Reviewerly/exHarmony huggingface dataset:
High-speed recursive-separable image processing filters with variable scanning aperture sizes

In the process of development of computer technologies, the number of areas of their application naturally grows and, along with it, the complexity of the tasks to be solved, which entails the need for new research. Similar tasks include digital filtering of images in the field of medical technologies and active-pulse television measuring systems. There are many methods and algorithms of digital filtering designed to solve the problem of improving the quality; algorithms that can improve the quality of images while reducing computational costs are widely used. High demands, which are made due to the constant growth in the size of the generated images, as well as the requirement for modern television systems, is real-time operation. When solving practical problems, it is required to use different filter aperture sizes, which provide an increase in quality and preservation of image details. The solution of these problems was the reason for the emergence of adaptive filters that are able to change the parameters in the process of processing the received data, while not spending additional time on processing with an increase in the size of the aperture. The paper presents the principles of constructing adaptive image processing filters, which, by obtaining an input parameter indicating the required dimension of a multi-element aperture, are able to implement the construction of the required aperture. The Laplacian “Truncated Pyramid” filter and the “double pyramid” Laplacian were modified. A feature of these filters is the oddness of the multi-element aperture, so the coefficient used to build the mask is always set to odd. When using these filters, it is possible to use two coefficients that are responsible for increasing the filtration efficiency, since, in their original form, the Laplacian filters have a sum of coefficients equal to zero. The experiment shows a comparison with high-dimensional filters that work when using classical two-dimensional convolution. The next stage of the presented research will be the application of parallel computing techniques, which will increase the speed of the developed filters.
Application of Multiple-Optimization Filtering Algorithm in Remote Sensing Image Denoising

Denoising remote sensing images is crucial in the application and research of remote sensing imagery. Noise in remote sensing images originates from sensor characteristics, signal transmission, and environmental conditions, among which Gaussian noise is the most common type. In this paper, we proposed a multiple-optimization bilateral filtering (MOBF) algorithm based on edge detection and differential evolution (DE) methods. The proposed algorithm optimizes the spatial domain filtering kernel and the spatial domain Gaussian kernel by using the standard deviation and width of the edge response. By employing the DE algorithm, the individuals in the population based on the standard deviation of the gray value domain are subjected to iterative mutation, crossover, and selection operations to refine the latent solution vectors and determine the optimal color space for optimizing the standard deviation of the pixel range domain kernel. As a result, the MOBF algorithm, which does not require any parameter input, is realized. To verify the feasibility and effectiveness of the proposed algorithm, denoising experiments were conducted on remote sensing images by using evaluation metrics such as the mean squared error, peak signal-to-noise ratio, and structural similarity index. The experimental results revealed that the MOBF algorithm outperforms traditional algorithms for all three evaluation metrics.
Experiment‐based statistical distribution of buckling loads of cylindrical shells

Abstract A silo structure is usually constructed in the form of a cylindrical steel shell. It has the advantages of being lightweight, having a short construction period, and possessing a large storage space. It has been widely used in many fields of industry. Thin‐walled cylindrical shells often exhibit buckling failure and the experimental buckling load is usually lower than calculation results from classical theory and simulation without geometrical imperfection. Besides, test results with carefully conducted similar specimens still have substantial scatter due to imperfection sensitivity. The nonlinear analysis with FEM can obtain a high‐precision result comparing the experiment if the geometric parameters of the shell are fully known. However, this is almost impossible in practical engineering. The initial geometric imperfections and shell thickness of cylindrical shells are complex and random properties. Theoretically, these geometric imperfections can be described using a random field. This paper presents the experimental investigation of buckling analysis of cylindrical shells under axial compression considering the randomness of geometric imperfections and thickness. A total of 12 cylindrical shell specimens were fabricated and tested. Based on the test results, the optimal statistical distribution is obtained by the maximum entropy fitting method and the obtained results were compared with geometric imperfections based on laser scan measurements.
Determination of the level of university digitalization by the method of the higher education institutional ranking of the Kyrgyz Republic

The article is devoted to the consideration of the key factor in the creation and successful functioning of the digital economy ecosystem in the Kyrgyz Republic - Digital transformation in the education system. The key areas of work on the digitalization of education in the Kyrgyz Republic have been identified. The article considers a comprehensive assessment of the level of university digitalization according to the methodology of the national ranking of universities of the Kyrgyz Republic. The mechanism for calculating rating indicators is described. The methodology for calculating the level of digitalization of the university is described, indicators are given.
Hypert: hypernymy-aware BERT with Hearst pattern exploitation for hypernym discovery

Abstract Hypernym discovery is challenging because it aims to find suitable instances for a given hyponym from a predefined hypernym vocabulary. Existing hypernym discovery methods used supervised learning with word embedding from word2vec. However, word2vec embedding suffers from low embedding quality regarding unseen or rare noun phrases because entire noun phrases are embedded into a single vector. Recently, prompting methods have attempted to find hypernyms using pretrained language models with masked prompts. Although language models alleviate the problem of w embeddings, general-purpose language models are ineffective for capturing hypernym relationships. Considering the hypernym relationship to be a linguistic domain, we introduce , which is further pretrained using masked language modeling with Hearst pattern sentences. To the best of our knowledge, this is the first attempt in the hypernym relationship discovery field. We also present a fine-tuning strategy for training with special input prompts for the hypernym discovery task. The proposed method outperformed the comparison methods and achieved statistically significant results in three subtasks of hypernym discovery. Additionally, we demonstrate the effectiveness of the several proposed components through an in-depth analysis. The code is available at: https://github.com/Gun1Yun/Hypert .
A Novel Fuzzy Relative-Position-Coding Transformer for Breast Cancer Diagnosis Using Ultrasonography

Breast cancer is a leading cause of death in women worldwide, and early detection is crucial for successful treatment. Computer-aided diagnosis (CAD) systems have been developed to assist doctors in identifying breast cancer on ultrasound images. In this paper, we propose a novel fuzzy relative-position-coding (FRPC) Transformer to classify breast ultrasound (BUS) images for breast cancer diagnosis. The proposed FRPC Transformer utilizes the self-attention mechanism of Transformer networks combined with fuzzy relative-position-coding to capture global and local features of the BUS images. The performance of the proposed method is evaluated on one benchmark dataset and compared with those obtained by existing Transformer approaches using various metrics. The experimental outcomes distinctly establish the superiority of the proposed method in achieving elevated levels of accuracy, sensitivity, specificity, and F1 score (all at 90.52%), as well as a heightened area under the receiver operating characteristic (ROC) curve (0.91), surpassing those attained by the original Transformer model (at 89.54%, 89.54%, 89.54%, and 0.89, respectively). Overall, the proposed FRPC Transformer is a promising approach for breast cancer diagnosis. It has potential applications in clinical practice and can contribute to the early detection of breast cancer.
GENERAL: GENerative, Explainable and Reasonable Artificial Learning

The GENERAL (GENerative, Explainable and Reasonable Artificial Learning) workshop, held at CHITALY 2023, delves into the advancements in General and Generative Artificial Intelligence (GGAI), with a focus on breakthroughs in natural language processing (NLP) and computer vision (CV). The workshop highlights the capabilities of Large Language Models (LLMs) and Latent Diffusion Models (LDMs) in generating human-like content across text and images. It emphasizes the importance of AI explainability, aiming to understand, explain, and control the complexities of these AI systems in terms of fairness, accountability, and transparency. The workshop encourages interdisciplinary collaboration across fields like HCI, psychology, social studies, and the arts to better understand AI’s societal and cultural impacts. Topics of interest include user perceptions of generative AIs, machine psychology, AI assistants, ethical issues in Generative AI, and safety and control mechanisms for large language models.
Blockchain in agriculture: a PESTELS analysis.

Abstract Blockchain (BC) represents a disruptive technology that has been extensively used to ensure immutability of digital transactions. Starting as an underlying mechanism in the digital currency sector, it has been applicable in a wide range of sectors and application domains. Agricultural sector represents a sector of significance for overall sustainability challenges that is benefiting from digitalization and technological evolution and the enforcement of Industry 4.0 paradigm shift towards precision agriculture. Introduction of Internet of Things, and Cyber-Physical Systems increase overall complexity, with Big Data analysis and Machine Learning technologies providing innovative applications. BC appears to be a promising technology for agriculture providing mechanisms for tracing of products and overall agricultural supply chain (SC) management from the farm to the fork. Authors investigate the challenges and open issues for the application of BC in agriculture performing a state of the art analysis along the PESTELS framework. A large number of challenges including technological ones, creates big research potential for the evolution of the area.
Exchange Rate Volatility and Nigerian Stock Market Development

Purpose: The study aims to evaluate the impact of exchange rate volatility on the indicators of stock market, such as the returns, capitalization, liquidity, transaction volume, based on the Nigerian evidence. Theoretical framework: The paper considers extant model of exchange rate-stock market interactions. Because the diversity of the market is important the paper follows suggestions by El-Wassal (2013) to examine variety of stock market indicators in order to offer accurate depiction of how the stock market drives exchange rate volatility. Design/Methodology/Approach: The study employs a simple static regression model of stock market indicators with autoregressive adjustment component which absorb autocorrelation. The models are tested on annual data (1985-2020). Findings: The study finds that the exchange rate volatility has negative impact on stock market development - returns, capitalization and volume. The paper reveals further that other correlated controls factors impinge different impacts on the stock market indicators. Research, Practical &amp; Social implications: One implication of the finding is that the volatility may discourage investors, reduce firm performance, and lead to reduction on the returns of firm shares. Policy makers should put in place growth-inducing infrastructural investments to make the business environment more conducive to attract foreign capital, while also positioning the capital market with several initiatives to increase trading activities. Originality/Value: The connection between the foreign exchange and financial markets is of policy importance. Although the focus is on the Nigerian markets, the evidence could be extended to other African and global markets, to serve as guide to foreign investors.
FIONA: Fine-grained Incoherent Optical DNN Accelerator Search for Superior Efficiency and Robustness

Incoherent optical DNN accelerators (OAs) are booming thanks to unparalleled performance-per-watt and excellent scalability. To boost their innovative development, a recent work revolutionarily proposed automatic OA search. However, the robustness and the acceleration performance of the generated OAs are below expectation, because the impacts of inter-tile data transfer and fabrication process & thermal variations (i.e., PTVs) on OAs were ignored. Both hinder OA design automation from being a reality. To resolve theses challenges, we develop FIONA, a novel framework for Fine-grained Incoherent Optical DNN Accelerator search towards both superior acceleration efficiency and inference robustness. Compared against 5 state-of-the-art incoherent OAs on 9 DNN benchmarks, extensive experiments and ablation studies validate the effectiveness of FIONA, achieving up to 198.01× acceleration efficiency improvement based on guaranteed robustness.
Decision-Making Method for High-speed Rail Early Warning System in Complex Earthquake Situations

Abstract To address the shortcomings in decision-making methods for ground motion threshold warning models in High-speed rail (HSR) earthquake early warning systems (HSREEWs), we propose a dual judgment method and corresponding early warning process for earthquake early warning decisions based on joint peak ground acceleration (PGA) and complex earthquake environmental risk evaluation (ERE) values. First, we analyze the characteristics of four complex earthquake environments based on the characteristics of HSR operating environments. Second, we establish an earthquake environmental risk evaluation index system and propose an adversarial interpretive structure modeling method-based complex earthquake situation evaluation model (AISM-based ESEM). The AISM method firstly evaluates the proximity by the TOPSIS method, then effectively rank targets with fuzzy attributes through opposite hierarchical extraction rules without sacrificing system functionality. Since PGA can reflect the current size of earthquake energy, combining PGA thresholds with ESEM-derived values of ERE can effectively determine the risk status of each train and make decisions on the most appropriate alarm form and control measures for that status. Finally, case analysis results under the background of Wenchuan Earthquake show that the new early warning decision-making method accurately assesses environmental risks in affected areas and provides corresponding warning levels as a supplement to existing HSREEWs warning models.
FS-Real: A Real-World Cross-Device Federated Learning Platform

Federated learning (FL) is a general distributed machine learning paradigm that provides solutions for tasks where data cannot be shared directly. Due to the difficulties in communication management and heterogeneity of distributed data and devices, initiating and using an FL algorithm for real-world cross-device scenarios requires significant repetitive effort but may not be transferable to similar projects. To reduce the effort required for developing and deploying FL algorithms, we present FS-Real, an open-source FL platform designed to address the need of a general and efficient infrastructure for real-world cross-device FL. In this paper, we introduce the key components of FS-Real and demonstrate that FS-Real has the following capabilities: 1) reducing the programming burden of FL algorithm development with plug-and-play and adaptable runtimes on Android and other Internet of Things (IoT) devices; 2) handling a large number of heterogeneous devices efficiently and robustly with our communication management components; 3) supporting a wide range of advanced FL algorithms with flexible configuration and extension; 4) alleviating the costs and efforts for deployment, evaluation, simulation, and performance optimization of FL algorithms with automatized tool kits.
Single-Qubit Error Mitigation by Simulating Non-Markovian Dynamics

Quantum simulation is a powerful tool to study the properties of quantum systems. The dynamics of open quantum systems are often described by completely positive ($CP$) maps, for which several quantum simulation schemes exist. Such maps, however, represent only a subset of a larger class of maps: the general dynamical maps which are linear, Hermitian preserving, and trace preserving but not necessarily positivity preserving. Here we present a simulation scheme for these general dynamical maps, which occur when the underlying system-reservoir model undergoes entangling (and thus non-Markovian) dynamics. Such maps also arise as the inverse of $CP$ maps, which are commonly used in error mitigation. We illustrate our simulation scheme on an IBM quantum processor, demonstrating its ability to recover the initial state of a Lindblad evolution. This paves the way for a novel form of quantum error mitigation. Our scheme only requires one ancilla qubit as an overhead and a small number of one and two qubit gates. Consequently, we expect it to be of practical use in near-term quantum devices.
Ordinal Regression for Difficulty Prediction of StepMania Levels

StepMania is a popular open-source clone of a rhythm-based video game. As is common in popular games, there is a large number of community-designed levels. It is often difficult for players and level authors to determine the difficulty level of such community contributions. In this work, we formalize and analyze the difficulty prediction task on StepMania levels as an ordinal regression (OR) task. We standardize a more extensive and diverse selection of this data resulting in five data sets, two of which are extensions of previous work. We evaluate many competitive OR and non-OR models, demonstrating that neural network-based models significantly outperform the state of the art and that StepMania-level data makes for an excellent test bed for deep OR models. We conclude with a user experiment showing our models’ superhuman performance.
The Antecedents of Mobile-Assisted Language Learning Applications Continuance Intention

The purpose of the research is to analyze the factors that influence the continuance intention to use Mobile-Assisted Language Learning (MALL) applications in the context of language courses in Indonesia. The study aims to understand the key factors that contribute to users' intention to continue using MALL applications, particularly in the context of the COVID-19 pandemic and the shift towards online and remote language learning. The research used the expectation-confirmation model and self-determination theory. Moreover, the research utilized a mixed-methods approach to achieve its objectives. A quantitative approach was employed, involving a survey with 445 respondents who had experience using MALL applications. The survey data was then analyzed using covariance-based structural equation modeling. Additionally, qualitative research was conducted through interviews with 17 respondents to gain a deeper understanding of rejected hypotheses and gather qualitative insights. The most important results of the research include identifying the factors that influence the continuance intention to use MALL applications. The study found that users' perceived usefulness of the application, their satisfaction with its usage, and their self-regulation ability significantly influence their intention to continue using MALL. These findings highlight the importance of these factors in shaping users' decision to persist with MALL applications for language learning. By understanding the factors that influence users' intention to continue using MALL, educational institutions and application providers can improve their offerings and tailor them to meet the needs and expectations of learners. This research advances the e-learning area by shedding light on the factors that influence the continuance intention to use MALL applications. By uncovering the importance of perceived usefulness, satisfaction, and self-regulation ability, the study contributes to the understanding of user behavior and decision-making in the context of e-learning. The findings can inform the design and implementation of MALL applications, leading to more engaging and effective language learning experiences in online and remote settings. This research contributes to the advancement of e-learning practices by providing evidence-based insights into the factors that drive user intention and adoption of technology in language learning.
Comment on J. N. Blakely, “Quantum illumination with a parametrically amplified idler” [Phys. Lett. A 400 (2021) 127319]

We show that the quantum illumination setup proposed in Blakely (2021) [1] does not afford any quantum advantage over a coherent-state transmitter.
Comparative Analysis of the Clustering Quality in Self-Organizing Maps for Human Posture Classification

The objective of this article is to develop a methodology for selecting the appropriate number of clusters to group and identify human postures using neural networks with unsupervised self-organizing maps. Although unsupervised clustering algorithms have proven effective in recognizing human postures, many works are limited to testing which data are correctly or incorrectly recognized. They often neglect the task of selecting the appropriate number of groups (where the number of clusters corresponds to the number of output neurons, i.e., the number of postures) using clustering quality assessments. The use of quality scores to determine the number of clusters frees the expert to make subjective decisions about the number of postures, enabling the use of unsupervised learning. Due to high dimensionality and data variability, expert decisions (referred to as data labeling) can be difficult and time-consuming. In our case, there is no manual labeling step. We introduce a new clustering quality score: the discriminant score (DS). We describe the process of selecting the most suitable number of postures using human activity records captured by RGB-D cameras. Comparative studies on the usefulness of popular clustering quality scores—such as the silhouette coefficient, Dunn index, Calinski–Harabasz index, Davies–Bouldin index, and DS—for posture classification tasks are presented, along with graphical illustrations of the results produced by DS. The findings show that DS offers good quality in posture recognition, effectively following postural transitions and similarities.