Reviewerly/exHarmony huggingface dataset:
Virtualization Technology

AbstractVirtualization technology emerged with the emergence of computer technology and has always played an essential role in computer technology development. From the introduction of the concept of virtualization in the 1950s to the commercialization of virtualization on mainframes by IBM in the 1960s, from the virtual memory of the operating system to the Java virtual machine, to the server virtualization technology based on the x86 architecture The vigorous development of virtualization has added extremely rich connotations to the seemingly abstract concept of virtualization. In recent years, with the popularization of server virtualization technology, new data center deployment and management methods have emerged, bringing efficient and convenient management experience to data center administrators. This technology can also improve the resource utilization rate of the data center and reduce energy consumption. All of this makes virtualization technology the focus of the entire information industry.
Anthropogenic transformation of forest ecosystems by geochemical and photosynthetic parameters

Geochemical and physiological indices gained at the background area and at the area under anthropogenic impact were compared. The study sites comprise 30 sample areas each. Overall, 600 samples of pine needles were designated for the delayed chlorophyll fluorescence measurement as well as 60 samples of soil and pine needle correspondingly were selected to perform the geochemical analysis. The biological uptake of the pine urban ecosystems is represented in the range: Mn (10,16) &gt; P (7,19) &gt; Ag (2,78) &gt; Ba (2,76) &gt; Cu (2,31) &gt; Sr (1,85) &gt; Ni (1,80) &gt; Zn (1,75) &gt; Pb (0,86) &gt; Co (0,48) &gt; Cr (0,40) &gt; Zr (0,37) &gt; V (0,36) &gt; Ga (0,33) &gt; Ti (0,21). In comparison with the background site data the biological uptake is increased in regard to V, Ti, Ni, Cu, Cr. The methodology implies the method of recording the relative index of delayed fluorescence with the help of fluorimeter “Photon 10”. The data accumulated in the course of the geochemical analysis allowed to distinguish common geochemical features of the study sites and revealed differences in soil and needle qualities. The value of relative index of delayed fluorescence (RIDF) under the impact of considerable anthropogenic impact is lower by 25% and more related to the background site. This deviation testifies that the human impact is present even on the early stage of ecosystem degradation.
Research and Analysis on Japanese Teaching Mode of Online Education under Multimedia Network Environment

Japanese is among the international languages in extreme demand in the modern education system. There is a technological revolution in language learning, with blended learning (BL) in the classroom and online resources offering the possibility of autonomous learning education. The advancement of Information Communication Technology (ICT) and the development of the Internet, predominantly the Web, has transformed the way students get curriculum materials in online environments. Teaching Japanese languages has experienced crucial changes with multimedia technologies aimed at simulating a native-speaking environment in audition and oral communication. Evaluating student readiness for online learning is the beginning point for online course design using the multimedia network. Hence, this paper proposes the Multimedia Network-assisted Online Japanese Language Teaching Method (MN-OJLTM) to enhance student engagement in the online learning environment. This study identifies probabilities for better incorporation of technology and Japanese language learning and learners’ interest and desires via tactical education of Japanese online courses. This paper examines the role and influence of an intelligent learning environment of ICT multimedia in Japanese education. Experimental outcomes show that the suggested MN-OJLTM method enhances the accuracy of usage and Japanese language students’ fluency and expression in writing and speaking. The simulation outcomes demonstrate that the proposed MN-OJLTM method improves student learning ability ratio by 98.4%, language fluency level by 96.2%, performance ratio by 97.5%, student engagement ratio by 95.6%, and efficiency ratio by 97.9% compared to other popular methods.
Trends in Intelligent and AI-Based Software Engineering Processes: A Deep Learning-Based Software Process Model Recommendation Method

In recent years, numerous studies have successfully implemented machine learning strategies in a wide range of application areas. Therefore, several different deep learning models exist, each one tailored to a certain software task. Using deep learning models provides numerous advantages for the software development industry. Testing and maintaining software is a critical concern today. Software engineers have many responsibilities while developing a software system, including coding, testing, and delivering the software to users via the cloud. From this list, it is easy to see that each task calls for extensive organization and preparation, as well as access to a variety of resources. A developer may consult other code repositories, websites with related programming content, and even colleagues for information before attempting to build and test a solution to the problem at hand. In this investigation, we aim to identify the factors that led to developing the recommender. This system analyzes the recommender’s performance and provides suggestions for improving the software based on users’ opinions.
Effect of Vibrations on Impression of Walking and Embodiment With First- and Third-Person Avatar

We investigate how underfoot vibrotactile feedback can be used to increase the impression of walking and embodiment of static users represented by a first- or third-person avatar. We designed a multi-sensory setup involving avatar displayed on an HMD, and a set of vibrotactile effects displayed at every footstep. In a first study (N = 44), we compared the impression of walking in 3 vibrotactile conditions : 1) with a "constant" vibrotactile rendering reproducing simple contact information, 2) with a more sophisticated "phase-based" vibrotactile rendering the successive contacts of a walking cycle and 3) without vibrotactile feedback. The results show that overall both constant and phase-based rendering significantly improve the impression of walking in first and third-person perspective. Interestingly, the more realistic phase-based rendering seems to increase significantly the impression of walking in the third-person condition, but not in the first-person condition. In a second study (N=28), we evaluated the embodiment towards first- and third-person avatar while receiving no vibrotactile feedback or by receiving vibrotactile feedback. The results show that vibrotactile feedback improves embodiment in both perspectives of the avatar. Taken together, our results support the use of vibrotactile feedback when users observe first- and third-person avatar. They also suggest that constant and phase-based rendering could be used with first-person avatar and support the use of phase-based rendering with third-person avatar. They provide valuable insight for stimulations in any VR applications in which the impression of walking is prominent such as for virtual visits, walking rehabilitation, video games, etc.
Comparative Analysis of Different Trust Metrics of User-User Trust-Based Recommendation System

Information overload is the biggest challenge nowadays for any website, especially e-commerce websites. However, this challenge arises for the fast growth of information on the web (WWW) with easy access to the internet. Collaborative filtering based recommender system is the most useful application to solve the information overload problem by filtering relevant information for the users according to their interests. But, the existing system faces some significant limitations such as data sparsity, low accuracy, cold-start, and malicious attacks. To alleviate the mentioned issues, the relationship of trust incorporates in the system where it can be between the users or items, and such system is known as the trust-based recommender system (TBRS). From the user perspective, the motive of the TBRS is to utilize the reliability between the users to generate more accurate and trusted recommendations. However, the study aims to present a comparative analysis of different trust metrics in the context of the type of trust definition of TBRS. Also, the study accomplishes twenty-four trust metrics in terms of the methodology, trust properties \&amp; measurement, validation approaches, and the experimented dataset.
Visual Modeling System for Optimization-Based Real-Time Trajectory Planning for Autonomous Aerial Drones

In this paper, we present a visual modeling system to enable users to seamlessly describe the constraints of trajectory planning problems for autonomous aerial drones. The proposed modeling system comes with an intuitive GUI-based interface that enables the user to specify trajectory objectives, add and remove motion constraints, and update the constraint parameters in real-time. The interface algorithm acts as a high-level parser to convert graphically specified constraints into a standard form of the underlying optimal control problem. Then a sequence of convex optimization problems, convex subproblems, are generated whose solutions will converge to a solution of the trajectory planning problem. This convex optimization based method is referred to as successive convexification (SCvx) [1]. Beneath the interface, there is another low-level layer of problem parsing, which aims to model each convex subproblem as a Second Order Cone Programming (SOCP) problem in a standard form. Once each SOCP is formulated in this standard form, it can be passed to our in-house developed primal-dual interior point method (IPM) SOCP solver [2], [3] to obtain a solution for each convex subproblem within SCvx. This paper is aimed to describe the functional architecture of the visual modeling system and its core algorithms, and also presents some illustrative flight experiments.
Quadripartite EPR steering based on energy-level cascaded four-wave mixing processes

Abstract Multipartite quantum steering has important applications in secure quantum communication networks. In this work, three different energy-level cascaded four-wave mixing (FWM) processes are proposed to generate quadripartite quantum steering. The covariance matrix among different modes combination is constructed. The properties of quantum steering of the three cascaded models were studied. The results show that all three model structures can achieve various types of quantum steering, and the steerability varies with the amplitude gain of the FWM processes. The results provide a theoretical basis for the experimental generation of multipartite quantum steering and a significant reference for secure quantum communication.
Single underwater image haze removal with a learning-based approach to blurriness estimation

Underwater images are usually degraded due to light scattering and absorption. To recover the scene radiance of degraded underwater images, a new haze removal method is presented by incorporating a learning-based approach to blurriness estimation with the image formation model. Firstly, the image blurriness is estimated with a linear model trained on a set of selected grayscale images, the average Gaussian images and blurriness images. With the estimated image blurriness, three intermediate background lights (BLs) are computed to obtain the synthesized BL. Then the scene depth is calculated by using the estimated image blurriness and BL to construct a transmission map and restore the scene radiance. Compared with other haze removal methods, haze in degraded underwater images can be removed more accurately with our proposed method. Moreover, visual inspection, quantitative evaluation and application test demonstrate that our method is superior to the compared methods and beneficial to high-level vision tasks.
A Cognitive Systems Engineering Approach Using Unsupervised Fuzzy C-Means Technique, Exploratory Factor Analysis and Network Analysis—A Preliminary Statistical Investigation of the Bean Counter Profiling Scale Robustness

A bean counter is defined as an accountant or economist who makes financial decisions for a company or government, especially someone who wants to severely limit the amount of money spent. The rise of the bean counter in both public and private companies has motivated us to develop a Bean Counter Profiling Scale in order to further depict this personality typology in real organizational contexts. Since there are no scales to measure such traits in personnel, we have followed the methodological steps for elaborating the scale's items from the available qualitative literature and further employed a cognitive systems engineering approach based on statistical architecture, employing cluster, factor and items network analysis to statistically depict the best mathematical design of the scale. The statistical architecture will further employ a hierarchical clustering analysis using the unsupervised fuzzy c-means technique, an exploratory factor analysis and items network analysis technique. The network analysis which employs the use of networks and graph theory is used to depict relations among items and to analyze the structures that emerge from the recurrence of these relations. During this preliminary investigation, all statistical techniques employed yielded a six-element structural architecture of the 68 items of the Bean Counter Profiling Scale. This research represents one of the first scale validation studies employing the fuzzy c-means technique along with a factor analysis comparative design.
Security-enhanced watermark embedding and transmission based on synchronized chaotic semiconductor lasers

In this paper, we propose and numerically demonstrate a chaotic watermarking encryption and secure transmission scheme based on two synchronized response lasers (RLs) subject to identical injection from a driving laser (DL). In this proposed system, the chaotic signal from DL can be optimized after passing through a phase modulator and a dispersion component, then the chaotic signal with large bandwidth and low time delay signature is obtained. Simultaneously injecting this chaotic signal into two identical RLs, high-quality chaos synchronization between two RLs can be realized. Both synchronized chaotic signals are, respectively, divided into two parts. One is used to generate shared keys required by the watermarking encryption, and the other is used as chaotic carrier to realize the secure message transmission. After introducing the synchronized optical chaos into the watermarking encryption algorithm, the normalized correlation of the extracted watermark without long-distance transmission is 0.9554 and can still maintain above 0.9 over 200 km transmission fiber. The message transmission and watermark extraction have good robustness to the fiber parameter mismatches, and excellent performances can be maintained even within 20% mismatches range. In our method, synchronized high-quality optical chaotic signals from two RLs are used to watermark embedding and transmission, and double encryption can be realized, which can open a new path for future high-speed and security-enhanced watermark embedding and transmission.
Robust Pedestrian Detection and Path Prediction using Improved YOLOv5

In vision-based surveillance systems, pedestrian recognition and path prediction are critical concerns. Advanced computer vision applications, on the other hand, confront numerous challengesdue to differences in pedestrian postures and scales, backdrops, and occlusion. To tackle these challenges, we present a YOLOv5-based deep learning-based pedestrian recognition and path prediction method. The updated YOLOv5 model was first used to detect pedestrians of various sizes and proportions. The proposed path prediction method is then used to estimate the pedestrian's path based on motion data. The suggested method deals with partial occlusion circumstances to reduce object occlusion-induced progression and loss, and links recognition results with motion attributes. After then, the path prediction algorithm uses motion and directional data to estimate the pedestrian movement's direction. The proposed method outperforms the existing methods, according to the results of the experiments. Finally, we come to a conclusion and look into future study.
Trajectory Prediction from Hierarchical Perspective

Predicting the future trajectories of multiple agents is essential for various applications in real life, such as surveillance systems, autonomous driving and social robots. The trajectory prediction task is influenced by many factors, including the individual historical trajectory, interactions between agents and fuzzy nature of an agent's motion. While existing methods have made great progress on the topic of trajectory prediction, they treat all the information uniformly, which limits the sufficiency of using information. To this end, in this paper, we propose to regard all the information in a two-level hierarchical view. Particularly, the first-level view is the inter-trajectory view. In this level, we observe that the difficulty to predict different trajectory samples is different. We define trajectory difficulty and train the proposed model in an "easy-to-hard'' schema. The second-level view is the intra-trajectory level. We find the influencing factors for a particular trajectory can be divided into two parts. The first part is global features, which keep stable within a trajectory, i.e., the expected destination. The second part is local features, which change over time, i.e., the current position. We believe that the two types of information should be handled in different ways. The hierarchical view is beneficial to take full advantage of the information in a fine-grained way. Experimental results validate the effectiveness of the proposed model.
Image Forgery Detection Using Tamper-Guided Dual Self-Attention Network with Multiresolution Hybrid Feature

Image forgery detection can efficiently capture the difference between the tampered area and the nontampered area. However, existing work usually overemphasizes pixel-level localization, ignoring image-level detection. As a result, false detection for tampered image maybe cause a large number of false positives. To address this problem, we propose an end-to-end fully convolutional neural network. In this framework, multiresolution hybrid features from RGB stream and noise stream are firstly fused to learn visual artifacts and compression inconsistency artifacts, which can efficiently identify the tampered images. Furthermore, a tamper-guided dual self-attention (TDSA) module is designed, which can focus the network’s attention on the tampered areas and segment them from the image by capturing the difference between the tampered area and the nontampered area. Extensive experiments demonstrate that compared to existing schemes, our scheme can simultaneously effectively achieve pixel-level forgery localization and image-level forgery detection while maintaining higher detection accuracy and stronger robustness.
An Analysis of the Gory and Glory of Artificial Intelligence

In era of travel destinations planning to moon and mars Artificial Intelligence to overcome all the natural phenomena is not erratic. A time has come to thing of natural ways to sustain life. A time has come to know the benefits of Glory of Artificial Intelligence(AI) in the Gory of Life is an imperative aspect. The AI is to be protected under the Intellectual Property Rights(IPR). Laws pertaining to Anti-trust, anti-monopoly, IPR, National and International treaties to protect organic farming and varieties and so on are to brought in to effect to protect the Gory and Glory of Artificial Intelligence.
Data acquisition and telemetry

This chapter reviews the existing techniques in data acquisition and telemetry applied to volcanic monitoring and briefly describes a limited number of representative systems. The instruments used in volcano monitoring must satisfy a number of requirements which distinguish them from electronic equipment used in the laboratory. When defining a system to provide information from sensors placed on a volcano, a choice has to be made between several basic options. The most important criterion is whether the system will be digital or analogue, if digital, it is important to decide at which point the analogue-to-digital conversion will be made, thus determining which parts of the systems will be analogue and which digital. Numerous radio or telephone links, and digital acquisition and transmission systems are currently being used to monitor active volcanoes. The choice of data storage depends on the data type and the storage duration required.
Prospects for volcano surveillance

It is estimated that more than 10% of the world's population currently lives in close proximity to an active volcano. In particular, it is quite clear that volcano surveillance on a global scale is at present inadequate and functioning on an ad hoc basis, a situation which must be improved if the future impact of volcanic eruptions is to be minimized. Although designed for implementation during the current decade, the proposed projects are likely to be largely opened ended, and will continue to form a major part of a strategy for volcanic disaster reduction well into the twenty-first century. While by no means demeaning the importance of future technological developments in volcano surveillance, it may well be that the best way to avoid such a situation in the future, is for every volcanologist to become as much an educator and communicator as a scientist.
Indirect Positioning of a 3D Point on a Soft Object Using RGB-D Visual Servoing and a Mass-Spring Model

In this paper, we present a complete pipeline for positioning a feature point of a soft object to a desired 3D position, by acting on a different manipulation point using a robotic manipulator. For that purpose, the analytic relation between the feature point displacement and the robot motion is derived using a coarse mass-spring model (MSM), while taking into consideration the propagation delay introduced by a MSM. From this modeling step, a novel closed-loop controller is designed for performing the positioning task. To get rid of the model approximations, the object is tracked in real-time using a RGB-D sensor, thus allowing to correct on-line any drift between the object and its model. Our model-based and vision-based controller is validated in real experiments for two different soft objects and the results show promising performance in terms of accuracy, efficiency and robustness.