Reviewerly/exHarmony huggingface dataset:
Distributed lévy sailfish optimizer optimizes XGBoost fraud detection

In credit card, shopping, and insurance systems, a small amount of fraud may sometimes exist in the massive transaction data, so fraud detection is widely used in these systems. Extreme Gradient Boost (XGBoost) is outstanding in the competition, many scholars use it in various different fields of classification and prediction. The hyperparameters set by XGBoost determine its performance strength or weakness, and the many hyperparameters make it difficult to have good choices. In this paper, we use the improved Lévy sailfish optimizer (LSFO) to solve the parameter selection problem. As a relatively new intelligent optimization algorithm, the sailfish optimizer chooses spiral updating to optimize the search process of the sailfish and adds Lévy flights to increase its performance. To validate the improved performance, several common optimization algorithms such as PSO, HHO, and WOA are compared using the Lévy Sailfish Optimizer. A fraudulent dataset is used to evaluate the performance of Lévy Sailfish Optimizer Optimization XGBoost (LSFO-XGBoost) in comparison with Decision Tree (DT), Random Forest (RF), SFO-XGBoost and PSO-XGBoost. In LSFO-XGBoost hyperparameter optimization, due to its long training time, the distributed Spark framework is applied to improve its training in a distributed manner. The experimental results show that the proposed LSFO is effective in solving the XGBoost hyperparameter problem, and the distributed LSFO-XGBoost algorithm is better in terms of time consumption and performance.
The research of streaming compression methods on cloud-rendering systems

Nowadays, cloud-rendering systems have attracted the intense interest in the public and been used in many areas including video games and online education. In this paper, we will focus on the streaming compression (i.e., command-and video-streaming), an important metric playing an indispensable role in the performance of Cloud-rendering Systems, especially for lowering the network bandwidth consumption. Based on the analysis of the characteristics of rendering commands and generated frames, we propose an effective method to compress them and reduce the bandwidth consumption. Finally, we will show the compression ratio we achieved when applying these compression methods.
Stereo 3D Object Detection Using a Feature Attention Module

Stereo 3D object detection remains a crucial challenge within the realm of 3D vision. In the pursuit of enhancing stereo 3D object detection, feature fusion has emerged as a potent strategy. However, the design of the feature fusion module and the determination of pivotal features in this fusion process remain critical. This paper proposes a novel feature attention module tailored for stereo 3D object detection. Serving as a pivotal element for feature fusion, this module not only discerns feature importance but also facilitates informed enhancements based on its conclusions. This study delved into the various facets aided by the feature attention module. Firstly, a interpretability analysis was conducted concerning the function of the image segmentation methods. Secondly, we explored the augmentation of the feature fusion module through a category reweighting strategy. Lastly, we investigated global feature fusion methods and model compression strategies. The models devised through our proposed design underwent an effective analysis, yielding commendable performance, especially in small object detection within the pedestrian category.
Weighted bootstrapped DQN: efficient exploration via uncertainty quantification

Uncertainty quantification is an essential method for sample-efficient deep reinforcement learning. There is a growing literature on uncertainty-based deep reinforcement learning algorithms, but many of the previous approaches failed to capture different sources of uncertainty. We highlight why this can be a crucial shortcoming for sample-efficient algorithms and provide a sophisticated analysis of the uncertainty in the interaction between agent and environment. Based on that, we propose Weighted Bootstrapped DQN, an exploration-efficient method that combines network ensembles and variance weighting. We use aleatoric uncertainty estimation together with epistemic uncertainty to improve the exploration ability of the algorithm. We prove that our new approach has a significant improvement in sample efficiency on different gym tasks, even compared with the previous state-of-the-art approaches.
Scope and limitations of library metrics for the assessment of ebook usage: COUNTER R5 and link resolver

Abstract Data is at the heart of electronic resource management in academic libraries. Assessing the usage data of electronic resources has become a prevalent approach to demonstrate the value of digital collections, justify library expenditures, and gain insights into how users interact with library materials. This study analyses the usage statistics of electronic books (ebooks) generated locally by the OpenURL link resolver in an academic library, and statistics collected by platform vendors based on Release 5 of the Counting Online Usage of Networked Electronic Resource (COUNTER R5). Three content provider platforms (Cambridge Core, EBSCOhost and ScienceDirect) were analysed as data sources. The COUNTER and link resolver statistics were examined to determine the degree of association between these two metrics. The Spearman correlation coefficient was moderate (rs &amp;gt; 0.561 and &amp;lt;0.678) and statistically significant (p &amp;lt;0.01). This suggests that these metrics capture different aspects of the usage of ebooks in different contexts. Other factors, such as the types of access to electronic resources and the units of content delivered, were also examined. The study concludes with a discussion regarding the scope and limitations of link resolver and COUNTER R5 as library metrics for measuring the usage of ebooks. Peer Review https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00279
Entanglement-enhanced quantum strategies for accurate estimation of multibody-group motion and moving-object characteristics

This study presents a quantum strategy for simultaneous estimation of two physical quantities using different entanglement resources. We explore the utilization of positively or negatively time-correlated photons. The proposed method enables the detection of the central position and relative velocity of multibody systems, as well as precise measurement of the size and velocity of moving objects. Comparative analysis with other strategies reveals the superior quantum advantage of our approach, particularly when appropriate entanglement sources with a high degree of entanglement are employed. These findings contribute to advancing our understanding of quantum strategies for accurate measurements.
Introduction to Computer Programming and Python

Computer programming – commonly referred to as “coding” by the cool kids – is the art of creating applications or software. These programs allow us to do everything from solving simple math problems and watching our favorite TikTok videos (I can’t get enough of skydiving bulldogs) to destroying hordes of rampant aliens in our favorite video games and even launching a real-life spaceship into outer space.
Saving Time with Functions, Modules, and Built-Ins

Now that we have officially created our first ever, full-blown Python application (back in Chapter 6 if you are skipping around!), it is time to begin learning how to truly harness our programming powers and become the best programm we can be.
Illustrator’s 2D and 3D Effects for Creating Infographics

In this chapter, we will look at how to apply 3D effects to your infographics.
Illustrator’s Graphing Tools: Creating a 2D Infographic – Part 2

In this chapter, you will continue to work with Illustrator graphing and drawing tools to create an infographic.
Python for Gaming

It is only appropriate that we have a chapter where we discuss creating video games in Python – after all, it is that very interest that got me started programming all those years ago when I was a kid. Things have progressed a lot since then; at the time, PC games were text-based with the only images consisting of really poor-quality graphics or, worse, made out of ASCII characters.
Illustrator’s 2D Effects for Creating Infographics

In this chapter, we will focus on applying a 2D look to your infographics.
Interactive Infographics with SVG

In this chapter, you will learn how to create basic interactivity for your infographic online using SVG (Scalable Vector Graphics) files.
Altering Graphics Using the Image Trace Panel and Project Ideas

This chapter looks at how to edit graphs that are copied from another Microsoft application and pasted into Illustrator. We will also look at using the Image Trace panel to create unique graphic creations. As we progress through the chapter, we will look few graph projects and consider some ideas.
Python Career

Well, young hero, it has been a long journey. We have overcome many foes – nefarious villains like Jack Hammer and the vile Algebro. We have read through this mystical tome and gained insight and wisdom that have allowed us to enhance our powers to heights previously unknown. We are talking Mount Everest types of heights.
Legal Efficiency of Secure Transactions Framework in India

Virtual technological development has revolutionized banking, financial, and capital market transactions in the world. A majority of transactions are taking place through online banking, net banking, or Unified Payments Interface (UPI) transactions, with few other portals giving the ease and comfort to do transaction in one's comfort zone without going to banks or withdrawing money in offline modes to be paid. The rapid evolution of online and UPI transactions has transformed the way transactions are conducted, shifting a significant portion of commerce to the digital realm. In this context, the efficiency of legal frameworks for secure transactions is of paramount importance to ensure trust, reduce risks, and foster economic growth without sabotaging the customers transactions security concerns. The transactions taking place in past in offline mode and presently in online mode had delves into the nexus between secure transactions and electronic transactions, exploring the impact of emerging technologies such as blockchain, UPI, and net banking that have posed challenges.
Loops and Logic

Sometimes fighting crime can make you feel like you are running around in circles. Day in and day out you seem to have to tackle the same struggles: a bank robber here, a cat stuck in a tree over there, an evil genius trying to take over the universe. It is almost as if you are stuck in some sort of…loop.
Illustrator’s Graphing Tools: Creating a 2D Infographic – Part 1

In this chapter, you will be working with Illustrator graphing and drawing tools to create an infographic.
Python Files

So far, we have worked primarily within one file; that is, all of our code has been saved in a single .py file and run from that same file. However, in the real world, a lot of our programs will be stored in multiple files. What’s more, we are likely to save some of our favorite code snippets and functions in files for later use. It is just the way we programmers – which include you now – work.
Collaborative optimization of bonded fuel supply chain based on combined swarm intelligence algorithm

Considering that the traditional bonded fuel supply chain has problems such as weak trust relationship and low cooperation efficiency, which affect the profits of supply chain node enterprises. To this end, a two-level supply chain game model based on suppliers and distributors is built. First, the particle swarm optimization algorithm is used to estimate the supplier's profit function, and the initial game strategy of the supplier is obtained accordingly. Add the neural network to the ant swarm intelligence algorithm to make it optimize the operation, and finally realize the optimal matching of supplier and distributor business. Finally, the effectiveness of the proposed method for improving the collaborative optimization of the bonded fuel supply chain is verified by experiments.
Human Translation and Natural Language Processing Towards a New Consensus?

Artificial intelligence technologies are currently challenging both machine translation and natural language processing. A better understanding of the social, economic and ethical stakes is urgently needed. This collective work explores the possibility and the contours of a new consensus between the human uses of language and the contributions of the machine.
SOFT SKILLS, TRABALHO REMOTO E INDÚSTRIA 4.0: A REESTRUTURAÇÃO DAS RELAÇÕES DE TRABALHO NO SÉCULO XXI

This work addresses, quite succinctly, the importance of soft skills in the Industry 4.0 scenario, demonstrating how these personal characteristics of the employee can be interesting for their professional success in this new Industrial Revolution, where men and machines need to coexist in the work environment. work, in a cooperative way, since the machines have ceased to be mere work tools and have become effective collaborators in the execution of the work necessary for the productive activity. This work managed to reach a conclusion about the main challenges, to take advantage of good employees, with the necessary soft skills to deal with this new scenario, pointing out, in the Brazilian scenario, the need to encourage professional qualification, so that these employees are not excluded from the labor market in the near future. As a methodology for carrying out this research, this work used bibliographical and documentary research, using national and foreign materials on the subject.
Controllable Privacy-Preserving Online Diagnosis with Outsourced SVM over Encrypted Medical Data

With the widespread application of online diagnosis systems, users can upload their physical characteristics anytime and from anywhere to receive clinical diagnoses. However, for privacy and intellectual property considerations, users' physical characteristics, diagnosis results, and the medical diagnosis model should be protected. To achieve an efficient and secure online diagnosis, secure outsourcing and low burden become research objectives. However, few of the existing privacy-preserving schemes focus on the secure outsourcing of the training process, and few consider the supervision of the hospital for the online diagnosis process. By introducing a four-party architecture with two non-colluding servers, a hospital and users, in this paper, we propose a controllable privacy-preserving online diagnosis scheme (CPPOD) with outsourced SVM over encrypted medical data. Concretely, an integer vector homomorphic encryption is employed to protect medical data and user requests. In the encrypted domain, a series of collaborative protocols including data collection, sequence minimum optimization solver, SVM model building, and online diagnosis are constructed and take place between different participants, while no significant increase in computation on either the hospital or user side. CPPOD enables the hospital to delegate online diagnosis services to a cloud server while ensuring that its regulatory capabilities cannot be bypassed unauthorized. Security analysis and performance evaluation suggest that CPPOD performs well regarding security and efficiency.
КОМУНІКАЦІЙНІ ТЕХНОЛОГІЇ ДИСТАНЦІЙНОГО НАВЧАННЯ: БІБЛІОМЕТРИЧНИЙ АНАЛІЗ ПУБЛІКАЦІЙНИХ ПОТОКІВ

&lt;p&gt;&lt;strong&gt;&lt;em&gt;The article aims&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; to identify the trends and dynamics of the publication flow regarding the use and implementation of online learning technologies..&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Research methodology.&lt;/em&gt;&lt;/strong&gt;&lt;strong&gt;&lt;em&gt; &lt;/em&gt;&lt;/strong&gt;&lt;em&gt;The research methodology based on bibliometric analysis, comparison method, descriptive method and visualization method. By means of bibliometric analysis, diachronic, geographical and linguistic sections of the publication flow on the use and implementation of online learning technologies were made. The comparison method was used to determine the features of the publication streams presented in the Scopus and Web of Science databases.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Results.&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; A diachronic analysis of the search results using the keywords 1) «online learning» or «educational platform» and 2) «online learning school» or «educational platform school» confirmed the positive dynamics of publication flows. At the same time, the scientific content of Scopus and Web of Science demonstrates significant differences regarding the nature of the intensification of publishing activity in the context of the impact of the Covid-19 pandemic. The geographical section of the publication flow determined the dominant countries-sources of scientific content: USA, China, Great Britain. The share of domestic content is insignificant and amounts to 0.4% and 0.5% in Scopus and Web of Science, respectively. The language slice of the publication stream showed the dominance of English, Spanish and Portuguese scientific content in both databases.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;The novelty of the obtained research results lies in the application of bibliometric analysis tools to determine the dynamics and trends of the publication flow regarding the use and implementation of online learning technologies.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Practical meaning.&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; The obtained results can serve as a methodological basis for further studies of publication flows in other areas of scientific knowledge.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Key&lt;/em&gt;&lt;/strong&gt;&lt;strong&gt;&lt;em&gt; &lt;/em&gt;&lt;/strong&gt;&lt;strong&gt;&lt;em&gt;words:&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; publication flow, scientific content, bibliometric analysis, distance learning, educational platforms, database&lt;/em&gt;&lt;em&gt;.&lt;/em&gt;&lt;/p&gt;
5 Hybrid entropy-based support vector machine with genetic algorithm for classification

In a real-world database, there are a large number of traits and information. These datasets also contain missing values, which might lead to incorrect categorization predictions. Extraction of concealed predictive data is a difficult and time-consuming operation. Developing classifier model is one of the major research areas in knowledge mining using datasets. Classification is a mechanism that labels data enabling economical and effective performance in valuable analysis. Research has indicated that the quality of the feature may cause a backlash to the classification performance. In this research work, a classifier is modeled and designed using hybrid entropy-based support vector machine with genetic algorithm (HESVMGA). There are three stages in developing the classifier. The first stage handles missing values using rejection and imputation by the nearest neighbor value. The nearest neighbor is identified by Euclidean distance measure. The second stage selects relevant features from the keel dataset using entropy- based support vector machine (SVM). The process of feature selection is applied in two ways: forward feature selection and backward feature elimination. The third stage is development of classifier using hybrid SVM and genetic algorithm. The proposed research HESVMGA algorithm is applied for four datasets, such as Ecoli, Wisconsin Breast Cancer, Glass, and CHD2, and calculates the performance evaluation. Qualitative assessment of proposed HESVMGA classification mechanism has been made with classification accuracy of 94.7% and better precision time, respectively. The proposed HESVMGA compared the three types of conventional methods, such as SVM, naive Bayes, and decision tree, after the depth evaluation proposed the HESVMGA method to produce better efficiency, recall, precision, and time responsibility. Statistical analysis of accuracy values and computational time portrays that the proposed schemes provide compromising results over existent methods.
8 A blockchain-based business model to promote COVID-19 via cloud services

The pandemic’s global spread resulted in nearly 3 million deaths and millions of people’s health deterioration. In addition to curfews, other restrictions were imposed in response to the risk of COVID-19. These restrictions disrupted education and working life, especially in places where people gathered in large groups. Aside from that, workforce planning in both the public and private sectors has changed with the adoption of remote working and flexible working methods from the beginning. By outsourcing their required services, businesses can reduce their total costs using cloud computing. Outsourcing changes the data protection landscape in definitions of trustworthiness, accuracy, and privacy. Consequently, public cloud has emerged as a major differentiator or competitive advantage for cloud providers. The use of block chain in cloud services is currently one of the most common inventions that can solve security problems in cloud computing. Data integrity, security, and anonymity are all provided by blockchain, a decentralized information management system. Cloud computing security issues and blockchain solutions are examined in detail in this chapter. Finding and evaluating articles from respected journals is made easier with the help of a variety of filters and global databases. Integration, trust, or privacy are all considered when it comes to cloud security. The results show that blockchain is an effective platform for this purpose. The most pressing issue, however, is the need for increased security. An action plan for prospective research and policy is also included in the chapter.
9 Evolutionary computation and streaming analytics machine learning with IoT for urban intelligent systems

In the Internet of things (IoT) era, for a wide range of fields and applications, a vast number of detecting gadgets collect or potentially produce different tactile information after a while. These gadgets can cause large or fast/constant streams of information. Examining these information sources in order to find new data predict future bits of knowledge, and decide on control choices is a critical process that makes IoT a commendable worldview for organizations and a personal satisfaction that enhances innovation. In this chapter, we give a careful review on the use of a class of cutting-edge artificial intelligence (AI) systems to advance deep learning (DL), thus enhancing IoT space inspection and encouraging learning. We begin by articulating IoT information attributes and recognizing two essential IoT information medicines from an AI perspective: IoT massive evolutionary computation and streaming information analysis and IoT gushing information review are two expels of IoT. We also explore why DL is a good way to perfectly handle investigation in these kinds of data and applications. The capacity to use DL procedures for investigation of IoT information is then addressed, and its guarantees and difficulties are identified. On various DL systems and measurements, we pose a far-ranging base. We also research and detail major research projects that used DL in IoT space. Furthermore, elegant IoT gadgets that fused DL into their knowledge base are investigated. On the basis of IoT applications, the mist and cloud-based DL implementation approach is also overviewed. We finally shed light on some problems and possible reasons that need further research.