Reviewerly/exHarmony huggingface dataset:
GPU and FPGA Based Deployment of Blockchain for Cryptocurrency – A Systematic Review

Blockchain Technology is considered as an attention-seeking technology and still in its early years according to many researchers. Cryptocurrencies like Bitcoin, Ethereum, Dogecoin and many more have emerged as new area of research for the fintech community as they are using blockchain technology. Bitcoin (BTC) emerged as the first real-world application of the blockchain technology. Bitcoin and other cryptocurrencies depend on the mining of the raw data which is going to be stored in the blocks. These cryptocurrencies depend on crypto mining, which is a complex computation of mathematical equations and problems. To solve these equations and problems, high-powered computing is required. The Graphics Processing Units work on the principle of high-power and high-performance computing. They are also extremely useful for solving complex computation of mathematical equation and problems. We have observed the usage of GPUs to deploy blockchain for mining of cryptocurrencies like Bitcoin, Ethereum, and Dogecoin. The research describes about the applications of GPU and FPGA for deployment of Blockchain for Cryptocurrency.
Compatibility Checking of Compound Business Rules Expressed in Natural Language Against Domain Specification

The following paper is the next step of research on automatic processing of business rules expressed in natural language. Such rules are used to describe a selected universe of discourse - its properties and constraints. They are usually written with a text editor as a set of free-form sentences. The purpose of the paper is to propose a method for verifying the compatibility of business rules with a domain specification in the form of a UML class diagram. Such verification is performed at the syntax level. While our previous research has focused on processing only simple sentences, this paper presents a method for analyzing compound sentences. The usefulness of our ideas has been experimentally demonstrated.
Conceptual Framework of Blockchain Technology Adoption in Saudi Public Hospitals Using TOE Framework

Despite the rapid growing of blockchain technology, the literature shows no evidence of its use in Arab countries’ hospitals, particularly in Saudi Arabia. It is vital to understand the level of user acceptance of any new technology before implementing it. This study aims to identify the various predictors that influence the adoption of blockchain technology in public hospitals in Saudi Arabia. The framework is based on the technological-organisational-environmental (TOE) framework and developed to investigate IT employees’ points of view to find which predictor will affect blockchain adoption. Understanding these predictors in a better way may help in making sure that blockchain technology, which is still in its initial stages, is implemented in a way that is acceptable to healthcare organisations, so as to promote the meaningful use of the technology. It is expected that the implications of this study will be useful from both a theoretical and practical perspective.
Machine learning algorithms in forensic science: A response to Morrison et al. (2022)

In Swofford & Champod (2022), we report the results of semi-structured interviews to various criminal justice stakeholders, including laboratory managers, prosecutors, defense attorneys, judges, and other academic scholars, on issues related to interpretation and reporting practices and the use of computational algorithms in forensic science within the American criminal justice system. Morrison et al. (2022) responded to that article claiming the interview protocol used a leading question with a false premise relating to the opaqueness of machine-learning methods. We disagree with the assertions of Morrison et al. (2022) and contend the premise to the question was relevant and appropriate.
A Literature Study to Characterize Continuous Experimentation in Software Engineering

Continuous Experimentation (CE) has become increasingly popular across industry and academic communities. Given its rapid evolution in software engineering (SE), the lack of a common understanding of CE can jeopardize new implementations and justify research efforts. Therefore, this literature study characterizes CE in SE based on its definitions, processes, and strategies for experimentation available in the technical literature. Seventy-six sources of information provided many different definitions, processes, and experimental procedures used to describe CE in SE. Despite the increasing use of CE in SE, it is impossible to observe a common terminology yet to support its characterization and use.
Identifying Fraud Rings Using Domain Aware Weighted Community Detection

With the increase in online platforms, the surface area of malicious activities has increased manifold. Bad actors abuse policies and services like claims, coupons, payouts, etc., to gain material benefits. These fraudsters often work collusively (rings), and it is difficult to identify underlying relationships between them when analyzing individual actors. Fraud rings identification can be modeled as a community detection problem on graphs where nodes are the actors, and the edges represent common attributes between them. However, the challenge lies in incorporating the attributes' domain-informed importance and hierarchy in coming up with edge weights. Treating all edge types as equal (and binary) can be fairly naive; we show that using domain knowledge considerably outperforms other methods. For community detection itself, while the weight information is expected to be learned automatically in deep learning-based methods like Graph Neural Networks (GNN), it is explicitly provided in traditional methods. In this paper, we propose a scalable and extensible end-to-end framework based on domain-aware weighted community detection to detect fraud rings. We first convert a multi-edge weighted graph into a homogeneous weighted graph and perform domain-aware edge-weight optimization to maximize modularity using the Leiden community detection algorithm. We then use features of communities and nodes to classify both community and a node as fraud or not. We show that our methods achieve up to 9.92% lift in F1-score on internal data, which is significant at our scale, and up to 4.81% F1-score lift on two open datasets (Amazon, Yelp) vs. an XGBoost based baseline.
Evolving subgraph matching on temporal graphs

Given a set of query graphs and specified orders between these query graphs, the evolving subgraph matching problem finds all subgraph sets from the temporal graph that not only match the given query graph set but also satisfy the specified orders. This problem motivates many significant application, e.g., social network analysis, financial fraud crimes detection, biological information mining, and so on. To efficiently address this problem, an evolving subgraph matching algorithm based on two key techniques is proposed. First, a memory-efficient index structure called EMI is designed to compactly store all necessary information required for finding all evolving matchings. To make the EMI compact and to construct the EMI efficiently, several techniques, such as four well-designed filters and adaptive filling orders, are proposed. Second, an evolving subgraph matching enumeration method is designed to immediately run on the EMI instead of on the temporal graph. The enumeration method expands evolving matchings in an edge-by-edge manner. Since the EMI is compactly maintained in the main memory and generally smaller than the temporal graph, the enumeration method runs very fast on the EMI. An extensive experimental evaluation has been carried out to evaluate the proposed algorithm. The experimental results show that the enumeration method executed on the EMI is an order of magnitude faster and requires significantly less memory than the baseline algorithms. To the best of our knowledge, there is no prior work on the evolving subgraph matching on temporal graphs.
Deep Learning Segmentation, Visualization, and Automated 3D Assessment of Ciliary Body in 3D Ultrasound Biomicroscopy Images

This study aimed to develop a fully automated deep learning ciliary body segmentation and assessment approach in three-dimensional ultrasound biomicroscopy (3D-UBM) images.Each 3D-UBM eye volume was aligned to the optic axis via multiplanar reformatting. Ciliary muscle and processes were manually annotated, and Deeplab-v3+ models with different loss functions were trained to segment the ciliary body (ciliary muscle and processes) in both en face and radial images.We trained and tested the models on 4320 radial and 3864 en face images from 12 cadaver eye volumes. Deep learning models trained on radial images with Dice loss achieved the highest mean F1-score (0.89) for ciliary body segmentation. For three-class segmentation (ciliary muscle, processes, and background), radial images with Dice loss achieved the highest mean F1-score (0.75 for the ciliary process and 0.82 for the ciliary muscle). Part of the ciliary muscle (10.9%) was misclassified as the ciliary process and vice versa, which occurred owing to the difficulty in differentiating the ciliary muscle-processes border, even by experts. Deep learning segmentation made further editing by experts at least seven times faster than a fully manual approach. In eight cadaver eyes, the average ciliary muscle, process, and body volumes were 56 ± 9, 43 ± 13, and 99 ± 18 mm3, respectively. The average surface area of the ciliary muscle, process, and body were 346 ± 45, 363 ± 83, and 709 ± 80 mm2, respectively. We performed transscleral cyclophotocoagulation in cadaver eyes to shrink the ciliary processes. Both manual and automated measurements from deep learning segmentation show a decrease in volume, surface area, and 360° cross-sectional area measurements.The proposed deep learning segmentation of the ciliary body and 3D measurements showed transscleral cyclophotocoagulation-related changes in the ciliary body.Automated ciliary body assessment using 3D-UBM has the translational potential for ophthalmic treatment planning and monitoring.
RASIPAM: Interactive Pattern Mining of Multivariate Event Sequences in Racket Sports

Experts in racket sports like tennis and badminton use tactical analysis to gain insight into competitors' playing styles. Many data-driven methods apply pattern mining to racket sports data - which is often recorded as multivariate event sequences - to uncover sports tactics. However, tactics obtained in this way are often inconsistent with those deduced by experts through their domain knowledge, which can be confusing to those experts. This work introduces RASIPAM, a RAcket-Sports Interactive PAttern Mining system, which allows experts to incorporate their knowledge into data mining algorithms to discover meaningful tactics interactively. RASIPAM consists of a constraint-based pattern mining algorithm that responds to the analysis demands of experts: Experts provide suggestions for finding tactics in intuitive written language, and these suggestions are translated into constraints to run the algorithm. RASIPAM further introduces a tailored visual interface that allows experts to compare the new tactics with the original ones and decide whether to apply a given adjustment. This interactive workflow iteratively progresses until experts are satisfied with all tactics. We conduct a quantitative experiment to show that our algorithm supports real-time interaction. Two case studies in tennis and in badminton respectively, each involving two domain experts, are conducted to show the effectiveness and usefulness of the system.
PLN: Parasitic-Like Network for Barely Supervised Medical Image Segmentation

It is known that annotations for 3D medical image segmentation tasks are laborious, time-consuming and expensive. Considering the similarities existing in inter-slice and inter-volume, we believe that the delineation way and the model architecture should be tightly coupled. In this paper, by introducing an extremely sparse annotation way of labeling only one slice per 3D image, we investigate a novel barely-supervised segmentation setting with only a few sparsely-labeled images along with a large amount of unlabeled images. To achieve this goal, we present a new parasitic-like network including a registration module (as host) and a semi-supervised segmentation module (as parasite) to deal with inter-slice label propagation and inter-volume segmentation prediction, respectively. Specifically, our parasitism mechanism effectively achieves the collaboration of these two modules through three stages of infection, development and eclosion, providing accurate pseudo-labels for training. Extensive results demonstrate that our framework is capable of achieving high performance on extremely sparse annotation tasks, e.g., we achieve Dice of 84.83% on LA dataset with only 16 labeled slices. The code is available athttps://github.com/ShumengLI/PLN.
Evaluating the Performance of SOBEK Text Mining Keyword Extraction Algorithm

This article presents a validation study of the algorithm implemented in the text mining tool called SOBEK, comparing it with YAKE!', a known unsupervised keyword extraction algorithm. Both algorithms identify keywords from single documents using mainly a statistical method, providing context independent information. The article describes briefly previous uses of SOBEK in the literature, and presents a detailed description of its text mining algorithm. The validation study presented in the paper compares SOBEK with YAKE!. Both systems were used to extract keywords from texts belonging to fourteen public text databases, each containing several documents. In general, their performance was found to be equivalent, with the algorithms outperforming one another in a batch of tests, and reaching similar results in others. Understanding why each algorithm outperformed the other in different circumstances may shed light on the advantages and disadvantages of specific features of keyword extraction methods.
Novel hybrid classification model for multi-class imbalanced lithology dataset

Deep learning methods and applications assist geologists in predicting and identifying lithologies in different surveys, hence lowering operational costs and uptime. This allows accurate data analysis and completion of scientific research on data obtained at geologically different places. This study used 4 lithologies datasets with high dimensionality and multiclass imbalance problems for analysis and classification. The imbalance of data classification is one of the most important problems facing current data analysis. Data imbalance can considerably influence classification performance, especially when dealing with other difficulty variables such as the presence of overlapping class distributions. This impact is especially obvious in multi-class conditions when mutual imbalance relations across classes complicate matters even further. Moreover, the problem of high dimensionality can lead to increased computing complexity and overfitting, and thus these issues can affect classification performance. To overcome these problems, we developed a new hybrid deep learning multi-class imbalanced learning method that combines Synthetic Minority Oversampling (SMOTE) to resample the data, and Recursive Feature Elimination (RFE) to identify the most useful predictive features. Finally, we believe that our developments can help improve geology research by providing accurate classification and rapid answers about interpreting data obtained in various study areas.
On Reliability Scores for Knowledge Graphs

The Instacart KG is a central data store which contains facts regarding grocery products, ranging from taxonomic classifications to product nutritional information. With a view towards providing reliable and complete information for downstream applications, we propose an automated system for providing these facts with a score based on their reliability. This system passes data through a series of contextualized unit tests; the outcome of these tests are aggregated in order to provide a fact with a discrete score: reliable, questionable, or unreliable. These unit tests are written with explainability, scalability, and correctability in mind.
Learning in virtual reality: Effects of instruction type and emotional arousal on learning performance

Virtual reality (VR) provides a powerful tool in education and training due to its ability to create immersion and arouse emotions compared to the traditional approaches like paper-based and screen-based. This study investigates the effects of instruction type and emotional arousal on learning performance in the VR environment. We employed a kitchen scenario where participants learned the safety-related knowledge of three electric appliances. In the experiment, 72 participants were assigned to one of the six VR conditions, i.e., 2 (emotional arousal: arousal and non-arousal) × 3 (instruction type: text, video, and interactive). Participants in the arousal group had to stay in a virtual, burning room for five minutes before the learning task, whereas participants in the non-arousal group started the learning task directly. The results suggested that the virtual, burning room successfully aroused fear-related emotions in participants and enhanced learning performance compared to participants in the non-arousal group. Additionally, participants who experienced the interactive instructions showed better learning performance than those who experienced text- or video-based instructions. The results confirmed VR’s advantages in emotion induction and learning promotion. • A virtual, burning room successfully evoked fear-related emotions in participants. • Emotional arousal enhanced learning performance in the VR environment. • Interactive instructions promoted learning performance than text or video instructions.
Fast Hubness-Reduced Nearest Neighbor Search for Entity Alignment in Knowledge Graphs

Abstract The flexibility of Knowledge Graphs to represent heterogeneous entities and relations of many types is challenging for conventional data integration frameworks. In order to address this challenge the use of Knowledge Graph Embeddings (KGEs) to encode entities from different data sources into a common lower-dimensional embedding space has been a highly active research field. It was recently discovered however that KGEs suffer from the so-called hubness phenomenon. If a dataset suffers from hubness some entities become hubs, that dominate the nearest neighbor search results of the other entities. Since nearest neighbor search is an integral step in the entity alignment procedure when using KGEs, hubness is detrimental to the alignment quality. We investigate a variety of hubness reduction techniques and (approximate) nearest neighbor libraries to show we can perform hubness-reduced nearest neighbor search at practically no cost w.r.t speed, while reaping a significant improvement in quality. We ensure the statistical significance of our results with a Bayesian analysis. For practical use and future research we provide the open-source python library at https://github.com/dobraczka/kiez .
Performance Analysis of Hybrid Memory Based Dragonfly Algorithm in Engineering Problems

Hybrid Memory Based Dragonfly Algorithm with Differential Evolution (DADE) is one of the most prominent swarm-based optimization techniques due to its better computational complexity and high convergence rate for achieving optimum results. In DADE, the best solution is memorized and processed with Differential Evolution (DE) for enhanced diversity and balanced exploration and exploitation rate. DADE brings two distinct advantages: First, the superior convergence rate due to continuous update of personal best individual in the search process. Second, better exploration due to the inclusion of global best and global worst individuals in the hybridization process. Comparative simulations have been performed on 24 standard benchmark functions along with the benchmark function of CEC2005 and CEC2017. Comparative analysis of the result demonstrates the competitiveness of the DADE algorithm in terms of optimal cost, computational complexity and convergence characteristics compared to other considered optimization algorithms.
General Multivariate Arctangent Function Induced Neural Network Approximations

Here we expose multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or $$\mathbb {R}^{N},$$ $$N\in \mathbb {N}$$ , by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We treat also the case of approximation by iterated operators of the last four types. These approximations are derived by establishing multidimensional Jackson type inequalities involving the multivariate modulus of continuity of the engaged function or its high order Fréchet derivatives. Our multivariate operators are defined by using a multidimensional density function induced by the arctangent function. The approximations are pointwise and uniform. The related feed-forward neural network is with one hidden layer. It follows [12].
Examining voice assistants in the context of children’s speech

An estimated 3.25 billion voice assistants (VAs) are in homes around the world, but these devices are not always able to recognize and respond to children’s speech. To inform the design of VAs that support kids, we report on a lab study where 28 5- to 10-year-old participants interacted with a commercial VA to: (1) attempt to execute common VA-supported requests (such as setting an alarm), (2) recite a set of such scripts verbatim, and (3) engage in unstructured conversation. We find that devices only respond appropriately to the content of children’s speech half of the time. Frequency of appropriate responses increased with children’s age and as their discourse became more standardized. Based on themes in participants’ speech, we identify design opportunities in child-VA interaction, such as exploring a topic or responding to a conversational bid. In addition to our empirical findings, we contribute a structured corpus of children’s speech.
Abstract Multivariate Gudermannian Function Induced Neural Network Approximations

Here we present multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or $$\mathbb {R}^{N},$$ $$N\in \mathbb {N}$$ , by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We examine also the case of approximation by iterated operators of the last four types. These approximations are achieved by establishing multidimensional Jackson type inequalities involving the multivariate modulus of continuity of the engaged function or its high order Fréchet derivatives. Our multivariate operators are defined by using a multidimensional density function induced by the Gudermannian sigmoid function. The approximations are pointwise and uniform. The related feed-forward neural network is with one hidden layer. It follows [14].