Reviewerly/exHarmony huggingface dataset:
Event-triggered model-free adaptive control for nonlinear systems using intuitionistic fuzzy neural network: simulation and experimental validation

Abstract This article presents model-free adaptive control based on an intuitionistic fuzzy neural network for nonlinear systems with event-triggered output. Essentially, model-free adaptive control (MFAC) is constructed by establishing an online approximate model of the controlled system using the pseudo-partial derivative (PPD) form. By the proposed scheme, first, an intuitionistic fuzzy neural network (IFNN) is developed as an estimator for time-varying PPD in both compact-form dynamic linearization (CFDL) and partial-form dynamic linearization (PFDL) for the MFAC technique. Second, two periodic event-triggered output methods are integrated with the proposed IFNN-based MFAC in both forms to save communication resources and reduce the computation burden and energy consumption. Based on the Lyapunov theory and BIBO stability approach, necessary conditions are established to guarantee the convergence of the adaptive law of the IFNN controller and the boundary of the tracking error of the closed loop system. Third, regarding the feasibility and the effectiveness of the developed control method, two simulation examples including the continuous stirred-tank reactor (CSTR) system and the heat exchanger system are given. Finally, the practical validation of the proposed data-driven control method is conducted via the speed control of a DC motor.
Effects of Internal and External Factors on Hospital Data Breaches: Quantitative Study

Background Health care data breaches are the most rapidly increasing type of cybercrime; however, the predictors of health care data breaches are uncertain. Objective This quantitative study aims to develop a predictive model to explain the number of hospital data breaches at the county level. Methods This study evaluated data consolidated at the county level from 1032 short-term acute care hospitals. We considered the association between data breach occurrence (a dichotomous variable), predictors based on county demographics, and socioeconomics, average hospital workload, facility type, and average performance on several hospital financial metrics using 3 model types: logistic regression, perceptron, and support vector machine. Results The model coefficient performance metrics indicated convergent validity across the 3 model types for all variables except bad debt and the factor level accounting for counties with &gt;20% and up to 40% Hispanic populations, both of which had mixed coefficient directionality. The support vector machine model performed the classification task best based on all metrics (accuracy, precision, recall, F1-score). All the 3 models performed the classification task well with directional congruence of weights. From the logistic regression model, the top 5 odds ratios (indicating a higher risk of breach) included inpatient workload, medical center status, pediatric trauma center status, accounts receivable, and the number of outpatient visits, in high to low order. The bottom 5 odds ratios (indicating the lowest odds of experiencing a data breach) occurred for counties with Black populations of &gt;20% and &lt;40%, &gt;80% and &lt;100%, and &gt;40% but &lt;60%, as well as counties with ≤20% Asian or between 80% and 100% Hispanic individuals. Our results are in line with those of other studies that determined that patient workload, facility type, and financial outcomes were associated with the likelihood of health care data breach occurrence. Conclusions The results of this study provide a predictive model for health care data breaches that may guide health care managers to reduce the risk of data breaches by raising awareness of the risk factors.
Improved faster R-CNN algorithm for object detection in remote sensing image

To enhance the precision of detecting small targets in remote sensing images, a target detection algorithm based on improved faster R-CNN is proposed in this paper. In order to enhance the ability of feature extraction for image object category, the algorithm uses the large network model EfficientNet in the backbone feature extraction network. introduce an improved FPN (feature pyramid network) model to reduce that loss of key information of small target caused by the deep neural network, enhancing the capability of extracting feature information of the small target of the fast R-CNN model to the remote sensing image, and being capable of better coping with the remote sensing image with complex background and drastic size change, thereby reducing the omission rate. Finally, CBAM attention module is introduced into the feature graph output of the network model to enhance the interest of the model in feature information and improve the detection ability. Experiments on the NWPU_VHR-10 dataset show that the proposed algorithm improves the accuracy by 8.82%.
Securing End User Computing Environments Using Blockchain

COVID-19's phenomenal effect has expedited the adoption of digital technologies over several years, indicating that several of these breakthroughs are here to stay. Several enabling technologies are currently being implemented as major solutions for improving and responding to the pandemic's numerous issues, with blockchain being one of the preferred solution options. Blockchain can be used to re-structure processes, resulting in most effective operational and business models (e.g., democratising quality cancer detection with advanced artificial intelligence based radiomics technologies). The authors posit here that while there is a lot of anticipation about using blockchain to improve business capabilities, the lessons learned from the many pilots and proofs of concept so far should be considered. The necessity for a structured, formal decision-making process, based on good business logic and an awareness of the problem's process lifecycle, is however critical. Blockchain is a means to an end, not an end in and of itself.
Introduction to special issue on machine learning applications in geophysical exploration and monitoring

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Combining Regular Expressions and Supervised Algorithms for Clinical Text Classification

Clinical text classification allows assigning labels to content-based data using machine learning algorithms. However, unlike other study domains, clinical texts present complex linguistic diversity, including abbreviations, typos, and numerical patterns that are difficult to represent by the most-used classification algorithms. In this sense, sequences of character strings and symbols, known as Regular Expressions (RegExs), offer an alternative to represent complex patterns from the texts and could be used jointly with the most commonly used classification algorithms for accurate text classification. Thus, a classification algorithm can label test texts when RegExs produce no matches. This work proposes a method that combines automatically-generated RegExs and supervised algorithms for classifying clinical texts. RegExs are automatically generated using alignment algorithms in a supervised manner, filtering out those that do not meet a minimum confidence threshold and do not contain specific keywords for the classification problem. At prediction time, our method assigns the class of the most confident RegEx that matches a test text. When no RegExs matches a test text, a supervised algorithm assigns a class. Three clinical datasets with textual information on obesity and smoking habits were used to assess the performance of four classifiers based on Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), and Bidirectional Encoder Representations from Transformers (BERT). Classification results indicate that our method, on average, improved the classifiers’ performance by up to 12% in all performance metrics. These results show the ability of our method to generate confident RegExs that capture representative patterns from the texts for use with supervised algorithms.
ICU Patients’ Pattern Recognition and Correlation Identification of Vital Parameters Using Optimized Machine Learning Models

Early detection of patient deterioration in the Intensive Care Unit (ICU) can play a crucial role in improving patient outcomes. Conventional severity scales currently used to predict patient deterioration are based on a number of factors, the majority of which consist of multiple investigations. Recent advancements in machine learning (ML) within the healthcare domain offer the potential to alleviate the burden of continuous patient monitoring. In this study, we propose an optimized ML model designed to leverage variations in vital signs observed during the final 24 hours of an ICU stay for outcome predictions. Further, we elucidate the relative contributions of distinct vital parameters to these outcomes The dataset compiled in real-time encompasses six pivotal vital parameters: systolic (0) and diastolic (1) blood pressure, pulse rate (2), respiratory rate (3), oxygen saturation (SpO2) (4), and temperature (5). Of these vital parameters, systolic blood pressure emerges as the most significant predictor associated with mortality prediction. Using a fivefold cross-validation method, several ML classifiers are used to categorize the last 24 hours of time series data after ICU admission into three groups: recovery, death, and intubation. Notably, the optimized Gradient Boosting classifier exhibited the highest performance in detecting mortality, achieving an area under the receiver-operator curve (AUC) of 0.95. Through the integration of electronic health records with this ML software, there is the promise of early notifications regarding adverse outcomes, potentially several hours before the onset of hemodynamic instability.
Evidence-Based Argumentation and Its Incremental Semantics

Arguments cannot be evaluated in isolation; their reliability hinges on the evidence that supports them. However, in some extensions to Dung’s seminal work, the crucial role that evidence plays has not been adequately captured. To address this issue, this paper proposes an extension of Dung’s abstract argumentation framework that includes a set of evidence and the concept of evidential support. Under this framework, arguments derive support from evidence and can then defeat their opponents. Moreover, the incremental semantics of the framework explicate the fallibility and cumulative nature of evidence. It largely increases the expressive power of formal argumentation.
Syntax Tree Constrained Graph Network for Visual Question Answering

Visual Question Answering (VQA) aims to automatically answer natural language questions related to given image content. Existing VQA methods integrate vision modeling and language understanding to explore the deep semantics of the question. However, these methods ignore the significant syntax information of the question, which plays a vital role in understanding the essential semantics of the question and guiding the visual feature refinement. To fill the gap, we suggested a novel Syntax Tree Constrained Graph Network (STCGN) for VQA based on entity message passing and syntax tree. This model is able to extract a syntax tree from questions and obtain more precise syntax information. Specifically, we parse questions and obtain the question syntax tree using the Stanford syntax parsing tool. From the word level and phrase level, syntactic phrase features and question features are extracted using a hierarchical tree convolutional network. We then design a message-passing mechanism for phrase-aware visual entities and capture entity features according to a given visual context. Extensive experiments on VQA2.0 datasets demonstrate the superiority of our proposed model.
Quantitative comparison of the computational complexity of optical, digital and hybrid neural network architectures for image classification tasks

By implementing neuromorphic paradigms in processing visual information, machine learning became crucial in an ever-increasing number of applications of our everyday lives, ever more performing but also computationally demanding. While a pre-processing of the information passively in the optical domain, before optical-electronic conversion, can reduce the computational requirements for a machine learning task, a comprehensive analysis of computational requirements for hybrid optical-digital neural networks is thus far missing. In this work we critically compare and analyze the performance of different optical, digital and hybrid neural network architectures with respect to their classification accuracy and computational requirements for analog classification tasks of different complexity. We show that certain hybrid architectures exhibit a reduction of computational requirements of a factor &gt;10 while maintaining their performance. This may inspire a new generation of co-designed optical-digital neural network architectures, aimed for applications that require low power consumption like remote sensing devices.
DLT and PIL from the Perspective of Liechtenstein

Blockchain, Distributed Ledger Technology (DLT) and tokens based on these technologies come in different forms and shapes. Some jurisdictions have already developed specific legislation or have made selective legal adaptations to provide legal certainty for the emerging ‘Token Economy’. Liechtenstein has introduced a comprehensive legal framework, which covers a wide range of possible applications. Some crypto enthusiasts might follow the spirit of ‘code is law’ and may not intend digital transactions to be governed by any law at all. Nevertheless, when it comes to judicial proceedings, the court will have to determine the law applicable to the specific case. Due to the non-uniform characterisation of crypto assets and digital transactions in different legal systems, there are uncertainties regarding the legal consequences. It is of utmost interest to the market participants to achieve the applicability of the law of a jurisdiction that recognises the intended legal effects of token transactions. It is the role of Private International Law to determine the applicable law. The intangible and decentralised nature of blockchain-based crypto assets hinders the search for connecting factors within the categories of conflicts of laws. This chapter describes Liechtenstein’s innovative legal framework for the Token Economy, which also covers the issuance and transfer of blockchain-based uncertificated securities. Not all situations can be clearly classified within the existing rules of PIL. This ultimately leads to the situation that the choice of the applicable law will be crucial to provide legal certainty.
A High-Performance MST Implementation for GPUs

Finding a minimum spanning tree (MST) is a fundamental graph algorithm with applications in many fields. This paper presents ECL-MST, a fast MST implementation designed specifically for GPUs. ECL-MST is based on a parallelization approach that unifies Kruskal's and Borůvka's algorithm and incorporates new and existing optimizations from the literature, including implicit path compression and edge-centric operation. On two test systems, it outperforms leading GPU and CPU codes from the literature on all of our 17 input graphs from various domains. On a Titan V GPU, ECL-MST is, on average, 4.6 times faster than the next fastest code, and on an RTX 3080 Ti GPU, it is 4.5 times faster. On both systems, ECL-MST running on the GPU is roughly 30 times faster than the fastest parallel CPU code.
Towards Hierarchical Clustered Federated Learning with Model Stability on Mobile Devices

Clustered federated learning (CFL) has proved to be an effective way to alleviate the non-IID (not independently and identically distributed) data challenge, which severely restricts the wider application of federated learning. However, existing approaches either lack adaptability, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , they require an additional number of clusters as a guide when clustering, or lack effectiveness in terms of communication. In this paper, we explore the differences in the ability of different layers in a model to represent non-IID data, and propose a hierarchical CFL approach, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HiCFL</i> , which considers both adaptivity and communication efficiency. The improvement of communication efficiency is due to our proposed novel concept of model stability, which characterizes the variation of model weights during training. Based on model stability, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HiCFL</i> can find the proper time to bi-partition the clusters of mobile devices in a hierarchical manner more quickly. We conduct extensive experiments based on popular datasets with various non-IID data settings. The results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HiCFL</i> achieves excellent performance effectiveness and efficiency. Compared to state-of-the-art approaches, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HiCFL</i> can improve the model accuracy by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2.0\% \sim 9.0\%$</tex-math></inline-formula> , while reducing the communication overheads by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$27.3\% \sim 80.6\%$</tex-math></inline-formula> .
Performance Prediction of a Dual-axis Tracking Solar Trough Collector Based on Artificial Neural Network

Abstract A dual-axis tracking parabolic trough solar collector, using a certain straight-trough tube, was set up to experimentally investigate the heat collecting performance. An artificial neural network(ANN) model was developed. Experimental data were used to train and predict the mean temperature of Heat transfer fluid in the solar trough collector based on the developed model. The Levenberg-Marquardt (LM) method was also applied to optimize the weights and thresholds for the classic BP Newton algorithm, providing an ANN model with 9 hidden nodes and 30,000 training times. The predicted values are all in good agreement with the experimental data, with a mean relative error of 0.21% and a maximum error of 1.2%. In comparison, the mean relative error of the one-dimensional steady-state model reaches 1.07%. It indicates that the ANN exhibits excellent performance in predicting the export temperature of the solar collector with a specific flow rate of Heat transfer fluid. This ANN model is promising to predict the performance of solar trough collectors under different operating and environmental conditions.
Implementasi Pembelajaran Tarjamah Melalui Whatsappgroup Bagi Siswa Kelas VII MTs Muhammadiyah Cambajawaya Gowa Sulawesi Selatan

The purpose of this study is 1. To find out how the implementation of learning through Whats App Group (WAG) for students VII MTs Muhammadiyah Cambajawaya Gowa, South Sulawesi. 2. To find out the supporting and inhibiting factors for the Implementation of Learning Tarjamah through Whats App Group for Class VII students of MTs Muhammadiyah Cambajawaya Gowa, South Sulawesi. 3. To find out the efforts made in the implementation of Tarjamah learning through Whats App Group (WAG) for Class VII students of MTs Muhammadiyah Cambajawaya Gowa, South Sulawesi. The type of research used is qualitative research. The subject of this research is the seventh grade teacher of MTs Muhammadiyah Camabajawaya Gowa South Sulawesi. The data collection technique used is the observation technique. interviews, documentation. The results showed that the 1. Application of tarjamah learning through Whats App Group was ineffective and less flexible because there were several obstacles. 2. There ara factors that influence namely signal interference, cellphone memory full of lack of interaction, difficulty in knowing seriousness in learning, lack of student learning motivation, lack of online learning support facilities, and difficult to understand the material provided, 3. Efforts made to overcome the obstacles that occur from the use of Whats App Group as a medium in online learning it is suggested that teachers are able to make variations in learning, especially by using features in the Whats App Group.
Land Registry System Using Smart Contract of Blockchain Technology

The existing setup for land registration is very slow, inconvenient, and involves a lot of vulnerabilities and paperwork that give rise to disputes and corruption. It also involves a lot of intermediaries like property dealers, who put trust in the land registration system, which leads to additional expenses. This paper discusses a proposed secure land registry system, based on the Ethereum blockchain. The Ethereum blockchain has great potential for storing all the transactions that have taken place during the process of land ownership transfer in a secure and immutable way. The proposed model can automatically trigger various events like the registration of buyers and sellers, the addition of land, approval of requested land, and the transfer of funds by using the concept of smart contracts in blockchain technology. The decentralized, immutable, and transparent features of blockchain technology make the process of land registration secure, cost-effective, reliable, and flexible. It greatly reduces the chances of deception in the transfer of ownership along with tracking of land ownership transfers. It will also benefit parties involved in the land registration process, such as buyers, sellers, and land inspectors, to transfer land ownership from the seller to the new buyer.
QuardTropy: Detecting and Quantifying Unauthorized Information Leakage in Hardware Designs using g-entropy

Ensuring the safeguarding of sensitive information from unauthorized disclosure is a core aspect of secure hardware. In modern SoC designs, information flow security (IFS) may be compromised by designer mistakes or maliciously-inserted functions at RTL/gate-level design abstractions. Developing unified IFS verification solutions that are scalable across abstraction levels and threat models, however, faces numerous challenges. In this paper, we provide the first formulation of the generalized information theoretic metric ‘g-entropy’ for hardware designs which measures the vulnerability of a design asset to leakage. Additionally, we propose QuardTropy, a novel unified IFS verification and quantification framework, which uses g-entropy to model and quantify unauthorized information leakage instances. The experimental results show that QuardTropy can successfully detect such unauthorized leakage paths, by finding outlier values in g-entropy compared to those of nominal paths, in various hardware design benchmarks regardless of their structural features.
An Evaluation Method for Large Language Models’ Code Generation Capability

Large language models are becoming increasingly popular in various professional fields. One of their applications is providing code suggestions. However, the differences in code generation capabilities of different large language models and the problems they may make in giving code suggestions have not been well studied. This paper proposes a method for evaluating the code generation capabilities of large language models and applies it to several commonly used models, including ChatGPT, Claude, Spark, and Bing AI. Through experimental evaluation and data analysis, we find that search-based large language models, such as Bing AI, exhibit stronger code generation capabilities than pre-trained models, such as ChatGPT, Claude, and Spark. We also find that the current large language models possess strong natural language understanding abilities, and errors in code suggestions are more likely to be due to code problems rather than understanding problems.