Reviewerly/exHarmony huggingface dataset:
HPCache: memory-efficient OLAP through proportional caching revisited

Abstract Analytical engines rely on in-memory data caching to avoid storage accesses and provide timely responses by keeping the most frequently accessed data in memory. Purely frequency- and time-based caching decisions, however, are a proxy of the expected query execution speedup only when storage accesses are significantly slower than in-memory query processing. On the other hand, fast storage offers loading times that approach fully in-memory query response times, rendering purely frequency-based statistics incapable of capturing the impact of a caching decision on query execution. For example, caching the input of a frequent query that spends most of its time processing joins is less beneficial than caching a page for a slightly less frequent but scan-heavy query. Thus, existing caching policies waste valuable memory space to cache input data that offer little-to-no acceleration for analytics. This paper proposes HPCache, a buffer management policy that enables fast analytics on high-bandwidth storage by efficiently using the available in-memory space. HPCache caches data based on the speedup potential instead of relying on frequency-based statistics. We show that, with fast storage, the benefit of in-memory caching varies significantly across queries; therefore, we quantify the efficiency of caching decisions and formulate an optimization problem. We implement HPCache in Proteus and show that (i) estimating speedup potential improves memory space utilization, and (ii) simple runtime statistics suffice to infer speedup. We show that HPCache achieves up to a 1.75x speed-up over frequency-based caching policies by caching column proportions and automatically tuning them. Overall, HPCache enables efficient use of the in-memory space for input caching in the presence of fast storage, without requiring workload predictions.
ExD: Explainable Deletion

This paper focuses on a critical yet often overlooked aspect of data in digital systems and services—deletion. Through a review of existing literature we highlight the challenges that user face when attempting to delete data from systems and services, the lack of transparency in how such requests are handled or processed and the lack of clear assurance that the data has been deleted. We highlight that this not only impacts users’ agency over their data but also poses issues with regards to compliance with fundamental legal rights such as the right to be forgotten. We propose a new paradigm – explainable deletion – to improve users’ agency and control over their data and enable systems to deliver effective assurance, transparency and compliance. We discuss the properties required of such explanations and their relevance and benefit for various individuals and groups involved or having an interest in data deletion processes and implications. We discuss various design implications pertaining to explainable deletion and present a research agenda for the community.
Research on the Construction of the Curriculum System of “Post-Certification Course Integration” in Higher Vocational Accounting Major under the Background of “Big Data Intelligent Mobile Cloud”

In recent years, information technology represented by big data, artificial intelligence, mobile internet and cloud computing has become a new universal technology by profoundly changing the economic and social environment and driving a new round of technological revolution and industrial change. Under the impact of new technologies and ideas, what kind of accounting talents are needed in the market and how to improve the quality of accounting talents training are important issues that need to be studied by accounting majors in universities. Based on the " big data intelligent mobile cloud ", while insisting on" student-centered " and result-oriented, this paper defines and matches the job requirements accurately through big data text analysis, then integrates 1+X certificate and accounting professional skills competition, reconstructs the teaching contents, strengthens modular teaching, and explores the construction of the curriculum system of "integration of post, certificate, class and competition" for higher vocational accounting majors.
Standardized CycleGAN training for unsupervised stain adaptation in invasive carcinoma classification for breast histopathology

Generalization is one of the main challenges of computational pathology. Slide preparation heterogeneity and the diversity of scanners lead to poor model performance when used on data from medical centers not seen during training. In order to achieve stain invariance in breast invasive carcinoma patch classification, we implement a stain translation strategy using cycleGANs for unsupervised image-to-image translation. Those models often suffer from a lack of proper metrics to monitor and stop the training at a particular point. We also introduce a method to solve this issue.We compare three CycleGAN-based approaches to a baseline classification model obtained without any stain invariance strategy. Two of the proposed approaches use CycleGAN's translations at inference or training to build stain-specific classification models. The last method uses them for stain data augmentation during training. This constrains the classification model to learn stain-invariant features. Regarding CycleGANs' training monitoring, we leverage Fréchet inception distance between generated and real samples and use it as a stopping criterion. We compare CycleGANs' models stopped using this criterion and models stopped at a fixed number of epochs.Baseline metrics are set by training and testing the baseline classification model on a reference stain. We assessed performances using three medical centers with H&E and H&E&S staining. Every approach tested in this study improves baseline metrics without needing labels on target stains. The stain augmentation-based approach produced the best results on every stain. Each method's pros and cons are studied and discussed. Moreover, FID stopping criterion proves superiority to methods using a predefined number of training epoch and has the benefit of not requiring any visual inspection of CycleGAN results.We introduce a method to attain stain invariance for breast invasive carcinoma classification by leveraging CycleGAN's abilities to produce realistic translations between various stains. Moreover, we propose a systematical method for scheduling CycleGANs' trainings by using FID as a stopping criterion and prove its superiority to other methods. Finally, we give an insight on the minimal amount of data required for CycleGAN training in a digital histopathology setting.
UTD-YOLO: underwater trash detection model based on improved YOLOv5

Underwater trash detection faces many problems. For example, the resolution of underwater trash datasets is generally low, and trash may show irregular shapes, sizes, and scales, especially small objects that are difficult to detect. To solve the above problems, we propose UTD-YOLO, an improved YOLOv5s trash detection model. This model proposes a cross-layer aggregation spatial dimensionality reduction module (CASDRM) and channel-PAN-FPN with improved channel information. CASDRM preserves the information of the channel dimension and utilizes the receptive field between different layers to enhance the feature extraction ability while strengthening the ability to adapt to the geometric changes of objects. Channel-PAN-FPN realizes two-way information transmission and improves the recognition accuracy of the model. UTD-YOLO can achieve good recognition results for smaller-scale targets in low-resolution datasets. The experimental results show that the UTD-YOLO model improves the mAP50 to 73.2% on the Trashcan dataset, which is higher than the original YOLOv5 algorithm.
What rating they will probably give: A cognitive diagnosis approach for recommending items based on polytomous responses and latent attributes

Recommendation Systems have become prevalent in recent years, attracting the attention of researchers to investigate different methods to filter relevant information for users. This information is not always explicit and different proposals have emerged to obtain the latent values of individuals through their behaviour. In educational areas, latent attributes of test-takers can be acquired by psychometric models such as the Cognitive Diagnostic Model. These models attempt to create a user’s profile in order to explore the connections between students and subjects, just like a recommendation system does with its users and the products to be recommended. The objective of this work is to develop a new recommendation approach that incorporates Cognitive Diagnostic Models applied to data from media defined by discrete content (such as genres in movies and series) in order to generate its polytomous response in the form of the rating prediction that a user would give to each item. The proposed approach was applied to two datasets (MovieLens20M Dataset and Anime Recommendation Database).The new proposal was also considered with additional information regarding the popularity of the items, in an enhanced version of our model, and compared to classic recommendation systems found in the literature. Finally, this work also explored the performance of the models in ranking items to be recommended for the users. In general, the new method obtained better results than the classic recommendation ones for both the predicted rating and the item ranking.
MINIMIZATION OF BITSLICED-REPRESENTATION OF 4×4 S-BOXES BASED ON TERNARY LOGIC INSTRUCTION

In this paper, the author suggests a modification of the thread pool algorithm that was presented by Sean Parent at NDC London 2017. The suggested algorithm is as simple as the original implementation and demonstrates similar performance, while eliminating a potential drawback of the original implementation consisting in the fact that under certain circumstances, multiple tasks can be executed on the same thread, while other threads may be waiting for a task. The suggested idea consists in tracking the total number of tasks that are in the queues of the thread pool. When the main thread pushes a new task to one of the queues, the tasks counter is incremented. When a task is removed from the queue, the task counter is decremented. When a thread wants to get a task, it keeps checking the queues until it succeeds in getting a task from one of the queues, or until the tasks counter becomes equal to zero. When the tasks counter becomes equal to zero, the thread becomes idle until the counter becomes non-zero again. Then, one of the threads wakes up and starts checking the queues. An important point is to maintain even distribution of tasks in the queues since it has a significant impact on the performance of the algorithm. Keywords: multithreading, thread pool, task stealing.
EATURES OF ALGORITHMIC AND SOFTWARE TOOLS FOR FRAMING FUZZY IMAGES

Abstract: This article examines the features of algorithmic and software tools for processing fuzzy images. The work uses three filters: CIGaussianBlur, CIUnsharpMask and CIBlendWithAlphaMask. The described filters allow you to improve image quality, reduce noise and reproduce details.The initial task is to process the blurring of images. For this, the CIGaussianBlur filter is used, which applies a Gaussian blur to the image. This blur reduces high-frequency noise and adds smoothness to the contours of objects.The second filter, CIUnsharpMask, is used to restore image details. This filter subtracts the blurred version from the original image, which allows you to highlight important details and increase the clarity of the image. The last filter, CIBlendWithAlphaMask, is used to blend two images using an alpha mask. This filter allows you to control the transparency and adjust how the images are blended. As a result, a more realistic and aesthetic image can be achieved. The article considers the principles of operation of each of the filters, gives examples of their use and describes the results obtained. Research shows that using these filters can improve the quality of blurry images, reduce noise, and sharpen details. The results of this work can be useful for use in the field of image processing, computer vision and graphic design. Using the described filters can help improve the visual characteristics of images and provide a more accurate interpretation of fuzzy data. Keywords: fuzzy images, algorithmic tools, software tools, image processing, filters.
Open Quantum Dynamics: Memory Effects and Superactivation of Backflow of Information

We investigate the divisibility properties of the tensor products Λt(1)⊗Λt(2) of open quantum dynamics Λt(1,2) with time-dependent generators. These dynamical maps emerge from a compound open system S1+S2 that interacts with its own environment in such a way that memory effects remain when the environment is traced away. This study is motivated by the following intriguing effect: one can have Backflow of Information (BFI) from the environment to S1+S2 without the same phenomenon occurring for either S1 and S2. We shall refer to this effect as the Superactivation of BFI (SBFI).
Application of YOLOv8 and Detectron2 for Bullet Hole Detection and Score Calculation from Shooting Cards

Scoring targets in shooting sports is a crucial and time-consuming task that relies on manually counting bullet holes. This paper introduces an automatic score detection model using object detection techniques. The study contributes to the field of computer vision by comparing the performance of seven models (belonging to two different architectural setups) and by making the dataset publicly available. Another value-added aspect is the inclusion of three variants of the object detection model, YOLOv8, recently released in 2023 (at the time of writing). Five of the used models are single-shot detectors, while two belong to the two-shot detectors category. The dataset was manually captured from the shooting range and expanded by generating more versatile data using Python code. Before the dataset was trained to develop models, it was resized (640 × 640) and augmented using Roboflow API. The trained models were then assessed on the test dataset, and their performance was compared using matrices like mAP50, mAP50-90, precision, and recall. The results showed that YOLOv8 models can detect multiple objects with good confidence scores. Among these models, YOLOv8m performed the best, with the highest mAP50 value of 96.7%, followed by the performance of YOLOv8s with the mAP50 value of 96.5%. It is suggested that if the system is to be implemented in a real-time environment, YOLOv8s is a better choice since it took significantly less inference time (2.3 ms) than YOLOv8m (5.7 ms) and yet generated a competitive mAP50 of 96.5%.
Digitalizing the Humanitarian Discourse: Modern Technologies in Research and Educational Space of a University

Introduction. The article is devoted to the analysis and assessment of the methods of the humanitarian discourse digitalization while implementing them in the educational process. Digitalization of the educational space is associated with the use of discourse as an especially complicated communicative phenomenon, as well as with the use of digital technologies and the Internet space. At the same time, the question on the right implementation of the modern technologies in the educational process constantly rises. Purpose. In our study, we will consider the possibilities of the implementation of digital discourse technologies in the educational process. Theoretical justification. We investigated the capabilities of the «Ngram Viewer» search service, the «Cyborg Writer» experimental text editor, the «AI Search» text creation tool, the Russian National Corpus internet service, including its sections: «Educational Corpus», «Poetry Corpus», and «British National Corpus» in English. We also looked into potential of the «Genial.ly» platform, the website in the form of a landing page, the «Wordpress» platform and its potential in the refinement of humanitarian discourse. From the discourse point of view, we described the electronic research and educational space of Don State Technical University, and the scientific electronic library e-library.ru. Examples of projects implemented in the «Fundamentals of the Project Management» and «Business Communication» subjects' frames by students, and in which modern digital technologies were applied successfully, are considered. Discussion . The digitalization of humanities knowledge under modern conditions appears as an ambiguous process. On the one hand, digital technologies can supplement and improve the educational process. On the other hand, in this relatively new conditions, the researcher's activity and its results lose importance to a significant extent. Meanwhile, the digital transformation of education presents new tools to humanities teachers for broadening research, speeding up analysis and data processing, and improving accuracy in studies.
Digital Transformation and Access to Scholarly Research: ETD Management and Digital Repository of Indian Agricultural System

The Universities and Institutes of Higher Education and Research has vast intellectual and scholarly research information of student scholarship in form of theses and dissertations, besides other gray literature that usually remains unused or underutilized due restricted access in physical form. Institutional repositories paves the way to hold these valuable information and provides unrestricted Open Access to these valuable research content and maximizes research impact making research more productive. This article discusses the creation and developments of Digital Repository of the first Indian Agricultural University of the country and looks into the transitions made from print Theses and Dissertations (TD) into digital formats, its accessibility and management.
Enhancing qubit readout with Bayesian learning

The authors have developed a scheme to measure the states of qubits with improved efficiency and accuracy. Their method leverages Bayesian inference, creating a probability distribution for each qubit state based on the response of the measurement device. The approach is able to embed the effects of different sources of noise which affect current quantum computers, thus improving results of quantum computation.
The Perception Features of Various Educational Material Forms: Efficiency Question

Introduction. In modern world, the dynamic development of information technologies leads to corresponding changes in educational process, that is, to a great extent, founded on implemented technologies. The new forms of information presentation are declared by designers as the most effective for teaching. Therefore, the question of their real value for education and their impact on the students' perception of educational content arises. The study of virtual reality, which is in principal a new form, impact on students' perception of educational material appears to be of importance. Specifically, we mean it in comparison with traditional forms, which are video and text. Purpose . In our article, we will assess students' perception features of learning material presented in various formats, thus its efficiency for educational process. Materials and Methods. We used the questionnaire method. The study participants were students of Don State Technical University. They were asked to assess materials presented in various forms: video, text, and VR. Results. The conducted research showed that the students highly assessed VR and video forms of information presentation. They think the stated forms are the easiest for perception, they are engaging and allow students to memorize material in the best way possible. While giving them the highest priority for further education, respondents also noted the difficulties with full immersion in the material. Discussion . The implementation of new forms of information presentation and VR specifically can help induce full immersion in the material. However, it requires familiarization for students with the content provided, which is organized in video and text form.
Digital Pedagogy: Challenges and Readiness for Learning in Digital Environment

Introduction. The article outlines the development trends of digital pedagogy and learning conditions in the new educational environment. The urgency of the study is justified through changes in technology and methods of providing education, as well as implications in the education sector with digital technologies and digital landscape. All of this helps improve the efficiency of educational activity and its outcome. However, it also requires digital literacy from its users. The novelty lies in identification of digital pedagogy problems and assessment of students' readiness for the implementation of digital tools in learning. Purpose. In our study, we will consider capabilities, challenges, and risks of education in a digital environment in an integrated way. Materials and Methods. We used theoretical methods for the analysis of digitalization state in the Russian and foreign education systems, and current requirements for digital skills of educational process participants. In the empirical part, the questionnaire method regarding teachers’ and students’ readiness level for the digital shift and ability to fully operate with digital educational environment was taken on the broad matter. Results . Various strategies and practices, models, and pedagogical innovations in the didactics development area were analyzed. We have revealed strengths and weaknesses, positive and problematic moments, organizational and didactic issues of learning in the digital landscape. Theoretical and methodological foundations of didactics under the conditions of digital transformation were structured. We analyzed the responses of teachers and students and draw a conclusion about their readiness for the digital shift in education. Discussion. The main factors that impact effective leaning in the digital landscape are stressed. It is proposed that digital pedagogy, as a new direction in science, is bound to answer plenty of questions regarding the digital shift. We have to enrich traditional methodology capabilities with a new content, modern tools and methods of teaching using digital technologies, as well as information and communications means, leaving the best to its side.
Genetic Programming to Remove Impulse Noise in Color Images

This paper presents a new filter to remove impulse noise in digital color images. The filter is adaptive in the sense that it uses a detection stage to only correct noisy pixels. Detecting noisy pixels is performed by a binary classification model generated via genetic programming, a paradigm of evolutionary computing based on natural biological selection. The classification model training considers three impulse noise models in color images: salt and pepper, uniform, and correlated. This is the first filter generated by genetic programming exploiting the correlation among the color image channels. The correction stage consists of a vector median filter version that modifies color channel values if some are noisy. An experimental study was performed to compare the proposed filter with others in the state-of-the-art related to color image denoising. Their performance was measured objectively through the image quality metrics PSNR, MAE, SSIM, and FSIM. Experimental findings reveal substantial variability among filters based on noise model and image characteristics. The findings also indicate that, on average, the proposed filter consistently exhibited top-tier performance values for the three impulse noise models, surpassed only by a filter employing a deep learning-based approach. Unlike deep learning filters, which are black boxes with internal workings invisible to the user, the proposed filter has a high interpretability with a performance close to an equilibrium point for all images and noise models used in the experiment.