Reviewerly/exHarmony huggingface dataset:
Swarm Behaviour Classification Using LSTM and Deep neural networks

Swarm behaviour was first simulated in 1986 and the agents are made to have motion per some basic rules. Swarm intelligent systems consists of simple agents known as boids, boids is an artificial program simulating the flocking behaviour of birds, these boids interact with one another and with their environment. Efforts are made to use results and observations of swarm intelligence to solve practical problems.As the behaviour is resembled to different classes of birds,insects etc hence the classification is necessary that what kind of swarm is it based on the features that are collected and doing all the processes. The artificial Intelligence concept can be used in the classification and the behaviour of it needs to be classified as the end output. The main motive of the paper that has been defined by the authors is to use LSTM which is a kind of recurrent neural network along with deep neural network and find a novel accuracy and after it compare the models based on different metrics.
Multi-Dimensional Measurement and Analysis Platform for Modern Guiding Value Mining in Smart Sports Classroom based on Camera Array Technology

Multi-dimensional measurement and analysis platform for modern guiding value mining in smart sports classroom based on camera array technology is the primary topic of this study. In terms of geometric model optimization, the method of constructing internal and external error compensation models separately and solving them as a whole can achieve certain results in the local scene or adjacent scene images. To better analyze the collected image, the novel CCD image model is designed. Using the anisotropic Gaussian directional derivative filter can effectively extract the characteristics of grayscale changes at the corners, and adopting a multi-directional strategy can detect corners more accurately. With this idea, the platform for modern guiding value mining in smart sports classroom is considered as the sample application. Comparing with the other state-of-the-art models, the designed approach is efficient.
A Study on Native English Instructors’ Experience with Online Asynchronous and Synchronous Classes

The purpose of this study was to investigate the experiences of English instructors in terms of asynchronous classes and synchronous classes during the COVID-19 pandemic. To this end, an online survey was conducted regarding the methods of each type of class in terms of strengths and weaknesses, class activities, assignments, quizzes, midterm exams, and communication tools. A total of 19 native English-speaking college instructors participated in the survey. The results of this study showed that the instructors pointed out the low level of learner participation among the students for both types of class, and that they recognized the different strengths and weaknesses of each class type. The advantages of LMS-based classes were that students were able to learn in a self-directed manner and the instructors could easily manage their classes. The disadvantages were that it was impossible for the students and the instructors to interact in real-time, and that it was difficult to build rapport with the learners and to motivate them as well. Therefore, the LMS-based method needs to be mixed with face-to-face classes or online synchronous classes.&lt;br/&gt; The perceived advantages of Zoom-based classes according to the instructors were that interaction, English practice opportunities, and class material sharing was possible in real-time. The disadvantages were that it was difficult for instructors to generate participation among the learners, and to reduce their level of “Zoom fatigue.” To cope with these issues, instructors used several strategies in order to get their students to turn on their cameras and to engage more actively in the class. In the LMS classes, learning activities were used to give chances to the students for self-directed learning, while in the Zoom classes the students were encouraged to participate in real-time. Quizzes were used to check the progress of the learners in the LMS classes, whereas in the Zoom classes, the students were given real-time speaking opportunities to practice their English skills. There was no significant difference between the two types of classes in regards to homework nor the type of midterm tests that were given. However, the communication tool was used more frequently in the asynchronous classes. The results of this study have implications for instructors, and should be taken into consideration when they are designing their online classes in the future.
Human Interface Guidelines for Interaction Zones in AR

In Augmented, Virtual and Mixed Reality, users interact with content in 3D space. Currently, surprisingly little is known about where these user interfaces should be placed in 3D space for optimal comfort and productivity. In this presentation, we will discuss factors that affect content placement and put forward a set of best practices for interaction zones in 3D. The output of the project is a set of Human Interface Guidelines (HIG) for Interaction Zones in AR. The Guidelines can be thought of as an AR version of the original Human Interface Guidelines put forward by Macintosh in 1995. The purpose of the Guidelines is to help application designers and developers in AR to design user-centric content in AR. Given the limited research that exists on the range of factors affecting interaction zones in AR, this document will contribute towards growing shared knowledge on this topic. This project combined knowledge of human physiological capabilities and limitations, Anthropometric ranges of the American target population, and current ergonomics guidelines and principles, as well as functional capabilities of Magic Leap 2 mixed reality device in the establishment of the HIG. Specific focus areas of the Human Interface Guidelines (HIG) for Interaction Zones in AR include: Ergonomics and Visual Comfort, Direct vs Indirect Interactions (gesture, gaze, controller), Field of View, and UI Behavior. In Ergonomics and Visual Comfort, we provide recommendations on content placement based on natural human line of sight, ergonomically comfortable head movement, and natural range of eye rotation. In Direct vs Indirect Interactions, we describe that where content is presented relative to the user depends on the way in which the user is expected to interact with the content. For example, content placement for direct (i.e. touch) interactions may be dictated by arm length anthropometry, as well as the field of view of the gesture-perceiving cameras. In Field of View, we review the relationship between field of view and viewing distance, and provide some techniques that designers can use to mitigate the noticeability of cut-off content due to limited FOV. In UI Behavior, we put forward best practices for how the UI moves with the user to prevent discomfort. The methodology to create the Human Interface Guidelines for Interaction Zones in AR is a literature review of existing research and guidelines on ergonomics as well as primary research in AR. The Guidelines will provide functional instructions for Developers and Designers of Augmented, Virtual and Mixed Reality applications, informing the design of content that is comfortable, convenient and optimally placed for the user.
A Comprehensive Analysis of Linear Programming in Image Processing

AbstractThis paper provides a basic background consisting of mathematical concepts of linear programming (LP) and implementation to process the images. This analysis consists of a basic introduction to optimization techniques used for and image processing. Image processing consists of various problem domains like segmentation, color-based segmentation, multiple objects tracking problems in video streams, image registration and image de-noising etc. Various algorithmic approaches devised to address these challenges using LP. The use of linear programming facilitates the designing of digital filters, utilization of prior knowledge for occlusion detection and efficient data fitting for image restoration along with the enhancement of the image quality. Reconstructed image exhibits optimal results then counterparts. LP helps in minimizing errors using estimators, extended dynamic programming for object tracking. It is also capable to separate meaningful regions, or interactive colors and with the integration of correlation clustering for segmentation of images. This analysis gives a insights versatility of the linear programming to implement, integrate and solving any problem. The analysis helps to conclude the future scopes and ideas to target and modify the problem. KeywordsOptimizationLinear programmingImage processingObjective functionConstraintsSegmentationRestorationTone mappingObject tracking
Multi-grained Semantics-aware Graph Neural Networks

Graph Neural Networks (GNNs) are powerful techniques in representation learning for graphs and have been increasingly deployed in a multitude of different applications that involve node-and graph-wise tasks.Most existing studies solve either the node-wise task or the graph-wise task independently while they are inherently correlated.This work proposes a unified model, AdamGNN, to interactively learn node and graph representations in a mutual-optimisation manner.Compared with existing GNN models and graph pooling methods, AdamGNN enhances the node representation with the learned multi-grained semantics and avoids losing node features and graph structure information during pooling.Specifically, a differentiable pooling operator is proposed to adaptively generate a multi-grained structure that involves meso-and macro-level semantic information in the graph.We also devise the unpooling operator and the flyback aggregator in AdamGNN to better leverage the multi-grained semantics to enhance node representations.The updated node representations can further adjust the graph representation in the next iteration.Experiments on 14 real-world graph datasets show that AdamGNN can significantly outperform 17 competing models on both node-and graph-wise tasks.The ablation studies confirm the effectiveness of AdamGNN's components, and the last empirical analysis further reveals the ingenious ability of AdamGNN in capturing long-range interactions.
Research on online emotion of COVID-19 based on text sentiment analysis

The growth of internet users and the convenience of internet communication provide a foundation for the formation of internet emotions. As the internet and real-life interactions become closer, the influence of internet emotions on society is increasing. Therefore, taking the spread of COVID-19 in Xinjiang in 2020 as an example, 43,111 related micro-blog texts were collected. After a series of operations such as Chinese word segmentation, POS tagging, data cleaning, text representation, feature extraction and so on, thematic extraction and text sentiment analysis were carried out to get people's comment themes, emotional tendencies and COVID-19's network emotional situation. The results show that the public will have a better understanding of the cause of COVID-19 disease and its infectiousness, preventive measures and cure as time goes on. The research of this paper can help the relevant government departments to perceive and guide the network emotional situation.
3D virtual learning environments to reinforce stock market competences

One of the major interests of higher education institutions is the creation of transformative methods that favor the instructional process, where students participate in their academic training through the generation of dynamic and open areas of interaction. This study describes the development of a strategy to understand the stock market with the application of technological tools based on 3D virtual environments. These environments known as Metaverses are immersive platforms that create experiences in an extended reality. The strategy consisted of designing and building a virtual learning environment on the Second Life platform to strengthen students' stock market skills. The instructional design was developed with the ADDIE (Analysis, Design, Development, Implementation and Evaluation) methodology focused on five themes: The world financial system, capital markets, products traded on the stock exchange, stock market indexes and famous cases in stock markets. The proposal was evaluated with 73 students of business administration career of a private university in Ecuador. According to the statistical information, 56% of the students have not used a metaverse before, 56% have not even practiced in real environments or simulators to learn the dynamics of the stock markets, 85% have not interacted with an avatar in cyberspace before, 78% consider the use of interactive spaces interesting, 79% were motivated to participate in 3D virtual environment strategies, 95% of students were engagement with active methodologies, 97% support the continuation of classes in the Second Life platform and 100% mention that it is necessary to create metaverses that allow them to interact and learn in a better way. The implementation of virtual worlds in e-learning platforms reinforces knowledge and management skills in the stock market
Working with Interfaces

This chapter builds upon your current understanding of object-oriented development by examining the topic of interface-based programming. Here, you will learn how to define and implement interfaces and come to understand the benefits of building types that support multiple behaviors. Along the way, you will look at several related topics, such as obtaining interface references, implementing explicit interfaces, and constructing interface hierarchies. You will also examine several standard interfaces defined within the .NET Core base class libraries. Also covered are the new features introduced in C# 8 regarding interfaces, including default interface methods, static members, and access modifiers. As you will see, your custom classes and structures are free to implement these predefined interfaces to support several useful behaviors, such as object cloning, object enumeration, and object sorting.
Trust management scheme for authentication in secure cloud computing using double encryption method

In cloud computing and banking, the consumer as well as supplier required for their service as protection and confidence. In this document suggest the belief value oriented verification procedure by the aid of encryption procedure, this verification segment bank marketing database are measured to the kernel fuzzy c-means clustering (KFCM) method. Clustered data's are accumulated in the cloud to the confidence data verification procedure. In the verification segment, the consumer verification is confirmed and acquires the verification key then encrypts the file by the double encryption algorithm. Primarily the confidence finest data implemented homomorphic encryption to encrypt the data by blowfish algorithm and then encrypted data are accumulated in cloud data core. This procedure oriented the banking data will be steadily legalised in cloud computing procedure. The outcomes are exemplify the improved encryption time and extremely legitimate the data in the cloud.
Conclusion

As we said at the start of Chapter 1, ontologies have become increasingly important as the use of knowledge graphs, machine learning, natural language processing (NLP), and the amount of data generated on a daily basis has exploded. As of 2014, 90% of the data in the digital universe was generated in the two years prior, and the volume of data was projected to grow from 3.2 zettabytes to 40 zettabytes in the next six years.111 The very real issues that government, research, and commercial organizations are facing in order to sift through this amount of information to support decision-making alone mandate increasing automation. Yet, the data profiling, NLP, and learning algorithms that are ground-zero for data integration, manipulation, and search provide less than satisfactory results unless they utilize terms with unambiguous semantics, such as those found in ontologies and well-formed rule sets. There are a number of books available on the market today, many that are part of this series, on the nuts and bolts of the Resource Description Framework (RDF) and the Web Ontology Language (OWL). The gap that we observed when we started working on this book with respect to methodology and best practices for ontology engineering had not been addressed until now, however. This gap is really our motivation for writing this book. It is a work in progress, representing the material we give in the first 5–6 weeks of a 13-week course, but we intend to continue to update and add content, and keep it current as the field evolves.
The Implementation of Modified Questioning The Author (MQTA) Strategy to Improve Students’ Reading Comprehension

This study was aimed i) to find out whether there was a difference of students’ reading achievement between students taught by using Questioning the Author (QtA) and those taught by using Modified Questioning the Author (MQtA), ii) to find out aspect of reading comprehension which score mostly increased after the implementation of QtA and MQtA, and iii) to find out the students’ responses toward the implementation of MQtA. The subjects of this research were the eighth grade students of SMP IT Ar Raihan Bandar Lampung. The result showed that i) there was a statistically significant difference in students’ reading comprehension achievement between students taught by using QtA and those taught by using MQtA, ii) from five aspects of reading comprehension; determining Main idea, Supporting details, Reference, Inferences and Vocabularies, the reading aspect which gained highest score in experimental group was in determining Main idea and Inferences, while in control group it was in determining References, and iii) the students’ responses toward the implementation of MQtA was positive. It can be said that MQtA strategy is more effective to increase the students’ reading comprehension achievement. However both QtA and MQtA can be implemented to every classroom with different students’ situation and background knowledge.
An Empirical Inquiry into the Perceptual Qualities of Spatial Enclosures in Head Mounted Display Driven VR Systems - Quantifying the 'Intangibles' of Space

This research is an inquiry into correlations between specific configurations of virtual spatial enclosures and corresponding perceptual responses in subjects. The experiments comprised of three sets - opening configurations, volume configurations and partition configurations. The perceptual parameters tested were Degree of Enclosure (E), Degree of Separation (P), and Spaciousness (S) respectively. Immersive virtual environments depicting enclosures with these different configurations were presented to 25 subjects through a head mounted VR gear. Responses were recorded in the form of verbal ratings. The results revealed that one's visual field along the horizontal axis at eye level plays a major role in the way specific attributes of spatial enclosures are perceived. One's perception of 'openness' in an enclosure correlated strongly with the amount of physical opening that was present along the horizontal axis at eye level, while the perception of 'spaciousness' correlated strongly with the amount of visual obstruction within one's horizontal visual field at eye level. It was found that larger unified openings between enclosures along eye level created a weaker sense of visual separation as compared smaller dispersed openings of equal cumulative area.
Simultaneous multi-view instance detection with learned geometric soft-constraints

We propose to jointly learn multi-view geometry and warping between views of the same object instances for robust cross-view object detection. What makes multi-view object instance detection difficult are strong changes in viewpoint, lighting conditions, high similarity of neighbouring objects, and strong variability in scale. By turning object detection and instance re-identification in different views into a joint learning task, we are able to incorporate both image appearance and geometric soft constraints into a single, multi-view detection process that is learnable end-to-end. We validate our method on a new, large data set of street-level panoramas of urban objects and show superior performance compared to various baselines. Our contribution is threefold: a large-scale, publicly available data set for multi-view instance detection and re-identification; an annotation tool custom-tailored for multi-view instance detection; and a novel, holistic multi-view instance detection and re-identification method that jointly models geometry and appearance across views.
The Use of Collocation to Improve English Vocabulary

&#x0D; &#x0D; &#x0D; &#x0D; Learning English is very useful, especially regarding the role of the tourism education institute to provide human resources in the tourism sector who have the ability to communicate with tourists and fills labour needs such as selling souvenirs, working in hotels, or being a tour guide. The tourism information service will use at least one foreign language, especially English. However, the reality shows that existing graduate students lack the required English language skills. This is due to the lack of vocabulary owned by students majoring in tourism. This study will use a quasi-experimental research model with one group pre-test and post-test design. The data will be analyzed using a quantitative approach and a qualitative approach using statistical analysis (test score results), and a descriptive qualitative approach (Observation, questionnaire, interview). This study aimed to determine whether there was an effect of using collocation in increasing the vocabulary (vocabulary breadth) of the STP Mataram students in the hospitality study program. Researchers took 50 students as research samples. This study used a quasi-experimental research model with one group pre-test and post-test design. Based on the table of results of the paired sample test t-test with SPSS, the sig value is obtained. (2-tailed) is 0.000 with a 5% probability is less than 0.005. So, it can be said that there is a difference in the effect of using collocation in improving the vocabulary skills of STP Mataram students.&#x0D; &#x0D; &#x0D; &#x0D;
ASPDC: Accelerated SPDC Regularized Empirical Risk Minimization for Ill-Conditioned Problems in Large-Scale Machine Learning

This paper aims to improve the response speed of SPDC (stochastic primal–dual coordinate ascent) in large-scale machine learning, as the complexity of per-iteration of SPDC is not satisfactory. We propose an accelerated stochastic primal–dual coordinate ascent called ASPDC and its further accelerated variant, ASPDC-i. Our proposed ASPDC methods achieve a good balance between low per-iteration computation complexity and fast convergence speed, even when the condition number becomes very large. The large condition number causes ill-conditioned problems, which usually requires many more iterations before convergence and longer per-iteration times in data training for machine learning. We performed experiments on various machine learning problems. The experimental results demonstrate that ASPDC and ASPDC-i converge faster than their counterparts, and enjoy low per-iteration complexity as well.