for the former and interpretability for the latter. Furthermore, due to the rarity of detected fraud cases, the associated data is usually imbalanced, which seriously degrades the performance of classification algorithms. In this paper, we propose deep boosting decision trees (DBDT), a novel approach for fraud detection based on gradient boosting and neural networks. In order to combine the advantages of both conventional methods and deep learning, we first construct soft decision tree (SDT), a decision tree structured model with neural networks as its nodes, and then ensemble SDTs using the idea of gradient boosting. In this way we embed neural networks into gradient boosting to improve its representation learning capability and meanwhile maintain the interpretability. Furthermore, aiming at the rarity of detected fraud cases, in the model training phase we propose a compositional AUC maximization approach to deal with data imbalances at algorithm level. Extensive experiments on several real-life fraud detection datasets show that DBDT can significantly improve the performance and meanwhile maintain good interpretability. Our code is available at https://github.com/freshmanXB/DBDT.
Inelastic zone around crack tip in polyacrylamide hydrogel identified using digital image correlation

Prior to fracture, a polyacrylamide hydrogel has a stress-stretch curve of nearly perfect elasticity, but it has been suggested that an inelastic zone exists around a crack tip. This inelastic zone, however, has never been observed directly in a polyacrylamide hydrogel. Here we identify the inelastic zone using digital image correlation (DIC). We prepare a polyacrylamide hydrogel with a precut crack. While a sample of the hydrogel is stretched, the speckle patterns are recorded using a microscope or a camera, with pixel size 2.3 μm and 22.7 μm, respectively. The speckle patterns recorded by the microscope and camera are processed using the DIC software, and merged to provide the deformation field over the entire sample. The measured field of deformation is used to calculate the field of energy density according to the neo-Hookean model. When the body is perfectly elastic, the field of energy density around the crack tip is inversely proportional to the distance from the crack tip. The difference between the measured field and the predicted elastic field identifies the inelastic zone. The measured size of the inelastic zone is ∼ 0.6 mm. We further confirm that, when a sample is much larger than the inelastic zone, an annulus exists, in which the elastic crack tip field prevails.
Effective hybrid attention network based on pseudo-color enhancement in ultrasound image segmentation

Ultrasound image segmentation plays a vital role in the early diagnosis of human diseases. It helps diagnose many diseases, such as breast cancer, hemangioma, and other gynecological disorders. However, the intrinsic imaging characteristics of ultrasound images result in substantially lower resolution and clarity than CT, MRI, and other imaging modalities, and they are sensitive to interference from external influences. With its inherent artifacts, blurred lesion boundaries, and uneven intensity distribution, ultrasound images present a challenging task when it comes to segmenting lesion areas accurately. In recent years, convolutional neural networks (CNNs) have achieved remarkable results in medical image segmentation tasks. However, CNNs are limited in capturing the remote dependencies of the input image, leading to degraded accuracy in segmenting ultrasound lesions. In this paper, we developed a deep convolutional neural network that incorporates the pseudo-color enhancement algorithm and hybrid attention modules that enhance the network’s ability to extract fine features and remote modeling capabilities. We propose a novel multi-scale channel attention-based decoder that efficiently uses the feature maps from the encoder as a complement and fuses them with the upsampled feature maps. The hybrid attention combination captures cross-channel interactions efficiently and enhances the context modeling capability, further improving the extraction of coarse and delicate features, and resulting in significant performance improvements. We found that the dice performance improved by 2.54%, 2.47%, 1.39%, 0.99%, and 1.23% on the BUL, BUSI, Hemangioma, BP, and VUI. Results from four public datasets and one self-collected dataset indicate that the proposed method outperforms other medical image segmentation methods for ultrasound image lesion segmentation.
Equitable Data Governance Models for the Participatory Sciences

Abstract When participants share data to a central entity, those who have taken on the responsibility of accepting the data and handling its management may also have control of decisions about the data, including its use, re‐use, accessibility, and more. Such concentrated control of data is often a default practice across many forms of participatory sciences, which can be extractive in some contexts and a way to protect participants in other contexts. To avoid extractive practices and related harms, projects can adopt structures so that those who make decisions about the data set and/or each datum are different from those responsible for executing the subsequent decisions about data management. We propose two alternative models for improving equity in data governance, each model representing a spectrum of options. With an individualized control model, each participant can place their data in a central repository while still retaining control of it, such as through simple opt‐in or opt‐out features or through blockchain technology. With a shared control model, representatives of salient participant groups, such as through participant advisory boards, collectively make decisions on behalf of their constituents. These equitable models are relevant to all participatory science systems, and particularly necessary in contexts where dominant‐culture institutions engage marginalized peoples.
Combined image encryption and steganography technique for enhanced security using multiple chaotic maps

This paper proposes a novel security algorithm which combines the concepts of Cryptography and Steganography to provide enhanced security to digital images. The proposed algorithm, named CIEST, enciphers the secret image (cryptography) which is hidden into the cover image (steganography) for secured transfer of information. A two-stage encryption process, with one of the stages being DNA encoding is used here. The cover image is also modified before the data embedding phase to improve the effectiveness of the algorithm. Multiple Combined Chaotic maps are utilised in the encryption and embedding stages to improve the randomness of the algorithm. The use of chaotic maps also makes the algorithm more robust to different types of attacks. The CIEST scheme is shown to be robust, offering high embedding capacity with high sensitivity with a near zero correlation value, information entropy value of 7.9972, maximum SSIM of 0.9992, and a maximum PSNR of 56.1071. The experimental results presented establish the immunity of the proposed method against attacks along with the feasibility and effectiveness of the algorithm in real time.
Speckle noise reduction for medical ultrasound images based on cycle-consistent generative adversarial network

Medical ultrasound (US) images are corrupted by speckle noise, which can adversely affect the disease diagnosis and treatment. Recently, the cycle-consistent adversarial network (CycleGAN) has been used in the style transfer for both natural and medical images. In this study, we aim to develop a US despeckling method based on the CycleGAN by the style transfer between the speckled noisy data domain and noise-free data domain by forming a bi-directional universal mapping. The inputs of noisy and noise-free images are designed in the CycleGAN model. For simulation work, we use both the multiplicative model and the spatial impulse response model to obtain noisy images from noise-free images. However, noise-free US images are not clinically available. Hence, for the real US despeckling scenario, the clinical images of hearts, lymph nodes, and breast tumors are used as noisy images; and the high-quality images that are derived from the clinical images by despeckling with the Gabor-based anisotropic diffusion (GAD) and selected with a new metric named the edge-to-noise ratio, are used as the noise-free images. We compare our CycleGAN based denoising method with nine existing denoising methods, i.e., the speckle reduction anisotropic diffusion, GAD, non-local means, wavelet transform, unbiased non-local means, statistical nearest-neighbors, TVHTVM, improved non-local self-similarity measures, and generative adversarial network. Our method outperforms other methods by visual assessment and quantitative comparison, which demonstrates its superiority for noise reduction and detail preservation.
Making inferences in incomplete Bayesian networks: A Dempster-Shafer belief function approach

How do you make inferences from a Bayesian network (BN) model with missing information? For example, we may not have priors for some variables or may not have conditionals for some states of the parent variables. It is well-known that the Dempster-Shafer (D-S) belief function theory is a generalization of probability theory. So, a solution is to embed an incomplete BN model in a D-S belief function model, omit the missing data, and then make inferences from the belief function model. We will demonstrate this using an implementation of a local computation algorithm for D-S belief function models called the “Belief function machine.” One advantage of this approach is that we get interval estimates of the probabilities of interest. Using Laplacian (equally likely) or maximum entropy priors or conditionals for missing data in a BN may lead to point estimates for the probabilities of interest, masking the uncertainty in these estimates. Bayesian reasoning is unable to reason from an incomplete model. A Bayesian sensitivity analysis of the missing parameters is not a substitute for a belief-function analysis.
FPGA-Based Chaotic Image Encryption Using Systolic Arrays

Along with the recent advancements in video streaming, concerns over the security of transferred data have increased. Thus, the development of fast and reliable image encryption methodologies has become an emerging research area in the field of communications. In this paper, a systolic array-based image encryption architecture is proposed. Systolic arrays are used to apply the convolution operation, and a Lü–Chen chaotic oscillator is used to obtain a convolutional filter. To decrease resource consumption, a method to fuse confusion and diffusion processes by using systolic arrays is also proposed in this paper. The results show that the proposed method is highly secure against some differential and statistical attacks. It is also shown that the proposed method has a high speed of encryption compared to other methods.
Lerngelegenheiten, technologisch-pädagogisches Wissen und personale Faktoren von angehenden Lehrkräften: Zum Verständnis der Verbindung zwischen Programmmerkmalen und Lernergebnissen von Lehramtsstudierenden in Zeiten der Digitalisierung

Abstract Despite the need to foster pre-service teacher competence with respect to information and communication technology (ICT) integration in school during the current era of digitalization, scientific understanding of the correlation between the relevant characteristics of teacher education programs and student teachers’ learning outcomes remains limited. This paper thus examines the relationship between student teachers’ opportunities to learn (OTL) and technological pedagogical knowledge (TPK) with the aim of obtaining insights into their learning processes and the effectiveness of teachers’ preparation upon completing their bachelor studies. A sample of 338 student teachers in their 6th semester at the University of Cologne was used. Findings from path modeling reveal that measures of OTL relate to TPK. While no direct effect of technological pedagogical OTL on TPK was identified, an indirect effect between conventional pedagogical OTL and TPK, mediated by student teachers’ general pedagogical knowledge (GPK) was found. Among the personal factors that affect student teachers, their motivation for using ICT reveals a direct effect on TPK. Further factors, such as gender and teacher education program type have no effect on TPK. The findings will be discussed in relation to expectations of teacher education effectiveness.
Suitable site selection for the development of solar based smart hydrogen energy plant in the Gangwon-do region, South Korea using big data: A geospatial approach

The selection of optimal sites for solar based smart hydrogen energy plants (SHEP) requires a spatial evaluation of various conditions, such as technical aspects, economic factors, environmental and social considerations, and as well as the assessment of natural catastrophe conditions within the chosen region. The present study used a multi-criterion decision (MCDA) support model to conduct an optimal site selection analysis for a smart hydrogen energy plant focusing on solar power plants in the Gangwon region of South Korea. Geographic Information Systems (GIS) and big data were utilized to locate and evaluate potential sites for a solar based SHEP project. The evaluation considered 18 sub-factors, including the presence of green energy sources, the demand for hydrogen, existing infrastructure, environmental effect, and the impact of natural disasters. The suitable areas for solar based SHEP installation were identified using a holistic GIS approach by defining appropriate ranks and weights through MCDA. The results showed that 3.74% (627.67 km2 ) of the study region was very high suitable for constructing solar power plants, particularly in Cheorwon-gun, Chuncheon-si, and Wonju-si regions. The suitability zones determined through the analysis were compared with the global solar plant inventory data, and the results showed good consistency between the two. This suggests that the identified optimal areas can be utilized for the installation of the solar based SHEP. Furthermore, the framework developed in the study can be used to assess the suitable sites for solar energy in other parts of the country.
Dispersed differential hunger games search for high dimensional gene data feature selection

The realms of modern medicine and biology have provided substantial data sets of genetic roots that exhibit a high dimensionality. Clinical practice and associated processes are primarily dependent on data-driven decision-making. However, the high dimensionality of the data in these domains increases the complexity and size of processing. It can be challenging to determine representative genes while reducing the data's dimensionality. A successful gene selection will serve to mitigate the computing costs and refine the accuracy of the classification by eliminating superfluous or duplicative features. To address this concern, this research suggests a wrapper gene selection approach based on the HGS, combined with a dispersed foraging strategy and a differential evolution strategy, to form a new algorithm named DDHGS. Introducing the DDHGS algorithm to the global optimization field and its binary derivative bDDHGS to the feature selection problem is anticipated to refine the existing search balance between explorative and exploitative cores. We assess and confirm the efficacy of our proposed method, DDHGS, by comparing it with DE and HGS combined with a single strategy, seven classic algorithms, and ten advanced algorithms on the IEEE CEC 2017 test suite. Furthermore, to further evaluate DDHGS' performance, we compare it with several CEC winners and DE-based techniques of great efficiency on 23 popular optimization functions and the IEEE CEC 2014 benchmark test suite. The experimentation asserted that the bDDHGS approach was able to surpass bHGS and a variety of existing methods when applied to fourteen feature selection datasets from the UCI repository. The metrics measured--classification accuracy, the number of selected features, fitness scores, and execution time--all showed marked improvements with the use of bDDHGS. Considering all results, it can be concluded that bDDHGS is an optimal optimizer and an effective feature selection tool in the wrapper mode.
A survey of state-of-the-art sharding blockchains: Models, components, and attack surfaces

Blockchain has been widely used in various fields, such as health management, finance, the Internet of Things (IoT), identity management, and supply chains. However, the performance of blockchain, including its throughput and transaction processing latency, has hindered its large-scale practical application, which is known as the scalability issue of blockchain. Many on-chain and off-chain solutions have been proposed to solve this problem, such as directed acyclic graph (DAG) blockchain and Rollup schemes. Despite these schemes' ability to enhance the scalability of the blockchain, they compromise on other essential properties. The consensus is that the DAG blockchain can never provide deterministic security for transactions, and Rollup is disputed because it makes a trade-off of decentralization for scalability. Sharding technology is a proposed solution for these limitations, promising to tackle the scalability issue while maintaining security and decentralization at the same time. To date, no survey has been done on sharding design models and their key components and corresponding attack surfaces. To fill this gap, in this survey, we first provide a detailed analysis and comparison of both permissioned and permissionless sharding blockchains. Then, we describe the common building blocks chosen by the above sharding schemes as key components. Finally, we investigate how these components may be attacked and suggest corresponding countermeasures. Several open challenges are also given as future research directions. The survey aims to help researchers, engineers, and educators quickly grasp a consolidated body of knowledge about current sharding blockchain development.
A statistical comparison between Matthews correlation coefficient (MCC), prevalence threshold, and Fowlkes–Mallows index

Even if assessing binary classifications is a common task in scientific research, no consensus on a single statistic summarizing the confusion matrix has been reached so far. In recent studies, we demonstrated the advantages of the Matthews correlation coefficient (MCC) over other popular rates such as cross-entropy error, F1 score, accuracy, balanced accuracy, bookmaker informedness, diagnostic odds ratio, Brier score, and Cohen's kappa. In this study, we compared the MCC to other two statistics: prevalence threshold (PT), frequently used in obstetrics and gynecology, and Fowlkes-Mallows index, a metric employed in fuzzy logic and drug discovery. Through the investigation of the mutual relations among three metrics and the study of some relevant use cases, we show that, when positive data elements and negative data elements have the same importance, the Matthews correlation coefficient can be more informative than its two competitors, even this time.
Forest GUMP: a tool for verification and explanation

Abstract In this paper, we present Forest GUMP (for Generalized, Unifying Merge Process) a tool for verification and precise explanation of Random forests. Besides pre/post-condition-based verification and equivalence checking, Forest GUMP also supports three concepts of explanation, the well-known model explanation and outcome explanation , as well as class characterization , i.e., the precise characterization of all samples that are equally classified. Key technology to achieve these results is algebraic aggregation, i.e., the transformation of a Random Forest into a semantically equivalent, concise white-box representation in terms of Algebraic Decision Diagrams (ADDs). The paper sketches the method and demonstrates the use of Forest GUMP along illustrative examples. This way readers should acquire an intuition about the tool, and the way how it should be used to increase the understanding not only of the considered dataset, but also of the character of Random Forests and the ADD technology, here enriched to comprise infeasible path elimination. As Forest GUMP is publicly available all experiments can be reproduced, modified, and complemented using any dataset that is available in the ARFF format.
Metaverse in business research: a systematic literature review

This study reviews metaverse literature related to the business discipline. Our first search yielded 323 papers, further filtered based on predefined standards. Accordingly, since our research focuses on the metaverse of business, we limited the Scopus search results by choosing only two categories: business, management, and accounting, as well as economics, econometrics, and finance. Using the PRISMA flowchart, this study reviewed 29 selected documents published by publication outlets in Scopus. The study reveals that most metaverse-business studies employed qualitative methods and were categorized as conceptual papers because the research is in its infancy. Marketing has become the dominant business area since most studies relate metaverse with various marketing activities. Besides, management information and finance come second and third in the number of publications. This result indicates that through metaverse, businesses can attract new customers, retain existing customers, improve firm operations, and streamline business processes. This study’s novelty is investigating metaverse studies related to business function areas, which is absent in metaverse literature. One limitation of this study is the single database, namely, Scopus. Further study may employ other databases like Web of Science or Google Scholar. Besides, future studies may explore more metaverse studies concerning more specific topics, such as accounting, auditing, and banking.