Reviewerly/exHarmony huggingface dataset:
Blockchain Technologies for Validation of Priority Vehicles in ITS

The analysis of the possibility of using distributed networks, in particular, the blockchain network, smart contracts, as a control mechanism for controlling messaging and ensuring the digital security of the network is carried out in the work. The paper describes the concept of priority passage and its provision using distributed network mechanisms. Two priority passage scenarios are considered, as well as a messaging scheme for general cases of priority passage and the case of overlapping priority routes. Also, the paper presents mathematical studies of evaluating the possibility of a blockchain network depending on its performance, as well as approbation of calculations on model stands on networks with PoW and LPoS consensus algorithms.
Machine Learning and Modeling of Ultrasonic Signals for High-Fidelity Data Compression

Ultrasonic systems are widely used in imaging applications for non-destructive evaluation, quality assurance, and medical diagnosis. These applications require large volumes of data to be processed, stored, and/or transmitted in real time. It is essential to compress the ultrasonic RF signal without inadvertently degrading desirable signal features. This study explores the development of learning models for massive data compression based on wavelet packet transformation, using machine learning techniques. Furthermore, this study utilizes the fast chirplet transform algorithm to successively estimate broadband, narrowband, symmetric, skewed, nondispersive, or dispersive echoes. These parameters not only have significant physical interpretations for radar, sonar, seismic, and ultrasonic applications but also, yield a method for efficient and high-precision data compression. Signal modeling and parameter estimation of the nonstationary ultrasonic echoes are critical for image analysis, target detection, and object recognition. The objective of this study is to design computationally efficient algorithms and the implementation of 3D ultrasonic data compression.
Comparative study of Video/Image denoising algorithms based on Convolutional neural network CNN

In this paper, we present a comparative study of different algorithms used to denoise images and videos, some of them are especially used in deep neural networks so we can see the effects of its performances using different computational criteria such as PSNR and SSIM. The algorithms for image denoising are BM3D, DnCNN, FFDNet, and the others for video denoising are SPTWO, VBM4D, VNLB, DVDnet, and Fast DVDnet. The purpose of this study is to see the powerful effect of convolutional neural network CNN used in image & video denoising compared to classical techniques.
Intelligent System for Skin Disease Detection of Dogs with Ontology Based Clinical Information Extraction

The largest organ in dogs, the epidermis, is crucial in supplying immunological responses. Skin will preserve all the nutrients and safeguard the cells while warding off harmful or pathogenic substances. Most dog owners today are not aware that their pet dog has a skin condition. Although they were aware of these ailments, they had no notion of how to cure them. In such a situation, the dog may experience pain and an aggravation of the condition. Owners should therefore take their dogs to the vet, even if the skin condition is minor. It can, however, be a costly procedure. There aren’t many forums where dog owners may get advice from professionals and ask inquiries regarding their pets. The solution suggests a fully functional mobile application which is a combination of disease identification feature, disease severity level detection feature, domain specific knowledge base with semantic web development and a domain specific AI based chat-bot to the dog owners to overcome this problem using Convolutional Neural Network (CNN) and natural language processing (NLP).System will extract the necessary features from the images of the lesion to classify the skin condition and Severity level of the disease. The results obtained show disease type classification is within the accuracy range of 77.78% to 100% which tested again 4 CNN base models. As for the severity level identification accuracy situated around 99.62%.
SOA-based nonlinear reservoir for echo-state networks

We introduce a new approach to reservoir computing (RC) in which single nonlinear device – semiconductor optical amplifier, replaces the entire nonlinear reservoir to perform computations. To study the performance of the proposed scheme, we use it for the benchmark prediction task of learning the Mackey-Glass chaotic attractor. Mildly chaotic attractor with tau = 17 and wilder chaotic behavior with tau = 30 are considered.
Super-resolution reconstruction for extremely low-light imaging by using intensified CCD or CMOS camera

It is difficult for normal CCD or CMOS camera to obtain high quality images under extremely low-light conditions for example the new moon or the quarter moon because the photons arriving at the detector are so few that signal to noise ratio (SNR) is much lower than what is necessary to resolve finer details in the nighttime scenario. To solve this problem, the intensified CCD or CMOS camera is adopted and the few photons is amplified to improve the SNR a lot. However, the intensifier is mainly composed of the cathode, MCP (Micro-channel-plate) and fluorescent screen and this complex structure and the multiple photoelectric conversion during the photon amplification process will lead to a big equivalent pitch size, which degrades the spatial resolution. Therefore in this manuscript, by improving the classical iterative back projection (IBP) algorithm a super-resolution reconstruction algorithm is proposed. By fusing multiple quite noisy lowlight images having sub-pixel displacements between each other, both the spatial resolution and the SNR could be enhanced. In the in-lab experiments, the spatial resolution can be increased to nearly 1.8 times the original one. Besides that, the increment in SNR bigger than 6dB and 9dB could be obtained for the quarter moon and the new moon light condition respectively. The out-door experiments show the similar results and besides that by fusing sub-pixel shifted low-light images corresponding to different low-light conditions together, the reconstructed high-resolution images will have even better visual performance.
Array camera crowd counting method based on Yolov5

Array cameras can effectively balance the contradiction between large field of view and high resolution in existing imaging systems, and can effectively solve the problem of high-density crowd counting in large scenes such as stadiums and parks. In order to realize the intelligence of the array camera, a crowd counting method of the array camera based on yolov5 is proposed, which is of great significance to realize the intelligence of public security management. Through the rtsp video stream input by the array sub-camera, the position coordinate conversion relationship between the sub-image and the large image is established, and then the panoramic image is output in real time to meet the needs of video surveillance. Then preprocess each rtsp video stream, use the tensorrt-accelerated yolov5 target detection method to identify the target in each sub-image, and convert the target coordinates into the coordinates of the large image, and use the NMS method to filter out duplicate targets in the overlapping area of each sub-image. , and finally summarize the results to output the target number. The system can be deployed on Win10, Linux and embedded system, working reliably with high precision to meet the practical application.
Infrared image super-resolution reconstruction based on high-frequency prior convolutional neural network

Image super-resolution technology successfully overcomes the limitation of excessively large pixel size in infrared detectors and meets the increasing demand for high-resolution infrared image information. In this paper, the superresolution reconstruction of infrared images based on a convolutional neural network with a priori for high frequency information is reported. The main network structure is based on residual blocks, BN blocks that are not suitable for the super-resolution task are removed. The introduction of residual learning reduces computational complexity and accelerates network convergence. Multiple convolution layers and deconvolution layers respectively implement the extraction and restoration of the features in infrared images. images are divided into high frequency and low frequency parts. The low frequency part is the image of down-sampling, while the high frequency information is obeyed a simple case-agnostic distribution, which is equivalent to having a prior of high frequency information for the super-resolution network, Which is captures some knowledge on the lost information in the form of its distribution and embeds it into model’s parameters to mitigate the ill-posedness. Compared with the other previously proposed methods for infrared information restoration, our proposed method shows obvious advantages in the ability of high-resolution details acquisition.
A fast facial expression recognition algorithm in the teaching and learning environments

Learner’s Emotional state has an important impact on affective and cognitive processes for classroom teaching. It is quite necessary to detect learner’s emotion state unconsciously in teaching and learning processes. A fast facial expression recognition algorithm is presented to detect the emotional state of the learner in real learning environment. Gabor convolutional network (GCN) is used to classify the facial expression. The image extracted from teaching and learning environment need to be preprocessed for accelerating the expression recognition. A skin color segmentation model, generalized Gaussian mixture distribution (GGMD), is designed by using expectation and maximization (EM) algorithm to detect the facial area rapidly. Then a fast facial expression recognition algorithm is designed by using the skin color model and the GCN. Experiment results show the satisfied accuracy and excellent time performance of the system.
DLT design and robustness verification based on random graph theory

Distributed Ledger Technology (DLT), commonly referred to as blockchains, provide a new approach to security. Rather than securing items by blocking access, data is made immutable by creating a massively distributed data structure. Entries are secured using cryptographic primitives. Coordination is done using consensus algorithms. We start by introducing random graph theory and presenting its major result: the emergence of a giant component. We explain how and when giant components arise. We explain why the existence, or not, of giant components result in the presence, or not, of graph properties of interest. We then introduce the most widely used consensus algorithms, concentrating on potential problems. We then describe a simple light weight mining (LWM) alternative. We use Erdös's random graph theory to prove the effectiveness and efficiency of LWM. This is an example of how random graph theory can be used to design and implement robust DLT systems
P2DF: Privacy-Preserving Data Fusion Protocol

In IoT environments, a large amount of heterogeneous data is generated from different devices in a short time. In all applications of IoT, data quality is important in decision-making. Data fusion is an approach to increase the quality of data collected from different sources and decrease the amount of wrong and conflicting data. Collecting data from different sources and fusing them has many benefits. However, there is concern about privacy violations for data owners. Designing a privacy-preserving protocol for data fusion is important. To the best of our knowledge, no protocol has been provided for private data fusion.In this paper, we propose a private protocol for data fusion in IoT environments based on the DFIOT method. The computational and communication complexity of our proposed protocol is O(n). We prove the security of our proposed protocol based on the real-ideal simulation paradigm. Also, we consider the problem of achieving security in the presence of semi-honest parties and adversaries.
Towards Fast and Flexible Simulation of Cloud Resource Management

Resource management, including the allocation of resources of physical machines to virtual machines, is a critical part of operating the modern large-scale cloud infrastructures. This is an active area of research with many problem statements and remaining challenges. The research efficiency in this area is hampered by the lack of general, flexible and performant frameworks for realistic simulation of IaaS clouds. To address this issue we have developed DSLab IaaS - a simulation framework that implements a model of IaaS infrastructure, including all the main components needed to study the VM allocation problems and evaluate the proposed solutions. The unique feature of the framework is support for modeling of multiple concurrent VM schedulers operating on local, asynchronously updated copies of resource pool state. The framework is extensible by supporting the user-defined VM placement and migration algorithms and models for VM load and host energy consumption. The framework also facilitates the use of currently available cloud traces. The framework has been evaluated on several examples and has demonstrated simulation performance and scalability significantly surpassing the state-of-the-art solution thereby enabling the modeling of resource management in modern large-scale cloud infrastructures.
Label-affinity-Scheduler: Considering Business Requirements in Container Scheduling for Multi-Cloud and Multi-Tenant Environments

The goal of this paper is to present a scheduling strategy that addresses a set of customer-specific business requirements when deploying containers, targeting multi-user and multi-cloud environments. This is done by extending the label scheme used to specify attributes for compute nodes and requirements for applications, and by associating workloads to the nodes with the highest affinity. The proposal is validated by implementing a custom scheduler for Kubernetes orchestrator. The custom scheduler was validated in an environment consisting of 25 nodes distributed across 4 providers with different hardware configurations and geographical locations. The results confirm the effectiveness of our scheduler in different scenarios, granting the business requirements assigned to each application.
Virtual tutors and their relationship with student learning at Universidad Nacional del Callao

The present study aimed to determine the type of correlation between the use of Information and Communication Technologies (ICTs) as virtual tutors and e-learning in the context of the Covid-19 health emergency. A group of students from the National University of Callao (UNAC) was taken as a case study. The study group consisted of 60 students of the statistics course and the flexibility, didactics and motivation provided by the virtual tutors were taken as important factors, as well as the conceptual, attitudinal and procedural learning of the course. The result by means of Spearman's Rho obtained a value of r = 0.688 which concludes that there is a positive and significant correlation indicating that the use of Virtual Tutors is related to the learning level of the students of a course and generates an impact on the improvement of the knowledge of the subject.
Role changes in software development projects using agile methodologies: A case study of private companies in Guatemala City

This new era of software development began to generate changes in the way software development teams are organized, new roles and functions are established in project management. In many cases this role does not exist and a similar role must be chosen and the project manager must adapt. This research seeks to know the changes made by software project managers in private companies in Guatemala City. The research hypothesizes that the implementation of agile methodologies produces an update in the functions and roles in the area of project management in private institutions. For this purpose, a four-phase study was carried out: Design of the instrument, Distribution of the instrument, Processing of the information and Analysis of the information. A sample of 35 private companies was worked with the participation of software development team leaders. The results obtained from an online survey allowed us to discover that there are considerable changes in the roles and responsibilities of project leaders in organizations that migrate from traditional methodology to agile methodology.
Key elements for UX-based design, applied in the improvement of the academic service of Peruvian university education in the context of COVID-19

The support systems for academic management are of vital importance for the development of activities in a higher education institution and their integration with learning platforms are key for a quality educational service, a requirement intensified during the COVID-19 pandemic. In most universities in Peru, these applications are far from the necessary critical requirements and especially if seen in terms of user experience (UX), however, it is still observed that they do not apply the methodologies or techniques of this approach, they don't even consider the profile of the key user, the student, according to the context in which it operates. That is why a study was conducted through interviews applied to IT staff, which includes five dimensions and surveys to 611 students, with the purpose of characterizing it, based on three conditions, as well as identifying the key elements for the UX design of the academic management support system, through eight criteria with the objective of improving the service provided. The results of the evaluation raise the need to improve usability, based on the characterization of the student, identifying as key elements, that the students are digital natives, they use the various technological media very frequently, although there was a lack of mastery of digital skills. They make use of the academic management support system but not through the mobile mainly because it is not designed for that environment; in visual logic, the majority specifies that the elements of the same theme must be close to each other; regarding performance, access time should be reduced and interaction with the user should be improved; Finally, online support must be improved, as well as the immediacy in dealing with incidents, as a result of the pandemic.
Evaluation of training projects based on socio-training: the case of the Quality Control project

Currently, training projects are a necessary instrument for teacher planning, so that expected learning must be focused on the reality of the context in which students develop. The purpose of this study is to establish the learning outcomes in a clear and concrete way according to the socio-training approach, under which the learning outcome and the product resulting from it must provide a solution to a problem of the context. The methodology applied was focused on action-training research so that through the analysis and evaluation processes of expected learning, changes or improvements can be made to the Quality Control training project. The main results show that most of the expected learning is somewhat theoretical and does not contribute to the solution of a real problem. By improving the training project, the learning results are intended to be of a practical nature, contributing to the improvement of the community's living conditions. The quality control training project was improved to a great extent, through the planning of the teacher's activities, autonomous and practical; the contents were structured according to the socio-training approach, so that the students deal with real cases of their context.
Ensemble Machine Translation to Filter Low Quality Corpus

The popularization of ubiquitous computing equipment and the development of network communication technology have greatly improved the productivity of human language information. The quality of large-scale language information is a key factor affecting the effectiveness of deep learning algorithms. How to filter low quality corpus from large-scale corpus that is unstructured, non-standardized, and even contains fallacies, to obtain high quality corpus has become a research issue of great application value. We focus on the specific filtering issue of bilingual parallel corpus, re-examine the manual filtering process, and propose an ensemble machine translation filtering idea. From the perspective of translation direction, we design and implement a single-engine-based ensemble machine translation filtering framework and algorithm, and from the translation system perspective, we design and implement a multi-engine-based framework and algorithm respectively. Experimental results show that the single-engine-based algorithm can integrate the advantages of different machine translation directions, and the multi-engine-based one can integrate the advantages of different machine translation systems. Using the straightforward methods of Levenshtein string morphological similarity and linear weighted ensemble can implement efficient industrial-grade corpus filtering.
Unified Voice Embedding Through Multi-task Learning

In this work, we describe a multi-task deep metric learning system to learn a single unified voice embedding that can be used to power multiple audio/speaker-specific tasks. The result obtained from using unified embeddings of the input audio outperforms our benchmark models: The deep speaker and VGGVox. Experiments on two distinct datasets suggest that our model performs with an accuracy of 85% for speaker identification task and a Misclassification Rate (MR) of 0.021 for speaker clustering task. In addition to that, the unified aspect of the embedding paved the way to analyse the concept of explainable audio embedding in terms of paralinguistic features. We demonstrate that our proposed unified embedding improves both performance and engagement in terms of specific speaker based tasks: speaker recognition and speaker clustering with accent and language generalization along with embedding explainability through comprehensive evaluations on multiple audio datasets, user studies, and experiments.
Paragraph-level Tibetan Question Generation for Machine Reading Comprehension

The question generation task can automatically generate large-scale questions to provide training data for reading comprehension tasks and QA systems, which are crucial for low-resource languages such as Tibetan. At present, due to the emergence of large-scale datasets and pre-trained language models in the Chinese and English domains, the task of question generation in the Chinese and English domains has been well developed, while the research on question generation in the Tibetan is still in its infancy. The main reason is the lack of datasets and the relatively backward development of various models in Tibetan. To solve the above questions, this paper constructs a Tibetan pre-trained language model TiBERT to provide a basis for the development of various downstream tasks, to expand the datasets of Tibetan machine reading comprehension, this paper proposes a Tibetan question generation model named TQGR. The model consists of two parts, the question generation and question quality assessment. The question generation adopts the classic seq2seq architecture to generate questions, and the question quality assessment improves the quality of generated questions by evaluating the fluency reward score, word repetition rate reward score and interrogative words classification auxiliary task. Finally, the experimental results show that our model has higher performance than baseline models, and ablation experiments demonstrate the effectiveness of the three mechanism.