Reviewerly/exHarmony huggingface dataset:
Real-time and accurate deep learning-based multi-organ nucleus segmentation in histology images

Abstract Automated nucleus segmentation is considered the gold standard for diagnosing some severe diseases. Accurate instance segmentation of nuclei is still very challenging because of the large number of clustered nuclei, and the different appearance of nuclei for different tissue types. In this paper, a neural network is proposed for fast and accurate instance segmentation of nuclei in histopathology images. The network is inspired by the Unet and residual nets. The main contribution of the proposed model is enhancing the classification accuracy of nuclear boundaries by moderately preserving the spatial features by relatively d the size of feature maps. Then, a proposed 2D convolution layer is used instead of the conventional 3D convolution layer, the core of CNN-based architectures, where the feature maps are first compacted before being convolved by 2D kernel filters. This significantly reduces the processing time and avoids the out of memory problem of the GPU. Also, more features are extracted when getting deeper into the network without degrading the spatial features dramatically. Hence, the number of layers, required to compensate the loss of spatial features, is reduced that also reduces the processing time. The proposed approach is applied to two multi-organ datasets and evaluated by the Aggregated Jaccard Index (AJI), F1-score and the number of frames per second. Also, the formula of AJI is modified to reflect the object- and pixel-level errors more accurately. The proposed model is compared to some state-of-the-art architectures, and it shows better performance in terms of the segmentation speed and accuracy.
High-frequency channel attention and contrastive learning for image super-resolution

Abstract Over the last decade, convolutional neural networks (CNNs) have allowed remarkable advances in single image super-resolution (SISR). In general, recovering high-frequency features is crucial for high-performance models. High-frequency features suffer more serious damages than low-frequency features during downscaling, making it hard to recover edges and textures. In this paper, we attempt to guide the network to focus more on high-frequency features in restoration from both channel and spatial perspectives. Specifically, we propose a high-frequency channel attention (HFCA) module and a frequency contrastive learning (FCL) loss to aid the process. For the channel-wise perspective, the HFCA module rescales channels by predicting statistical similarity metrics of the feature maps and their high-frequency components. For the spatial perspective, the FCL loss introduces contrastive learning to train a spatial mask that adaptively assigns high-frequency areas with large scaling factors. We incorporate the proposed HFCA module and FCL loss into an EDSR baseline model to construct the proposed lightweight high-frequency channel contrastive network (HFCCN). Extensive experimental results show that it can yield markedly improved or competitive performances compared to the state-of-the-art networks of similar model parameters.
NLU-V: A Family of Instruction Set Extensions for Efficient Symmetric Cryptography on RISC-V

Cryptographic primitives nowadays are not only implemented in high-performance systems but also in small-scale systems, which are increasingly powered by open-source processors, such as RISC-V. In this work, we leverage RISC-V’s modular base instruction set and architecture to propose a generic instruction set extension (ISE) for symmetric cryptography. We adapt the work from Engels et al. in ARITH’13, the non-linear/linear instruction set extension (NLU), which presents a generic hardware/software co-design solution for efficient symmetric crypto implementations through a hardware unit extending the 8-bit AVR instruction set. These new instructions realize non-linear and linear layers, which are widely used to implement the block ciphers in symmetric cryptography. Our proposal modifies and extends the NLU instructions to a 32-bit RISC-V architecture; hence, we call the proposed ISE ‘NLU-V’. The proposed architecture is integrated into the open-source RISC-V implementation ‘Icicle’ and synthesized on a Xilinx Kintex-7 XC7K160T FPGA. The area overhead for the proposed NLU-V ISE is 1088 slice registers and 4520 LUTs. As case studies, the PRESENT and AES block ciphers are implemented using the new ISE on RISC-V in assembly. Our evaluation metric to showcase the performance gain, Z ‘time-area-product (TAP)’ (the execution time in clock cycles times code memory consumption), reflects the impact of the proposed family of instructions on the performance of the cipher implementations. The simulations show that the NLU-V achieves 89% gain for PRESENT and 68% gain for AES. Further, the NLU-V requires 44% less lines of code for the PRESENT and 23% less for the AES implementation.
Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems

This paper presents a pioneering methodology for refining product recommender systems, introducing a synergistic integration of unsupervised models—K-means clustering, content-based filtering (CBF), and hierarchical clustering—with the cutting-edge GPT-4 large language model (LLM). Its innovation lies in utilizing GPT-4 for model evaluation, harnessing its advanced natural language understanding capabilities to enhance the precision and relevance of product recommendations. A flask-based API simplifies its implementation for e-commerce owners, allowing for the seamless training and evaluation of the models using CSV-formatted product data. The unique aspect of this approach lies in its ability to empower e-commerce with sophisticated unsupervised recommender system algorithms, while the GPT model significantly contributes to refining the semantic context of product features, resulting in a more personalized and effective product recommendation system. The experimental results underscore the superiority of this integrated framework, marking a significant advancement in the field of recommender systems and providing businesses with an efficient and scalable solution to optimize their product recommendations.
Mono‐MVS: textureless‐aware multi‐view stereo assisted by monocular prediction

Abstract The learning‐based multi‐view stereo (MVS) methods have made remarkable progress in recent years. However, these methods exhibit limited robustness when faced with occlusion, weak or repetitive texture regions in the image. These factors often lead to holes in the final point cloud model due to excessive pixel‐matching errors. To address these challenges, we propose a novel MVS network assisted by monocular prediction for 3D reconstruction. Our approach combines the strengths of both monocular and multi‐view branches, leveraging the internal semantic information extracted from a single image through monocular prediction, along with the strict geometric relationships between multiple images. Moreover, we adopt a coarse‐to‐fine strategy to gradually reduce the number of assumed depth planes and minimise the interval between them as the resolution of the input images increases during the network iteration. This strategy can achieve a balance between the computational resource consumption and the effectiveness of the model. Experiments on the DTU, Tanks and Temples, and BlendedMVS datasets demonstrate that our method achieves outstanding results, particularly in textureless regions.
Increasing Students' Digital Literacy Through the Use of Google Cardboard Media on the Theme of My Dreams In Class IV of Elementary School

Purpose of the study: This research aims to determine and improve students' digital literacy using Google Cardboard media for class IV students at Elementary School and assessing the extent to which students' participation and involvement in learning activities using Google Cardboard can motivate them to actively learn.&#x0D; Methodology: This research is classroom action research. The subjects of this research were 25 grade IV students at Elementary School. The object of the research is to increase students' digital literacy through Google Cardboard media. Data techniques use tests and observations. The research instrument uses observation sheets and test questions or evaluation of learning outcomes.&#x0D; Main Findings: The results of the study showed that before the implementation of Google Cardboard media on the theme of my dreams, 5 students (20%) had completed it and 20 students (80%) had not completed it and the class average score was 60%. However, after learning using Google Cardboard media in learning the theme of my dreams in cycles 1 and II, data was obtained that student learning outcomes increased. With the increase that occurred in students who had reached 88%.&#x0D; Novelty/Originality of this study: The novelty of this research is that through the use of Google Cardboard with a focus on the theme "My Dreams", class IV students elementary school can develop their digital literacy skills while gaining a deeper understanding of technology and information, thereby increasing students' digital literacy. through more contextual learning experiences.
Faithful quantum teleportation through common and independent qubit-noise configurations and multi-parameter estimation in the output of teleported state

Quantum teleportation allows the transmission of unknown quantum states over arbitrary distances. This paper studies quantum teleportation via two non-interacting qubits coupled to local fields and Ornstein Uhlenbeck noise. We consider two different qubit-noise configurations, i.e., common qubit-noise interactions and independent qubit-noise interactions. We introduce a Gaussian Ornstein Uhlenbeck process to take into account the noisy effects of the local external fields. Furthermore, we address the intrinsic behavior of classical fields toward single- and two-qubit quantum teleportation as a function of various parameters. Additionally, using a quantum estimation theory, we study single- and multi-parameter estimation of the teleported state output for single and two-qubit quantum teleportation scenarios. One important application of this work is obtaining more valuable information in quantum remote sensing.
Advances in AI and machine learning for predictive medicine

The field of omics, driven by advances in high-throughput sequencing, faces a data explosion. This abundance of data offers unprecedented opportunities for predictive modeling in precision medicine, but also presents formidable challenges in data analysis and interpretation. Traditional machine learning (ML) techniques have been partly successful in generating predictive models for omics analysis but exhibit limitations in handling potential relationships within the data for more accurate prediction. This review explores a revolutionary shift in predictive modeling through the application of deep learning (DL), specifically convolutional neural networks (CNNs). Using transformation methods such as DeepInsight, omics data with independent variables in tabular (table-like, including vector) form can be turned into image-like representations, enabling CNNs to capture latent features effectively. This approach not only enhances predictive power but also leverages transfer learning, reducing computational time, and improving performance. However, integrating CNNs in predictive omics data analysis is not without challenges, including issues related to model interpretability, data heterogeneity, and data size. Addressing these challenges requires a multidisciplinary approach, involving collaborations between ML experts, bioinformatics researchers, biologists, and medical doctors. This review illuminates these complexities and charts a course for future research to unlock the full predictive potential of CNNs in omics data analysis and related fields.
Distracted by Previous Experience: Integrating Selection History, Current Task Demands and Saliency in an Algorithmic Model

Abstract Attention can be biased by previous learning and experience. We present an algorithmic-level model of this selection history bias in visual attention that predicts quantitatively how stimulus-driven processes, goal-driven control and selection history compete to control attention. In the model, the output of saliency maps as stimulus-driven guidance interacts with a history map that encodes learning effects and a goal-driven task control to prioritize visual features. The model works on coded features rather than image pixels which is common in many traditional saliency models. We test the model on a reaction time (RT) data from a psychophysical experiment. The model accurately predicts parameters of reaction time distributions from an integrated priority map that is comprised of an optimal, weighted combination of separate maps. Analysis of the weights confirms selection history effects on attention guidance. The model is able to capture individual differences between participants’ RTs and response probabilities per group. Moreover, we demonstrate that a model with a reduced set of maps performs worse, indicating that integrating history, saliency and task information are required for a quantitative description of human attention. Besides, we show that adding intertrial effect to the model (as another lingering bias) improves the model’s predictive performance.
Analyzing variational quantum landscapes with information content

Abstract The parameters of the quantum circuit in a variational quantum algorithm induce a landscape that contains the relevant information regarding its optimization hardness. In this work, we investigate such landscapes through the lens of information content, a measure of the variability between points in parameter space. Our major contribution connects the information content to the average norm of the gradient, for which we provide robust analytical bounds on its estimators. This result holds for any (classical or quantum) variational landscape. We validate the analytical understating by numerically studying the scaling of the gradient in an instance of the barren plateau problem. In such instance, we are able to estimate the scaling pre-factors in the gradient. Our work provides a way to analyze variational quantum algorithms in a data-driven fashion well-suited for near-term quantum computers.
SC-Unext: A Lightweight Image Segmentation Model with Cellular Mechanism for Breast Ultrasound Tumor Diagnosis

Automatic breast ultrasound image segmentation plays an important role in medical image processing. However, current methods for breast ultrasound segmentation suffer from high computational complexity and large model parameters, particularly when dealing with complex images. In this paper, we take the Unext network as a basis and utilize its encoder-decoder features. And taking inspiration from the mechanisms of cellular apoptosis and division, we design apoptosis and division algorithms to improve model performance. We propose a novel segmentation model which integrates the division and apoptosis algorithms and introduces spatial and channel convolution blocks into the model. Our proposed model not only improves the segmentation performance of breast ultrasound tumors, but also reduces the model parameters and computational resource consumption time. The model was evaluated on the breast ultrasound image dataset and our collected dataset. The experiments show that the SC-Unext model achieved Dice scores of 75.29% and accuracy of 97.09% on the BUSI dataset, and on the collected dataset, it reached Dice scores of 90.62% and accuracy of 98.37%. Meanwhile, we conducted a comparison of the model’s inference speed on CPUs to verify its efficiency in resource-constrained environments. The results indicated that the SC-Unext model achieved an inference speed of 92.72 ms per instance on devices equipped only with CPUs. The model’s number of parameters and computational resource consumption are 1.46M and 2.13 GFlops, respectively, which are lower compared to other network models. Due to its lightweight nature, the model holds significant value for various practical applications in the medical field.
Developer Ecosystems for Software Safety

How to design and implement information systems so that they are safe and secure is a complex topic. Both high-level design principles and implementation guidance for software safety and security are well established and broadly accepted. For example, Jerome Saltzer and Michael Schroeder's seminal overview of principles of secure design was published almost 50 years ago, and various community and governmental bodies have published comprehensive best practices about how to avoid common software weaknesses. This article argues, based on experience at Google, that focusing on developer ecosystems is both practical and effective, and can achieve a drastic reduction in the rate of common classes of defects across hundreds of applications being developed by thousands of developers.
The Nexus of Smart Contracts and Digital Twins Transforming Green Finance With Automated Transactions in Investment Agreements

In recent years, the integration of digital twins and smart contracts has become a revolutionary force in the field of green finance. The purpose of this chapter is to examine how smart contracts and digital twins are interwoven, particularly in relation to how they have significant influence in the field of green finance. It looks into the way these innovations work together to transform agreements regarding investments by making automated transactions possible and maximising effectiveness and transparency. This investigation demonstrates how working together can simplify complicated financing processes and increase responsibility and dependability for sustainable investment projects. The chapter demonstrates the real-world use of digital twins and smart contracts via case studies and theoretical insights, highlighting their revolutionary influence on changing the face of green finance.
Blockchain-Based Financial Systems Deployment and Challenges

Web3 encompasses decentralized finance (DeFi), which leverages blockchain and smart contracts to create an open and accessible financial system. They promote decentralization, trust, and innovation across industries. However, challenges include scalability, energy consumption, price volatility, regulatory uncertainty, and security vulnerabilities. Governments are working on regulatory frameworks, and adoption is growing. Education and awareness are crucial. In particular, there is a need to identify and address the technical, economic, social, and legal issues that may arise from the adoption and implementation of Web 3.0, including concerns around governance, scalability, interoperability, security, and regulation. Therefore, the purpose of the book chapter is to cover the key issues and challenges associated with blockchain technology and to develop a comprehensive understanding of the opportunities and limitations of this technology in order to inform the development and adoption of Web 3.0 applications and services.
Blockchain and Digital Twins

Digital twins and blockchain technology have revolutionized data management and accessibility in a variety of areas and opened a plethora of new and profitable business opportunities. The “foundations of blockchain and digital twins” book chapter delves into the advantageous relationship between these two cutting-edge technologies. The basic ideas, unique characteristics, and real-world use cases of blockchain technology have been covered in the first section of this chapter. Blockchain technology's decentralized, transparent, and unchangeable features have the potential to completely transform a number of industries, including finance, healthcare, and supply chain management. The chapter also examines the consensus, processes, smart contracts, and cryptographic techniques that support the blockchain's dependability and security. The final section investigates the interesting relationship between blockchain and digital twins, assessing their individual benefits. Blockchain technology has the potential to improve the reliability, authenticity, and safety of digital twins.
Blockchain in Human Resource Management

This chapter aims to provide fresh ideas from philosophy to help human resources managers adopt blockchain technology in their work. In order to categorize the 25 significant items, the authors examined the 126 publications that were made available for acceptance between 2016 and 2021 in the Scopus and Web of Science databases. Using bibliometric data, the headings, titles, and keyword phrases of each of those 25 papers have all been meticulously analysed. Depending on the bibliometric data gathered, word relationship network maps were produced using VOS viewer. There are four topic groupings that were made using the linked visualisations. It has been decided that the ideas of employee-systems interface (ESI) and blockchain framework (BCF) for HR are going to encourage the adoption and application of distributed computing technologies in the area of the administration of human resources. After reading all 25 articles, the authors thoroughly and in-depth examined the key terms that supported the two philosophical subjects and determined the validity of the results.
Advanced Data Analytics in Green Finance

This chapter explores how cutting-edge technologies, such as big data analytics, digital twins, and blockchain, are revolutionizing the green finance industry. It examines how these technologies' convergence improves sustainable financial practices efficiency, transparency, and decision-making while providing a fresh viewpoint on their complementary effects. The chapter describes the distinct functions of digital twins in asset optimization and risk management, blockchain in guaranteeing security and trust, and sophisticated data analytics in facilitating well-informed financial decisions.
Blockchain-Driven NFTs

Blockchain technology and non-fungible tokens (NFTs) are transforming industries like finance, art, and gaming. This chapter explores their intersection and profound implications for sustainable investments. Blockchain's transparency and immutability enhance trust in sustainability projects, while NFTs tokenizing real-world assets democratize access and promote eco-friendly initiatives. Real-world cases demonstrate the practical impact of blockchain-driven NFTs in sectors like renewable energy, sustainable agriculture, and carbon credits. Challenges include regulatory frameworks and environmental concerns, but the future promises a more robust ecosystem, increased accessibility, and greater transparency in sustainable finance.
Foundations of Blockchain and Digital Twins

The rapid evolution of transformative technologies, including blockchain and digital twins, is reshaping numerous industries. Blockchain, recognized for its distributed ledger capabilities, is gaining widespread acceptance owing to its secure and transparent data management features. Simultaneously, digital twins, virtual replicas of physical assets, are proving instrumental in offering real-time insights and enabling remote monitoring and control. This chapter explores the convergence of these two technologies, emphasizing their potential in the real world. Commencing with an overview of blockchain and digital twins, the chapter proceeds to scrutinize their architecture and consensus mechanisms. Subsequently, it delves into the concept of digital twins and their versatile applications across various sectors. Finally, it articulates the advantages derived from the integration of blockchain and digital twins.