Reviewerly/exHarmony huggingface dataset:
An extended proximity relation and quantified aggregation for designing robust fuzzy query engine

In this article, we propose a novel model of a robust fuzzy query engine that addresses vagueness in data and users’ requirements. It aims to assist users in recommending similar products or services by retrieving the most suitable entities when the limitations of queries and recommendation approaches are recognized. The proposed fuzzy engine model considers various complex aspects, including imprecise preferences explained by linguistic terms, uncertain data in datasets, connections among elementary requirements, and the lack of historical data necessary for personalized recommendations. To achieve this goal, we propose a similarity matching based on the extended proximity relation and an adapted conformance measure for elementary requirements. In this direction, a new monotonicity property for proximity relation is introduced to ensure consistency in similarities among ordinal categorical data (including binary data) expressed by crisp or fuzzy numbers. Therefore, the conformance measure used to evaluate the similarity between user requirements and attribute values is expressed as a fuzzy number. Next, we propose a quantified aggregation of elementary requirements by strictly monotone fuzzy relative quantifiers. The flexibility is further extended by a convex combination of possibility and necessity measures. A hotel selection experiment is being carried out to explore the potential of the proposed fuzzy query engine. Finally, the limitations and usefulness of the proposed approach are addressed.
Constructing the three-dimensional extinction density maps using V-net

ABSTRACT One of the major challenges we face is how to quickly and accurately create the three-dimensional (3D) density distributions of interstellar dust in the Milky Way using extinction and distance measurements of large samples of stars. In this study, we introduce a novel machine-learning approach that utilizes a convolution neural network, specifically a V-net, to infer the 3D distribution of dust density. Experiments are performed within two regions located towards the Galactic anticentre. The neural network is trained and tested using 10 000 simulations of dust density and line-of-sight extinction maps. Evaluation of the test sample confirms the successful generation of dust density maps from extinction maps by our model. Additionally, the performance of the trained network is evaluated using data from the literature. Our results demonstrate that our model is capable of capturing detailed dust density variations and can recover dust density maps while reducing the ‘fingers of god’ effect. Moving forward, we plan to apply this model to real observational data to obtain the fine distribution of dust at large and small scales in the Milky Way.
CFAuditChain: Audit BlockChain Based On Cuckoo Filter

Abstract Log blockchain can be used to ensure the integrity of log data. However, current methods are facing the problems of throughput mismatch and rough audit granularity. Packaging multiple logs to generate integrity proofs improves throughput but reduces audit granularity, and the auditor can only locate the tampering log packet instead of specific records. This paper proposes CFAuditChain, an audit blockchain based on a cuckoo filter, where the proof of existence (PoE) of the single log is calculated while calculating the integrity proof of packet logs. The PoE is saved in the cuckoo filter and stored using blockchain for immutability. Therefore, the auditor can verify the credibility of each log based on the filter when the integrity proof of a log packet does not match. The theoretical analysis and experimental results show that CFAuditChain provides the granularity of audit logs down to the records level at an acceptable cost.
Identity-Based Encryption with (Almost) Tight Security in the Multi-instance, Multi-ciphertext Setting

Abstract We construct an identity-based encryption (IBE) scheme that is tightly secure in a very strong sense. Specifically, we consider a setting with many instances of the scheme and many encryptions per instance. In this setting, we reduce the security of our scheme to a variant of a simple assumption used for a similar purpose by Chen and Wee (CRYPTO 2013, Springer, 2013). The security loss of our reduction is $$\textbf{O} (k)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mi>k</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> (where $$k $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>k</mml:mi> </mml:math> is the security parameter). Our scheme is the first IBE scheme to achieve this strong flavor of tightness under a simple assumption. Technically, our scheme is a variation of the IBE scheme by Chen and Wee. However, in order to “lift” their results to the multi-instance, multi-ciphertext case, we need to develop new ideas. In particular, while we build on (and extend) their high-level proof strategy, we deviate significantly in the low-level proof steps.
To the 75th Anniversary of Academician of the Russian Academy of Sciences Evgeny Lvovich Nasonov

On September 4, 2023, the 75th anniversary of the largest scientist, clinician and innovator, a talented teacher in the field of rheumatology, an outstanding healthcare organizer, scientific director of the Federal State Budgetary Scientific Institution “V.A. Nasonova Research Institute of Rheumatology”, Honored Scientist of the Russian Federation, Academician of the Russian Academy of Sciences Evgeny Lvovich Nasonov.
Line segment detection algorithm in image extraction improvement study

In recent years, image processing technology has been developing and maturing, but due to the influence of many interfering factors in the acquisition process, there is a large amount of redundant information in the images obtained. The line segment detection algorithm in image extraction needs to be improved. This study utilizes computer technology to improve the line segment detection technology, and designs a line segment detection algorithm based on the linear detection improvement. Firstly, based on the basic principle of straight line detection algorithm, for the problems of line segment breakage and missing in straight line detection, RGB three-channel grayscale map is applied to detect line segments. Then the detected line segments are connected, merged and deleted. The test results show that the line segment detection algorithm improved based on straight line detection has the highest accuracy rate of 94.50 %, and the average processing time per image is also the lowest at 0.2 s. The algorithm runs faster at 0.25 s and has a higher F-value. It is able to detect the boundaries of a variety of rectangular targets, using the improved line segment detection algorithm has a wide range of applicability, lower error rate, and strong anti-interference ability. The improved line segment detection algorithm has a greater advantage in rectangular target extraction for document, text and book type images.
A penalized criterion for selecting the number of clusters for K-medians

Clustering is a usual unsupervised machine learning technique for grouping the data points into groups based upon similar features. We focus here on unsupervised clustering for contaminated data, i.e in the case where K-medians algorithm should be preferred to K-means because of its robustness. More precisely, we concentrate on a common question in clustering: how to chose the number of clusters? The answer proposed here is to consider the choice of the optimal number of clusters as the minimization of a penalized criterion. In this paper, we obtain a suitable penalty shape for our criterion and derive an associated oracle-type inequality. Finally, the performance of this approach with different types of K-medians algorithms is compared on a simulation study with other popular techniques. All studied algorithms are available in the R package Kmedians on CRAN.
Taiyi: a bilingual fine-tuned large language model for diverse biomedical tasks

Most existing fine-tuned biomedical large language models (LLMs) focus on enhancing performance in monolingual biomedical question answering and conversation tasks. To investigate the effectiveness of the fine-tuned LLMs on diverse biomedical natural language processing (NLP) tasks in different languages, we present Taiyi, a bilingual fine-tuned LLM for diverse biomedical NLP tasks.We first curated a comprehensive collection of 140 existing biomedical text mining datasets (102 English and 38 Chinese datasets) across over 10 task types. Subsequently, these corpora were converted to the instruction data used to fine-tune the general LLM. During the supervised fine-tuning phase, a 2-stage strategy is proposed to optimize the model performance across various tasks.Experimental results on 13 test sets, which include named entity recognition, relation extraction, text classification, and question answering tasks, demonstrate that Taiyi achieves superior performance compared to general LLMs. The case study involving additional biomedical NLP tasks further shows Taiyi's considerable potential for bilingual biomedical multitasking.Leveraging rich high-quality biomedical corpora and developing effective fine-tuning strategies can significantly improve the performance of LLMs within the biomedical domain. Taiyi shows the bilingual multitasking capability through supervised fine-tuning. However, those tasks such as information extraction that are not generation tasks in nature remain challenging for LLM-based generative approaches, and they still underperform the conventional discriminative approaches using smaller language models.
Sustainable deployment of clinical prediction tools—a 360° approach to model maintenance

As the enthusiasm for integrating artificial intelligence (AI) into clinical care grows, so has our understanding of the challenges associated with deploying impactful and sustainable clinical AI models. Complex dataset shifts resulting from evolving clinical environments strain the longevity of AI models as predictive accuracy and associated utility deteriorate over time.Responsible practice thus necessitates the lifecycle of AI models be extended to include ongoing monitoring and maintenance strategies within health system algorithmovigilance programs. We describe a framework encompassing a 360° continuum of preventive, preemptive, responsive, and reactive approaches to address model monitoring and maintenance from critically different angles.We describe the complementary advantages and limitations of these four approaches and highlight the importance of such a coordinated strategy to help ensure the promise of clinical AI is not short-lived.
What is a blink? Classifying and characterizing blinks in eye openness signals

Blinks, the closing and opening of the eyelids, are used in a wide array of fields where human function and behavior are studied. In data from video-based eye trackers, blink rate and duration are often estimated from the pupil-size signal. However, blinks and their parameters can be estimated only indirectly from this signal, since it does not explicitly contain information about the eyelid position. We ask whether blinks detected from an eye openness signal that estimates the distance between the eyelids (EO blinks) are comparable to blinks detected with a traditional algorithm using the pupil-size signal (PS blinks) and how robust blink detection is when data quality is low. In terms of rate, there was an almost-perfect overlap between EO and PS blink (F1 score: 0.98) when the head was in the center of the eye tracker's tracking range where data quality was high and a high overlap (F1 score 0.94) when the head was at the edge of the tracking range where data quality was worse. When there was a difference in blink rate between EO and PS blinks, it was mainly due to data loss in the pupil-size signal. Blink durations were about 60 ms longer in EO blinks compared to PS blinks. Moreover, the dynamics of EO blinks was similar to results from previous literature. We conclude that the eye openness signal together with our proposed blink detection algorithm provides an advantageous method to detect and describe blinks in greater detail.
Fuzzy cognitive maps for municipal governance improvement

This paper applies Fuzzy Cognitive Maps (FCMs) to understand the diverse behavior of municipal governments in Ecuador to find common elements that influence the well-being of citizens in the short and long term. Information gathering was conducted in two stages: in the first one, a group of 16 national experts was consulted to develop the initial FCM; in the second stage, local experts from 220 municipalities were interviewed to collect information on the general validity of initial FCMs and specific values given to concepts and relationships in their municipalities. Results show the importance of certain concepts for long-term municipal performance, such as the need for a competitive entrepreneurial sector, improving human resources in the municipality, and, particularly, having a competent mayor with leadership skills and a forward-looking vision that enables the development of municipal projects required to reach an efficient and equitable coverage of goods and services throughout the city. Through the application of genetic algorithms, the FCM was calibrated to ascertain the long-term dynamics of municipal development and the optimal values of the concepts that would optimize the attainment of the set objectives. The derived outcomes suggest the desirability of the maintenance of, in principle, unwanted structures like financial transfers from the central government and the need to exploit natural resources to attain urban development.
Klasik ve Bulanık Kenar Algılama Yöntemlerinin Karşılaştırılması

Edge detection is one of the challenging problems in image processing. Four different classical edge detection methods—Sobel, Prewitt, Roberts, and Canny—and type-1 and type-2 fuzzy logic-based edge detection methods are applied to analyze two separate datasets with various properties. The datasets are STARE which contains medical images of the retina and BIPED which contains images of the street. Furthermore, two separate hybrid fuzzy logic methods are implemented. The type-1 and type-2 fuzzy inference techniques are combined to produce the hybrid-1 and hybrid-2 approaches, using the "AND" and "OR" logic operators. We compare the simulation results for each technique using three different image quality metrics. These are Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR), and Structural Similarity Index (SSIM). The type-2 fuzzy technique outperformed the hybrid-1 fuzzy method in visual quality metrics comparison, demonstrating superior blood vessel recognition on the STARE retinal image dataset—a dataset that more closely resembles the human visual system. Using the BIPED street image dataset, the hybrid-1 fuzzy approach outperformed the Roberts method. The hybrid-1 fuzzy technique showed good results in the second order for both kinds of datasets. Any data and general applications can take advantage of it.
Generalization of Deep-Learning Models for Classification of Local Distance Earthquakes and Explosions across Various Geologic Settings

Abstract Although accurately classifying signals from earthquakes and explosions at local distance (&amp;lt;250 km) remains an important task for seismic network operations, the growing volume of available seismic data presents a challenge for analysts using traditional source discrimination techniques. In recent years, deep-learning models have proven effective at discriminating between low-magnitude earthquakes and explosions measured at local distances, but it is not clear how well these models are capable of generalizing across different geological settings. To address the issue of generalization between regions, we train deep-learning models (convolutional neural networks [CNNs]) on time–frequency representations (scalograms) of three-component earthquake and explosion signals from eight different regions in the continental United States. We explore scenarios where models are trained on data from all regions, individual regions, or all but one region. We find that although CNN models trained on individual regions do not necessarily generalize well across different settings, models trained on multiple regions that include diverse path coverage generalize to new regions, with station-level accuracy of up to 90% or more for data sets from unseen regions. In general, CNN-based discrimination models significantly outperform models based on uncorrected P/S ratio (measured in the 10–18 Hz frequency band), even when CNN models are tested on data from entirely unseen regions.
To the 75th Anniversary of Academician of the Russian Academy of Sciences Gennady Petrovich Kotelnikov

On January 11, 2024, the largest scientist in the field of traumatology and orthopedics, a talented organizer of healthcare and higher education, a public figure, laureate of the State Prize of the Russian Federation in the field of science and technology, three times laureate of the Government of the Russian Federation, Honored Scientist of the Russian Federation, academician, turned 75 RAS to Gennady Petrovich Kotelnikov.
Constructing polynomial libraries for reservoir computing in nonlinear dynamical system forecasting

Reservoir computing is an effective model for learning and predicting nonlinear and chaotic dynamical systems; however, there remains a challenge in achieving a more dependable evolution for such systems. Based on the foundation of Koopman operator theory, considering the effectiveness of the sparse identification of nonlinear dynamics algorithm to construct candidate nonlinear libraries in the application of nonlinear data, an alternative reservoir computing method is proposed, which creates the linear Hilbert space of the nonlinear system by including nonlinear terms in the optimization process of reservoir computing, allowing for the application of linear optimization. We introduce an implementation that incorporates a polynomial transformation of arbitrary order when fitting the readout matrix. Constructing polynomial libraries with reservoir-state vectors as elements enhances the nonlinear representation of reservoir states and more easily captures the complexity of nonlinear systems. The Lorenz-63 system, the Lorenz-96 system, and the Kuramoto-Sivashinsky equation are used to validate the effectiveness of constructing polynomial libraries for reservoir states in the field of state-evolution prediction of nonlinear and chaotic dynamical systems. This study not only promotes the theoretical study of reservoir computing, but also provides a theoretical and practical method for the prediction of nonlinear and chaotic dynamical system evolution.
Readout-Induced Suppression and Enhancement of Superconducting Qubit Lifetimes

It has long been known that the lifetimes of superconducting qubits suffer during readout, increasing readout errors. We show that this degradation is due to the anti-Zeno effect, as readout-induced dephasing broadens the qubit so that it overlaps "hot spots" of strong dissipation, likely due to two-level systems in the qubit's bath. Using a flux-tunable qubit to probe the qubit's frequency-dependent loss, we accurately predict the change in lifetime during readout with a new self-consistent master equation that incorporates the modification to qubit relaxation due to measurement-induced dephasing. Moreover, we controllably demonstrate both the Zeno and anti-Zeno effects, which can explain both suppression and the rarer enhancement of qubit lifetimes during readout.
Image Division Using Threshold Schemes with Privileges

Threshold schemes are used among cryptographic techniques for splitting visual data. Such methods allow the generation of a number of secret shares, a certain number of which need to be assembled in order to reconstruct the original image. Traditional techniques for partitioning secret information generate equal shares, i.e., each share has the same value when reconstructing the original secret. However, it turns out that it is possible to develop and use partitioning protocols that allow the generation of privileged shares, i.e., those that allow the reconstruction of secret data in even smaller numbers. This paper will therefore describe new information sharing protocols that create privileged shares, which will also use visual authorization codes based on subject knowledge to select privileged shares for secret restoration. For the protocols described, examples of their functioning will be presented, and their complexity and potential for use in practical applications will be determined.
Impressive predictive model for Breast Cancer based on Machine Learning

INTRODUCTION: Breast cancer is a major health concern for women all over the world.&#x0D; OBJECTIVES: In order to reduce mortality rates and provide the most effective treatment, Histopathology image prognosis is essential. When a pathologist examines a biopsy specimen under a microscope, they are engaging in histopathology. The pathologist looks for the picture, determines its type, labels it, and assigns a grade.&#x0D; METHODS: Tissue architecture, cell distribution, and cellular form all play a role in determining whether a histopathological scan is benign or malignant. Manual picture classification is the slowest and most error-prone method. Automated diagnosis based on machine learning is necessary for early and precise diagnosis, but this challenge has prevented it from being addressed thus far. In this study, we apply curvelet transform to a picture that has been segmented using k-means clustering to isolate individual cell nuclei.&#x0D; RESULTS: We analysed data from the Wisconsin Diagnosis Breast Cancer database for this article in the context of similar studies in the literature.&#x0D; CONCLUSION: It is demonstrated that compared to another machine learning algorithm, the IICA-ANN IICA-KNN and IICA-SVM-KNN method using the logistic algorithm achieves 98.04% accuracy.
Assessing non-Markovian dynamics through moments of the Choi state

Non-Markovian effects in open quantum system dynamics usually manifest the backflow of information from the environment to the system, indicating complete-positive divisibility breaking of the dynamics. We provide a criterion for witnessing such non-Markovian dynamics exhibiting information backflow, based on partial moments of Choi matrices. The moment condition determined by the positive semi-definiteness of a matrix does not hold for a Choi state describing non-Markovian dynamics. We then present some explicit examples in support of our proposed non-Markovianity detection scheme. Finally, a moment-based measure of non-Markovianity for unital dynamics is formulated.
Discrimination and certification of unknown quantum measurements

We study the discrimination of von Neumann measurements in the scenario when we are given a reference measurement and some other measurement. The aim of the discrimination is to determine whether the other measurement is the same as the first one. We consider the cases when the reference measurement is given without the classical description and when its classical description is known. Both cases are studied in the symmetric and asymmetric discrimination setups. Moreover, we provide optimal certification schemes enabling us to certify a known quantum measurement against the unknown one.