Reviewerly/exHarmony huggingface dataset:
Combinations of L-Complex Fuzzy t-Norms and t-Conorms

This paper investigates the study of -complex fuzzy sets. The -complex fuzzy set, where &nbsp;is a completely distributive lattice, is a generalization of the complex fuzzy set. The fundamental set theoretic operations on -complex fuzzy sets are discussed properly, including -complex fuzzy complement, union and intersection. New procedures are presented to combine the novel concepts of -complex fuzzy -norms and -conorms and look into the conditions that lead to a comparable representation theorem. We have used the axiomatic method, in the sense that our underlying assumptions, especially about , are abstract; it can thus be ascertained to what extent our results apply to some new problem. On the other hand, our method shows that if mathematics, as we use it, is consistent, so is fuzziness, as we formulate it.
Novel O/W Emulsions by Utilizing a Vesicle/Disc Transformation of Polyether Modified Silicone

Emulsification is an important technology in the field of cosmetics and household products. Emulsions are in non-equilibrium state; therefore, the products vary depending on the preparation process, and their state changes with time. Furthermore, it is known empirically that different types of oils have different emulsification properties (preparation and stability). For these reasons, the variables in emulsification research are numerous and complicated to analyze. As a result, many industrial applications have had to rely on empirical rules. In this study, emulsions with a lamellar liquid crystalline phase as an adsorption layer at the emulsion interface were investigated. The characteristics of O/W emulsions formed with the excess solvent phases (aqueous and oil phases) separated from the lamellar liquid crystalline phase were investigated based on the phase equilibrium of the ternary system.
Retracted: Analysis of the Impact of the Traditional Literature Environment Based on Big Data Technology on Overseas Literature

[This retracts the article DOI: 10.1155/2022/2834363.].
Retracted: Application Model of Public Health Visual Art Creation Concept Based on Artificial Intelligence Technology in Venice Biennale

[This retracts the article DOI: 10.1155/2022/6546357.].
Retracted: Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

[This retracts the article DOI: 10.1155/2022/1359019.].
Retracted: Using an Optimized Learning Vector Quantization- (LVQ-) Based Neural Network in Accounting Fraud Recognition

[This retracts the article DOI: 10.1155/2021/4113237.].
Retracted: Feedforward Control Combined with 4F Management on Postoperative Nursing Effects and Motor Function of Meniscus Sports Injuries: Based on a Prospective Case Analysis

[This retracts the article DOI: 10.1155/2022/5447509.].
Retracted: Application of CNN Algorithm Based on Chaotic Recursive Diagonal Model in Medical Image Processing

[This retracts the article DOI: 10.1155/2021/6168562.].
Retracted: Determination of Bridge Prestress Loss under Fatigue Load Based on PSO-BP Neural Network

[This retracts the article DOI: 10.1155/2021/4520571.].
Retracted: Efficacy of Rosuvastatin Combined with rt-PA Intravenous Thrombolytic Therapy for Elderly Acute Ischemic Stroke Patients

[This retracts the article DOI: 10.1155/2022/9403693.].
Retracted: A Virtual Reality Algorithm for the Study of Clinical Efficacy of Sports Injury Rehabilitation Training

[This retracts the article DOI: 10.1155/2021/6725625.].
Retracted: Chinese Language Feature Analysis Based on Multilayer Self-Organizing Neural Network and Data Mining Techniques

[This retracts the article DOI: 10.1155/2021/4105784.].
Retracted: Construction and Application of Comprehensive Nursing Information Service Platform Based on Internet of Things Technology

[This retracts the article DOI: 10.1155/2022/7178531.].
Base Algorithm and Open Data of Auditing the e-Learning Digital Accessibility

Enhancement the methodology for assessing the digital accessibility of e-learning is an important condition for improving the quality of modern educational services. In order to develop a basic algorithm for auditing digital accessibility, the expert data from 173 e-learning resources (56 Massive Open Online Courses (MOOCs) in mathematics, 65 MOOCs in computer science and programming, 22 intra-university online courses in mathematics, computer science and programming, 30 MOOCs in cardiopulmonary resuscitation) were systematized and analyzed. The paper considers: methods and process of collecting expert data, the content of data sets, the procedure for empirical analysis of the results of testing the e-learning content accessibility. The structure of the proposed algorithm of auditing the digital accessibility includes the following stages: preparation for examination; automatic and expert testing of digital accessibility and generation of data sets; data analysis; formulating a final conclusion and recommendations for improving digital accessibility. Further use of the basic algorithm and data sets of audits can be useful for the development of accessible education, the training of e-learning specialists, and the strengthening of regulatory and control mechanisms in the field of education.&#x0D;
Retracted: Analysis of Body Behavior Characteristics after Sports Training Based on Convolution Neural Network

[This retracts the article DOI: 10.1155/2021/7006541.].
Retracted: Design and Research of Mathematics Teaching Intelligent Classroom Based on PCA-NN Algorithm

[This retracts the article DOI: 10.1155/2021/3884587.].
Retracted: Optimization and Simulation of Manuscript Management System Based on Fuzzy Genetic Neural Network

[This retracts the article DOI: 10.1155/2021/3155765.].
A Data Set for Fall Detection with Smart Floor Sensors

This article describes a data set of falls and activities of daily living recorded with a pressure floor sensor.These signals have been recorded under two settings, one constrained -with volunteers following a predefined protocol, and one unconstrained -where data were collected in a partner nursing home.Overall 157 hours of signal are made available along with 563 manually annotated falls and 333 manually annotated activities (e.g.running, walking).For ease of use, code snippets and an online interface are also provided.
An Algorithm for Learning Orthonormal Matrix Codebooks for Adaptive Transform Coding

This paper proposes a novel data-driven approach to designing orthonormal transform matrix codebooks for adaptive transform coding of any non-stationary vector processes which can be considered locally stationary. Our algorithm, which belongs to the class of block-coordinate descent algorithms, relies on simple probability models such as Gaussian or Laplacian for transform coefficients to directly minimize with respect to the orthonormal transform matrix the mean square error (MSE) of scalar quantization and entropy coding of transform coefficients. A difficulty commonly encountered in such minimization problems is imposing the orthonormality constraint on the matrix solution. We get around this difficulty by mapping the constrained problem in Euclidean space to an unconstrained problem on the Stiefel manifold and leveraging known algorithms for unconstrained optimization on manifolds. While the basic design algorithm directly applies to non-separable transforms, an extension to separable transforms is also proposed. We present experimental results for adaptive transform coding of still images and video inter-frame prediction residuals, comparing the transforms designed using the proposed method and a number of other content-adaptive transforms recently reported in the literature.
Enhancing Remote Sensing Image Super-Resolution Guided by Bicubic-Downsampled Low-Resolution Image

Image super-resolution (SR) is a significant technique in image processing as it enhances the spatial resolution of images, enabling various downstream applications. Based on recent achievements in SR studies in computer vision, deep-learning-based SR methods have been widely investigated for remote sensing images. In this study, we proposed a two-stage approach called bicubic-downsampled low-resolution (LR) image-guided generative adversarial network (BLG-GAN) for remote sensing image super-resolution. The proposed BLG-GAN method divides the image super-resolution procedure into two stages: LR image transfer and super-resolution. In the LR image transfer stage, real-world LR images are restored to less blurry and noisy bicubic-like LR images using guidance from synthetic LR images obtained through bicubic downsampling. Subsequently, the generated bicubic-like LR images are used as inputs to the SR network, which learns the mapping between the bicubic-like LR image and the corresponding high-resolution (HR) image. By approaching the SR problem as finding optimal solutions for subproblems, the BLG-GAN achieves superior results compared to state-of-the-art models, even with a smaller overall capacity of the SR network. As the BLG-GAN utilizes a synthetic LR image as a bridge between real-world LR and HR images, the proposed method shows improved image quality compared to the SR models trained to learn the direct mapping from a real-world LR image to an HR image. Experimental results on HR satellite image datasets demonstrate the effectiveness of the proposed method in improving perceptual quality and preserving image fidelity.
Soft Skills of a Modern Successful Accountant

Abstract. Introduction. Professional accountants of today have careers that use a wide variety of skills applicable to highly specialized roles. International Accounting Education Standards prescribe the technical competencies and skills accounting professionals need to develop. But there is another category of skills no less important for a financial specialist soft skills. Numerous foreign publications consider the accountant's professional skills in tandem with their soft skills. Purpose. The research aims to analyse the content, role and factors of development of soft skills of professional accountants in the context of business requirements in the current economic environment. Results. The study shows, as requirements for hiring, professionals who demonstrate, in addition to the hard skills exclusive to accountants skills, such as critical thinking, problem solving and teamwork. In a world that is becoming more digital, computerized, and automated, soft skills can be the differentiator between two employees competing for the same promotion or position. Poor organisation and time management skills could cause unnecessary stress, and accountants could risk tarnishing their reputation if important deadlines are missed. Accountants have to stay organised throughout their processes and work. Regulations are constantly changing, as is the technological environment in which accountants function, so the best accountants are open to change and remain adaptive and flexible in approaching their day-to-day activities. Effective communication is essential to all business roles, accountants included. They’re responsible for translating data in a visualised and easy-to-understand format. Since accounting and finance teams touch every area of the business, they are expected to work cross-functionally and collaborate well with other employees. Learning and education are constant in the accounting profession. Accountants are required to remain objective, ethical, and honest with regard to their tasks. The contribution of this research is to provide institutions, educators and professionals in the area, with insights into the requirements demanded by the job market for accounting professionals. Conclusions. To develop certain soft skills is always necessary in tandem with hard skills. Some of soft skills are character traits, the rest are acquired with life and professional experience. However, it is possible to develop soft skills at any age, the main thing is to set up such a goal and clearly determine the competencies that require improvement. The study found that the importance of both groups of skills to perform professional tasks by the accountant is undeniable. The success of the accountant's career depends directly on achieving the balance of soft and hard skills.
Advancements in Geological Disaster Monitoring and Early Warning Systems: A Deep Learning and Computer Vision Approach

Geological disasters, characterized by their destructive nature, pose significant threats to both human life and ecological environments.The advent of remote sensing technology has rendered hyperspectral remote sensing images an integral data source in monitoring and predicting these phenomena.However, it is noted that minor variations and detailed nuances within the images are often overlooked by traditional computer vision and deep learning techniques.Furthermore, data imbalances during the training of deep learning models have been identified as a potential hindrance to optimal performance.Recognizing these issues and the inherent unpredictability of geological disasters, an innovative approach has been developed.This approach encapsulates an optical flow-based method for enhancing the edges of geological remote sensing images, an improved geological disaster monitoring model leveraging the Isolation Forest algorithm, and an efficient implementation strategy.The suggested methods present numerous advantages, including the acceleration of computations to augment real-time monitoring of geological disasters, an enhanced capacity for handling extensive data, an improved system stability and fault tolerance, and the preservation of fundamental strengths such as linear computational complexity, unsupervised learning, and non-parametric methodologies.By synthesizing these methodological improvements and advantages, a swift, efficient, and flexible strategy for enhancing the Isolation Forest model is put forth.This research supports the development of geological disaster monitoring and early warning systems grounded in computer vision and deep learning, presenting substantial technical aid for related tasks.
An Examination Monocular Vision Gaze Point Tracking under the Theory of 'Machines Displacing Workers' in the Philosophy of Technology

With the continuous development of technology, we have the opportunity to quantitatively study the internal behaviors of personnel, such as attention, in a suitable way, in order to better solve the "machine compensation" for workers and achieve the development of human-machine fusion.An exploration of how varying lighting conditions directly affect the eyes and, by extension, the individual's attention has been undertaken in this study.Image sensors capture the subject's eye movements during task participation, with the location of the eye's (pupil's) center aiding in gaze point recognition and facilitating the quantification of attention.A quantifiable model of "lighting-attention" has been developed through controlled lighting conditions and replicable experimental circumstances, thereby studying the alteration of attention under different lighting and road conditions.An initial calibration of a monocular vision gaze tracking system was achieved with two commercialgrade eye trackers and calibration software, achieving a precision level of 2 cm.Nine college students aged 20-21, divided into male and female control test groups, exhibited similar attention characteristics under various lighting conditions.Overall, a negative correlation between illumination intensity and attention is observed when exceeding a certain threshold.On average, female participants maintained higher attention levels for longer durations.The efficacy of the model proposed in this study has been proven through a series of tests, providing a quantifiable reference for the mechanisms influencing attention.So it can provide the basis for the study of the benefits of factory workers.
Enhancing Biometric Fingerprint Security Through Integrated Watermarking and Cipher Block Chaining Techniques

This study presents a combined watermarking-cryptography approach to bolster the security of biometric fingerprint data.In the initial stage, a digital watermark is embedded within the host image (fingerprint scan) using the Least Significant Bit (LSB) technique.Upon completion of the embedding process, the watermarked image undergoes encryption using the Advanced Encryption Standard (AES) in Cipher Block Chaining (CBC) mode, increasing complexity and ensuring secure communication.Conversely, the extraction procedure involves reversing the embedding steps by first decrypting the received image and subsequently applying the extraction algorithm to the unencrypted image to recover the embedded watermark.The proposed method demonstrates significant imperceptibility, as measured by the Peak Signal-to-Noise Ratio (PSNR) and Normalized Correlation (NCC) metrics.Furthermore, the watermark exhibits resilience against signal processing attacks, including noise and filtering.The heightened key sensitivity of the CBC cryptosystem renders the proposed scheme more resistant to statistical attacks compared to existing methods in the literature.
A Multi-layer Approach for Mobile Devices: Behavioral Fingerprinting

In distributed systems such as mobile networks, problems like heterogeneity and real-time reliable responses represent constant challenges.Furthermore, reliable identification of the participants in mobile networks is a challenging research problem because of the susceptibility to security attacks.Also, reliable identification of participants means to be able to distinguish between the device and its user.In this paper, a multi-layer mechanism is proposed for reliable identification of devices and their users in mobile networks, based on the smartphone sensor data.The proposed model can uniquely identify both the device and the users by leveraging a mobile application that harvests mobile sensor data in various usage scenarios and analyse different usage patterns.Moreover, in order to achieve data confidentiality, a cryptographic scheme is integrated, based on the public key exchange mechanism.The obtained results show that the model proposed in this paper provides meaningful insights about user behavioral patterns, without special permissions and without disclosing the actual owner identity or biometric data.
SIERRA: A robust bilateral feature upsampler for dense prediction

Feature upsampling is a fundamental operation in modern deep network architectures. Existing upsamplers, however, are prone to cause negative upsampling—performance of an upsampler falls behind naive interpolation. For instance, the recent dynamic upsampler CARAFE is the best performing operator in semantic segmentation, but it turns out to be the worst one in image matting. In this work, we present robuSt bIlatERal featuRe upsAmpler (SIERRA), a simple, task-robust, plug-and-play, and ultra lightweight upsampler. Its key idea is to use an efficient gradient-prior kernel to modulate a (shifted) distance-prior kernel to control which feature points participate in interpolation, which shares a similar spirit to joint bilateral filtering (JBF). Yet, in contrast to JBF that requires high-res guidance, SIERRA generates kernels from the low-res decoder feature alone. Extensive experiments demonstrate the superiority and robustness of SIERRA on five dense prediction tasks. Code will be available online.
Using Unsupervised Learning for Mining Behavioural Patterns from Data. A Case Study for the Baccalaureate Exam in Romania

Behavioural data mining is an interesting paradigm in the field of knowledge discovery focused on uncovering meaningful patterns that describe behavioural characteristics.With the broader goal of improving decision-making in various areas, behaviour mining has proven to be useful in several application domains ranging from information systems to social science studies.This paper addresses the topic of behaviour mining in the field of educational data mining and analyses, as a proof of concept, the application of unsupervised learning-based models (self-organising maps and association rules) for identifying certain patterns in the behaviour of the high school students from the Real Sciences specializations when choosing the optional exam item for the Romanian baccalaureate.The experiments conducted for real data sets collected from Romanian high school students have shown that features like class specialization, gender, motivational patterns, or the average score obtained at a certain school subject influence the students' choosing or rejecting that subject as a baccalaureate exam item.The uncovered behavioural patterns are useful in outlining the profile of the present-day high school student and may be integrated in a recommender system for assisting students and teachers in the educational processes.
Model‐Based Systems Engineering as an Enabler of Agility

ABSTRACT Model‐based systems engineering (MBSE) with agility can help systems engineering programs which deal with both increasing complexity and frequent changes in environment and usages, shorter time‐to‐market, uncertainty of the needs, and more sophisticated industrial schemes. Agile approaches originated in software engineering can be extended and tailored to a certain extent to complex systems engineering and particularly to MBSE. Main benefits of agility are provision of a minimum viable product as early as possible in the schedule, early capture of changes of needs, enabling to deliver a system answering to the real needs, and securing of the value proposal. It includes also potential reduction in rework of the final system through regular customer feedback throughout development (left shift for the defect correction with early exposure), and efficiency of the use of resources. Concerning MBSE, the use of models as a single source of truth for completeness and consistency is useful to share and secure the design by improving communication within engineering teams and the building and support of the development strategy, and to help to automate some tasks such as model exchange and synchronization. In addition to the benefits of each approach, combining them may help to: ▪ Organize and synchronize the development and validation effort of one or multiple engineering teams. ▪ Faster impact analysis including trade‐off studies/options and hence a faster reaction to evolutions in expectations and constraints, that is, the agility of systems. ▪ Show regularly “end to end” value to the customer and other stakeholders.
Automatic Meter Detection and Recognition: A Robust Approach Based on Image Processing Techniques

Abstract: To address the issue of manual reading, Automatic Meter Reading (AMR) is introduced, which is a combination of detection and recognition that is efficiently applied to identify the meter and accurately read the digits on electric meters. The initial step in meter detection is the collection of the dataset, after which the annotation process generates XML files that are converted to CSV files and the label map is generated. Using a pre-trained classifier SSD ResNet, the model is trained and evaluated. The identified photos are used as input meter images in meter recognition, where a sequence of pre-processing techniques is performed. After which, using tesseract and leptonica, a model is generated from source, and digit recognition is conducted. The accuracy of 95.45% is then generated after training and evaluating on 2000 images of different kinds of electric meters in various surroundings.
System Volume and Brightness Control Using Voice Commands

Abstract: This paper proposes a missing feature of windows voice assistant Cortana that voice assistants of android Operating Systems, iPhone Operating Systems. Android operating system has Google Assistant and IOS has Siri, which can adjust the system volume and brightness followed by user’svoice commands, while Cortana can’t adjust the system volumeand brightness