Reviewerly/exHarmony huggingface dataset:
Interpretable machine learning text classification for clinical computed tomography reports – a case study of temporal bone fracture

Machine learning (ML) has demonstrated success in classifying patients' diagnostic outcomes in free-text clinical notes. However, due to the machine learning model's complexity, interpreting the mechanism behind classification results remains difficult. We investigated interpretable representations of text-based machine learning classification models. We created machine learning models to classify temporal bone fractures based on 164 temporal bone computed tomography (CT) text reports. We adopted the XGBoost, Support Vector Machine, Logistic Regression, and Random Forest algorithms. To interpret models, we used two major methodologies: (1) We calculated the average word frequency score (WFS) for keywords. The word frequency score shows the frequency gap between positively and negatively classified cases. (2) We used Local Interpretable Model-Agnostic Explanations (LIME) to show the word-level contribution to bone fracture classification. In temporal bone fracture classification, the random forest model achieved an average F1-score of 0.93. WFS revealed a difference in keyword usage between fracture and non-fracture cases. Additionally, LIME visualized the keywords' contributions to the classification results. The evaluation of LIME-based interpretation achieved the highest interpreting accuracy of 0.97. The interpretable text explainer can improve physicians' understanding of machine learning predictions. By providing simple visualization, our model can increase the trust of computerized models. Our model supports more transparent computerized decision-making in clinical settings.
Bibliometric analysis of endocrinology, diabetes and metabolism research in South Asia from (2012–2021): Comparison with five developed countries

In the present study the research output of the South Asian region (India, Pakistan, Bangladesh, Sri Lanka, Nepal, Maldives, and Bhutan) in endocrinology, diabetes and metabolism (EDM) is highlighted. It was compared with five scientifically advanced countries i.e. USA, UK, Italy, Japan and China.On September 13, 2022, the data was retrieved from the Scopus database. The analysis focused on the number of publications, total citations (TC), citations per paper (CPP), field-weighted citations impact (FWCI), and degree of international collaboration.In South Asia, India produced the highest number of publications (n = 7048), followed by Pakistan (n = 799), Bangladesh (n = 345), Sri Lanka (n = 256), Nepal (n = 144), Maldives (n = 12) and Bhutan (n = 4). The highest CPP (n = 19.4) and FWCI (n = 1.18) was recorded for Sri Lanka. Furthermore, USA (n = 64022), China (n = 23991), UK (n = 21449), Italy (n = 18884), and Japan (n = 12875), published the highest number of documents with the highest citations and FWCI in the world. It was noted that India published the highest number of documents (n = 47.28%) in the quartiles (Q) 6 and Q7. Pakistan produced the highest number of documents (n = 64.22%) in the top 50% of journals (Q1 to Q5). South Asian countries produced 8332 publications, with 130382 TC, 15.6 CPP and 1.06 FWCI. Importantly 46.50% of documents from South Asian countries were published in Q6 and Q7 journals. In contrast USA, UK, Italy, Japan and China published 77% documents in top 50% journals.Although the South Asian research publications have increased yearly (from 2012 to 2021), but approximately 50% of the South Asian output were in the lower quartile journals. Consequently, significant measures are needed to improve the quantity and quality of EDM research produced in South Asian coutries.
The Design and Implementation of a Secure File Storage on the Cloud using Hybrid Cryptography

In recent years, cloud computing has become a popular way of storing and sharing data. However, security concerns have been raised about the safety of sensitive information stored on the cloud. Hybrid cryptography, which combines symmetric and asymmetric encryption, has been proposed as a solution to these concerns. This paper proposes a new hybrid cryptography approach for secure file storage on the cloud regardless of the type of deployment model i.e., either public cloud, private cloud, or hybrid cloud. The proposed approach uses a combination of the Advanced Encryption Standard (AES) algorithm, Triple Data Encryption Standard (DES) algorithm, and Rivest cipher 6 (RC6) algorithms are used to provide block-wise security to data. LSB steganography technique is introduced for key information security.
A traceable and revocable multi-authority attribute-based access control scheme for mineral industry data secure storage in blockchain

Abstract With the rapid advancements of the mineral industry, the data generated by this industry chain have increased dramatically. To reduce the growing pressure of data storage and security risks, we design a credible on-chain and off-chain collaborative dual storage system that integrates blockchain technology and Interplanetary file system (IPFS), also construct a traceable and revocable multi-authority ciphertext-policy attributed-based encryption (CP-ABE) algorithm to meet the demand of privacy protection and dynamic fine-grained access control. Furthermore, the multi-authority layered authorization with a central authority model distributes system overhead while enabling the platform can be regulated. More importantly, our scheme achieves accurate trace of the malicious users by white-box traceability and capable of implementing indirect immediate user and attribute revocation without requiring key or ciphertext updates. Finally, the proposed scheme is indistinguishably secure under chosen-plaintext attack (IND-CPA) in the standard model. And the performance analysis demonstrates that our scheme is feature-rich, practical and efficient.
Promotion Mechanism to Information Protection through Virtual-Real Fusion Network

To address the increasingly serious information leakage problem, the quantitative mechanism of implementing information protection was explored using information fragmentation tools and the general mechanism of enhancing information protection strength was examined in the virtual-real fusion network. Information fragmentation protection is fragmenting the information content sufficiently and decentralizing the store address sufficiently to prevent others from illegally accessing the information content. The validation analysis using arithmetic examples proved the effectiveness and feasibility of the constructed model for improving information protection. Compared with the virtual network, the virtual-real fusion network can increase the information fragmentation protection strength strength to the infinite status.
“CONCRETE METAPHYSICS” OF THE SCIENCE OF THE PHILOSOPHER FR. PAVEL FLORENSKY

The name of Pavel Alexandrovich Florensky is widely known as a philosopher, an Orthodox priest who tragically died in 1937. It is little known that he is also an outstanding mathematician, physicist, electrical engineer, who founded a new scientific direction - materials science, who made a great contribution to the development of this direction. His works devoted to science contain many deep thoughts of philosophical content, relevant now. His, as he defined it - “concretely metaphysical” ideas, are of interest, both from the point of view of the philosophical approach and the applied content. In particular, he substantiated the geometric and physical meaning of imaginary numbers and put forward the idea of imaginary space; approach to physics and mathematics as a symbolic language; computing devices based on various physical laws. This article is devoted to these and his other ideas.
Estimating Time-To-Compromise for Industrial Control System Attack Techniques Through Vulnerability Data

Abstract When protecting the Industrial Control Systems against cyber attacks, it is important to have as much information as possible to allocate defensive resources properly. In this paper we estimate the Time-To-Compromise of different Industrial Control Systems attack techniques by MITRE ATT&amp;CK. The Time-To-Compromise is estimated using an equation that takes into consideration the vulnerability data that exists for a specific asset and category of vulnerability. The vulnerability data is derived from an Industrial Control Systems specific vulnerability dataset. As a result, we present the mapping of the attack techniques to assets and categories of vulnerability, which makes it possible to apply specific vulnerabilities to the technique. We also present the method of how to estimate the Time-To-Compromise of the techniques and finally the values of Time-To-Compromise. After mapping the attack techniques to assets and category of vulnerability we are able to estimate the Time-To-Compromise and discuss its trustworthiness.
PHYSICAL AND SUB-PHYSICAL PARADIGMS

Promising tasks in three dualistic metaphysical paradigms are noted. The prospects of the relational-statistical interpretation (interpretation) of quantum mechanics are indicated. In relational physics, the importance of studying the possibility of controlling gravity due to an electromagnetic field is noted. In the field-theoretic approach, it is necessary to investigate the possibility of developing a gauge theory of time. A way of constructing the geometry of praspinors based on unary systems of complex relations is proposed. The hypothesis of pre-physical reality and the prospect of creating binary geometrosubphysics are discussed.
Research on Low Illumination Image Enhancement Algorithm and Its Application in Driver Monitoring System

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The driver monitoring system (DMS) plays an essential role in reducing traffic accidents caused by human errors due to driver distraction and fatigue. The vision-based DMS has been the most widely used because of its advantages of non-contact and high recognition accuracy. However, the traditional RGB camera-based DMS has poor recognition accuracy under complex lighting conditions, while the IR-based DMS has a high cost. In order to improve the recognition accuracy of conventional RGB camera-based DMS under complicated illumination conditions, this paper proposes a lightweight low-illumination image enhancement network inspired by the Retinex theory. The lightweight aspect of the network structure is realized by introducing a pixel-wise adjustment function. In addition, the optimization bottleneck problem is solved by introducing the shortcut mechanism. Model performance comparison test results demonstrate that the Structure Similarity Index Measure index of the proposed model is 7.04% and 31.03% higher than that of Multi-Scale Retinex with Color Restoration and Contrast Limiting Adaptive Histogram Equalization, respectively. In the use case, the proposed model is capable to process videos with a resolution of 400×600 at a speed of 20fps on average, which meets the requirements of DMS video stream processing speed. Furthermore, a MobileNet based distraction state recognition network pre-trained on the SFD3 dataset is adopted as the back-end to verify its application in the DMS system. The results show that the accuracy in the driver's distracted behavior recognition with a low-light environment is improved by 75.39% compared to before use.&lt;/div&gt;&lt;/div&gt;
Mexican Sign Language Corpus: Towards an Automatic Translator

The development of the Sign Language Corpus has been motivated by its great utility and application to various purposes and research areas. However, some countries do not have their own Sign Language Corpus. Developing a corpus thereby benefits the community of people with speech disabilities in diverse areas such as education. Thus, the motivation to develop this work is to present an advance toward constructing an RGB-D corpus of Mexican Sign Language captured by a Kinect sensor. A total of 90,000 samples of 570 words and 30 phrases interpreted by 150 people who commonly use Mexican Sign Language were collected. Of the participants, 86 were women and 64 were men, aged between 12 and 60 years old. The Mexican Sign Language Corpus was recorded by signers from three different regions of the south of Mexico. The constructed corpus contains depth, color, point clouds, and human skeleton positions. Six hundred of the most used words were selected from 17 semantic fields, considering the variability in the movement of both hands. After training a neural network, the performance developed by the recognition system was 98.62%.
Extending the classical side-channel analysis framework to access-driven cache attacks

Cryptographic devices are vulnerable to side-channel attacks, which have attracted broad attention in the hardware security field. The side-channel analysis framework proposed at Eurocrypt 2009 has been widely adopted in academia, containing key elements such as points of interest, leakage models, distinguishers, and security metrics. This classical framework, however, is not intuitively compatible with recent micro-architectural attacks such as cache attacks, therefore, few attempts have been made to link them. In this paper, we extend the classical side-channel analysis framework and migrate its ideas to access-driven cache attacks. We find that cache attacks can be effectively incorporated into this framework. Specifically, we propose a leakage model called cache access pattern vector according to the cache timing leakage characteristics. Furthermore, we propose data preprocessing schemes such as noise reduction, dimensionality reduction, and vector expansion to implement efficient attacks. Subsequently, we proposed seven distinguishers in three categories: difference of means-based analysis, vector distance-based analysis, and mutual information-based analysis. Moreover, we attacked the last round of the AES fast software implementation algorithm based on the Chipyard platform. According to the experimental results, all proposed methods can successfully extract the key, and five of them are comparable to mainstream cache analysis in attack efficiency. Finally, we evaluate the five efficient distinguishers under three different cache micro-architectures using guessing entropy. Based on the experimental environment of this paper, vector distance-based distinguishers have the best attack efficiency, and the difference of mean-based analysis has the worst. Surprisingly, the most general mutual information-based attacks can achieve excellent medium results in a few attack rounds without the help of static analysis tools. Meanwhile, the experimental results demonstrate that cache micro-architecture directly impacts the attack effect, in which the smaller cache line size benefits the attacks, while the random replacement policy increases the noise and difficulty of the attacks.
Dynamics of Pair of Entangled Spin-1/2 Particles and Quantification of the Dynamics in terms of Correlations

The dynamics of an identical pair of entangled spin-1/2 particles, both subjected to the same random magnetic field, are studied. The dynamics of the pure joint state of the pair are derived using stochastic calculus. An ensemble of such pure states is combined using the modified spin joint density matrix, and the joint relaxation time for the pair of spin-1/2 particles is obtained. The dynamics can be interpreted as a special kind of correlation involving the spatial components of the Bloch polarization vectors of the constituent entangled spin-1/2 particles.
Exploring the economics of conversational search sessions

Purpose Turn-taking is beneficial to conversational search success, but the increase in turns and time can also increase the cognitive load of the user. Therefore, in this research paper, the authors view conversational search sessions through the lens of economic theory and use the economic models of search to analyze the various costs and benefits of information-seeking interactions. Design/methodology/approach First, the authors built a cost-benefit model for conversational search sessions by defining action types and performing an intellectual mapping of actual sessions into sequences of these actions (using thematic analyses). The authors used the hypothesized cost and benefit actions (obtained from the user-system dialogs), along with the number of turns, utterances and time-related parameters, to propose the mathematical model. Next, the authors tested the model empirically by comparing the model scores to the user satisfaction and task success scores (collected through questionnaires). By representing each session as a bag of actions, the authors developed linear regression models to predict task success and user satisfaction. Findings Through feature analysis and significance testing, the authors identify the different parameters that contribute significantly to user satisfaction and task success scores. Error analysis shows that the model predicts task success and user satisfaction reasonably well, with the average prediction error being 0.5 for both (on a 5-point scale). Originality/value The authors' research is an initial step toward building a mathematical model for predicting user satisfaction and task success in conversational search sessions.
Theory and Practice of Relational-to-RDF Temporal Data Exchange and Query Answering

We consider the problem of answering temporal queries on RDF stores, in presence of atemporal RDFS domain ontologies, of relational data sources that include temporal information, and of rules that map the domain information in the source schemas into the target ontology. Our proposed practice-oriented solution consists of two rule-based domain-independent algorithms. The first algorithm materializes target RDF data via a version of data exchange that enriches both the data and the ontology with temporal information from the relational sources. The second algorithm accepts as inputs temporal queries expressed in terms of the domain ontology using a lightweight temporal extension of SPARQL, and ensures successful evaluation of the queries on the materialized temporally-enriched RDF data. To study the quality of the information generated by the algorithms, we develop a general framework that formalizes the relational-to-RDF temporal data-exchange problem. The framework includes a chase formalism and a formal solution for the problem of answering temporal queries in the context of relational-to-RDF temporal data exchange. In this article, we present the algorithms and the formal framework that proves correctness of the information output by the algorithms, and also report on the algorithm implementation and experimental results for two application domains.
Metrics for Machine Learning Models to Facilitate SOTIF Analysis in Autonomous Vehicles

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Machine Learning (ML) components are widely adopted in autonomous vehicles to perform tasks such as perception and planning. Despite the multiple uses of machine learning components and their benefits, incorrect outputs from machine learning components can compromise the safety of the system. The limitations of the machine learning algorithms and their acceptable level of performance that results in a reasonable level of residual risk are considered as a part of ISO 21448, the safety of the intended functionality (SOTIF) standard. Currently, to measure the performance of machine learning models, statistical metrics such as accuracy, recall, precision, and F1-measure are often used depending on the nature of the data and task. While these metrics help in understanding which machine learning model is better and can be chosen as a part of the vehicle’s architecture, they do not provide much information regarding safety, in particular, SOTIF. There is a need for new metrics to better assess safety corresponding to these machine learning models. The new metrics need to focus more if an incorrect output from the model results in crashes and near crashes and aid in proposing design changes that help to reduce the residual risk of the vehicle. To achieve this goal, in this paper we discuss the limitation of current metrics with an example architecture that uses machine learning models and propose new scenario-based metrics that help in better analysis of machine learning models for SOTIF.&lt;/div&gt;&lt;/div&gt;
Blockchain Technology In Healthcare Services

The recent ransomware attack on AIIMS (All India Institute of Medical Sciences) has highlighted internal system weaknesses in the healthcare sector, and blockchain technology has emerged as a potential solution to improve security and reduce the risk of future attacks. The decentralized blockchain technology makes it much more difficult for ransomware attackers to focus on a single point of failure. Additionally, blockchain technology provides recipients and data providers with the reassurance that their data has not been changed, enabling enterprises to have confidence and assurance in the integrity of their sensitive data. The sharing of data also restores control of data to its owners. The information is dispersed so that no one company can control it, yet it is still organized around the owner. Thamrin and Xu's research paper proposes a framework for healthcare data storage that includes hospital, city, and state blockchain networks. The proposed system uses a private cloud, but the researchers recommend a hybrid cloud for improved efficiency and adaptability. This innovative approach has the potential to enhance healthcare systems' data security, privacy, and accountability. However, a comprehensive analysis of the problem and an assessment of the potential effectiveness of blockchain technology is necessary before implementing any solutions.
Neural network operators with hyperbolic tangent functions

We determine the global errors occurring as a result of applying the method of approximate approximations to a function defined on a compact interval. By the method of extending a function to a wider interval, we obtain upper bounds on the error estimates in the uniform norm for continuous and differentiable functions by using these approximation tools. We extend this study to the bivariate case by constructing the associated approximate approximation neural network operators.
Fusing feature and output space for unsupervised domain adaptation on medical image segmentation

Abstract Image segmentation requires large amounts of annotated data. However, collecting massive datasets with annotations is difficult since they are expensive and labor‐intensive. The unsupervised domain adaptation (UDA) for image segmentation is a promising approach to address the label‐scare problem on the target domain, which enables the trained model on the source labeled domain to be adaptive to the target domain. The adversarial‐based methods encourage extracting the domain‐invariant features by training a domain discriminator to mitigate the domain gap. Existing UDA segmentation methods fail to obtain satisfied segmentation results as they only consider the global knowledge of output space while neglecting the local information of feature space. In this paper, a fusing feature and output (FFO) space method is proposed for UDA, which in the context of medical image segmentation. The proposed model is learned by training a more powerful domain discriminator, which considers features extracted from both feature space and output space. Extensive experiments carried out on several medical image datasets show the adaptation effectiveness of our approach in improving the segmentation performance.