Reviewerly/exHarmony huggingface dataset:
Key Features and Formation of Transactions in the Case of Using UTxO, EUTxO and Account Based Data Storage Models

At the moment there are various logical concepts of constructing data storage and making transactions in blockchain networks. These concepts can be divided into account-based and (Extended) Unspent Transaction Output (E)UTxO. Account-based is a system with multiple accounts that can interact with each other through the creation and signing the transaction by a private key. While (E)UTxO is a model in which the values at the transaction outputs determine the current state of both a single smart contract and the entire blockchain network. To move from one state to another, transactions use the unspent outputs of previous transactions. The unspent outputs then become spent outputs: inputs for the new transaction also forming its unspent transaction outputs. These outputs can store both ADA and native tokens assigned to the public key. They can also store other information depending on the implementation of the script. The paper investigates the architectural features and differences of systems with two concepts of data storage construction, application features, as well as transaction construction models and the impact of models on network performance considered on the example of a practical case - development of an authorization module in the system.
Research on calibration method of extrinsic parameters of lidar and camera carried by UAV

The colorful image of the camera and the point cloud of the lidar can provide rich information about the surrounding environment.With the widespread application of UAV in various industries, loading lidars and cameras on UAV can effectively help UAV realize functions such as identification, mapping, and navigation in unfamiliar environments. Aiming at the current research hotspot, this paper combines hand-eye calibration with edge calibration. The former provides the calibration initial value for the latter, and the latter optimizes the results of the former. Finally, a high-precision automatic calibration system without artificial initial value is realized. In addition, this paper proposes a Gauss-Helmert model with scale estimation and outlier removal to solve hand-eye calibration. Experiments show that the method has higher accuracy and better robustness.
Privacy-preserving data augmentation for thermal sensation dataset based on variational autoencoder

Machine learning-based methods show high performance in estimating the thermal sensation of a person. These methods are based on a huge amount of personal data. The dataset used for the training of the estimator includes personal physiological data, which includes people's private information. Generative models have received significant attention to anonymize such data, including private information. In this paper, we propose privacy-preserving data augmentation for the thermal sensation dataset, including the subject's physiological data using Variational Autoencoder. The generative model trained with a thermal sensation dataset collected in the uncontrolled environment tends to be biased because subjects in the environment rarely report extreme thermal sensation labels. To tackle this problem, we introduce a weighted loss function for the generative model to mitigate the bias of the thermal sensation labels. The evaluation result shows that our method generates an anonymized dataset that works to train a thermal sensation estimator as well as the original dataset.
SWOT Analysis for Object Detection in Traffic Engineering Based on YOLO Implementation – Case Study

Over decades, traffic engineers are focusing on different ways to increase the drivers and pedestrian safety during the daily traffic on the streets. The main reason for that is the growing number of cars, pedestrians, and road density across the whole world [1]. Also, the dramatic increase of computational power, decreasing price of technical equipment enable enrolling object detection topics also in the real time application withing traffic engineering topics. Special focus on mobility topics is not the only one application of this computer technology. It has a broad impact also to many different fields of interests starting from medicine, face-recognition, industry, or art. Three decades of algorithms development, increasing of the computational power led to wide spread of methods and usage. Many important milestones were reached and presented. One should have read the complete evolution, or even browse deeply dedicated pages and forums, but this all can lead to misleading conclusions of usage or performance. This paper should briefly demonstrate and illustrate the implementation of object detection algorithms at a specific, predefined location.
Integer Syndrome Decoding in the Presence of Noise

Code-based cryptography received attention after the NIST started the post-quantum cryptography standardization process in 2016. A central NP-hard problem is the binary syndrome decoding problem, on which the security of many code-based cryptosystems lies. The best known methods to solve this problem all stem from the information-set decoding strategy. A recent line of work considers augmented versions of this strategy, with hints provided by side-channel information. In this work, we consider the integer syndrome decoding problem, where the integer syndrome is available but might be noisy. We study how the performance of the decoder is affected by the noise. We provide experimental results on cryptographic parameters for the Classic McEliece and BIKE cryptosystems, which are in the fourth round of the NIST standardization process.
Improved League Championship Algorithm (ILCA) for Load Balancing in Cloud Computing

You can’t obtain the outcomes you need without planning, thus it’s at the heart of cloud computing. This article’s major goal is to decrease value-added time, increase resource utilisation, and make cloud services viable for a single activity. In recent years, metaheuristic algorithms have drew attention to the correct functioning of work scheduling algorithms among the many job scheduling techniques. With sports leagues, the algorithm based on the League Championship (LCA) is fascinating because it can be used to identify the best team/task for programming.This article uses the Improved League Championship Algorithm (ILCA) to schedule tasks, reducing deployment time, cloud usage, and cost. The ILCA is implemented through the Cloudsim simulator and the Java programming language with a nonpreventive planning strategy. ILCA also enhances economies of scale and minimises the value of using the cloud. As it has proven to be versatile in terms of time to manufacture, resource usage and economics, ILCA could be a good candidate for a cloud broker as it has proven to be versatile in termsof time to manufacture, resource usage and economics usage.
Security Issues in Deep Learning

Deep learning has created substantial improvements for industries and set the tempo for a destiny constructed on artificial intelligence (AI) technology. Nowadays, deep learning is turning into an increasing number of vital in our everyday lifestyles. The appearance of deep learning in many applications in life relates to prediction and classification such as self-driving, product recommendation, classified ads and healthcare. Therefore, if a deep learning model causes false predictions and misclassification, it may do notable harm. This is largely a critical difficulty inside the deep learning model. In addition, deep learning models use big quantities of facts inside the training/learning phases, which in corporate touchy facts can motivate misprediction on the way to compromise its integrity and efficiency. Therefore, while deep learning models are utilized in real-world programs, it's mile required to guard the privateness facts used inside the model. The countless opportunities and technological abilities that system learning has added to the arena have concurrently created new safety dangers that threaten development and organizational development. Understanding system learning safety dangers is one of every of our contemporary technological time's maximum vital undertakings due to the fact the results are extraordinarily high, mainly for industries along with healthcare in which lives are at the line. We talk about the forms of system mastering safety dangers that you may stumble upon so you may be higher organized to stand them head-on.
The Second Language Acquisition: System of Extended Integrative Control for Linguistic Knowledges and Communication Skills Sustainability

The article is devoted to the search for new approaches and forms of control over the assimilation of students’ knowledge at the initial stage within the framework of a worldview approach to teaching Russian as a foreign language. Based on the analysis of existing forms and types of control, a conclusion was made about their incomplete compliance with the goals of teaching live communication in Russian. The main problem is the lack of motivation to form an oral or written statement. The thematic principle for the organization of control over the assimilation of listening / speaking skills is outdated, it does not reflect the specifics of modern interpersonal communication, including socially conditioned one. Based on this conclusion, the article outlines approaches to the creation of an expanded comprehensive control system aimed, on the one hand, at testing (control) skills of free communication, and, on the other hand, at psychological stimulation (psychological boost) of communication.
Artificial Intelligence Applications in Education

The aim of this research is to use the descriptive correlative approach because of its suitability to the nature of the objectives of this study. A scale was built to measure the skills of using artificial intelligence applications in education which is consisting of (50) items on a five-point scale (excellent, very good, good, average, acceptable). The sample of the research consisted of (200) male and female students from the computer departments in the faculty of education and faculty of science in the universities of the Middle Euphrates in Iraq. The study tools were applied in the first semester of the academic year (2021–2022). A number of statistical methods were used, including the t-test, the equation of the coefficient of excellence for paragraphs, chi-square, Pearson's correlation coefficient, the Alpha-Cronbach equation, the Alpha-Cronbach, Kuder–Richardson-20 equation, and the Spearman-Brown equation. It appears that the T value for students is (3.59) which is higher than its tabular value (1.65) with a significance level (0.05) and a degree of freedom (199). This indicates that there is a statistically significant difference between the average performance of students in computer departments (science and education). Also according to the gender variable, it appears there are no statistically significant differences between the average performance of males and females on the scale skills of using artificial intelligence applications.
AmericasNLI: Machine translation and natural language inference systems for Indigenous languages of the Americas

Little attention has been paid to the development of human language technology for truly low-resource languages—i.e., languages with limited amounts of digitally available text data, such as Indigenous languages. However, it has been shown that pretrained multilingual models are able to perform crosslingual transfer in a zero-shot setting even for low-resource languages which are unseen during pretraining. Yet, prior work evaluating performance on unseen languages has largely been limited to shallow token-level tasks. It remains unclear if zero-shot learning of deeper semantic tasks is possible for unseen languages. To explore this question, we present AmericasNLI, a natural language inference dataset covering 10 Indigenous languages of the Americas. We conduct experiments with pretrained models, exploring zero-shot learning in combination with model adaptation. Furthermore, as AmericasNLI is a multiway parallel dataset, we use it to benchmark the performance of different machine translation models for those languages. Finally, using a standard transformer model, we explore translation-based approaches for natural language inference. We find that the zero-shot performance of pretrained models without adaptation is poor for all languages in AmericasNLI, but model adaptation via continued pretraining results in improvements. All machine translation models are rather weak, but, surprisingly, translation-based approaches to natural language inference outperform all other models on that task.
Crypto Convulsions, Digital Delusions, and the Inexorable Logic of Finance Capitalism

The crypto winter, Ramaa Vasudevan writes, is here. Cryptocurrency, far from being a democratizing force in finance, has led to only further concentrations of wealth and power and increased precarity within the financial sector. The recent fall of Sam Bankman-Fried is only the beginning.
A graph attention feature pyramid network for 3D object detection in point clouds

Graph Neural Networks have been recently applied to 3D object detection in point clouds. The works, however, have the problem of insufficient detection accuracy for small objects and objects in complex backgrounds. Towards this end, a graph attention feature pyramid network is proposed for 3D point clouds object detection. Specifically, the network constructs a near-neighbors graph in a point cloud which is downsampled; and then, a graph attention feature pyramid network is designed to extract features of the point cloud at different levels; finally, a feature fusion module is employed to fuse the features before point classification and object detection. Compared with the benchmark network Point-GNN on the KITTI dataset, the detection accuracy of cars in complex scenes is improved by 2.53%, and the detection accuracy of pedestrian and cyclist categories in moderate scenes and complex scenes is improved by 5.17%. and 3.62%. The experiments show that the designed method is more effective for the detection of small objects and objects in complex backgrounds in 3D object detection.
DIGITALIZATION OF THE SCIENTIFIC LIBRARY OF THE OMSK STATE TECHNICAL UNIVERSITY

The article describes the process of digitalisation of the scientific library of the Omsk State Technical University by creating a new information system. Online services that the library will provide to users, and innovations, such as an electronic library card, are proposed and described. A functional layout is presented and the design of the information system is described. The results of digitalisation and their impact on the interaction of the library with the user are analysed.
Palette-based Image Search with Color Weights

We propose a novel image search system with color palettes. By querying color palettes, users can search for inspiring images, which is helpful for design exploration. Although few such systems can accept palettes as queries, they have several constraints on query palettes (e.g., limited palette size) or search results. Our system accepts palettes with color weights and any sizes as the inputs and returns results with enough diversity that can stimulate users’ design inspiration. To achieve these, we extract color themes from our database with a perceptually-based model and calculate the palette similarity based on a weighted palette distance. Visual comparisons demonstrate that our system has more potential for helping users’ design inspiration than the existing systems.
TexSR: Image Super-Resolution for High-Quality Texture Mapping

We introduce an image super-resolution technique for high-quality texture mapping in this poster. We first get upscaled textures from an existing image super-resolution (SR) method. We then perform a post-color correction algorithm to restore color tones and details lost in the SR algorithm. Finally, we compress the textures with variable compression ratios to reduce storage and memory overheads caused by the increased resolution. As a result, TexSR can improve the image quality of a state of the art, Real-ESRGAN.
MEMformer: Transformer-based 3D Human Motion Estimation from MoCap Markers

We address the problem of 3D human motion estimation from original MoCap optical markers. The original markers are noisy, disordered, and unlabeled, hence recovering 3D human motion from them is non-trivial. Existing works are either time-consuming or assuming the knowledge of the marker labels. We address these problems by presenting an end-to-end method for 3D human motion estimation by leveraging the capability of Transformer to model long-range dependencies. The method takes original markers as inputs and learns joint poses with a Transformer-like architecture. Experimental results show that our method is able to achieve better than centimeter-level errors.
Safety Helmet Detection Based on YOLOv7

Frequent safety accidents have posed a significant risk to workers' lives recently. In particular, the risk of head injury is significantly increased by workers not wearing helmets. However, manual supervision is inefficient and costly. Even though traditional object detection methods have achieved good results, accuracy is difficult to guarantee in complex conditions such as long detection distances and sandy weather. The performance of the models YOLOV5 and YOLOv7 on 7581 hard hat datasets was compared in this paper. As a result, YOLOv7 has the best detection performance on the helmet datasets, with 96.5% accuracy and 62FPS speed. This result represents that YOLOv7 has high effectiveness in helmet detection.
Malicious URL Detection An Evaluation of Feature Extraction and Machine Learning Algorithm

Cyber attacks are increasing rapidly today, and have a great influence on network security. Many of cyber attacks take place via malicious Uniform Resource Locators (URLs). As a result, various approaches have been developed to detect malicious URLs. One of the most competitive techniques is machine learning and deep learning. However, the detailed techniques concerning feature extraction for URLs and machine learning algorithm are still in the process of development. This paper aims to provide some references for screening out the methods of feature extraction and machine learning algorithm. In the designed experiment, the selected URLs are processed by two different methods of feature extraction, tokenization and vectorization, and lexical feature selection. The resultant constructs two different datasets (data1 and data2) for machine learning. Two traditional learning algorithms (Logistic Regression and SVM) and three ensemble learning algorithms (Random Forest, Gradient Boosting, and Bagging) are adopted as detection model for both datasets. The experimental results demonstrate that the method of tokenization and vectorization for feature extraction, together with ensemble learning algorithms can result in good predictive performance of malicious URL detection.
Multimodal interaction aware embedding for location-based social networks

Location-based social networks (LBSNs) have greatly promoted the development of the field of human mobility mining. However, the sparsity, multimodality and heterogeneity nature of the user check-in data remains a great concern for learning high-quality user or other entity representations, especially in the downstream application tasks, such as point-of-interest (POI) recommendation. Most existing methods focus on user preference modeling based on sequential POI tags without exploring the interaction between different modalities (e.g., user-user interactions, user-timestamp interactions, user-POI interactions, etc.). To this end, we introduce a multimodal interaction aware embedding framework to generate reliable entity embeddings on the heterogeneous socio-spatial network. At its core, first, multi-modal interaction sub-graph sampling techniques are designed to capture the heterogeneous contexts; then, a self-supervised contrastive learning technique is leveraged to extract intra-modality and inter-modality interactions in a light way. We conduct experiments on the next-POI recommendation tasks based on three real-world datasets. Experimental results demonstrate the superiority of our model over the state-of-the-art embedding learning algorithms.
CoAP

Open source is about leveraging building blocks implementing shared functionalities. This enables you to focus on your use case instead of reimplementing what countless others have already implemented. Since your time is a severely constrained resource, any minute you save by using open source components is a minute you can invest in meaningful differentiation for your solutions.
Transforming E-Learning Through the Use of Virtual and Augmented Reality: A Systematic Review

Until the end of the twentieth century, printed text, classroom lectures, and the projected screen remained the mainstays of pedagogical practices. With advances in innovationInnovation and technology, learningLearning environments have undergone a rapid transformation in the last two decades. Virtual and augmentedAugmented reality technologies have had a significant impactImpact in providing learnersLearners with an engaging and immersiveImmersive learningLearning experienceExperience. This paper performs a systematicSystematic literature review of articles that discuss using such technologies in transforming E-learningE-learning. Prominent studies from databases like GoogleGoogle Scholar, ScopusScopus, Web of Science, SpringerLink, and ResearchGateResearchGate were considered during the analysis. The peak periods of research and notable authors were identified through bibliometricBibliometrics and co-citationCo-citation analysis using VOSviewer, CiteSpaceCitespace, and HarzingHarzing. MAXQDAMaxqda and the VicinitasVicinitas word cloudsWordcloud were analyzed to illustrate common themes of interest and recognize emergingEmerging trends. The study showed the applicability of virtual and augmentedAugmented reality-based e-learningE-learning to fields other than school or university educationEducation. In addition to identifying research gaps, suggestions regarding future work are also offered.
An Image Enhancement Method for Autonomous Vehicles Driving in Poor Visibility Circumstances

In most cases, autonomous vehicles perform well in normal lighting conditions. However, their performance is significantly reduced when faced with poor visibility circumstances because of the lack of detail and information. These situations can easily cause collisions and many dangerous accidents for transportation. Many previous efforts have focused on improving images only in a single case, such as low-light, fog, or dust. This way leads to self-driving automobile systems embedding various image restoration models, increasing computation time, and not meeting real-time processing needs. Therefore, designing an enhancer algorithm restoring input image quality is more imperative than ever before. This paper proposes a method to enhance the image under poor visibility circumstances such as foggy and low light. On top of that, we introduce the defogging algorithm based on contrast energy, entropy, and sharpness characteristics. On the other hand, the inverse of an image captured in a dark environment will be equivalent to a daytime image obtained in a fog atmosphere. Inspired by this interpretation, we adopt the proposed defogging model to enhance images in low-light conditions. Experimental results demonstrated that our method was feasible to execute pre-processing input images for autonomous vehicles driving in poor visibility circumstances.