Reviewerly/exHarmony huggingface dataset:
Nuclei instance segmentation from histopathology images using Bayesian dropout based deep learning

The deterministic deep learning models have achieved state-of-the-art performance in various medical image analysis tasks, including nuclei segmentation from histopathology images. The deterministic models focus on improving the model prediction accuracy without assessing the confidence in the predictions.We propose a semantic segmentation model using Bayesian representation to segment nuclei from the histopathology images and to further quantify the epistemic uncertainty. We employ Bayesian approximation with Monte-Carlo (MC) dropout during the inference time to estimate the model's prediction uncertainty.We evaluate the performance of the proposed approach on the PanNuke dataset, which consists of 312 visual fields from 19 organ types. We compare the nuclei segmentation accuracy of our approach with that of a fully convolutional neural network, U-Net, SegNet, and the state-of-the-art Hover-net. We use F1-score and intersection over union (IoU) as the evaluation metrics. The proposed approach achieves a mean F1-score of 0.893 ± 0.008 and an IoU value of 0.868 ± 0.003 on the test set of the PanNuke dataset. These results outperform the Hover-net, which has a mean F1-score of 0.871 ± 0.010 and an IoU value of 0.840 ± 0.032.The proposed approach, which incorporates Bayesian representation and Monte-Carlo dropout, demonstrates superior performance in segmenting nuclei from histopathology images compared to existing models such as U-Net, SegNet, and Hover-net. By considering the epistemic uncertainty, our model provides a more reliable estimation of the prediction confidence. These findings highlight the potential of Bayesian deep learning for improving medical image analysis tasks and can contribute to the development of more accurate and reliable computer-aided diagnostic systems.
A Discrete Prey–Predator Algorithm for Cloud Task Scheduling

Cloud computing is considered a key Internet technology. Cloud providers offer services through the Internet, such as infrastructure, platforms, and software. The scheduling process of cloud providers’ tasks concerns allocating clients’ tasks to providers’ resources. Several mechanisms have been developed for task scheduling in cloud computing. Still, these mechanisms need to be optimized for execution time and makespan. This paper presents a new task-scheduling mechanism based on Discrete Prey–Predator to optimize the task-scheduling process in the cloud environment. The proposed Discrete Prey–Predator mechanism assigns each scheduling solution survival values. The proposed mechanism denotes the prey’s maximum surviving value and the predator’s minimum surviving value. The proposed Discrete Prey–Predator mechanism aims to minimize the execution time of tasks in cloud computing. This paper makes a significant contribution to the field of cloud task scheduling by introducing a new mechanism based on the Discrete Prey–Predator algorithm. The Discrete Prey–Predator mechanism presents distinct advantages, including optimized task execution, as the mechanism is purpose-built to optimize task execution times in cloud computing, improving overall system efficiency and resource utilization. Moreover, the proposed mechanism introduces a survival-value-based approach, as the mechanism introduces a unique approach for assigning survival values to scheduling solutions, differentiating between the prey’s maximum surviving value and the predator’s minimum surviving value. This improvement enhances decision-making precision in task allocation. To evaluate the proposed mechanism, simulations using the CloudSim simulator were conducted. The experiment phase considered different scenarios for testing the proposed mechanism in different states. The simulation results revealed that the proposed Discrete Prey–Predator mechanism has shorter execution times than the firefly algorithm. The average of the five execution times of the Discrete Prey–Predator mechanism was 270.97 s, while the average of the five execution times of the firefly algorithm was 315.10 s.
Distributed photovoltaic power output prediction based on satellite cloud map video frames

To address the challenge of predicting distributed photovoltaic (PV) power output for improved system integration and stability, this study proposes a novel method. Given the expanding scale of distributed PV systems and their economic constraints, accurate power output prediction becomes pivotal. Conventional prediction methods are hindered by the lack of meteorological stations at most distributed PV stations. In response, we present a dynamic convolutional generative adversarial network (DC-GAN) approach coupled with satellite cloud map video frames. By extracting shading features from satellite cloud images and utilizing DC-GAN, our method forecasts short-term cloud shading effects on future radiation. We further integrate radiation data from centralized PV stations, spatial correlations of distributed PV stations, and cloud shading characteristics. This information is used to construct a predictive model combining Convolutional Neural Networks (CNN) and Long short-term memory (LSTM), enhancing prediction accuracy. Comparative experiments confirm the superiority of our proposed method over traditional approaches, substantiating its effectiveness and practicality. Our method achieves notable accuracy improvements, establishing its value in predicting distributed PV power output. This research’s findings offer a valuable contribution to the field of renewable energy integration. In numerical assessments, our method demonstrates a significant increase in prediction accuracy, outperforming conventional techniques by 3.3%. This underscores the practical relevance and efficiency of our proposed approach in enhancing distributed PV power output prediction.
3D multiplexed tissue imaging reconstruction and optimized region of interest (ROI) selection through deep learning model of channels embedding

Introduction: Tissue-based sampling and diagnosis are defined as the extraction of information from certain limited spaces and its diagnostic significance of a certain object. Pathologists deal with issues related to tumor heterogeneity since analyzing a single sample does not necessarily capture a representative depiction of cancer, and a tissue biopsy usually only presents a small fraction of the tumor. Many multiplex tissue imaging platforms (MTIs) make the assumption that tissue microarrays (TMAs) containing small core samples of 2-dimensional (2D) tissue sections are a good approximation of bulk tumors although tumors are not 2D. However, emerging whole slide imaging (WSI) or 3D tumor atlases that use MTIs like cyclic immunofluorescence (CyCIF) strongly challenge this assumption. In spite of the additional insight gathered by measuring the tumor microenvironment in WSI or 3D, it can be prohibitively expensive and time-consuming to process tens or hundreds of tissue sections with CyCIF. Even when resources are not limited, the criteria for region of interest (ROI) selection in tissues for downstream analysis remain largely qualitative and subjective as stratified sampling requires the knowledge of objects and evaluates their features. Despite the fact TMAs fail to adequately approximate whole tissue features, a theoretical subsampling of tissue exists that can best represent the tumor in the whole slide image. Methods: To address these challenges, we propose deep learning approaches to learn multi-modal image translation tasks from two aspects: 1) generative modeling approach to reconstruct 3D CyCIF representation and 2) co-embedding CyCIF image and Hematoxylin and Eosin (H&E) section to learn multi-modal mappings by a cross-domain translation for minimum representative ROI selection. Results and discussion: We demonstrate that generative modeling enables a 3D virtual CyCIF reconstruction of a colorectal cancer specimen given a small subset of the imaging data at training time. By co-embedding histology and MTI features, we propose a simple convex optimization for objective ROI selection. We demonstrate the potential application of ROI selection and the efficiency of its performance with respect to cellular heterogeneity.
Empowering solar photovoltaic logistic operations through cloud-enabled blockchain technology: a sustainable approach

In a world where renewable energy solutions are in high demand, solar photovoltaic (PV) systems are at the forefront, emphasizing the need for efficient and eco-friendly logistics. This coincides with the rise of blockchain technology as a game-changer in PV system management. Additionally, cloud computing is playing a pivotal role in transforming the IT landscape, providing seamless access to shared computing resources via the Internet. This research introduces a novel framework that utilizes cloud computing to enhance blockchain-based PV logistics. It employs a sophisticated mathematical model to optimize logistics components such as transportation, storage, inventory management, and supply chain coordination, with the goal of minimizing costs and improving overall efficiency. At the heart of this transformative paradigm lies a visionary cloud-based system, endowing PV logistic planners with the power of real-time access to mission-critical data. This infusion of data-driven decision-making capabilities fosters a seamless symphony of communication and enriches collaboration among stakeholders, setting the stage for monumental shifts in efficiency and management, ushering in a new era of sustainability and scalability within the photovoltaic industry. This technological synergy is not just about immediate gains. It is a testament to our commitment to improved energy sustainability and a testament to our shared vision of a greener future. In lockstep with the global pursuit of renewable energy solutions, this research lights the way toward a brighter, more sustainable tomorrow.
Multifractality in stride-to-stride variations reveals that walking involves more movement tuning and adjusting than running

Introduction: The seemingly periodic human gait exhibits stride-to-stride variations as it adapts to the changing task constraints. The optimal movement variability hypothesis (OMVH) states that healthy stride-to-stride variations exhibit “fractality”—a specific temporal structure in consecutive strides that are ordered, stable but also variable, and adaptable. Previous research has primarily focused on a single fractality measure, “monofractality.” However, this measure can vary across time; strideto-stride variations can show “multifractality.” Greater multifractality in stride-tostride variations would highlight the ability to tune and adjust movements more. Methods: We investigated monofractality and multifractality in a cohort of eight healthy adults during self-paced walking and running trials, both on a treadmill and overground. Footfall data were collected through force-sensitive sensors positioned on their heels and feet. We examined the effects of self-paced walking vs. running and treadmill vs. overground locomotion on the measure of monofractality, α-DFA, in addition to the multifractal spectrum width, W, and the asymmetry in the multifractal spectrum, W Asym , of stride interval time series. Results: While the α-DFA was larger than 0.50 for almost all conditions, α-DFA was higher in running and locomoting overground than walking and locomoting on a treadmill. Similarly, W was greater while locomoting overground than on a treadmill, but an opposite trend indicated that W was greater in walking than running. Larger W Asym values in the negative direction suggest that walking exhibits more variation in the persistence of shorter stride intervals than running. However, the ability to tune and adjust movements does not differ between treadmill and overground, although both exhibit more variation in the persistence of shorter stride intervals. Discussion: Hence, greater heterogeneity in shorter than longer stride intervals contributed to greater multifractality in walking compared to running, indicated by larger negative W Asym values. Our results highlight the need to incorporate multifractal methods to test the predictions of the OMVH.
White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning

White blood cells (WBCs) are an indispensable constituent of the immune system. Efficient and accurate categorization of WBC is a critical task for disease diagnosis by medical experts. This categorization helps in the correct identification of medical problems. In this research work, WBC classes are categorized with the help of a transform learning model in combination with our proposed virtual hexagonal trellis (VHT) structure feature extraction method. The VHT feature extractor is a kernel-based filter model designed over a square lattice. In the first step, Graft Net CNN model is used to extract features of augmented data set images. Later, the VHT base feature extractor extracts useful features. The CNN-extracted features are passed to ant colony optimization (ACO) module for optimal features acquisition. Extracted features from the VHT base filter and ACO are serially merged to create a single feature vector. The merged features are passed to the support vector machine (SVM) variants for optimal classification. Our strategy yields 99.9% accuracy, which outperforms other existing methods.
A Specific Emitter Identification System Design for Crossing Signal Modes in the Air Traffic Control Radar Beacon System and Wireless Devices

To improve communication stability, more wireless devices transmit multi-modal signals while operating. The term 'modal' refers to signal waveforms or signal types. This poses challenges to traditional specific emitter identification (SEI) systems, e.g., unknown modal signals require extra open-set mode identification; different modes require different radio frequency fingerprint (RFF) extractors and SEI classifiers; and it is hard to collect and label all signals. To address these issues, we propose an enhanced SEI system consisting of a universal RFF extractor, denoted as multiple synchrosqueezed wavelet transformation of energy unified (MSWTEu), and a new generative adversarial network for feature transferring (FTGAN). MSWTEu extracts uniform RFF features for different modal signals, FTGAN transfers different modal features to a recognized distribution in an unsupervised manner, and a novel training strategy is proposed to achieve emitter identification across multi-modal signals using a single clustering method. To evaluate the system, we built a hybrid dataset, which consists of multi-modal signals transmitted by various emitters, and built a complete civil air traffic control radar beacon system (ATCRBS) dataset for airplanes. The experiments show that our enhanced SEI system can resolve the SEI problems associated with crossing signal modes. It directly achieves 86% accuracy in cross-modal emitter identification using an unsupervised classifier, and simultaneously obtains 99% accuracy in open-set recognition of signal mode.
Who could be behind QAnon? Authorship attribution with supervised machine-learning

Abstract A series of social media posts on 4chan then 8chan, signed under the pseudonym ‘Q’, started a movement known as QAnon, which led some of its most radical supporters to violent and illegal actions. To identify the person(s) behind Q, we evaluate the coincidence between the linguistic properties of the texts written by Q and to those written by a list of suspects provided by journalistic investigation. To identify the authors of these posts, serious challenges have to be addressed. The ‘Q drops’ are very short texts, written in a way that constitute a sort of literary genre in itself, with very peculiar features of style. These texts might have been written by different authors, whose other writings are often hard to find. After an online ethnography of the movement, necessary to collect enough material written by these thirteen potential authors, we use supervised machine learning to build stylistic profiles for each of them. We then performed a ‘rolling analysis’, looking repeatedly through a moving window for parts of Q’s writings matching our profiles. We conclude that two different individuals, Paul F. and Ron W., are the closest match to Q’s linguistic signature, and they could have successively written Q’s texts. These potential authors are not high-ranked personality from the US administration, but rather social media activists.
Translation Performance from the User’s Perspective of Large Language Models and Neural Machine Translation Systems

The rapid global expansion of ChatGPT, which plays a crucial role in interactive knowledge sharing and translation, underscores the importance of comparative performance assessments in artificial intelligence (AI) technology. This study concentrated on this crucial issue by exploring and contrasting the translation performances of large language models (LLMs) and neural machine translation (NMT) systems. For this aim, the APIs of Google Translate, Microsoft Translator, and OpenAI’s ChatGPT were utilized, leveraging parallel corpora from the Workshop on Machine Translation (WMT) 2018 and 2020 benchmarks. By applying recognized evaluation metrics such as BLEU, chrF, and TER, a comprehensive performance analysis across a variety of language pairs, translation directions, and reference token sizes was conducted. The findings reveal that while Google Translate and Microsoft Translator generally surpass ChatGPT in terms of their BLEU, chrF, and TER scores, ChatGPT exhibits superior performance in specific language pairs. Translations from non-English to English consistently yielded better results across all three systems compared with translations from English to non-English. Significantly, an improvement in translation system performance was observed as the token size increased, hinting at the potential benefits of training models on larger token sizes.
The EXtended Reality Quality Riddle: A Technological and Sociological Survey

This position paper surveys recent research on the latest findings regarding the Quality of Experience in XR from two distinct angles. Firstly, we present recent technical outcomes concerning media quality. Secondly, we extend our investigation to user experiences from a sociological perspective. This innovative multidisciplinary approach establishes a connection between various methodologies, enabling a more comprehensive understanding of XR quality and it opens up new possibilities for XR services design and performance measurements.
Illegal Accounts Detection on Ethereum Using Heterogeneous Graph Transformer Networks

Numerous applications based on Ethereum have been utilized in a variety of scenarios, such as financial services. However, due to the lack of effective regulation in the blockchain, a significant number of illegal users cash in on the anonymity of blockchain accounts, which has an extremely negative impact. Existing illegal account detection methods employ machine learning techniques to train fundamental account characteristics and fail to extract efficient high-order features by graph structures, leading to inaccuracies in account detection. To address this issue, we propose a novel illegal account identification method based on a heterogeneous transformer network. Specifically, we design an account-centric heterogeneous information network model to express real transaction data on Ethereum for the first time. This model can describe the network structure information more comprehensively. Additionally, we propose to apply the graph transformer network to automatically learn the multi-hop metapath and obtain high-order node information and links. These features, in turn, improve the quality and performance of our model. Finally, we employ the graph convolutional network to classify nodes and complete the account identification task and ensure the security of the Ethereum system. Furthermore, we compare our method with other existing detection models. Our experiments demonstrate that the proposed approach achieves an accuracy of 95.57%, which surpasses that of traditional machine learning models and existing detection schemes.
The Cryptocurrency Crash of 2022: Which Lessons for the Future?

Cryptocurrency crashes can be triggered by various factors, including speculatory bubbles, security breaches, market manipulation, and regulatory actions. The world’s major cryptocurrency defaults occurred in the field of cryptocurrencies and, most importantly, cryptocurrency exchanges. Another reason for their volatility is that most cryptocurrencies are related to exchanges where the prices depend on arbitrageurs and speculators who trade across them. The further cause of cryptocurrency variability is the concentration of ownership. Another risk indicator is the promise of other returns in the short term which is one of the pillars of Ponzi schemes. Investors often prefer to trust rumors about other people who have become millionaires overnight, but it is possible and necessary to conduct due diligence on the DeFi projects according to the classical schemes. The price of cryptocurrencies is influenced also by the trend of interest rates such as the stock market index, albeit with the variability of different amplitudes which means that it is used as a financial investment instrument.
A Triple-Stage Robust Ellipse Fitting Algorithm Based on Outlier Removal

Ellipse fitting is a fundamental yet critical task in computer vision, and the development of robust and accurate algorithms is crucial for various applications. In this study, we propose a triple-stage robust ellipse fitting algorithm to address the challenges posed by noise and outliers in the input data. Specifically, to overcome the sensitivity of existing methods to outliers, we introduce an adaptive outlier removal algorithm. This algorithm dynamically removes outliers based on the probability density of all data points, eliminating the need for manual parameter adjustment and enhancing robustness to outliers. Furthermore, we tackle the issue of multiple ellipses in the input data by projecting the filtered points into the polar coordinate system. The points are then divided into equal intervals based on the polar angle, facilitating linear clustering to identify the point sets belonging to candidate ellipses, which helps to avoid erroneous fits and improve accuracy. Finally, to avoid solving the geometric distance between the point and the quadratic curve, a simplified ellipse fitting objective function and its corresponding optimization scheme are developed, in which the ellipse parameters are iteratively solved. To verify the universality and accuracy of the algorithm, we tested it on both synthetic data and real-world images from various scenarios with state-of-the-art approaches. Additionally, experiments have been carried out on a physical spacecraft pose measurement platform. The experimental results demonstrate that the algorithm exhibits excellent performance in terms of fitting accuracy and robustness, with a position estimation error of less than 2 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mm</i> and an attitude estimation error of less than 0.1°.