Reviewerly/exHarmony huggingface dataset:
An Efficient and Secure Data Sharing Scheme for Ciphertext-Policy Attribute-based Signcryption for Cloud Storage Services

In this paper, an efficient retrievable attribute with Ciphertext-policy attribute-based signcryption with accountable and verifiable outsourced designcryption (CP-ABSc-AVODs) was proposed towards enable sharing of data in cloud in a secure manner. The cloud provides accurate control on data access, encryption and authenticity to data for granting confidentiality and integrity to personal messages. This protocol enables signing of message, depending on the access privileges given in tree structure mentioned embedded with the message. Users will be able to decrypt to get plaintext if and only if they have the necessary properties satisfying the structure specified for data access. In addition, CP-ABSc-AVODs provide access policy update functionality to decrypt unsigned or signed cloud storage server messages and redistribute user secret keys. The feature of access policy update in CP-ABSc-AVODs has no effect on the number of messages or its size received at the client site and hence there is a reduction in bandwidth and memory usage. The proposed protocol includes general attack resistance, message detection, data protection and fraud prevention. In addition, the proposed method’s performance is evaluated by comparing with other methods with respect to size of the key, functionality and time required for computation.
Application of Image Saturation Enhancement Algorithm based on OpenGL Aided Design System

The image recognition rate is reduced. Based on the monochromatic atmospheric scattering model and the prior law of dark primary colors, a new algorithm for the saturation of the HS I color model for visual perception is proposed to achieve image dehazing. For the minimum pixel point of the dehazed image, the maximum value and the minimum value are used. Estimate. It is conduded that “high efficiency, energy saving, green low carbon, clean and environmental protection” is the inevitable direction of the future development of DC welding power sources. Using the good 3D image generation function of the OpenGL graphics standard, the special finite element simulation system for the steel pipe tension reduction process developed by Yanshan University is based on the SketchUp platform. where the target is.
Fine margin between free speech and hate speech at new media: A Case-Study on Twitter

This research aimed to examine the dimensions and contents of hate speech shared on&#x0D; the official Twitter accounts of clubs in the fiercely competitive derbies of world&#x0D; football.&#x0D; In the scope of the research, competitors with intense competition in world football&#x0D; and the competitions with the highest competition rate were determined, and the&#x0D; Twitter reflections of the matches played by these clubs with each other in 2019 were&#x0D; examined on the official Twitter accounts of the clubs. Clubs under examination in the&#x0D; Turkish Super League were Fenerbahce and Galatasaray, Manchester United and&#x0D; Liverpool from England, Inter and Milan from Italy, Barcelona and Real Madrid from&#x0D; Spain, Schalke 04 and Borussia Dortmund from Germany, Celtic and Rangers from Scotland, Boca Juniors from Argentina and River Plate teams' official Twitter accounts&#x0D; were investigated.When total of 18249 tweets were examined the obtained results&#x0D; were examined, the intensity of sharing profanity-insulting content did not differ&#x0D; according to the countries, but there were differences when the people or teams in&#x0D; which these posts were directly considered. While insulting and profanity messages&#x0D; towards the opposing team came to the fore in the Turkish Super League, Spain La&#x0D; Liga, Argentine League, Italy Serie A and Scottish Premier League, the messages of&#x0D; the fans in this category in the English Premier League and the German Bundesliga&#x0D; came to the fore because of the hate speech directed to their teams. In humiliating or&#x0D; othering the opponent, the Turkish Super League was at the top, and the results in this&#x0D; league were similar to Spanish La Liga.
Deep Learning-Based Channel Estimation for HPO-MIMO Systems in IoV Scenario

Non-linear MIMO technology is proven to work out the excessive power consumption issue caused by the base station with more than 100 antenna pairs that have been adopted for the Internet of Vehicles (IoV). However, the non-linear MIMO scheme applied in the IoV scenario does not consider the real-world channel with the characteristics of vehicle motion. In addition, traditional channel estimation (CE) in non-linear MIMO technology are not robust under the variation of channel parameters in IoV.A channel estimation scheme of Half Phase Only (HPO)-MIMO based on Convolutional Neural Network (CNN) is proposed, which can get more accurate channel estimation results and achieve perfect robustness for changing channel parameters. Besides, the COST 2100 channel model is used, which is more suitable for simulating the IoV scenarios. Moreover, the channel estimation scheme based on CNN al-gorithm can be used favorably in the non-linear MIMO and IoV scenarios. Simulation results show that the CNN-based CE scheme we proposed achieves outstanding mean squared error (MSE) performance compared to the Generalized Approximate Messaging (GAMP) algorithm. Furthermore, the rationality of using the COST 2100 channel model is proven that have excellent performances.
Big Data Retrieval and Its Application in Image Annotation and Reconstruction

This paper discusses how to organically combine image retrieval with data mining, image reconstruction and other related technologies. It makes use of the prior knowledge and rich images contained in the big data, and the automatic annotation and reconstruction-sharing application in the images are driven by text messages, so as to realize the open real-time annotation of the large-scale images and the real-time sharing of the high-resolution photos in the mobile phones. In this paper, the big data retrieval and its application in the image annotation and reconstruction are analyzed.
Shoulder Position Estimation using Load Current Shape

This paper presents an improved shoulder position detection method in the body scanning process of a massage chair using flex and knead motor current shape. In order to improve the relaxing and massaging effect, the strength of rolling, kneading and tapping have to be adjusted according to the muscle position of the human body. And these massage regions of the body can be estimated by the shoulder and hip position. The hip position is automatically determined by the massage seat shape. But, the shoulder position of the user has to be determined by body scanning. In this paper, a novel shoulder position estimation scheme using flex and knead motor current shape is presented. In the proposed method, the load currents of the knead and flex motor in the constant speed condition are same as the friction load between the massage ball and human body. From the neck to shoulder moving, the friction between the massage ball and human body is suddenly changed. According to the periodic operation of knead and flex motion, the motor currents are analyzed by the periodic shape form. Then, the periodic current shapes of the knead and flex motor can be used to detect the shoulder position of the user. The proposed shoulder estimation scheme is tested by the practical electronic massage chair according to the users with different height and weight. In the practical test, the proposed method can well estimate the actual shoulder positions of various users.
Research on Discrimination Method of Superpixel Clustering Based on Support Vector Machine

The requirements of maneuverability, velocity and control ability of modern aircraft are getting higher and higher. Effectively controlling the aircraft when flying at a high attack angle can greatly improve the maneuverability of the aircraft and reduce the stall speed of the aircraft. Flush Air Data Sensing System can indirectly obtain the angle of attack, sideslip angle, and the change of the incoming flow speed which provides necessary parameters for the flight control of the aircraft and meets the parameter measurement requirements of the aircraft. The algorithm research on pressure model and model trimming was conducted based on the three-point method with good real-time performance and high accuracy after study of traditional pressure models. And a series of experiments were performed to modify the accuracy of the model. After experimental verification, the model can be used to calculate the attack angle, sideslip angle, and flow velocity in flight projects, which can provide certain parameters or basis for aircraft flight control.
Impact of Malmath Application on Students’ Achievement on Properties of Quadratic Graph

The study looked into how the MalMath Application integration affected how well students performed when learning about the characteristics of a quadratic graph and how they felt about using the program. A quasi-experimental design was adopted in the investigation. The pre-questionnaire results were used to create the control and experimental groups (i.e., students who have access to smartphones and those who do not have access to smartphones respectively). While receiving treatment, the experimental groups' lessons included the MalMath app, whereas the control group received traditional training. For the study in Ghana, a Technical Senior High School's sample of N = 80 students were readily and purposefully selected as control and experimental groups. This study's theoretical underpinnings were informed by constructivism. The experimental group's findings regarding the scores of male and female did not show a significant difference, but the post-test results for the two groups showed a significant difference in the findings regarding the scores of female students. The findings of the post-questionnaire showed that students' attitudes on the usage of MalMathApp in teaching quadratic graphs are favourable, and they are willing to utilise it for other mathematical topics. It was advised that teachers use MalMath App to teach and learn about quadratic graph characteristics and any other mathematics courses.
The Effect of Self-efficacy on Sustainable Development: The PetroMasila in Yemen

This research addresses the mechanism that the components of self-efficacy impact the sustainable development efficacy among employees of PetroMasila in Yemen. The methodology of the current study was quantitative. The online questionnaire was utilized to collect data from 342 employees who are working in the department of project management. The data analysis was conducted by the Structural Equation Model (SEM) in Smart-PLS Software. The findings of this study indicated that all components of self-efficacy, namely past performance, vicarious experiences, verbal persuasion, and emotional cues, have a significant and positive influence on the oil industry's sustainable development effectiveness. The model of research explained 45% of the whole variance in sustainable development efficacy. Furthermore, this study discusses the limitation and future studies.
Statistical Univariate Modeling

Univariate time series data analysis is the most popular type of temporal data, where a single numeric observation is recorded sequentially over equal time periods. Only the variable observed and its relation to time is considered in this analysis.
Impact and success factors of online education methods at university level in times of COVID-19: a case study of Paraguay

The drastic changes that COVID-19 has brought along include the necessity to introduce new forms of online university education. This article compares the impact of the change from face-to-face to online classes on student performance and satisfaction and identifies key success factors for effective online teaching. The study is based on the comparison of 34 face-to-face and online courses in 2019 and 2020 among 416 students of the 1e to 4e-year from the degree programs of the Faculty of Business Sciences of the German Paraguayan University in Paraguay. The study provides a methodology to identify and measure the success factors for an effective change to online education and provides practical lessons learned on the effective integration of digital tools in the teaching and learning process, relevant for other universities in developing and developed countries alike.
There is no place for hate speech in the world

Whenever I decide to write my column, I always want to pick a topic that I think will be interesting but also provide some guidance and advice to those of us who work in campus safety on college and university campuses.
High-precision polarized dual low-coherence scanning interferometry for rapid step height measurements

In this investigation, we propose a polarized dual low-coherence scanning interferometer. The spatial phase-shifting technique and the concept of the dual low coherence are adopted to overcome the scanning conditions of typical low-coherence scanning interferometers and reduce the measurement time. In the proposed interferometer, the scanning interval is not critical because the visibility and the phase are immediately obtained during a scanning procedure, and the whole scanning distance can be significantly reduced by the dual low coherence. To verify the system performance, the surface profiles of a plane mirror and a step height specimen were measured, and it was confirmed that the whole scanning distance was reduced 10 times without any scanning conditions.
Spam Detection in Connected Networks Using Particle Swarm and Genetic Algorithm Optimization: Youtube as a Case study

Although there are many networks security tools, both wire and wireless connected networks are still suffering from many types of attacks. YouTube's meteoric rise to prominence as a social platform speaks for itself. The sheer volume of comments on YouTube has made it an ideal medium for spammers to spread their malicious software. Phishing attacks, in which anyone who clicks on a bad link might be a victim, have contributed to this problem. Classification systems may be used to examine spam for its unique characteristics and identify it. This is why it is suggested that YouTube already has built-in mechanisms for identifying spam. A YouTube Spam detection framework was designed with the five stages of data collection, pre-processing, features extraction, classification, and detection, allowing for the execution of the tests. To analyze and validate each stage of the YouTube detection methodology presented in this study, two metaheuristic optimization methods are employed to optimize the parameters of a new voting ensemble classifier. These methods are the particle swarm optimization (PSO) and the Genetic Algorithm (GA). The ensemble model is based on three classifiers: neural. Results indicate that the proposed approach is accurate. In addition, statistical analysis is performed to emphasize the superiority and effectiveness of the proposed methodology.
Trustworthy Federated Graph Learning Framework for Wireless Internet of Things

As computational power has increased rapidly in recent years, deep learning techniques have found widespread use in wireless internet of things (IoT) networks, where they have shown remarkable results. In order to make the most of the data contained in graphs and their surrounding contexts, graph intelligence has seen extensive use in a wide variety of tailored wireless applications. However, the sensitive nature of client data poses serious challenges to conventional customization approaches, which depend on centralized graph learning on globe graphs. In this work, we introduce federated graph learning, dubbed FGL, that is capable of producing accurate personalization while still protecting clients' anonymity. To train graph intelligence models jointly based on distributed graphs inferred from local data, we employ a trustworthy model updating technique. In order to make use of graph knowledge beyond the scope of dynamic interplay, we present a trustworthy graph extension mechanism for incorporating high-level knowledge while yet maintaining confidentiality. Six customization datasets were used to show that with excellent trustworthy protection, FGL achieves 2.0% to 5.0% lower errors than the state-of-the-art federated customization approaches. For ethical and insightful personalization, FGL offers a potential path forward for mining distributed graph data.
AI-based video analysis for traffic monitoring

Video data from these surveillance cameras is ex-tremely huge. When it requires extracting information about an object or event from the surveillance camera without knowing the exact time, it is very challenging to immediately identify it from billions of video frames. Thus, how to effectively extract information from video surveillance has a strategically important role in practical applications. In this paper, we introduce a system to manage and retrieve surveillance videos based on the indexing of moving objects. The system contains object detection and features extraction, video indexing, retrieval processing mechanism, and fast previewing. Specifically, this system uses information from moving objects to index video content. As a result, in the retrieval phase, the system allows both text-based query and image-based query mechanisms. The system performance are evaluated on traffic surveillance videos and got promising results.
Security and Secrets Management

In this chapter, we’ll look into various different areas pertaining to the security of our application and infrastructure. We’ll discuss how to manage the database using AWS Secrets Manager and how it can directly be injected into the pods. Then we’ll look at a few small aspects of securing our ALB and restricting the network for the RDS. After that, we’ll look at how pods can authenticate to AWS and access different resources. Then we’ll look at how the disk of the EKS nodes can be encrypted using AWS KMS and how we can enforce Service Control Policies or SCPs on different OUs such that developers cannot spin up expensive EC2 resources. Finally, we’ll end by adding a tool called Checkov in the GitHub Actions pipeline, which will continuously spill out security issues on every PR.
AI/ML Trained Serverless Endpoints with Spring Cloud Function

This chapter looks at how Spring Cloud Function can be leveraged in AI/ML. You learn about the AI/ML process and learn where Spring Cloud Function fits in the process. You also learn about some of the offerings from the cloud providers, such as AWS, Google, and Azure.
Why Use Spring Cloud Function

This chapter discusses the Spring Cloud Function using a sample use case—an HRM system. The focus is on systems that reside in an enterprise. The chapter touches on the FaaS (Functions as a Service) concept and explains how it is gaining momentum in the enterprise. The chapter also digs deeper into its implementations in the cloud. You will learn about some of the portability issues present at the code and container level and read about concepts such as Knative on Kubernetes, which includes container portability. You will also learn about some high-level implementations of Spring Cloud Function on AWS, GCP, Azure, VMware Tanzu, and Red Hat OpenShift.
AutoML, AutoAI, and the Rise of NoLo UIs

In what is still so far a relatively short space of time, growth in machine and deep learning implementations in organizations across the world has been extraordinary. However, this hasn’t always translated into commercial success, with disappointingly low adoption rates in the retail sector (11.5% in the UK) and just over 50% of prototypes ending up in production across all industries. Historically many solutions have been operationally siloed with PhD-level statisticians left to explain code-heavy technical models.
Warping with Patterns

Chapter goal: Look at different ways that repeating patterns can be created and then warped.
Warping Text

Chapter goal: Learn different ways you can alter your text with various warps.
Perspective Warp and Content-Aware Scale

Chapter goal: Learn how to correctly use the features of the Perspective Warp, Content-Aware Scale, and Sky Replacement
The Importance of Classifying Artificial Intelligence as a Digital Asset. A Bibliometric Study.

The importance of Artificial Intelligence technology is manifested in the advancement of society. However, there is a growing need for the economic and business support that Artificial Intelligence can offer to be considered in its involvement as a digital asset. The motivation of this paper is precise to reflect the importance for academia of the concept of Artificial Intelligence as a digital asset since its capacity to generate intangible value for companies will make them more competitive. The main result is that Artificial Intelligence represents a significant percentage of the studies on digital assets, but not the other way around; this shows that most writings published so far have addressed the issue by focusing more on the technical aspect of the study (understood as the development of IT systems or solutions, for example). All shows the concern of the scientific community for the technological progress of the link between Artificial Intelligence and digital assets, but not in the sense of progress towards greater valuation of entities and organisations in economic and financial terms, which will make companies more competitive in their access to financing, for example. All of the above is refuted by the conceptualisation of current work, which works from the point of view of the advancement of artificial intelligence in specific areas of the company, such as marketing or finance, or sectors such as manufacturing, but not from an aggregate point of view as presented here.
Text Classification Models for Form Entity Linking

Forms are a widespread type of template-based document used in a great variety of fields including, among others, administration, medicine, finance, or insurance. The automatic extraction of the information included in these documents is greatly demanded due to the increasing volume of forms that are generated in a daily basis. However, this is not a straightforward task when working with scanned forms because of the great diversity of templates with different location of form entities, and the quality of the scanned documents. In this context, there is a feature that is shared by all forms: they contain a collection of interlinked entities built as key-value (or label-value) pairs, together with other entities such as headers or images. In this work, we have tacked the problem of entity linking in forms by combining image processing techniques and a text classification model based on the BERT architecture. This approach achieves state-of-the-art results with a F1-score of 0.80 on the FUNSD dataset, a 5% improvement regarding the best previous method. The code of this project is available at https://github.com/mavillot/FUNSD-Entity-Linking .
An Adaptive Clustering Approach for Distributed Outlier Detection in Data Streams

Many real-world problems deal with collections of high-dimensional data, i.e., data with many different features. A dataset exhibiting a high number of features incurs the so-called curse of dimensionality: when the dimensionality increases, the volume of the space increases at a fast rate, causing the sparseness of the data. This makes challenging clustering high-dimensional data for outlier detection purposes. In this paper, we design and implement a distributed peer to peer version of an algorithm that addresses the curse of dimensionality by generating candidate subspaces from the high-dimensional space through Principal Component Analysis. The experimental results show that if the parameters of the distributed algorithm are properly set, then the distributed algorithm converges to the results provided by the sequential algorithm, which is a fundamental and highly desirable property.