Reviewerly/exHarmony huggingface dataset:
Single-ended quality measurement of a music content via convolutional recurrent neural networks

The paper examines the usage of Convolutional Bidirectional Recurrent Neural Network (CBRNN) for a problem of quality measurement in a music content.The key contribution in this approach, compared to the existing research, is that the examined model is evaluated in terms of detecting acoustic anomalies without the requirement to provide a reference (clean) signal.Since real music content may include some modes of instrumental sounds, speech and singing voice or different audio effects, it is more complex to analyze than clean speech or artificial signals, especially without a comparison to the known reference content.The presented results might be treated as a proof of concept, since some specific types of artefacts are covered in this paper (examples of quantization defect, missing sound, distortion of gain characteristics, extra noise sound).However, the described model can be easily expanded to detect other impairments or used as a pre-trained model for other transfer learning processes.To examine the model efficiency several experiments have been performed and reported in the paper.The raw audio samples were transformed into Mel-scaled spectrograms and transferred as input to the model, first independently, then along with additional features (Zero Crossing Rate, Spectral Contrast).According to the obtained results, there is a significant increase in overall accuracy (by 10.1%), if Spectral Contrast information is provided together with Mel-scaled spectrograms.The paper examines also the influence of recursive layers on effectiveness of the artefact classification task.
A Real-Time Lightweight Detection Algorithm for Deck Crew and the Use of Fishing Nets Based on Improved YOLOv5s Network

A real-time monitoring system for the operational status of fishing vessels is an essential element for the modernization of the fishing industry. The operational status of fishing vessels can be identified by using onboard cameras to detect the deck crew and the use of fishing nets. Due to the typically limited processing capacity of shipboard equipment and the significant memory consumption of detection models, general target detection models are unable to perform real-time image detection to identify the operational status of fishing vessels. In this paper, we propose a lightweight real-time deck crew and the use of a fishing net detection method, YOLOv5s-SGC. It is based on the YOLOv5s model, which uses surveillance cameras to obtain video of fishing vessels operating at sea and enhances the dataset. YOLOv5s-SGC replaces the backbone of YOLOv5s with ShuffleNetV2, replaces the feature fusion network with a modified Generalized-FPN, and adds the CBAM attention module in front of the detection head.
Development of an Accessibility Testing System for the Virtual Machine Deployment Service in the Cloud

This paper presents a developed system for testing the accessibility of the virtual machine deployment service in the cloud. The methods used for monitoring cloud systems based on open-source solutions such as Kubernetes, Prometheus, and Selenium are discussed. The key stages of the system design are described, including requirements gathering and analysis, architecture, and implementation features. This system allows for the prevention of potential issues before they arise, as well as increasing IT service reliability. Integration with open-source systems helps to reduce the cost of system development and operation and speeds up implementation time. Additionally, this system can be quickly adapted and customized to meet specific needs. To analyze the feasibility of building the system, production statistics of virtual machine reservations were collected using the Prometheus monitoring system.
A universal model for solar radiation exergy accounting: Case study of Tunisia

The global solar radiation is the origin for all environmental processes on the earth and the majority of energy sources are derived from it.The data of solar radiation are required for the design and the study of solar application systems.The more important is the quality of the solar radiation which is defined by the maximum work can be provided by the solar radiation.This quality is measured by the exergy content of a solar radiation.In the present work, a universal pattern has been built to provide a prediction of solar exergy dependently to the geographic location.Fitting models have been developed for exergy account depending on geographic location, based on the linear, quadratic, cubic, logarithmic, exponential, power regression.The Petela model is adopted from literature for exergetic efficiency accounting of solar radiation.The global solar radiation according to ASHRAE model is expressed dependently of the cosine of zenith angle.The developed model is applied on Tunisia regions to predict exergy solar potential.The studied regions are classified regarding the exergy account, high, medium and low solar exergy locations.Results show that generally the solar radiation shows a low degree of exergy content, about 7% of difference.
Forensic Examination of Ceph

The concept of Software Defined Storage (SDS) has become very popular over the last few years. It is used in public, private, and hybrid clouds to store enterprise, private, and other kinds of data. Ceph is an open source software that implements an SDS stack. This article analyzes the data found on storage devices (Object Store Devices (OSDs)) used to store Ceph BlueStore data from a data forensics point of view. The Object Store Device (OSD) data is categorized using the model proposed by Carrier into the five categories file system, content, metadata, file name, and application category. It then describes how the different data can be connected to present useful information about the content of an OSD and presents the implementation of a forensic software tool for OSD analysis based on Ceph 12.2.4 luminous .
Algorithm Comparison and Evaluation of GAN Models Based on Image Transferring from Desert to Green Field

Some time-consuming and labor-intensive techniques, like manual drawing or interactive modeling with an image editing system, are often used to show how a desert area might look after being transformed into a green field (oasis) in an image way. In order to improve the rendering efficiency of image style transformation and increase the variety of renderings, we can build an algorithm for automatically generating style images based on machine learning. In this paper, after comparing seven generative adversarial network (GAN) models in the way of theory analysis, we propose a method for generating green fields using desert images as input data, and a comprehensive comparison is presented on how GANs are currently applied to solve the desert-to-oasis problem. Experimental results show that two GAN models, geometrically consistent GAN and cyclic consistent GAN, have the best transfer effect of a desert image to oasis one in the view of quantitative indicators, Fréchet inception distance, and learned perceptual image patch similarity.
Graph Neural Networks for Traffic Classification in Satellite Communication Channels: A Comparative Analysis

This paper presents a comprehensive comparison of graph neural networks, specifically Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT), for traffic classification in satellite communication channels. The performance of these GNN-based methods is benchmarked against traditional Multi-Layer Perceptron (MLP) algorithms. The results indicate that GNNs demonstrate superior accuracy and efficiency compared to MLPs, emphasizing their potential for application in satellite communication systems. Moreover, the study investigates the impact of various factors on GNN algorithm performance, providing insights into the most effective strategies for implementing GNNs in traffic classification tasks. This research offers valuable knowledge on the benefits and prospective use cases of GNNs within satellite communication systems.
Tinkercad environment as a tool for forming ideas about the areas of professional activity in the it industry when studying informa tics at the level of basic general education

The article presents the experience of using the Tinkercad environment to teach in basic school the traditional content of the informatics course related to the study of modeling, programming, and information technologies. The article describes a practical work for beginners, in which the creation of an LED beacon is presented step by step, and tasks for independent work, during which students create a programmed model of the device. The article also offers a task in the form of a case, for the successful completion of which students need to apply the skills they have acquired in practical work. The material will be useful for teachers of informatics and technology working in general education classes or information technology classes, when teaching a basic informatics course, as well as courses in robotics, modeling, programming.
Interactive seminar (teachers' council) using informa tion technologies

The article provides an example of how, using information and communication technologies, it is possible to carry out internal corporate training in an educational organization, aimed at the final point request of modern challenges of the education system. The proposed option for conducting an interactive training seminar (teachers' council) using the developed software product makes it possible to increase the effectiveness of training, since each teacher "lives through" all stages of learning in action. Teamwork allows you to increase the level of team cohesion, which is also important in the digital age. The practice of seminars held in two different educational institutions suggests that the project is scalable. After the seminar, each participant can take with him the completed work and refer to it at any time.
The possibilities of game dialog programs for teaching the solution of typical tasks of the "Programming" section of the school informatics course

Today, not enough attention is paid to teaching programming in schools, as it causes many difficulties for students associated with a misunderstanding of the abstract and difficult to assimilate new material. At the same time, a weak level of motivation to study and, in the future, to teach the section "Programming" is one of the problems of training future informatics teachers. As a means of motivation for both students and future informatics teachers, game dialogue programs (games in which the player enters into a dialogue with the computer) can be used. Such programs can be used by teachers to develop students' skills in solving typical problems in the "Programming" section. The article discusses the possibilities of using various types of game dialogue programs (text quests, games with random numbers, dice, bulls and cows) for learning to solve typical problems in the "Programming" section. In particular, these games can be used in the study of data processing algorithms using branches and loops, algorithms for finding the maximum and minimum, algorithms for extracting digits from a natural number. At the same time, it should be noted that these programs can be adapted for learning to solve typical problems of the "Programming" section based on other educational programming languages, such as Pascal, Java, C++, etc.
The additional professional advanced training program for educators "Application of ma chine learning in project and research activities of the natural sciences"

The article discusses the possibilities of the modern information technologies, in particular, machine learning, in the implementation of the additional professional advanced training program "Application of machine learning in project and research activities of the natural sciences" by teachers of the Perm State Humanitarian Pedagogical University. The course for educators of education organizations of general education is presented. The course is aimed at creating interest among educators in the development of interdisciplinary project and research activities of students in a school focused on working with modern and relevant projects in the field of natural sciences and artificial intelligence. The structure and content line of the advanced training program are described. Examples of students' work performed as part of the study of each of the sections of the additional professional advanced training program are given. Based on the experience of implementing the program, a number of conclusions have been formulated that characterize the results achieved.
Real-Time Reflection Reduction from Glasses in Videoconferences

Surrounding lighting conditions cannot always be sufficiently controlled during videoconferences, yielding situations in which disturbing reflections might appear on the participants glasses. In this article, we present a retrained neural network to convincingly reduce such reflections. For real time performance we propose an asynchronous processing pipeline accompanied by a head pose-based caching strategy to reuse intermediate processing results. The implementation as virtual webcam allows the system to be used with arbitrary videoconferencing systems.
Accuracy of Legendre Moments for Image Representation

Existing works on orthogonal moments are mainly focused on optimizing classical orthogonal Cartesian moments, such as Legendre moments, Gauss-Hermite moments, Gegenbauer moments, and Chebyshev moments. Research in this area generally includes accurate calculation, fast computation, robustness/invariance optimization, and the application of orthogonal moments. This paper presents the inclusion of the integration method proposed by Holoborodko to calculate the Legendre moments. The results obtained are compared with the traditional equation and the methods proposed by Hosny and Pawlak to approximate the integration computation.
Visualization of deviations between different geometries using a multi-level voxel-based representation

We present an approach for visualizing deviations between a 3d printed object and its digital twin. The corresponding 3d visualization for instance allows to highlight particularly critical sections that indicate high deviations along with corresponding annotations. Therefore, the 3d printing thus needs to be reconstructed in 3d, again. However, since the original 3d model that served as blueprint for the 3d printer typically differs topology-wise from the 3d reconstructed model, the corresponding geometries cannot simply be compared on a per-vertex basis. Thus, to be able to easily compare two topologically different geometries, we use a multi-level voxel-based representation for both data sets. Besides using different appearance properties to show deviations, a quantitative comparison of the voxel-sets based on statistical methods is added as input for the visualization. These methods are also compared to determine the best solution in terms of the shape differences and how the results differ, when comparing either voxelized volumes or hulls. The application VoxMesh integrates these concepts into an application and provides the possibility to save the results in form of voxel-sets, meshes and point clouds persistently, that can either be used by third party software or VoxMesh to efficiently reproduce and visualize the results of the shape analysis.
Multi-Track Path Planning of Outdoor Scanning Robot in Unknown Scene

As the digital age continues to evolve, automated scanning of outdoor environments ushers in challenges. There are currently two main problems with outdoor mobile robot scanning: firstly, the scanning path is too long; secondly, it is easy to collide with obstacles, and this thesis will focus on the above two problems. Firstly, a topological structure graph is constructed for the outdoor scene, and the set of accessible points is obtained. The high exploration value region is first explored by the greedy algorithm, and then the ant colony algorithm is used for path planning of the general exploration value region. Secondly, we make algorithmic improvements to the ant colony algorithm by adopting a multi-trajectory path planning algorithm that allows decision makers to obtain multiple solutions, proposing a negative feedback ant colony algorithm, and introducing guidance pheromones and alert pheromones to enable the ant colony algorithm to explore with a comprehensive consideration of the effects brought about by the environment. Finally, for the algorithm proposed in this thesis, we conducted simulation experiments using point cloud map and raster method respectively, our proposed algorithm has better performance on the map, and the improved ant colony algorithm can improve the scanning range by about 4% than the original algorithm when moving the same distance.
ArgFrame: A multi-layer, web, argument-based framework for quantitative reasoning

Multiple systems have been proposed to perform computational argumentation activities, but there is a lack of options for dealing with quantitative inferences. This multi-layer, web, argument-based framework has been proposed as a tool to perform automated reasoning with numerical data. It is able to use boolean logic for the creation of if-then rules and attacking rules. In turn, these rules/arguments can be activated or not by some input data, have their attacks solved (following some Dung or rank-based semantics), and finally aggregated in different fashions in order to produce a prediction (a number). The framework is implemented in PHP for the back-end. A JavaScript interface is provided for creating arguments, attacks among arguments, and performing case-by-case analyses.
Deep learning-based label-free hematology analysis framework using optical diffraction tomography

<h2>Abstract</h2> Hematology analysis, a common clinical test for screening various diseases, has conventionally required a chemical staining process that is time-consuming and labor-intensive. To reduce the costs of chemical staining, label-free imaging can be utilized in hematology analysis. In this work, we exploit optical diffraction tomography and the fully convolutional one-stage object detector or FCOS, a deep learning architecture for object detection, to develop a label-free hematology analysis framework. Detected cells are classified into four groups: red blood cell, abnormal red blood cell, platelet, and white blood cell. In the results, the trained object detection model showed superior detection performance for blood cells in refractive index tomograms (0.977 mAP) and also showed high accuracy in the four-class classification of blood cells (0.9708 weighted F1 score, 0.9712 total accuracy). For further verification, mean corpuscular volume (MCV) and mean corpuscular hemoglobin (MCH) were compared with values obtained from reference hematology equipment, with our results showing reasonable correlation in both MCV (0.905) and MCH (0.889). This study provides a successful demonstration of the proposed framework in detecting and classifying blood cells using optical diffraction tomography for label-free hematology analysis.
Persistence and pleasure in VR: Enhancing Exercise Endurance and Enjoyment through Virtual Environments

Virtual Reality (VR) provides an enjoyable addition to stationary physical exercise and can improve performance while exercising. The aim of this study is to explore the effectiveness of three interactive virtual environments (i.e., social, relaxing, stressful) on enjoyment and persistence during strength-based exercises. In a within-subjects experiment, 97 healthy young adults completed four consecutive sets of two strength-based exercises. Participants completed one set as baseline and then each participant completed three more sets in three different interactive environments, experienced through a VR headset. Results showed that both the stressful virtual environment, where participants were hanging suspended over a city, and the social virtual environment where participants were audibly encouraged in a stadium, increased participants' persistence in both exercises, when compared to the relaxing virtual environment. Specifically, the relaxing sunny beach environment caused poorer performances in a dead hang exercise among men (n = 30), and poorer performances in a core exercise among women (n = 66). Somewhat paradoxically, this relaxing virtual beach environment was considered the most enjoyable environment among both male and female participants. The potential of VR in exercise lies in its ability to provide pleasurable and performance-enhancing immersive environments that may be too expensive or dangerous in reality.
Breaking down linguistic complexities: A structured approach to aspect-based sentiment analysis

Aspect-based sentiment analysis refers to the task of determining the sentiment polarity associated with particular aspects mentioned in a sentence or document. Previous studies have used attention-based neural network models to connect aspect terms with context words, but these models often perform poorly due to limited interaction between aspect terms and opinion words. Furthermore, these models typically focus only on explicitly stated aspect objects, which can be overly restrictive in certain scenarios. Current sentiment analysis methods that rely on aspect categories also often fail to consider the implicit placement of aspect-category information within the context. While existing models may produce strong results, they often lack domain knowledge. To address these issues, this study proposes an Aspect-position and Entity-oriented Knowledge Convolutional Graph (APEKCG) consisting of two modules: the Aspect position-aware module (APA) and the Entity oriented Knowledge Dependency Convolutional Graph (EKDCG). The APA module is designed to integrate aspect-specific sentiment features for sentiment classification by incorporating information about aspect categories into different parts of the context. The EKDCG module incorporates entity-oriented knowledge, dependency labels, and syntactic path using a dependence graph. Experimental results on five benchmarks Natural Language Processing (NLP) datasets of the English language demonstrate the effectiveness of the proposed APEKCG framework. Furthermore, the APEKCG outperformed previous state-of-the-art models with its accuracy, achieving 89.13%, 84.32%, 89.02%, 79.64%, and 90.22% on the MAMS, Laptop, Restaurant, AWARE, and SemEval-15&16 datasets, respectively.
3DF-FCOS: Small object detection with 3D features based on FCOS

Lower resolution and fewer feature details appear in small objects, so achieving small object detection is a challenging task. To improve the detection ability in the case of low-resolution object details, we propose to exploit 3D features with FCOS for representing small objects. The proposed method employs a Global Spatial Block (GS-Block) in the backbone to guide the network to learn the picture’s shallow features and increase the model’s perceived capability for small objects. In addition, we introduce a 3D sparse convolution in the shallow input features of the detection heads to better capture the global and contextual information of the shallow feature elements and further improve the feature representation of small objects in the backgrounds, and the whole network is referred to as 3DF-FCOS. Finally, we conducted extensive experiments to verify the effectiveness of 3DF-FCOS on VisDrone2019Det. The results show that our method achieves a remarkable improvement in AP and APs compared to the baselines of 1.2% and 1.2%, respectively. Compared with current state-of-the-art (SOTA) algorithms, the performance of our proposed 3DF-FCOS is comparable.