Reviewerly/exHarmony huggingface dataset:
Taming Multi-node Accelerated Analytics: An Experience in Porting MATLAB to Scale with Python

High Performance Data Analytics (HPDA) at scale is a multifaceted problem that involves distributed computing resources, their location with respect to the data placement, fast networks, optimized I/O, hardware accelerators and a fairly complex software stack that draws both from the legacy of HPC and the cloud world. This complexity does not cope well with the needs of domain experts who desire to focus on their algorithms without having to understand all the features of the underlying infrastructure. Among these domain experts, engineers often rely on MATLAB to quickly model their complex numerical computations with a simple, textbook-like syntax and an effective Integrated Development Environment (IDE). On the other end, MATLAB was not designed with large-scale, out-of-core computations in mind, despite the introduction of some parallel computing tools (e.g., distributed arrays and spmd loops). In an ideal world, a domain expert should only focus on its application logic, while runtime/parallel computing experts should provide tools that act behind the scenes to efficiently distribute the computations on available resources and to provide optimal performance. Conversely, in real life it often happens that the domain expert prototypes its code with MATLAB on a small scale, then HPDA/HPC experts will leverage proper tools to deploy it at scale; this causes a significant effort overhead, as development needs to be performed twice, possibly with multiple iterations. The rise of Python as the language of choice for a huge number of data scientists, along with its open ecosystem, led to the development of many tools that tried to achieve a convergence between the goals of domain experts and the need for performance and scalability. Sometimes building upon existing frameworks (e.g. PySpark) or starting from scratch in pure Python (e.g. Dask), these tools bear promise to allow engineers to write their code with a reasonably textbook-like syntax, while retaining the capability to scale among multiple nodes (and accelerators) with minimal intervention to the application code. This paper discusses the process of porting an engineering application written in MATLAB (parallelized with existing toolboxes) to Python using Dask. An indication of the scalability results is also given, including a 20x speedup with respect to the MATLAB code using the same setup.
Crypto-Assets In The EU Internal Market

Crypto-assets are qualified as money, a special type of property, or technology respectively in different fields of law. Even in one jurisdiction, crypto-assets can have diverse legal qualifications. Dimitropoulos’ theory to explain such diversity is that the new underlying technology has created a regulatory space in which different regulators compete to define crypto-assets differently for their own regulatory concerns. The diversity might seem conflicting.Moreover, being different from the traditional highly regulated and centralized financial market, the market for crypto-assets is inherently decentralized due to advancement of distributed ledger technology (DLT). Due to this decentralized feature, crypto-assets might trigger fundamental freedoms under the Treaty of Functioning of the European Union. However, these freedoms do not always provide sufficient protection or framework for crypto-assets owners/users. Therefore, the EU’s harmonization action is justified: In 2020, European Commission launched the EU’s Digital Finance Package, and this Package embodies Dimitropoulos’ theory on crypto-assets and also includes three different legal qualifications for crypto-assets.Such different qualifications are not inconsistent, as they follow the legal-doctrinal rationale. The European Commission is in the right track. What can be improved, is to make the underlying concerns of these three different legal qualifications more explicit.
Design of Non Significant Region Adaptive Enhancement System for 3D Image of Martial Arts Movement

Aiming at the problems of low efficiency and poor effect in traditional enhancement system, an adaptive enhancement system based on reti η ex enhancement model optimized by center surround method is proposed. In the image space domain, the mean filtering method is used to denoise the three-dimensional image of martial arts movement. The gradient operator is used to calculate the gradient vector which can reflect the three-dimensional image of martial arts movement. In the image space domain, the differential operator is used to sharpen the three-dimensional image of martial arts movement. On this basis, the SSR algorithm is used to solve and weight the non significant areas in the three-dimensional image of martial arts movement, the center surround method is introduced to estimate the three-dimensional image of martial arts movement, and the Retinex enhancement model is introduced to adaptively enhance the non significant areas in the three-dimensional image of martial arts movement.
Research on the Intelligent Detection Technology of Multi-Objective Coarse Aggregates

In order to achieve the screening of multi-objective coarse aggregate particles and the detection of needle-like particles, this paper targets t he limestone coarse aggregate feature points obtained by Harris corner detection as the research object, using unsupervised clustering technology to intelligently cluster the corner coordinates. To achieve intelligent separation of coarse aggregates and screening of needle-like particles. At the same time, the Harris corners are sensitive to scale, the multi-scale corner information is fitted and the sieving critical value is regressed, and the needle flake determination threshold is integrated. Finally, the intelligent screening system of multi-objective coarse aggregate was integrated, and the correct recognition rate was counted. Studies have shown that the number of feature points of coarse aggregates with different particle sizes has a stable change at different resolutions. Intelligent clustering of feature points of coarse aggregates can replace image segmentation. There is a strong correlation between coarse aggregate texture information and particle size.
Introduction

In this chapter, the basic elements for framing the blockchain technology in the different application fields is given. The fundamental principles of its distributed operation are given against traditional centralized systems are described together with all the main components of its successful architectures. These components also physically give the possibility to implement the main features of the blockchain technology, ranging from the smart contracts to the consensus mechanisms. Finally, a general overview of the blockchain technology features against the European General Data Protection Regulation is given.
AIS Challenges in Evaluating the Selection of Learner Interventions

This paper identifies challenges related to the optimal selection of adaptive instructional system (AIS) learner interventions. There are major challenges associated with accurately assessing learner performance, deciding when and how to intervene with learner, and evaluating the effectiveness of any intervention (e.g., feedback, support, direction, change in content level of difficulty) delivered during adaptive instruction, and improving intervention selection over time. AISs are computer-based systems that accommodate individual differences and tailor instruction to match learner capabilities to acquire knowledge through a guided learning process. While AISs are highly effective learning tools, they are costly and require very high-level skills to develop them and create complex, effective, and efficient adaptive courses. In some well-defined domains of instruction (e.g., mathematics), AIS developers have focused on the identification of errors and the interventions selected are focused on correcting these errors. Common errors are often identified in a misconception library that associates content and practice required to overcome them. In more complex domains, learner data may be sparse, and the simple identification of errors may not be sufficient to support optimal selection of learner interventions. Under these conditions, the challenge is to identify and address poor learner performance by testing plausible root causes and addressing them directly instead of focusing on errors that may only address symptoms and not root causes of learner performance. This paper identifies several challenge areas, discusses potential solutions, and provides recommendations for future research to overcome difficult challenges associated with machine selection of adaptive instructional interventions.
Research on Image Denoising Method in Spatial Domain by Using MATLAB

In the process of image processing, image acquisition, acquisition and transmission, the image is usually affected by noise, resulting in “distortion” to varying degrees. Image denoising is a very important graphics processing method, which can effectively improve the quality of distributed images and solve the problem of image quality degradation after noise pollution in reality. This article introduces spatial denoising methods, including mean filter, Wiener filter and median filter, and analyzes these methods. By using MATLAB software, the method of image denoising in the spatial domain is studied and tested, and these results are analyzed.
Large Scale Graph Data Processing Technology in Cloud Computing Environment

Cloud computing is a new Internet related application mode. The emergence of cloud computing has led to a revolution in IT industry. The academia has also conducted effective exploration and Research on various situations and related problems in cloud computing. In recent years, the rapid growth of emerging applications such as bioinformatics network, semantic web analysis and bioinformatics network analysis, It improves the requirement standard of large-scale graph processing function utility, and increases the demand of large-scale graph data processing technology ability. In this paper, a brief overview of the analysis in the cloud computing environment for large-scale graph data processing technology related applications.
Prototypes Optimized with Bregman Soft Clustering for Few-Shot Learning

Few-Shot Learning (FSL) mainly studies that model aims to quickly learn and adapt to very limited new tasks. The FSL methods based on embedding model construct prototypes using support set. Then combining the query set and the prototypes to optimize the embedding model, such that the intra-class difference becomes smaller and the inter-class difference becomes larger for new tasks under the embedding model mapping. However, the prototypes only constructed from support set make them lack representation because the support set lacks samples. To overcome this limitation, this paper proposes an algorithm (BPN) that uses query set and its soft labels to optimize the prototypes. Moreover, to further optimize the embedding model, we use the strategy of data augmentation (Fixmatch) on labeled samples to increase the training samples. On the other hand, we introduce the idea of semi-supervised learning. We add unlabeled samples to further optimize the prototypes in BPN. Besides, we use a semi-supervised learning method (FFT) to select unlabeled samples with high confidence to optimize the embedding network. Finally, we combine Fixmatch and FFT methods in the BPN. Extensive experiments show that our method can improve greatly the accuracy of few-shot classification and prove the effectiveness of our proposed framework in semi-supervised few-shot learning.
A Mixed Method Study of Devops Challenges

DevOps practices combine software development and IT (Information Technology) operations. Our continuous needs for rapid but quality software development necessitates the adoption of high-quality DevOps tools. Therefore, it is important to learn the challenges developers face while using the currently available DevOps tools and techniques. We observed a growing number of DevOps related posts in popular online developer forum Stack Overflow (SO). While previous research analyzed SO posts related to build/release engineering, we are aware of no research that specifically focused on DevOps related discussions. In this paper, we first conduct an empirical study by applying topic modeling on 174K SO posts that contain DevOps discussions. We then validate and extend the empirical study findings with a survey of 21 professional DevOps practitioners. We find that: There are 23 DevOps topics grouped into four categories:Cloud & CI/CD Tools, Infrastructure as Code, Container & Orchestration, and Quality Assurance. In the followup survey, DevOps developers agree with our findings and mention that it requires hands-on experience before current DevOps tools can be considered easy. The survey participants raised the needs for better documentation and learning resources to learn the rapidly changing DevOps tools and techniques.
An Optimal Transport Framework for Collaborative Multi-view Clustering

Research on Multi-View ClusteringMulti-view clustering (MVC) has become more and more attractive thanks to the richness of its application in several fields. Unlike traditional clustering, each view maintains different information to complete each other. On the other hand, the collaborative framework aims mainly to exchange knowledge between collaborators to improve their local quality. The purpose of this chapter is to present a new framework for Collaborative Multi-View ClusteringMulti-view clustering (Co-MVC) based on Optimal TransportOptimal transport (OT) theory. The main idea of the proposed approaches is to perform a collaborative learningCollaborative learning step to create a coordination map between multiple views that will ensure an optimal fusion for building a consensus solution. The intuition behind performing the collaboration step is to exploit the pre-existing structure learned in each view to improve the classical consensus clustering. In this chapter we propose two approaches: Collaborative Consensus ProjectionProjection Approach (CoCP) that aims to perform the consensus in the global space of the data, and a Collaborative Consensus with New Representation (CoCNR) that seeks to encode a new data representation based on local ones. Both approaches are based on the entropy regularized Wasserstein distanceWasserstein distance and the Wasserstein barycenters between the data distribution in each view. Extensive experiments are conducted on multiple datasets to analyze and evaluate the proposed approaches.
Re-Annotation of Training Samples for Robust Maritime Object Detection

Machine Learning and specifically Deep Learning techniques address many of the issues faced in visual object detection and classification tasks. However, they have the caveat of needing large amounts of annotated training data. Specifically, in the maritime domain, the amount and quality of available data is lacking. In this work, we present a novel approach that can detect possible “poor” training samples and automatically re-annotates them, based on the current state of the object detector. We show the applicability of our approach on real-life maritime data and show that the poor annotation quality of the data sets used can be mitigated with our approach. We show that the performance gain of the proposed method with respect to the baseline approach is proportional to the amount of poorly annotated data in the data set. Finally, our approach allows for the iterative improvement of a given data set by providing a list of new possible annotations. Therefore, the mundane and tedious task of sorting through large data sets is reduced.
An Integrated Model to Evaluate the Transparency in Predicting Chronic Kidney Disease Using a Trio-Embedded Explainable Model

Why do clinicians doubt the applicability of artificial intelligence in the medical field? One of the primary challenges to implementation is a lack of transparency, as clinicians must be confident that the AI system can be trusted. Explainable AI has the ability to solve this problem and is a step toward AI that is trustworthy. Explainability improves learning for educational and research objectives by allowing comparisons of learned procedures with existing knowledge. This research proposes an explainable artificial intelligence (XAI) archetype utilizing shapely additive values (SHAP), local interpretable model-agnostic explanation (LIME), and DALEX to provide a qualitative knowledge of the association between the model and the parameters utilized in the model. Firstly five machine learning models (Logistic Regression, Random Forest, Gradient Boosting Machine, Support Vector Machine, and Multilayer Perceptron Classifier) are being interpreted with local models called LIME, SHAP, and DALEX and then a global model called EBM. This research also compares the performance of five classifiers with Explainable Boosting Machine (EBM), while EBM achieved the highest accuracy of 98.57%. Lastly, we use the XAI model to determine the most important factors that influence those predictions.
Deep Learning of Monocular Depth, Optical Flow and Ego-Motion with Geometric Guidance for Uav Navigation in Dynamic Environments

Computer vision-based metric depth estimation and visual odometry provide perceptual information useful for robot navigation tasks like obstacle avoidance. However, despite the proliferation of state-of-the-art deep convolutional neural network (CNN) models for monocular depth, ego-motion and optical flow estimation, a relatively low volume of work has been reported on their practical applications in UAV navigation. This is due to well-known challenges – real-time embedded hardware constraints, viewpoint variation, scarcity of aerial image datasets, and intricacies of dynamic environments. Consequently, we address these limitations to facilitate real-world deployment of CNN model for UAV navigation. First, we devise an efficient confidence weighted adaptive network (Cowan) training framework that iteratively leverages intermediate prediction confidences to fine-tune cross-task consistency constraints over corresponding image regions. This method achieves competitive accuracy with a lightweight multi-task CNN capable of real-time execution on resource-constraint Jetson TX2 processor. Second, we handle dynamic environments and adaptation to varying viewpoints by geometric guided refinement (GRR). We accomplish this using pixel correspondences from optical flow acquired during flight under different viewing conditions along with the UAV’s composition of motion to formulate 3D geometric constraints as domain-independent pseudo-ground-truth supervisory signals used to update our network at test time. We simultaneously update ego-motion estimation using pose information from the on-board inertial measurement unit (IMU). The test-time refinement vastly improved the resulting Cowan-GGR model’s accuracy and robustness. Field tests show the proposed model is capable of accurate scene depth and object-level motion perception in real-world dynamic environments, thus proving its efficacy in facilitating UAV navigation.
Theil-Sen Regressive Miyaguchi–Preneel Based Cryptographic Hash Blockchain for Secure Data Transmission Using Remote Sensing Data in Iot

In this manuscript TSR-MPCHB is proposed for secure data transmission using remotesensing data. At first, Theil-Sen Regression is used to analyze data collectedon basis of geometric neutrality. This process reduces processing time ofsecure data transfer. MPCHB is used for secure data transfer. Miyaguchi-Preneelcryptographic function is used to generate confusing value of each data. Hashdata then generated is transferred to authorized hub, avoids unauthorizedadditions. Proposed method is simulated using java language. Simulationperformance of proposed method provides higher data confidentiality rate, and minimumprocessing rate compared with existing method like BESDDC-IOD-RSD, BIoTHR-SDT-RSD, LMDSG-SDT-RSD and ACOMKSVM-SDT-RSD.
Predicting House Prices using Machine Learning and LightGBM.

Purchasing a home in today's environment is a time-consuming process. It is necessary to approach a real estate agent and obtain information, which is not safe and can result in theft. We are attempting to create an online efficient means of determining house prices for a certain region based on customer criteria using Machine Learning in this research project. To get the best results, we're using the LightGBM framework to train the dataset. For predicting property values, the most optimal and precise result will be chosen. LightGBM (Light Gradient Boosting Machine) is a free and open source distributed gradient boosting system for Machine Learning that was created by Microsoft. It is used for ranking, classification, and other machine learning applications and is based on decision tree algorithms. Performance and scalability are at the forefront of the development process. We also employed the Naive Bayes method to evaluate the likelihood of finding a house in a specific location where the costumer is interested in purchasing a home. The accuracy obtained utilising the methods demonstrated that this strategy offers the least error and highest accuracy when compared to other Gradient Boosting Machine frameworks.
An Exploratory Study on the Spotify Recommender System

Spotify is a world class platform for music streaming, and it offers various kinds of services. As with many digital platforms, Spotify uses artificial intelligence to personalize the user experience, also known as a recommender system. This study investigates what role Spotify’s recommender system plays in the use of Spotify and if there are any differences in satisfaction between different ages and gender. Therefore, we conducted a survey about how customers are using Spotify, which was shared in different forums. In total we received 159 answers with respondents from 21 different countries. One of the main findings was that the three services “Make your own playlist”, “Playlist made by Spotify” and “Recommended songs” are the most popular. Also, a correlation was made to investigate the relationship between the satisfaction of “Recommended songs” and if customers add them to their own playlists. The Spearman’s Rank Correlation Coefficient was 0.43 (significant at the 0.01 level), which is a moderate value. This means that almost half of the time the Spotify users place the recommended songs in their playlist. Further, two conclusions were arrived at. Firstly, the recommender system plays a major part in how customers use Spotify. Secondly, we cannot see that age and gender would significantly affect the satisfaction of the recommended songs that Spotify suggest.