Reviewerly/exHarmony huggingface dataset:
A Beginner's Guide to Federated Learning

The increasing prevalence of Internet of Things devices produces substantial volumes of data, thereby demanding the development of advanced machine learning (ML) models to maintain the data securely. Federated learning (FL) is such an advanced distributed ML paradigm that enables multiple devices or data centers to collaboratively train an ML model without the need to share the raw data. In recent years, it has got considerable interest because of its capability to overcome the challenges of traditional centralized ML models, such as data privacy and scalability. Even though there is such growing interest in FL, there are merely very few resources available in the literature to fundamentally understand and implement the FL concepts. In light of this, the objective of this paper is to provide basic guidance that is essential for beginners to develop FL-based solutions. This paper provides a clear intuition behind the FL, which includes a discussion on different types (horizontal FL, vertical FL, and federated transfer learning) and federated averaging (FedAvg) algorithm. It also highlights various key applications of FL. Finally, the paper emphasizes a few research directions that facilitate the development of FL.
Smart Prediction of Circulatory Failure: Machine Learning for Early Detection of Patient Deterioration

Circulatory failure, also known as shock, is a critical condition that can have serious consequences for one’s health. Early detection and timely intervention are crucial for improving patient outcomes. Machine learning (ML) models have shown promise in predicting circulatory failure based on clinical data. In our study, we examined different machine learning (ML) models to predict circulatory failure in patients who were admitted to the intensive care unit (ICU) with suspected circulatory problems. The ML model we developed used various algorithms like random forest, LG, XGB, Decision Tree, and SVM. It was trained using clinical information such as vital signs, laboratory values, and demographic data. By considering these different factors, our model aimed to accurately predict circulatory failure in critically ill patients. Furthermore, we employed feature selection techniques such as SelectKBest to identify the most informative clinical variables for prediction. Using receiver operating characteristic (ROC) curves and area under the curve (AUC) values, we evaluated the model’s performance. Our results show that machine learning models can contribute significantly to predicting circulatory failure in patients at high risk of deterioration.
Real and Fake Face Detection: A Comprehensive Evaluation of Machine Learning and Deep Learning Techniques for Improved Performance

The progress of digital manipulation methods has led to the creation of incredibly realistic fake faces, making it more challenging for humans to differentiate between genuine and fabricated ones while our brains are wired to interpret facial features, the use of sophisticated technology and artificial intelligence is blurring the line between real and manipulated images. As a consequence, techniques such as deep learning are becoming popular in distinguishing between real and fake faces with greater accuracy and reliability. In this study, a combination of machine learning and deep learning approaches was utilized to develop models for the detection of genuine and fabricated faces. The initial model implemented was an artificial neural network model, which utilized a Fourier-based technique for feature extraction. The model underwent training and testing using a benchmark dataset labeled as "Real and Fake Faces", and produced an accuracy rate of 0.57. ResNet18 approach involved the utilization of multiple models of convolutional neural networks that were trained on the same dataset and their results enhanced the overall precision of the classification process. The implementation of ResNet18 in deep learning has greatly enhanced the overall performance by achieving a substantially elevated accuracy level of 0.77.
Multifaceted perception-based blind omnidirectional image quality assessment

With the rapid development of computer vision, the omnidirectional image (OI) with a broader view and a higher resolution provides humans with an immersive visual experience and has attracted more researchers’ attention. But current image quality assessment models cannot perform well due to neglecting the difference between panoramic images and traditional images, limiting OIs’ further development. Inspired by the above problem, we propose a blind OI quality assessment model based on multi-frequency features, color features, and human visual system-based saliency features. Specifically, we utilize the high- and low-frequency information mapped to the frequency domain to measure the frequency domain loss. The local binary pattern operator is applied to encode the different color channel information. Then the local natural scene statistics and entropy features are extracted using the input image to weight the obtained saliency image by our model to reduce the model’s computational complexity. Finally, support vector regression is employed to predict the OIs’ quality scores. Experimental results on CVIQD and OIQA databases prove that our work performs better than state-of-the-art OIQA models.
How Quantum Computing Impacts Cyber Security

Cybersecurity confronts several challenges in the current and upcoming decades. Quantum computing might be one of these challenges that is the worst. Usually, cybersecurity provides techniques, standards, policies, configurations, and recommendations to protect classical system assets. According to the CIA triad (Confidentiality, Integrity, and Availability) fundamentals and the current domain structures used in the classical IT infrastructure systems, vulnerability may occur at any time and place, where several technical and non-technical foundations have been breached aggressively during the past ten years. These breaches are based on classical weaknesses, whether in the offensive or defensive platforms. Quantum computing might be the solution to these problems. However, quantum computing will be a part of the problem, since quantum usage requires a variety of devices, software, applications, and policies. Most of these requirements are still not ready to be approved or even exist. This paper focuses on the logical readings of the near future when quantum computers become available or before. In addition, some advantages and disadvantages are concluded to declare some circumstances that probably face cybersecurity in the upcoming decades.
Specialized Syntactic Quran Search Engines: Evaluation and Limitations

The Quran is the sacred text that provides guidance and teachings to the followers of Islam. This paper aims to analyze and evaluate the limitations of current specialized search engines used for retrieving information from the Quran. Also, this work includes an initial evaluation of Quran search with a large language model (LLM) employing prompt engineering. The study focuses on the syntactic aspect of information retrieval, while acknowledging the necessity of considering the semantic meaning of Quranic words and verses for a more comprehensive analysis. Furthermore, recommendations and guidelines for future research are proposed, stressing the significance of developing syntactic search capabilities to improve the accuracy and relevance of search results.
Comparison between Homodyne and PNR Detection Schemes for Quantum Key Distribution

In this paper, we study the comparison between two different methods of detection regarding key generation rate in a quantum key distribution system. We illustrate that after a threshold PNR detection outperforms homodyne detection.
On Data-Aware Global Explainability of Graph Neural Networks

Graph Neural Networks (GNNs) have significantly boosted the performance of many graph-based applications, yet they serve as black-box models. To understand how GNNs make decisions, explainability techniques have been extensively studied. While the majority of existing methods focus on local explainability, we propose DAG-Explainer in this work aiming for global explainability. Specifically, we observe three properties of superior explanations for a pretrained GNN: they should be highly recognized by the model, compliant with the data distribution and discriminative among all the classes. The first property entails an explanation to be faithful to the model, as the other two require the explanation to be convincing regarding the data distribution. Guided by these properties, we design metrics to quantify the quality of each single explanation and formulate the problem of finding data-aware global explanations for a pretrained GNN as an optimizing problem. We prove that the problem is NP-hard and adopt a randomized greedy algorithm to find a near optimal solution. Furthermore, we derive an improved bound of the approximation algorithm in our problem over the state-of-the-art (SOTA) best. Experimental results show that DAG-Explainer can efficiently produce meaningful and trustworthy explanations while preserving comparable quantitative evaluation results to the SOTA methods.
PET Image Representation and Reconstruction Based on Graph Filter

Due to count limitations, PET image reconstruction from low count data is challenging, resulting in the reconstructed image with high noise and blurred outline structures. By analyzing the graph spectrum of PET images reconstructed by different methods, we find that the most graph spectra of a high quality PET image generally reside in the low to middle frequencies in graph spectral domain. We therefore propose a novel PET image representation model based on a lowpass graph filter to represent PET images. Incorporating this model with MLEM, we propose a graph filter-based EM (GFEM) method to reconstruct the PET images with their graph spectra restricted to the low to middle frequencies. Extensive tests and comparisons show that GFEM achieves better visibility of small legions, and lower MSE, bias, and variance for dynamic PET image reconstruction than other methods, including MLEM with post-filter, the conventional regularization-based method, and kernelized EM (KEM). In the regions of interest, GFEM also achieves a better contrast recovery coefficient versus background noise standard deviation curve. Moreover, the graph filter-based representation model has a stronger representation ability than that of the kernel-based representation model in KEM, particularly in low count regions.
Correlating Healthcare Staff’s Stress Level and Cybersecurity Practices in Norway

This paper examines the relationship between stress levels and cybersecurity practices among hospital employees in Norway. As the healthcare sector increasingly uses technology to improve patient care, hospital data became more vulnerable to cyber attacks and the human factor remains a significant vulnerability that can be exploited by hackers. Stress is one such factor that can affect cybersecurity practices. This study hypothesized that employees with higher stress levels are more likely to engage in risky security practices. An online survey was conducted to collect data on demographic details, stress levels, and cybersecurity practices of healthcare staff. Respondents’ stress levels were assessed using the Perceived Stress Scale (PSS), and hospital staff’s hazardous cybersecurity practices were evaluated using the Hospital Staff’s Risky Cybersecurity Practices Scale (HS-RCPS). Regarding cybersecurity practices, the results show that hospital workers in Norway tend to have good cybersecurity practices. Our findings also indicate that there is no significant correlation between stress levels and cybersecurity practices (r=0.101). In addition, demographic variables including gender, age, position, and position level did not have a significant impact on healthcare professionals’ dangerous cybersecurity activities. However, years of working experience were found to be a crucial factor in determining cybersecurity practices among hospital employees ( F(3, 38) = 3.146, p = 0.036) with staff who had more than 25 years of work experience getting the highest average HS-RCPS score.
The waste generated by waste avoidance

Click to increase image sizeClick to decrease image size Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsCameron TonkinwiseCameron Tonkinwise is the Professor of Design Studies at the University of Technology Sydney who is best known for his work with former colleagues at Carnegie Mellon University on Transition Design.
Evaluation of a new technique using artificial intelligence for quantification of plasma cells on <scp>CD138</scp> immunohistochemistry

Abstract Introduction The diagnosis of plasma cell neoplasms depends on the accurate quantification of plasma cells, traditionally done by immunohistochemical CD138 staining of bone marrow biopsies. Currently, there is no fully satisfactory reference method for this quantification. In our previous study, we compared the commonly used overview estimation method (method A) with a novel method for counting plasma cells in three representative areas (method B). Results showed comparable concordance parameters between the two methods. In this follow‐up study, we compared the previously evaluated methods with a digital analysis method (method C) that uses artificial intelligence in open‐source software, QuPath. Methods Archived CD138 immunohistochemically stained trephine sections of bone marrow samples used in our previous study were used ( n = 33). Reviewers selected three representative areas on each sample by taking images with a light microscope and camera. Digital analysis was performed using the positive cell detection function in QuPath. The entire process was repeated by each reviewer to test intraobserver concordance (concordance correlation coefficient [CCC]) in addition to interobserver concordance (intraclass correlation coefficient [ICC]). Results Intraobserver concordance of method C showed strong agreement for all reviewers with the lowest CCC = 0.854. Interobserver concordance for method C using ICC was 0.909 and 0.949. This showed high interobserver agreement with significant differences between method C and previously assessed method A (ICC = 0.793 and 0.713) and method B (ICC = 0.657 and 0.658). Conclusion We were able to successfully count CD138‐positive plasma cells in bone marrow biopsies using artificial intelligence. This method is superior to both manual counting and overview estimation, regardless of tumour load.
Analysis of cellularity in H&amp;E-stained rat bone marrow tissue via deep learning

Our objective was to develop an automated deep-learning-based method to evaluate cellularity in rat bone marrow hematoxylin and eosin whole slide images for preclinical safety assessment. We trained a shallow CNN for segmenting marrow, 2 Mask R-CNN models for segmenting megakaryocytes (MKCs), and small hematopoietic cells (SHCs), and a SegNet model for segmenting red blood cells. We incorporated the models into a pipeline that identifies and counts MKCs and SHCs in rat bone marrow. We compared cell segmentation and counts that our method generated to those that pathologists generated on 10 slides with a range of cell depletion levels from 10 studies. For SHCs, we compared cell counts that our method generated to counts generated by Cellpose and Stardist. The median Dice and object Dice scores for MKCs using our method vs pathologist consensus and the inter- and intra-pathologist variation were comparable, with overlapping first-third quartile ranges. For SHCs, the median scores were close, with first-third quartile ranges partially overlapping intra-pathologist variation. For SHCs, in comparison to Cellpose and Stardist, counts from our method were closer to pathologist counts, with a smaller 95% limits of agreement range. The performance of the bone marrow analysis pipeline supports its incorporation into routine use as an aid for hematotoxicity assessment by pathologists. The pipeline could help expedite hematotoxicity assessment in preclinical studies and consequently could expedite drug development. The method may enable meta-analysis of rat bone marrow characteristics from future and historical whole slide images and may generate new biological insights from cross-study comparisons.
Inconsistency in the registration of the Digital Object Identifier (DOI) of articles on Web of Science and Scopus

Almost 25 years ago, the Digital Object Identifier (DOI) was implemented with the purpose of providing a unique and persistent form of document identification. As DOIs can be assigned to any object, journals rapidly adopted their use as it eases the process of identifying a specific document, thus increasing its visibility. However, while studying the impact of papers published by Chilean researchers in disciplines related to Social Sciences, Arts, and Humanities, we noticed that some journals published a considerable proportion of documents without DOIs. Thus, we questioned if this was due to a lack of DOI adoption by these journals or if it was a database processing error. Our findings indicate that while most journals have adopted the use of DOI for article-type documents, many of the Web of Science and Scopus records do not include this information. To overcome this issue, databases need to ensure the accuracy and consistency of their data, while the editorial management team of each journal ought to confirm that all the metadata from their articles has been properly registered by these databases. Undoubtedly, the use of DOI benefits all academics as it facilitates the discoverability and retrieval of the published articles.
Error level analysis (ELA)

ELA (Error Level Analysis) is a digital image analysis technique that has gained popularity in recent years in the field of modern forensics. It is a powerful method that can help experts determine if an image has been altered or manipulated in any way. ELA works by analyzing the levels of compression within an image and identifying areas that have been edited or manipulated at a different level than the rest of the image. This information can be crucial in criminal investigations, where the authenticity of an image can be key to solving a case. In this article, we will explore the use of ELA in modern forensics, its benefits, and limitations, as well as its potential impact on the future of forensic investigation.
FLAMA

Nowadays, feature models are the de facto standard when representing commonalities and variability, with modern examples spanning up to 7000 features. Manual analysis of such models is challenging and error-prone due to sheer size. To help in this task, automated analysis of feature models (AAFM) has emerged over the past three decades. However, the diversity of these tools and their supported languages presents a significant challenge that motivated the MOD-EVAR community to initiate a project for a new tool that supports the UVL language. Despite the rise of machine learning and data science, along with robust Python-based libraries, most AAFM tools have been implemented in Java, creating a collaboration gap. This paper introduces Flama, an innovative framework that automates the analysis of variability models. It focuses on UVL model analysis and aims for easy integration and extensibility to bridge this gap and foster better community and cross-community collaboration.
Variability-aware Behavioural Learning

Addressing variability proactively during software engineering activities means shifting from reasoning on individual systems to reasoning on families of systems. Adopting appropriate variability management techniques can yield important economies of scale and quality improvements. Conversely, variability can also be a curse, especially for Quality Assurance (QA), i.e., verification and testing of such systems, due to the combinatorial explosion of the number of software variants. Featured Transition Systems (FTSs) were introduced as a way to represent and reason about the behaviour of Variaility-intensive Systems (VISs). By labelling a transition system with feature expressions, FTSs capture multiple variants of a system in a single model, enabling reasoning at the family level. They have shown significant improvements in automated QA activities such as model-checking and model-based testing, as well as guiding design exploration activities. Yet, as most model-based approaches, FTS modelling requires both strong human expertise and significant effort that would be unaffordable in many cases, in particular for large legacy systems with outdated specifications and/or systems that evolve continuously.
Software Product Lines for Development of Evolutionary Robots

Evolutionary Robotics utilizes evolutionary algorithms for training robot controllers (e.g., neural networks) and adapting robot morphologies for different environments in design and runtime. One of the main challenges in robotics is the lack of reusability as AI-based robot controllers have to be trained from scratch for any change in the environment or a new task specification that a robot should adapt to. Training Artificial Neural Networks can be computationally heavy, time-consuming, and hard to reuse due to their monolithic black-box nature. The building blocks of emerging behaviors from Artificial Neural Networks cannot be fully separated or reused. We address the issue of reusability and propose an incremental approach for applying the reusability of behaviors. We implemented an Evolutionary Robotics framework to form a product family of robots. This product family is used to show the feasibility of our method for handling variability in a domain. Our results can be used to demonstrate a sample binding between the software product lines and machine learning domains.
The e4CompareFramework

Software product-line engineering (SPLE) provides structured reuse strategies reducing the time-to-market and decreasing development and maintenance effort when developing variant-rich software systems. In practice, however, unstructured reuse strategies such as clone-and-own are frequently used. While copying, pasting, and modifying artifacts to create new variants seems straightforward, it increases the maintenance effort. SPLE requires the extraction of a software product-line (SPL) from existing variants. However, this task needs complex analyses that are not feasible manually, making tool support crucial.
ChatEd.Mgr.com/SAP

Artificial intelligence surrounds us today, with its derivative ChatGPT suddenly capturing everyone's attention. Pertinent to our medical literature is the capability of this chatbot to generate original and quite impressive text suitable for the format of any journal. However, risks not yet determined need further investigation before this approach will be suitable to allow unimpeded progress in our specialty.