Reviewerly/exHarmony huggingface dataset:
Artificial intelligence in UX/UI design: a survey on current adoption and [future] practices

Artificial Intelligence (AI) technology has been disseminated across various industries, and algorithm-based activities are becoming common in design disciplines.Despite high expectations of collaborating with intelligent systems, whether designers are actually interested in working with algorithms has been little discussed.This paper summarises ongoing research findings that have probed the use of AI features in design practices.A survey of Brazilian UX/UI professionals was conducted to map their AI-supported activities and explore their perspectives on interacting with AI systems and future adoption scenarios.The findings indicate a low usage rate of AI tools in the Brazilian UX/UI industry and a limited operational perspective regarding the role, application, and impact of intelligent technologies on design.Surveyed UX/UI designers are more prone to adopt AI as a virtual assistant to facilitate practice and increase process efficiency rather than as a creative collaborator.
The Open Source Community Choice

Open source communities have had and continue to have a major influence on the evolution of the Internet. By their nature, such communities involve people with diverse coding cultures and skills. Automation has consequently been of major interest to open source software developers for a long time, and many open source tools have been developed to address code variability and sustainability challenges. This chapter discusses why open source communities must automate and the challenges they will face. Solutions and current examples of automation in open source projects are provided as a guide to what is achievable. OpenShift, OpenStack, and OPNFV communities are used to illustrate different approaches and best practices. Two recently initiated automation initiatives are detailed: “Cross Community Continuous Integration” (XCI) and “Cross Testing” (Xtesting). Finally, some recommendations are provided for new projects as a guide to ease adoption of appropriate tools and methods.
Learning Algorithms with Data Balancing in Credit Card Fraud Detection Application

There is a huge number of credit card transactions happening in the real world. But there is also the third-party person who monitors our activities and puts people in trouble. This was happening not only now but also about twenty years ago, and with the help of the latest technology algorithms, this fraud activity has been increased rapidly in certain areas such as online shopping, marketing, and so on. So, to detect these types of fraud activity, our research work focuses on identifying the percentage of the fraudulent in the given data set. Nowadays, technology had been improvised and frauds have been rising rapidly. In the banking sector, fraudulent activities in credit-card have been increased. In our research work, the main process is to make accurate predictions when balanced data is fed. With the use of Machine Learning [ML], this research study analyzes and summarizes the frauds in credit card transactions. Two library files such as PyCaret and SMOTE were used for data balancing. Five learning algorithms performed the detection of fraudulent activity, among them random Forest algorithm which feeds on a balanced dataset of SMOTE provides a very good estimate of the generalization error and to be resistant to overfitting.
Targeted Universal Adversarial Examples via Single Node Injection on Graph Convolutional Networks

Graph convolutional networks (GCNs) are fundamental graph neural networks used for solving node classification problems in graph-structured data. GCNs have been reported to be vulnerable to adversarial example attacks, posing a severe threat to their practical applications. In this study, we formulate targeted universal adversarial example attacks performed by injecting a single fake node into a graph. The proposed methods eliminate an unrealistic assumption required for previously proposed universal adversarial example attacks on GCNs. We show that GCNs are highly vulnerable to universal adversarial examples generated by only injecting a single node through experiments.
A Prediction Model of Domestic Animated Films and Audience Psychology Based on Facial Expression Recognition

By analyzing the relationship between the resistance and buoyancy of the swimmer and the water, exploring the mechanism of resistance and propulsion during swimming, and then proposing an optimization plan for technical movements, achieving the purpose of reducing resistance and improving propulsion efficiency. It is currently the most effective to improve the performance of competitive swimming. One of the means. With the rapid development of computer science, computer numerical simulation technology has shown great potential in the study of dynamic numerical modeling of swimming. Based on computational fluid dynamics, this paper carries out a numerical analysis of dynamic numerical simulations for different swimming postures. The analysis results show that different postures are closely related to the resistance of athletes to the water.
Design of a Light-Weight Key Scheduler for AES using LFSR for IoT Applications

This paper presents a new light-weight key scheduler in AES using linear feedback shift register (LFSR). The conventional substitution-box (S-box) based key scheduler in the AES suffers from a considerable hardware resource consumption. To improve overall hardware efficiency, the proposed key scheduler exploits an LFSR based structure with XOR operation. The proposed design is implemented in a 32-nm CMOS technology, it is shown that our LFSR-based scheduler reduces area, delay, power, and energy by 53%, 56%, 12%, and 61%, respectively, compared to the S-box based counterpart.
Underwater video transmission with video enhancement using reduce hazing algorithm

Abstract Underwater video regulation is an insightful research field that can help engineers with bettering investigation on the lowered condition. Submerged video preparing has been utilized in a many fields, such as submerged infinitesimal location, landscape examining, mine identification, media transmission connections, and self-proficient lowered vehicles. Be that as it may, submerged video experiences solid assimilation, dissipating, shading contortion, and clamor from the manufactured light sources, causing video obscure, cloudiness, and a somewhat blue or greenish tone. In this way, the improvement can be separated into two techniques, submerged video de-preliminaries and underexposed video concealing remaking. Relentless in remote correspondence structures, for instance 3G, 4G, and so on, a coming crisis is endless deftly of the nonattendance of consistently Radio Frequency (RF) resources; this deterrent in moving speed cannot strengthen the improvement notable for high information speed. So the new innovation of Light-Fidelity (Li-Fi) came into picture. This innovation can be contrasted to that of Wi-Fi and offers points of interest like expanded available spectrum efficiency, effectiveness, security, low idleness and a lot higher speed. Communication is accomplished by exchanging light-emitting diode (LED) lights on and off at a speed higher than what is detectable to the human eye. This paper presents the explanation behind underexposed picture corruption and surveys the cutting-edge knowledge calculations like video reduce hazing algorithm. In this calculation, it uses two different de-hazing methods, simple Dark Channel Prior (DCP) and Approximate Dark Channel Prior (ADCP), to reduce haze in a video.
COGNITIVE MODELING OF FINANCIAL AND ECONOMIC PROVISION OF QUALITY ACTIVATION OF HIGHER EDUCATION

Abstract. The article develops and substantiates the need to determine the causal links between concepts that contribute to the quality of higher education. The lack of motivation for radical reform of higher education is still hampered by attempts to use the successful international experience of the process of building a full-fledged system of quality assurance in the provision of educational services. The aim of the article is to develop a model for identifying causal links between the concepts of financial and economic support, which contribute to improving the quality of higher education. A system of concepts of the internal state and macro-environment of financial security has been formed, which has a positive or negative impact on the intensification of higher education. A fuzzy cognitive map of the impact of financial and economic support on improving the quality of higher education has been built. Scales and criteria for providing a qualitative assessment of the impact of the concepts of financial and economic support for the intensification of higher education in accordance with the introduced linguistic sets are calculated on the basis of the trapezoidal number method. The concepts of internal state and macroenvironment for activation of higher education are defined. The negative impact on the level of public spending on education and opportunities for access to ICT, which constrains the prospects for development and realization of the potential of participants in the higher education process, has been proved. The negative impact on the quality of higher education on the migration of students to study abroad has been identified, as access to higher education is almost unlimited due to significant government procurement and relatively low cost of contract education, and the return on higher education is relatively low. According to the simulation results, in order to intensify higher education, it is necessary to focus on the quality of teachers, provide opportunities for development, competence development, obtaining a higher level of qualification, which includes postgraduate and doctoral studies and academic degrees. Currently, an important priority of the European innovation system is the formation of the European Research Area. That is why there is a need to find effective mechanisms to influence the quality of research and innovation, which is represented by the number of publications / patents / CAT, investment and innovation projects, grants. Support for these concepts will provide an opportunity to unleash scientific and innovative potential, have a high social status in society and will improve the quality of educational services provided. Keywords: higher education, cognitive modeling, financial and economic support, consonance and dissonance, influence on the system, concepts of internal state, concepts of macroenvironment. JEL Classіfіcatіon І22, G17 Formulas: 9; fig.: 1; tabl.: 5; bibl.: 11.
From Seismic Instrumentation Towards Disaster Prevention and Mitigation

Abstract The present paper describes the current technical achievements in seismic instrumentation and monitoring within a national network and the role of this developed concept in disaster prevention and mitigation, in particular case of Romanian seismicity. Many studies are being conducted in the field of structural health monitoring, for seismically instrumented/monitored buildings, based on existed sensor technology, seismic data acquisition systems, data communication and information flow, computer hardware/software engineering, new solutions for seismic data transfer etc. Seismic records in free-field and on buildings are capitalised in anti-seismic design, development of technical and technological solutions in construction, seismic evaluation and rehabilitation of buildings, as well as in the process of education and earthquake preparedness. It aimed also to create a virtual seismic network (through Internet, WAN property networks, public analogue telephone network). It is a national priority creating a preventive culture in order to mitigate the seismic risk, starting with the strengthening of buildings, upgrading of the code for seismic design, seismic instrumentation as a usual practice and continuing with public communication and information actions, empowering communities and decision-makers related to the risks, prevention measures, what behaviour to be adopted. The efforts of last years show that Romania has taken important steps in preparing a response according to the challenges induced by the existing seismic sources from the entire territory of the country.
Extractive Text Summarization Using Convolutional Neural Network

Text summarization is a technique of briefing a large text document by extracting its significant information. Extractive text summarization involves direct extraction of sentences from the original document to form a summarized document. The considered task had been an intriguing one for a long time, and thus many approaches had been proposed for the same. This chapter proposes information extraction from a large text document using a convolutional neural network (CNN).
Regulatory basis for the formation of inclusive culture in student youth

The article analyzes scientific approaches and characterizes the concept of "inclusive culture", "inclusive culture of student youth". The main directions of inclusive culture formation in students of higher education institutions at: state, university, educational levels are clarified. The normative-legal support of organizational bases of formation of inclusive culture in student's youth is analyzed. The author identifies legal guarantees for the right to education of children and youth with special educational needs, their integration into society and the possibility of lifelong learning. The university level is characterized by internal regulations and the accessibility and accessibility of the educational environment of the educational institution. The educational level was characterized from the point of view of the organization and implementation of inclusive education, upbringing and socio-psychological support of students with special educational needs. On the basis of normative-legal documents the conclusion on necessity of introduction of specially organized educational process of formation of inclusive culture in student's youth is made.
A User Behavior-Based Random Distribution Scheme for Adversarial Example Generated CAPTCHA

The widespread and mature application of deep learning technology on human behavior analysis leads to the possibility that the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) may not be able to accurately determine whether or not the user is human, which makes the distributed denial-of-service (DDoS) attacks launched by deep learning-based malicious automatic computer programs become possible. The CAPTCHA generated by adversarial examples can defend against the automatic identification by the deep learning-based method, however, if the deployed CAPTCHAs are all adversarial examples solely, the adversary has the chance to collect sufficient adversarial examples, which also causes security issues. In this paper, a user behavior-based random distribution scheme for adversarial example generated CAPTCHA is proposed to tackle the above sole distribution problem. Specifically, we generated two kinds of CAPTCHAs, one is normal and the other is generated by the fast gradient signed method (FGSM), i.e., the adversarial examples. Meanwhile, user behaviors are analyzed and associated with a reasonable probability model. According to the probability selection, the normal CAPTCHAs or strong CAPTCHAs will be delivered to the users. The experimental results illustrate that our scheme has an excellent ability to distinguish computers from humans. Thus, it can protect the CAPTCHA systems from DDoS attacks.
A method for Style-Based Domain Conversion by Generative Adversarial Network

In recent, many methods for image generation using the Generative Adversarial Network(GAN) have been proposed. CycleGAN is one of the methods for image domain conversion which is an unsupervised learning method using two unpaired datasets. On the other hand, a method for image generation called StyleGAN is known as a method which improves accuracy by learning and incorporating the style of the image. In this paper, we proposed a method for domain conversion based on CycleGAN model, and added a new module that can learn the style and perform style transfer. The generated images change by inputting the noise parameters to different resolution during inverse convolution in the style transfer module. In experiments, we confirmed that the generated images were change depending on which resolution image the noise is input into.
Edge-cloud Collaborative Architecture for Scene Graph Application System

The SGG(sene graph generation) task is potentially of great significance for downstream visual reasoning. The application of scene graph has attracted more and more attention in computer vision field. At the same time, scene graph and knowledge graph have similar structure and significance. Scene graph can be regarded as a small knowledge graph, whereas knowledge graph can be regarded as the aggregation of scene graph. Starting from SGG, this paper explores the transformation relationship and combination method of scene graph and knowledge graph, excavates the application value of scene graph, and constructs the edge-cloud based architecture for scene graph application system(EcSGAS) according to their respective characteristics. Two applications scene description and scene recognition, are deployed to verify the effectiveness of the proposed application system.
Traversability-based Trajectory Planning with Quasi-Dynamic Vehicle Model in Loose Soil

This paper presents a framework for trajectory planning that explicitly considers robotic traversability based on a quasi-dynamic vehicle model of a mobile robot in loose soil. The quasi-dynamic model estimates the slip effect due to wheel-terrain interaction forces regardless of solving complicated multibody dynamics. Therefore, our proposed model is computationally efficient for quantifying how the robot safely traverses each trajectory segment generated by a planning algorithm. The trajectory planning in our framework exploits a sampling-based incremental search algorithm, i.e., Closed-Loop Rapidly-Exploring Random Trees (CL-RRT). In the tree extension process of the CL-RRT, the traversability assessment based on the quasi-dynamic vehicle model excludes the trajectory segment associated with a hazardous wheel slip ratio. As a result, a trajectory generated from the proposed framework is safely traversable for the robot even in high slip terrain. Simulation results show that the proposed vehicle model can run 57K times faster than the dynamic model and predict the robot motion 3 times more accurately than the kinematic model. Multiple trials of the trajectory planning simulation show that our proposed framework incorporated with the quasi-dynamic model reduces a wheel slip ratio by about 40 % as compared with the kinematic model.
Enriching Attributes from Knowledge Graph for Fine-grained Text-to-Image Synthesis

In this paper, we propose an Attribute-Rich Generative Adversarial Network (AttRiGAN) for text-to-image synthesis, which enriches the simple text description by associating knowledge graph and embedding it in the synthesis task in the form of an attribute matrix. Higher fine-grained images can be synthesized with AttRiGAN, and the synthesized sample are more similar to the objects that exist in the real world, since they are driven by attributes which are enriched from the knowledge graph. The experiments conducted on two widely-used fine-grained image datasets show that our AttRiGAN allows a significant improvement in fine-grained text-to-image synthesis.
James R. Martin, Yaegan J. Doran &amp; Giacomo Figueredo: Systemic functional language description: Making meaning matter

Article James R. Martin, Yaegan J. Doran & Giacomo Figueredo: Systemic functional language description: Making meaning matter was published on December 29, 2021 in the journal Journal of World Languages (volume 0, issue 0).
A Novel Complexity Feature Extraction Method for Classing Inter-Pulse Stagger Radar Signal

Inter-pulse stagger radar signals are widely used in electronic countermeasures due to their anti-reconnaissance characteristics, and the correct classification of them can greatly improve the effectiveness of countermeasures. The primary and pivotal part of classification is the construction of effective features. However, there are few effective features are established based on the characteristic of inter-pulse stagger radar signal and suitable for their classification, which makes correct classification of such signals a difficult problem. Here, a novel feature extraction method of inter-pulse stagger radar signal is proposed in this paper. In this method, a modified permutation entropy analysis method which combines permutation entropy with improved variability measure is first utilized for encoding original stagger radar signal. And then, the complexity feature model of encoding result is established according to Lempel-Ziv algorithm and entropy. Finally, based on the extracted feature, Support Vector Machines is employed to classify different stagger radar signals to verify the feasibility and effectiveness of this complexity feature. The simulation experiment results show that all classification accuracies have reached over 97%. It is worth mentioning that the simulation shows that complexity feature proposed in this paper is not sensitive to a certain proportion of missing pulses and spurious pulses.
A Comprehensive Survey on the Internet of Things (IoT) in Healthcare

In the era of fast-growing technology, Internet of things (IoT) is expanding, which involves ubiquitous Internet connectivity using standard devices, including global computing network, providing services to anyone who can access anything, anywhere, and anytime through the Internet. The IoT proves itself as a powerful domain where the fields of computer science and electronics have been merged. The IoT has autonomous control feature where the communication is made through machine to machine, which promises a great future for the technology. The IoT has extensive set of applications in heath, logistics, security, agriculture, environment, and industry. Another IoT idea allows the invention of various sensors, gadgets, and machines, to accumulate constant information fromnature. The nature of IoT 158being as a resource includes the tools required for a special authentication scheme that is not fully computing and energy resources. The development of IoT devices provides suitable solutions for comprehensive range of applications for smart cities, transportation, medical and healthcare, smart homes, emergency services, biomedical, etc. After introducing the IoT features, the quality and effectiveness of services are greatly achieved that provide appropriate solution for medical and healthcare services. In this chapter, we offer a comprehensive study to understand IoT technologies, applications, and smart industrial management trends in IoT-based medical and healthcare solutions. After this study, researchers highlight certain applications, statistics, and products that are derived from healthcare IoT fields.