Reviewerly/exHarmony huggingface dataset:
Modeling of the library collection acquisition process

Сonstantly increasing assortment of literature of various genres and directions offered by publishers sets a problem for the libraries when purchasing new literature in the conditions of limited budgeting. In this connection, the task of the qualitative acquisition of the library stock is urgent. The main conceptual idea for the solution of the problem is the maximum satisfaction of the information needs of various types of library resource users. To implement the users informational preferences, a general mathematical model of decision-making support has been developed in the task of optimizing library stock acquisition. It allows it to be replenished and renewed in order to meet the information needs of library resource users and takes into account the limited budgeting for acquiring new books. The demand for library resources is considered as the optimality criterion. A formalized approach to the process of completing the library fund with popular printed publications is proposed. To reduce the area dimension of possible solutions, a model approximation of the optimization problem has been constructed. The transition from the optimization problem to its model approximation is based on the ideas of identifying the most important features of the area of possible values and the segmentation of the readership. In the practical implementation of the constructed model, the method of data mining, the method of analyzing strategic alternatives and the theory of fuzzy sets in accordance with the specifics of the problem to be solved were used in determining each significant classification problem. The model, constructed and tested on the basis of the scientific library of the Orenburg State University (OSU), allows to take into account not only the influence of various factors on the quality of the library stock, but also the issues caused by the limited budgeting of libraries.
Visual Odometer Based on Image Region Texture Weights of ORB Features

ORB (Oriented FAST and Rotated Brief) features are the most commonly used features in visual SLAM and visual odometry that have high computational speed, and rotation scale invariance. However, due to the homogenization of ORB features, the corner properties of specific features are poor in some artificial environments, and it can lead to poor matching performance when the environmental texture is not rich. The underlying reason is that the pixel grayscale changes in the less-richly textured regions on the image are not obvious, leading to a certain degree of mis-matching in the matching process. At the same time, when the camera motion speed is low, there is much overlap between adjacent frames. This results in minimal or almost no changes in the projection of feature points, and the system is highly sensitive to errors, In this case, even small errors can cause significant fluctuations in the calculation results. In response to the above issues, an improved feature point method for stereo vision mileage calculation is proposed to solve the problem of system stability deterioration when cameras move at low speeds in artificial envi-ronments. Firstly, weight calculation based on different texture regions is used to solve the problem of low corner properties of feature points caused by environmental texture differences. Then, keyframes are used for motion model estimation to improve the system's stability under low-speed motion. The experimental results of the dataset showed that the key frame rate was optimal between 10% and 12%, and the positioning accuracy was higher than these open-source systems compared with three common open-source VO systems.
SVM-RFE enabled feature selection with DMN based centroid update model for incremental data clustering using COVID-19

This research introduces an efficacious model for incremental data clustering using Entropy weighted-Gradient Namib Beetle Mayfly Algorithm (NBMA). Here, feature selection is done based upon support vector machine recursive feature elimination (SVM-RFE), where the weight parameter is optimally fine-tuned using NBMA. After that, clustering is carried out utilizing entropy weighted power k-means clustering algorithm and weight is updated employing designed Gradient NBMA. Finally, incremental data clustering takes place in which centroid matching is carried out based on RV coefficient, whereas centroid is updated based on deep maxout network (DMN). Also, the result shows the better performance of the proposed method..
Application of Speech Recognition Technology Based on Channel Adversarial Training in the Field of Speech Recognition System

With the rapid development of big data, artificial intelligence, and Internet technologies, the human-human contact and human-machine interaction have produced an explosive growth of voice data. Rapidly identifying the speaker&amp;#039;s identity and retrieving and managing his or her speech data in the massive amount of speech data has become a major challenge for intelligent speech applications in the field of information security. This research proposes a vocal recognition technique based on information adversarial training for speaker identity recognition in massive audio and video, and speaker identification when oriented to the information security domain. The experimental results show that the method projects data from different scene channels all onto the same space and dynamically generates interactive speaker representations. It solves the channel mismatch problem and effectively improves the recognition of speaker&amp;#039;s voice patterns across channels and scenes. It is able to separate overlapping voices when multiple people speak at the same time and reduce speaker separation errors. It realizes speaker voice recognition for information security field and achieves 89% recall rate in massive database, which has practical application value for intelligent application field.
Robot Path Planning Method Combining Enhanced APF and Improved ACO Algorithm for Power Emergency Maintenance

Considering the limited adaptability of the existing substation inspection robot path planning (PP) algorithms, the authors propose a novel PP method for mobile robots (MR) based on the structure of the ultra-high voltage (UHV) substation inspection robot system. The proposed method combines the improved ant colony optimization (IACO) algorithm and the enhanced artificial potential field (EAPF) algorithm. To minimize the interference of the pheromones, they introduced a pheromone adjustment coefficient in the later iterations of the algorithm. Furthermore, they improved the global pheromone update method, which is beneficial to the MR to search for the optimal path (OP) rapidly. They constructed two environmental models using the grid method, and they used MATLAB to implement comparative experiments between the proposed algorithm and other advanced methods. The results demonstrate that the proposed algorithm outperforms other methods in terms of running time, convergence speed, and global optimization ability.
IRGAN: cGAN-based Indoor Radio Map Prediction

Radio map or radio coverage prediction in indoor and outdoor remains a challenge of great interest due to the large number of applications it allows. Many techniques such as data-driven interpolation methods, model-based data fitting methods, model-based prediction methods exist. However, each of them has their limitations (computation time, accuracy, level of input information required, generalization to different environments). Also indoors seems less studied than outdoors. In this paper, a multi-material model and its enhanced version boosted by attention mechanism for indoor radio map prediction are introduced. The proposed models are based on conditional Generative Adversarial Networks (cGANs) and take floor plan images as input. We also propose a new method for floor plan images preprocessing by segmentation to characterize the materials in the environment of interest, which alleviates user's effort. The validity and efficiency of our method in generating high quality radio maps in different environments and its ability to consider the electromagnetic properties of materials are verified on two simulated datasets. Numerical results show that our approach outperforms state of the art methods.
EBCDet: Energy-Based Curriculum for Robust Domain Adaptive Object Detection

This paper proposes a new method for addressing the problem of unsupervised domain adaptation for robust object detection.To this end, we propose an energy-based curriculum for progressively adapting a model, thereby mitigating the pseudo-label noise caused by domain shifts.Throughout the adaptation process, we also make use of spatial domain mixing as well as knowledge distillation to improve the pseudo-labels reliability.Our method does not require any modifications in the model architecture or any special training tricks or complications.Our end-to-end pipeline, although simple, proves effective in adapting object detector neural networks.To verify our method, we perform an extensive systematic set of experiments on: synthetic-to-real scenario, cross-camera setup, cross-domain artistic datasets, and image corruption benchmarks, and establish a new state-of-the-art in several cases.For example, compared to the best existing baselines, our Energy-Based Curriculum learning method for robust object Detection (EBCDet), achieves: 1-3 % AP50 improvement on Sim10k-to-Cityscapes and KITTI-to-Cityscapes, 3-4 % AP50 boost on Pascal-VOC-to-Comic, WaterColor, and ClipArt, and 1-5% relative robustness improvement on Pascal-C, COCO-C, and Cityscapes-C (1-2 % absolute mPC).Code is available at: https://github.com/AutomotiveML/EBCDet.
Few-Shot Learning with Class Imbalance

Few-Shot Learning (FSL) algorithms are commonly trained through Meta-Learning (ML), which exposes models to batches of tasks sampled from a meta-dataset to mimic tasks seen during evaluation. However, the standard training procedures overlook the real-world dynamics where classes commonly occur at different frequencies. While it is generally understood that class imbalance harms the performance of supervised methods, limited research examines the impact of imbalance on the FSL evaluation task. Our analysis compares 10 state-of-the-art meta-learning and FSL methods on different imbalance distributions and rebalancing techniques. Our results reveal that 1) some FSL methods display a natural disposition against imbalance while most other approaches produce a performance drop by up to 17\% compared to the balanced task without the appropriate mitigation; 2) contrary to popular belief, many meta-learning algorithms will not automatically learn to balance from exposure to imbalanced training tasks; 3) classical rebalancing strategies, such as random oversampling, can still be very effective, leading to state-of-the-art performances and should not be overlooked; 4) FSL methods are more robust against meta-dataset imbalance than imbalance at the task-level with a similar imbalance ratio ($\rho<20$), with the effect holding even in long-tail datasets under a larger imbalance ($\rho=65$).
A general framework for the semantics of type theory

Abstract We propose an abstract notion of a type theory to unify the semantics of various type theories including Martin–Löf type theory, two-level type theory, and cubical type theory. We establish basic results in the semantics of type theory: every type theory has a bi-initial model; every model of a type theory has its internal language; the category of theories over a type theory is bi-equivalent to a full sub-2-category of the 2-category of models of the type theory.
Penggunaan System Development Life Cycle (SDLC) Sebagai Sistem Informasi Penjualan Retail

This study aims to solve problems experienced at the Six Store Probolinggo Store where data recording and investigation of sales and purchase transactions still use manual goods receipts. When checking the size or stock of goods for each product when purchasing at offline user sales, the user still has to wait to find out whether stock is still there or not and the high error rate that occurs. The Six Store store needs a way to solve the problems that occur, so the researchers created a system design using the System Development Life Cycle (SDLC) method. This method was chosen because SDLC can measure and improve the sales process more effectively, minimize operational costs and increase revenue throughout the day. While programmers use VB 6.0-based applications and Ms. Access. It can be concluded if there is progress produced by the news system. This proves that regular financial bookkeeping can affect the results of the sale itself. This News System is able to obtain sales reports per note, per item variant, per item and inventory reports in such a short time according to user needs. The results of the application of the Information System show an increase in turnover of 75% every year at the Six Store Probolinggo Store
Effectiveness of Dynamic Archive System (SIKD) in Dynamic Archival Management in Vocational School

This study aims to measure the level of effectiveness of the Dynamic Archival Information System (SIKD) in managing archives. The data analysis technique used in this research is descriptive qualitative with simple tabulation calculations. Data collection techniques used interviews, literature studies and questionnaires given to 33 respondents using SIKD with 6 dimensions of the Delone and Mclean models namely system quality, information quality, service quality, use, customer satisfaction and added benefits. The results showed that the dynamic archive information system (SIKD) implemented in the School of Vocation of Universitas Sebelas Maret has been good and effective to help manage the dynamic archive. The lowest score of 2.85 is obtained in service quality dimension, because there are some types of users that can use. The 90% respondents constituting 27 letter recorders highly affect the response based on the feedback given by Structural Official user type. An in-depth interview indicates that the constraint found related to the slow response is due to human resource or the preoccupation of structural officials. Meanwhile, the overall results of the calculations show that SIKD is effective in managing official scripts in vocational schools.
Physics-Aware Semi-Supervised Underwater Image Enhancement

Underwater images normally suffer from degradation due to the transmission medium of water bodies. Both traditional prior-based approaches and deep learning-based methods have been used to address this problem. However, the inflexible assumption of the former often impairs their effectiveness in handling diverse underwater scenes, while the generalization of the latter to unseen images is usually weakened by insufficient data. In this study, we leverage both the physics-based underwater Image Formation Model (IFM) and deep learning techniques for Underwater Image Enhancement (UIE). To this end, we propose a novel Physics-Aware Dual-Stream Underwater Image Enhancement Network, i.e., PA-UIENet, which comprises a Transmission Estimation Steam (T-Stream) and an Ambient Light Estimation Stream (A-Stream). This network fulfills the UIE task by explicitly estimating the degradation parameters of the IFM. We also adopt an IFM-inspired semi-supervised learning framework, which exploits both the labeled and unlabeled images, to address the issue of insufficient data. Our method performs better than, or at least comparably to, eight baselines across five testing sets in the degradation estimation and UIE tasks. This should be due to the fact that it not only can model the degradation but also can learn the characteristics of diverse underwater scenes.
Kernel Geometric Mean Metric Learning

This paper propose a kernel geometric mean metric learning (KGMML) algorithm. The basic idea is to obtain the closed-form solution of the geometric mean metric learning (GMML) algorithm in the high-dimensional feature space determined by the kernel function. Then, the solution is generalized as a form of kernel matrix by using the integral representation of the weighted geometric mean and the Woodbury matrix in this new feature space. Experimental results on 15 datasets show that the proposed algorithm can effectively improve the accuracy of the GMML algorithm and other metric algorithms.
Practical Approaches and Guidance to Small Business Organization Cyber Risk and Threat Assessments

Cyber-attacks and breaches can occur in any organization type, and the areas of small businesses are not exempt from this nefarious activity. This research note and rapid review provide various cybersecurity tools, guidelines, and frameworks that a small business can consider when embarking on the action to assess its cybersecurity hygiene and defensive stance. The content was pulled together in response to the need for an easy-to-digest approach that a small business utilizes to gain valuable confidence to undertake a self-assessment or third-party review of an organization’s cybersecurity plans. Regarding cybersecurity concerns, doing nothing is not an option, and taking an initial step to review computing, information technology, and data systems practices will only be beneficial in attempting to sustain a business and organization.
Comparative Study of Open Access and Non-Open Access Journals in Subject Area Energy

The purpose of this research is to establish a comparative analysis between open access journals and those open access journals whose sole purpose is to research on energy as a socialized conduit.To achieve such objective three tools will be used, namely: 1) the bibliographic database of abstracts, 2) the citations of articles for scientific journals, including Scopus, and 3) bibliometric analysis.Different studies show research that has been published in non-open access journals is larger in content than those published in open access journals.Non-open access journals establish a trend indicating better SNIP and SJR scores in comparison to their open access journal counterparts.Such trends are considered very atypical, and they certainly cannot be pre-determined in other areas of knowledge which would include: medicine, agronomy, biochemistry, mathematics, and planetary sciences.So far, the United Kingdom has been the leading-edge country which has provided more funding for both non-open access journals and open access journals in terms of energy research.
Comparison of Smoothness, Movement Speed and Trajectory during Reaching Movements in Real and Virtual Spaces Using a Head-Mounted Display

Virtual reality is used in rehabilitation and training simulators. However, whether movements in real and virtual spaces are similar is yet to be elucidated. The study aimed to examine the smoothness, trajectory, and velocity of participants’ movements during task performance in real and virtual space. Ten participants performed the same motor task in these two spaces, reaching for targets placed at six distinct positions. A head-mounted display (HMD) presented the virtual space, which simulated the real space environment. The smoothness of movements during the task was quantified and analysed using normalised jerk cost. Trajectories were analysed using the actual trajectory length normalised by the shortest distance to the target, and velocity was analysed using the time of peak velocity. The analysis results showed no significant differences in smoothness and peak velocity time between the two spaces. No significant differences were found in the placement of the six targets between the two spaces. Conversely, significant differences were observed in trajectory length ratio and peak velocity time, albeit with small effect sizes. This outcome can potentially be attributed to the fact that the virtual space was presented from a first-person perspective using an HMD capable of presenting stereoscopic images through binocular parallax. Participants were able to obtain physiological depth information and directly perceive the distance between the target and the effector, such as a hand or a controller, in virtual space, similar to real space. The results suggest that training in virtual space using HMDs with binocular disparity may be a useful tool, as it allows the simulation of a variety of different environments.
Prompt-Based Tuning of Transformer Models for Multi-Center Medical Image Segmentation of Head and Neck Cancer

Medical image segmentation is a vital healthcare endeavor requiring precise and efficient models for appropriate diagnosis and treatment. Vision transformer (ViT)-based segmentation models have shown great performance in accomplishing this task. However, to build a powerful backbone, the self-attention block of ViT requires large-scale pre-training data. The present method of modifying pre-trained models entails updating all or some of the backbone parameters. This paper proposes a novel fine-tuning strategy for adapting a pretrained transformer-based segmentation model on data from a new medical center. This method introduces a small number of learnable parameters, termed prompts, into the input space (less than 1% of model parameters) while keeping the rest of the model parameters frozen. Extensive studies employing data from new unseen medical centers show that the prompt-based fine-tuning of medical segmentation models provides excellent performance regarding the new-center data with a negligible drop regarding the old centers. Additionally, our strategy delivers great accuracy with minimum re-training on new-center data, significantly decreasing the computational and time costs of fine-tuning pre-trained models. Our source code will be made publicly available.
Nonadiabatic holonomic quantum computation based on a commutation relation

Nonadiabatic holonomic quantum computation has received increasing attention due to the merits of both robustness against control errors and high-speed implementation. A crucial step in realizing nonadiabatic holonomic quantum computation is to remove the dynamical phase from the total phase. For this reason, previous schemes of nonadiabatic holonomic quantum computation have to resort to the parallel transport condition, i.e., requiring the instantaneous dynamical phase to be always zero. In this paper, we put forward a strategy to design nonadiabatic holonomic quantum computation, which is based on a commutation relation rather than the parallel transport condition. Instead of requiring the instantaneous dynamical phase to be always zero, the dynamical part of the total phase is separated from the geometric part and then removed by properly choosing evolution parameters. This strategy enhances the flexibility to realize nonadiabatic holonomic quantum computation as the commutation relation is more relaxed than the parallel transport condition. It provides more options for realizing nonadiabatic holonomic quantum computation and hence allows us to optimize realizations such as the evolution time and evolution paths.