Reviewerly/exHarmony huggingface dataset:
Capturing a snapshot in VR space

We propose a new way to capture snapshots in virtual space. We created a demonstration of how this method could be incorporated into other VR (virtual reality) contents. Using an Oculus Quest HMD (headmounted display), we created VR utility that runs on game engine Unity. We used bimanual controllers to take snapshots. The area framed by the bimanual controllers can be cropped and saved as a PNG image. It also has the functions to zoom in and out of the camera using the position of the player’s HMD, to capture non-horizontal images, to dynamically apply graphical effects, and to create stereoscopic imagery. This system is a more intuitive and fun way to capture virtual snapshots.
The Impact of Ethical Leadership and Motivation to Innovative Work Behavior with Friendly Relationship Knowledge Sharing Mediating Variables in Small Medium Enterprises (SMEs)

This research is aimed at determining the influence of ethical leadership and motivation on innovative work behavior (IWB) with Friendly Relationship Knowledge Sharing in Small and Medium Enterprises (SME) as the mediating variable.Furthermore, the method used is quantitative research that involves a sample size of 197 respondents within a population of SMEs in the culinary field in the City of Depok, South Tangerang, Indonesia.The Partial Least Square (PLS) technique of the SmartPLS computer software is used to analyze the research data.The findings of this research suggest that Ethical Leadership and Motivation have a positive effect on IWB, and Friendly Relationship Knowledge Sharing significantly mediates the influence of Ethical leadership on IWB.The results of this research will contribute immensely to SMEs to enable them to survive and compete with larger business enterprises.
RDNet: ResNet-18 with Dropout for Blood Cell Classification

(Aims) Blood cells are hematopoietic pluripotent stem cells derived from bone marrow. Blood diseases occur primarily in the hematopoietic system and can affect the hematopoietic system with abnormal blood changes, characterized by anemia, bleeding, and fever. It is helpful for doctors to diagnose blood diseases by classifying blood cells. However, doctors take a lot of time and energy to classify blood cells. The classification process is easily disturbed by external factors, such as doctors’ lack of rest, fatigue, etc. Many researchers use CNN to classify and detect red blood cells or white blood cells. However, using CNN has some problems in the classification or detection process. First, most researchers only classify blood cells into two categories, but there are many different types of blood cells. In addition, some studies are multi-classification of cells, but the results are often not ideal. (Methods) We propose a new model (RDNet) for the automatic classification of four types of blood cells to deal with these problems. The proposed RDNet selects the pre-trained ResNet-18 as the backbone. We transfer the pre-trained ResNet-18 because of the difference between the blood cell data set with the ImageNet data set. We add dropout to improve the classification performance. (Results) The accuracy of the proposed RDNet is 86.53%. The proposed RDNet obtains better accuracy than the transferred ResNet-18 because we add dropout in RDNet. Based on the accuracy, the proposed model is an effective tool to classify blood cells.
DCAL: A New Method for Defending Against Adversarial Examples

In recent years, deep learning has shown excellent performance in the field of computer vision. Nevertheless, researchers have found that the deep learning system does not have good robustness. Adding a insignificant amount of undetectable interference to the input of the deep learning system can lead to deep learning models fail, and these examples that make the model fail are called adversarial examples by researchers. The existence of adversarial examples will hinder the application and popularization of artificial intelligence-based deep learning systems. Therefore, we propose a denoising convolutional autoencoder incorporated with label knowledge (DCAL), a new method for defending against adversarial examples. The principle of which is DCAL as a pre-processing module before image classification, the image to be classified is denoised and reconstructed to obtain a innovative image, which is then sent to the classifier for classification. If we let the innovative image obtained by the adversarial examples through DCAL can make the classifier classify correctly, we will achieve the role of defending against the adversarial examples. The experimental results on two benchmark datasets including MNIST, CIFAR-10. Our experimental principally resisting the white-box attacks. The experimental results show that the proposed DCAL is superior to state-of-the-art defense methods in a white-box setting.
Multi-target panoramic digital pathology: from principle to application

The predecessor of China Optics was China Optics and Applied Optics Digest, which was founded in 1985. At that time, it was the only retrieval journal in the field of optics in China. At the end of 2008, China Optics and Applied Optics Digest was renamed
Cyclic codes and primitive idempotents in the finite cyclic group algebras

We parallelly discuss the construction of cyclic linear codes as ideals in the finite cyclic group ring as well as zero-divisors therein.We also determine a complete set of primitive idempotents in the finite cyclic group ring over a field of characteristic p.
Automated Probe Life-Cycle Management for Monitoring-As-a-Service

Cloud services must be continuously monitored to guarantee that misbehaviors can be timely revealed, compensated, and fixed. While simple applications can be easily monitored and controlled, monitoring non-trivial cloud systems with dynamic behavior requires the operators to be able to rapidly adapt the set of collected indicators. Although the currently available monitoring frameworks are equipped with a rich set of probes to virtually collect any indicator, they do not provide the automation capabilities required to quickly and easily change (i.e., deploy and undeploy) the probes used to monitor a target system. Indeed, changing the collected indicators beyond standard platform-level indicators can be an error-prone and expensive process, which often requires manual intervention. This paper presents a Monitoring-as-a-Service framework that provides the capability to automatically deploy and undeploy arbitrary probes based on a user-provided set of indicators to be collected. The life-cycle of the probes is fully governed by the framework, including the detection and resolution of the erroneous states at deployment time. The framework can be used jointly with existing monitoring technologies, without requiring the adoption of a specific probing technology. We experimented our framework with cloud systems based on containers and virtual machines, obtaining evidence of the efficiency and effectiveness of the proposed solution.
Human operator functions implementation by a manipulator

Robots application areas are considered, including directions of space robotics expected applications. It is shown that an important direction is a robotic manipulator to work with the onboard control panel controls. Such robotic manipulators’ development is an urgent and practically important task. A robotic manipulator organizing the autonomous operation problem is investigated. Manipulation objects are objects (controls) located on some technical object control panel. The subsystems that ensure the target task fulfillment are a robotic arm with a control system and a vision system. The robot manipulator interaction with objects should provide: on/off toggle switch, the switch handle turn, pressing a button, etc. At the same time, to manipulate objects, their position and orientation an assessment is required, which is realized with a computer vision system help. The subsystems’ operation coordination is necessary for planning and implementing manipulator controls, taking into account the objects relative position. The article presents a manipulator interaction diagram with a control panel, a methodology for organizing a vision system and a manipulator coordinated functioning when manipulating controls, including the interest objects position an assessment relative to a robot manipulator, and an algorithm for planning controls based on neural network technology. It is shown that the obtained control accuracy meets the requirements for a robot manipulator when working with a control panel.
Novel Rewinding-Time Technique Applied for Enhancing Meta-heuristic Algorithms

This paper proposes a new rewinding-time algorithm applied in meta-heuristic optimization techniques including PSO, JAYA to quickly and robustly obtain accurate and stable identified result. In standard optimization algorithms, initiated population are installed to look for the optimum solution with the fitness function value is responded to output requirements. With the proposed rewinding-time technique, the state of each generation, included all of information of population and gradient of fitness function, is stored. During optimize process, the rollback event will be triggered when gradient of fitness function satisfied the jumping condition. The current state is return to the previous state which is chosen if the rollback event active. The proposed technique is fully tested on benchmark test-functions including Shubert, Rosenbrock, Griewank, Salomon, Ackley and Michalewicz. The simulation results indicate that the proposed technique yield a competitive performance in both of precision and stability criteria compared to the standard algorithms.
Real-Time Object Detection and Tracking from Videos

This paper presents a novel approach to solve the problem of detection and continuous tracking of object(s) of interest in real-time from videos taken using stationary and moving cameras for various purposes like surveillance, vehicle navigation, biotechnology, human–computer interaction and sports analytics. The major challenges of this problem are low computational latency and high accuracy in spite of occlusion, non-uniform illumination, low quality videos, change of scale and orientation of object(s) in motion. The two-fold problem of object detection and tracking is solved using a transformer encoder-decoder architecture with Convolutional Neural Network (CNN) backbone called DEtection TRansformer or DETR and Point Tracking techniques respectively. We have implemented and tested the most popular Bayesian filters for point tracking like Kalman filter, Unscented Kalman filter and Particle filter independently. However, the tracking algorithm using Particle filter that also integrates an Auto-regressive model (ARM) for motion prediction, gives best result as compared to the other approaches. Our algorithm demonstrates accuracy and near real-time results which are on par with the well-established algorithms. The high overall efficiency is achieved by implementing high-performance techniques at both the stages of object detection and tracking.
Detection of Web User Clusters Through KPCA Dimensionality Reduction in Web Usage Mining

Dimensionality Reduction is a technique that performs feature extraction to enhance the evaluation of the model which uses data mining techniques. When the number of features in the model increases, the number of samples taken for tested also increases proportionally. The samples should also be selected to include all possible combination of the features. Web Mining plays a significant role in understanding the user’s browsing behavioral pattern on their web usage data. Web Usage mining records the user’s visited web page in their website. Each user’s visited umpteen number of pages as well as each website also contains more number of web pages. These pages help to build the model which finds the user’s behavioral pattern leads to a complex model which may have lot of inconsistent or redundant features in the data there by increase the computation time. When a greater number of features are included, there is also a high possibility of overfitting. Thus, to address the issue of overfitting and increase in computation time dimensionality reduction technique is introduced. Feature extraction technique helps to find the smaller set of new pages with the combination of the given independent web pages. Comparing three dimensionality reduction techniques such as PCA, Isomap and KPCA in which non-linear reduction technique of KPCA is used. Furthermore, unsupervised clustering technique of fuzzy Clustering technique is applied to identify the similar user behavioral pattern. Empirical study proved that the clustering accuracy is improved with the non-linear dimensionality reduction technique of KPCA is used with CTI dataset of DePaul University and MSNBC dataset is taken from the Server logs for msnbc.com
Using Multi-level Attention Based on Concept Embedding Enrichen Short Text to Classification

Aiming at the defects of short text, which lack context information and weak ability to describe topic, this paper proposes an attention network based solution for enriching topic information of short text, which can leverage both text information and concept embedding to represent short text. Specifically, short text encoder is used to enhance the representation of short texts in the semantic space. The concept encoder obtains the distribution representation of the concept through the attention network composed of C-ST attention and C-CS attention. Finally, Concatenating outputs from the two encoders creates a longer target representation of short text. Experimental results on two benchmark datasets show that our model achieves inspiring performance and outperforms baseline methods significantly.
Application Research of Virtual Reality as a Tool for Design

The aim of this study is to explore the advantages, feasibility and potential of applying head-mounted display (HMD)-based virtual reality (VR) in design work. The problems associated with working with desktop interfaces (DI) and the feasibility of using virtual reality as a solution was identified by research and review related paper. The advantages and problems of performing general operations under VR or DI condition was explored by a comparative experiment. According to the results of the review and the comparative experiment, we summary and analysis the advantages and problems of each aspect of nowadays DI and VR. Otherwise, we made a further review to discuss the solution of the problems of HMD-based VR. Finally, this paper proposes suggestions for the application of VR in design working, meanwhile, with the development of technology, VR will be applied better.
A Survey on Interoperability Issues at the SaaS Level Influencing the Adoption of Cloud Computing Technology

The study's conclusive solutions are two-fold: a 3-way generic solution mechanism into the other challenges bedeviling cloud adoption aside from interoperability is recommended and advanced. Also, a content analysis desk-search survey of the common solutions proposed by different researchers in cloud computing interoperability with special emphasis on the SaaS service-level interoperability with different initiatives which are structured on 6-key solution types is given. The analysis survey results from the 6-keys solutions advanced by researchers favors the standardization solution type more, with an implementation initiative of 40% and also the model-driven solution type, showing the highest number of authors implementation of 70%. Albeit more work on this area is needed to test and measure the effectiveness of the initiatives adopted in the survey in a single and broader context to determine their efficacy under the SaaS service level model of cloud interoperability.
Sign Language Interpreter

Sign language is the main source of communication for the deaf-mute people. These people go through many kinds of problems whilst communicating in person or through any other devices. To overcome this communication barrier, they need an interpreter which converts the sign language into text. In some situations, these impaired people may be unknown with sign language. Thus, necessity of sign interpreter is unpreventable. Developing this kind of interpreter needs a wide range of knowledge in fields such as deep learning, image processing, and convolution networking. The crucial point of this analysis is to know whether recognizing the gesture can succeed in assisting the self-learners in learning the sign language. This ideology can avoid their quarantine from the rest of the society notably. Results from this literature review could help in development of an efficient sign interpreter which helps for the communication between non-signer and a signer.
Support Vector Machine Performance Improvements by Using Sine Cosine Algorithm

Zivkovic, Miodrag Vukobrat, Nikola Chhabra, Amit Rashid, Tarik A. Venkatachalam, K. Bacanin, NebojsaThe optimization of parameters has a crucial influence on the solution efficacy and the accuracy of the support vector machine (SVM) in the machine learning domain. Some of the typical approaches for determining the parameters of the SVM consider the grid search approach (GS) and some of the representative swarm intelligence metaheuristics. On the other side, most of those SVM implementations take into the consideration only the margin, while ignoring the radius. In this paper, a novel radius–margin SVM approach is implemented that incorporates the enhanced sine cosine algorithm (eSCA). The proposed eSCA-SVM method takes into the account both maximizing the margin and minimizing the radius. The eSCA has been used to optimize the penalty and RBF parameter in SVM. The proposed eSCA-SVM method has been evaluated against four binary UCI datasets and compared to seven other algorithms. The experimental results suggest that the proposed eSCA-SVM approach has superior performances in terms of the average classification accuracy than other methods included in the comparative analysis.
Radial Visualization Model in Healthcare: A Survey

One of the main focuses of research in healthcare involves the processing of large amounts of data accumulated in Electronic Health Records and their communication in an interactive, understandable, and adequate way to the needs of each user, either healthcare professionals, patients, or care givers. One way of conveying the information to all of them is in a graphical way. There are, traditionally, two types of graphic models for data presentation: Cartesian/linear mod- els and radial models. From literature one can observe that there is an increasing interest in radial models to analyze and present large amounts of data. In this paper an exploratory study is presented aiming to understand how radial visualization model evolved throughout history, as well as its importance and relevance in data visualization with a particular interest in clinical data. The methodology used is focused on case studies found in literature, collecting all the relevant references about the radial visualization model and conclude on their weaknesses and strengths.
Bistatic Active Sonar Bayesian Sequential Automatic Detection and Tracking

Due to the influence of environment and measurement uncertainty, multi-target echo automatic detection and tracking of bistatic active sonar is a complex statistical inference problem. Besides, the parameters of bistatic sonar system to realize target localization and tracking are more complicated than that of the monostatic sonar system. Therefore, the implementation method should be more refined and adaptive. In this paper, a reliable sequential Bayesian detection and tracking method by combining Bayesian filter and generalized likelihood ratio detection (GLRT) framework is proposed. Firstly, the bistatic sonar observation model was constructed based on Bayesian theory, where Kalman filter and particle filter are considered as Bayesian filter which have been introduced to estimate the target tracking results, at the same time, the tracking results were used to auxiliary spectrum detection and guide the tracking of the next ping. This process is implemented in a sequential and recursive manner. Combined with the analysis of simulation data, it is proved that the proposed method has good real-time continuous detection performance and robust performance, and can effectively solve the problem of bistatic sonar target automatic detection and tracking.
A Conflicting Evidence Reasoning Approach Based on Hybrid Weight Under Interval-Valued Belief Structure

In the framework of Dempster-Shafer (D-S) theory, the combination of interval evidence is one of its core research questions. In this paper, we extend the ER rule with hybrid weight to interval evidence reasoning to be able to solve the conflicting interval evidence combination problems more efficiently. To do this, a means based on interval distance is established to objectively determine the importance of interval evidence. Focusing on the uncertainty of the belief distribution, a reliability measurement method based on belief entropy is constructed, which does not depend on the subjective setting of experts or decision makers. Further, we construct an improved optimization model in order to combine interval-valued belief structures. We also set up a series of examples to illustrate the proposed combination method, and compare it with some of the existing method, which shows its superiorities in better validity and high accuracy.
In Situ Statistical Distribution-Based Data Summarization and Visual Analysis

As the era of exascale computing approaches, the need for effective, scalable, and flexible data reduction techniques is becoming more and more prominent. As discussed in the introductory chapter, this need is primarily due to the bottleneck stemming from output data size and I/O speed compared to the ever-increasing computing speed. With this chapter, we consider a promising solution: data summarization techniques that work in the in situ environment while the data is getting produced, and preserve the important information from the data compactly, which minimizes information loss and enables a variety of post hoc analyses. Specifically, this chapter shows that statistical distribution-based in situ data summaries are a pragmatic solution and able to preserve important statistical data features. Using only the in situ generated statistical data summaries, which is significantly smaller in size compared to the original raw data, a wide range of data analysis and visualization tasks can be performed such as feature detection, extraction, tracking, query-driven analysis, etc. In addition, reconstruction of the full-resolution data is also possible, in order to visualize the data in its entirety with the added advantage of uncertainty quantification. To this end, this chapter presents several distribution-based data modeling algorithms, considering both their in situ performance and the usefulness of their distribution data summaries on several application studies.
Basics

This book introduces Git from the eyes of a circuit designer. In this chapter, we will see the basics of Git through hands-on exercises and discuss concepts through examples in the command-line environment. This will help you to interactively learn the concept.