Modeling Secure 3D Web Applications

2011 ◽  
pp. 263-283 ◽  
Author(s):  
Krzysztof Walczak
Keyword(s):  
Web Applications ◽  
Fine Grained ◽  
Dynamic Composition ◽  
Novel Approach ◽  
Access Privileges ◽  
3D Scene ◽  
High Level ◽  
And Behavior ◽  
Content Modeling ◽  
3D Web

This chapter describes a novel approach to building 3D web applications, called Flex-VR, which can be used a basis for implementing security solutions. Two key elements of the approach are described: scene structuralization and content modeling. The scene structuralization enables decomposition of a 3D scene into independent geometrical and behavioral objects, called VR-Beans. Virtual scenes with rich interactivity and behavior can be dynamically created by combining sets of independent VR-Beans. The second element – the content model – is a generalized high-level description of the application content. The model enables efficient manipulation of content elements and dynamic composition of virtual scenes. Flex-VR provides a fine-grained semantically-rich content structure, which can be used as a basis for defining access privileges for users and groups. Five levels of user privileges definition in the Flex-VR approach are described. An application of Flex-VR in the cultural heritage domain is presented. Examples demonstrate how user privileges can be defined at all levels.

scholarly journals Affective Multimodal Control of Virtual Characters

2020 ◽  
Vol 6 (4) ◽  
pp. 43-54 ◽  
Author(s):  
Martin Klesen ◽  
Patrick Gebhard
Keyword(s):  
Real Time ◽  
The Body ◽  
Temporal Constraints ◽  
Virtual Characters ◽  
Fine Grained ◽  
Novel Approach ◽  
Control Body ◽  
High Level ◽  
The Mind ◽  
Selection Of

In this paper we report about the use of computer generated affect to control body and mind of cognitively modeled virtual characters. We use the computational model of affect ALMA that is able to simulate three different affect types in real-time. The computation of affect is based on a novel approach of an appraisal language. Both the use of elements of the appraisal language and the simulation of different affect types has been evaluated. Affect is used to control facial expressions, facial complexions, affective animations, posture, and idle behavior on the body layer and the selection of dialogue strategies on the mind layer. To enable a fine-grained control of these aspects a Player Markup Language (PML) has been developed. The PML is player-independent and allows a sophisticated control of character actions coordinated by high-level temporal constraints. An Action Encoder module maps the output of ALMA to PML actions using affect display rules. These actions drive the real-time rendering of affect, gesture and speech parameters of virtual characters, which we call Virtual Humans. 

DESIGNING E-LEARNING RESOURCES FOR TEACHING MATHEMATICS IN PRIMARY SCHOOL WITH ADOBE FLASH AND HTML5 SYSTEMS

2018 ◽  
pp. 49-57
Author(s):  
N. A. Gluzman
Keyword(s):  
Primary School ◽  
Web Applications ◽  
Special Importance ◽  
Learning Resources ◽  
Multimedia Presentations ◽  
Adobe Flash ◽  
E Learning ◽  
High Level ◽  
And Training ◽  
Training Practice

In the modern educational space regarding the realities of the information society special importance is attached to issues related to the provision of a high level of informatization of education, which implies teachers’ mastering the necessary competencies and the ability to introduce e-learning resources into educational and training practice. Adobe Flash as one of the platforms for creating web applications and multimedia presentations enjoys greatest popularity with users including teachers. However, in connection with the announcement of discontinuing Adobe Flash support in 2020, the issue of choosing an analog to create web applications and presentations for use in teaching purposes is becoming particularly relevant. The article provides a comprehensive analysis of developing electronic educational resources by teachers using Adobe Flash and HTML5 for teaching math in primary school.

Bracketed Morality in Sport

2019 ◽  
Vol 6 (2) ◽  
pp. 34-45
Author(s):  
Visnja Djordjic
Keyword(s):  
Moral Reasoning ◽  
Positive Influence ◽  
Fair Play ◽  
Personal Factors ◽  
Moral Integrity ◽  
Moral Decision Making ◽  
Male Athletes ◽  
Moral Atmosphere ◽  
High Level ◽  
And Behavior

Summary Although sport can promote moral values and prosocial behavior in youth, numerous research shows that sports engagement alone does not guarantee that outcome. Instead of striving for fair-play and sport excellence which not exclude justness, solidarity and moral integrity, contemporary sport frequently follows the Lombardian ethic, where „winning isn’t everything, it’s the only thing”. Moral pause or bracketed morality, as described in sport, refers to the phenomenon of tolerance and acceptance of aggressive behavior or cheating, that will be morally condemned outside sports arenas. Accordingly, lower levels of moral reasoning and behavior have been identified in athletes and non-athletes in the sports-related situation in comparison to other life situations; in athletes when compared to non-athletes, in more experienced athletes, high-level athletes, team-sport athletes, and male athletes. Moral reasoning and behavior of athletes are influenced by contextual and personal factors, with coaches having a particularly important role to play. The positive influence of sport on the moral development of athletes might be related to pre-service and in-service education of coaches how to develop adequate moral atmosphere, and how to plan for moral decision-making as an integral part of everyday practice.

scholarly journals Towards a contemporary player learning in development framework for sports practitioners

2021 ◽  
pp. 174795412110023
Author(s):  
Mark O Sullivan ◽  
Carl T Woods ◽  
James Vaughan ◽  
Keith Davids
Keyword(s):  
Linear Process ◽  
Task Environment ◽  
Coach Education ◽  
Development Framework ◽  
Novel Approach ◽  
Non Linear ◽  
And Performance ◽  
Learning Development ◽  
High Level ◽  
User Friendly

As it is appreciated that learning is a non-linear process – implying that coaching methodologies in sport should be accommodative – it is reasonable to suggest that player development pathways should also account for this non-linearity. A constraints-led approach (CLA), predicated on the theory of ecological dynamics, has been suggested as a viable framework for capturing the non-linearity of learning, development and performance in sport. The CLA articulates how skills emerge through the interaction of different constraints (task-environment-performer). However, despite its well-established theoretical roots, there are challenges to implementing it in practice. Accordingly, to help practitioners navigate such challenges, this paper proposes a user-friendly framework that demonstrates the benefits of a CLA. Specifically, to conceptualize the non-linear and individualized nature of learning, and how it can inform player development, we apply Adolph’s notion of learning IN development to explain the fundamental ideas of a CLA. We then exemplify a learning IN development framework, based on a CLA, brought to life in a high-level youth football organization. We contend that this framework can provide a novel approach for presenting the key ideas of a CLA and its powerful pedagogic concepts to practitioners at all levels, informing coach education programs, player development frameworks and learning environment designs in sport.

scholarly journals Explaining Neural Networks Using Attentive Knowledge Distillation

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1280
Author(s):  
Hyeonseok Lee ◽  
Sungchan Kim
Keyword(s):  
Neural Networks ◽  
Model Prediction ◽  
Saliency Map ◽  
Model Parameters ◽  
Learning Capability ◽  
Fine Grained ◽  
Network Layers ◽  
Saliency Maps ◽  
Novel Approach ◽  
Knowledge Distillation

Explaining the prediction of deep neural networks makes the networks more understandable and trusted, leading to their use in various mission critical tasks. Recent progress in the learning capability of networks has primarily been due to the enormous number of model parameters, so that it is usually hard to interpret their operations, as opposed to classical white-box models. For this purpose, generating saliency maps is a popular approach to identify the important input features used for the model prediction. Existing explanation methods typically only use the output of the last convolution layer of the model to generate a saliency map, lacking the information included in intermediate layers. Thus, the corresponding explanations are coarse and result in limited accuracy. Although the accuracy can be improved by iteratively developing a saliency map, this is too time-consuming and is thus impractical. To address these problems, we proposed a novel approach to explain the model prediction by developing an attentive surrogate network using the knowledge distillation. The surrogate network aims to generate a fine-grained saliency map corresponding to the model prediction using meaningful regional information presented over all network layers. Experiments demonstrated that the saliency maps are the result of spatially attentive features learned from the distillation. Thus, they are useful for fine-grained classification tasks. Moreover, the proposed method runs at the rate of 24.3 frames per second, which is much faster than the existing methods by orders of magnitude.

scholarly journals Deep Reinforcement Learning for End-to-End Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2534
Author(s):  
Oualid Doukhi ◽  
Deok-Jin Lee
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Autonomous Navigation ◽  
Control Technique ◽  
Local Motion ◽  
Aerial Robots ◽  
Novel Approach ◽  
Outdoor Environments ◽  
Aerial Vehicle ◽  
High Level

Autonomous navigation and collision avoidance missions represent a significant challenge for robotics systems as they generally operate in dynamic environments that require a high level of autonomy and flexible decision-making capabilities. This challenge becomes more applicable in micro aerial vehicles (MAVs) due to their limited size and computational power. This paper presents a novel approach for enabling a micro aerial vehicle system equipped with a laser range finder to autonomously navigate among obstacles and achieve a user-specified goal location in a GPS-denied environment, without the need for mapping or path planning. The proposed system uses an actor–critic-based reinforcement learning technique to train the aerial robot in a Gazebo simulator to perform a point-goal navigation task by directly mapping the noisy MAV’s state and laser scan measurements to continuous motion control. The obtained policy can perform collision-free flight in the real world while being trained entirely on a 3D simulator. Intensive simulations and real-time experiments were conducted and compared with a nonlinear model predictive control technique to show the generalization capabilities to new unseen environments, and robustness against localization noise. The obtained results demonstrate our system’s effectiveness in flying safely and reaching the desired points by planning smooth forward linear velocity and heading rates.

A challenge for historical research: Making data FAIR using a collaborative ontology management environment (OntoME)

Semantic Web ◽  
2020 ◽  
pp. 1-16
Author(s):  
Francesco Beretta
Keyword(s):  
Semantic Analysis ◽  
Historical Research ◽  
Data Modelling ◽  
Fine Grained ◽  
Fair Principles ◽  
Active Research ◽  
New Research ◽  
High Level ◽  
Conceptual Data ◽  
Ontology Management

This paper addresses the issue of interoperability of data generated by historical research and heritage institutions in order to make them re-usable for new research agendas according to the FAIR principles. After introducing the symogih.org project’s ontology, it proposes a description of the essential aspects of the process of historical knowledge production. It then develops an epistemological and semantic analysis of conceptual data modelling applied to factual historical information, based on the foundational ontologies Constructive Descriptions and Situations and DOLCE, and discusses the reasons for adopting the CIDOC CRM as a core ontology for the field of historical research, but extending it with some relevant, missing high-level classes. Finally, it shows how collaborative data modelling carried out in the ontology management environment OntoME makes it possible to elaborate a communal fine-grained and adaptive ontology of the domain, provided an active research community engages in this process. With this in mind, the Data for history consortium was founded in 2017 and promotes the adoption of a shared conceptualization in the field of historical research.

One-IPC high-level simulation of microthreaded many-core architectures

2016 ◽  
Vol 31 (2) ◽  
pp. 152-162 ◽  
Author(s):  
Irfan Uddin
Keyword(s):  
Design Space Exploration ◽  
Instruction Set ◽  
Efficient Design ◽  
Simulation Framework ◽  
Fine Grained ◽  
Detailed Simulation ◽  
High Level ◽  
Many Core ◽  
The Cost ◽  
Multiple Clusters

The microthreaded many-core architecture is comprised of multiple clusters of fine-grained multi-threaded cores. The management of concurrency is supported in the instruction set architecture of the cores and the computational work in application is asynchronously delegated to different clusters of cores, where the cluster is allocated dynamically. Computer architects are always interested in analyzing the complex interaction amongst the dynamically allocated resources. Generally a detailed simulation with a cycle-accurate simulation of the execution time is used. However, the cycle-accurate simulator for the microthreaded architecture executes at the rate of 100,000 instructions per second, divided over the number of simulated cores. This means that the evaluation of a complex application executing on a contemporary multi-core machine can be very slow. To perform efficient design space exploration we present a co-simulation environment, where the detailed execution of instructions in the pipeline of microthreaded cores and the interactions amongst the hardware components are abstracted. We present the evaluation of the high-level simulation framework against the cycle-accurate simulation framework. The results show that the high-level simulator is faster and less complicated than the cycle-accurate simulator but with the cost of losing accuracy.

Stories as Technology: Past, Present, and Future

2021 ◽  
Author(s):  
Roger’s Bacon ◽  
Sergey Samsonau ◽  
Dario Krpan ◽  
◽  
Keyword(s):  
Social Cognitive ◽  
Fine Grained ◽  
Good Story ◽  
High Level ◽  
Future Technologies

What is it about a good story that causes it to have life-changing effects on one person and not another? I wonder if future technologies will enable us to develop the type of truly deep and fine-grained understanding of stories as social, cognitive, and emotional technologies that might allow us to answer this question with a high-level of precision.

scholarly journals Entity Type Recognition for Heterogeneous Semantic Graphs

AI Magazine ◽  
2015 ◽  
Vol 36 (1) ◽  
pp. 75-86 ◽  
Author(s):  
Jennifer Sleeman ◽  
Tim Finin ◽  
Anupam Joshi
Keyword(s):  
Machine Learning ◽  
Background Knowledge ◽  
Knowledge Bases ◽  
Heterogeneous Data ◽  
Unstructured Data ◽  
Supervised Machine Learning ◽  
Coreference Resolution ◽  
Multiple Sources ◽  
Fine Grained ◽  
High Level

We describe an approach for identifying fine-grained entity types in heterogeneous data graphs that is effective for unstructured data or when the underlying ontologies or semantic schemas are unknown. Identifying fine-grained entity types, rather than a few high-level types, supports coreference resolution in heterogeneous graphs by reducing the number of possible coreference relations that must be considered. Big data problems that involve integrating data from multiple sources can benefit from our approach when the datas ontologies are unknown, inaccessible or semantically trivial. For such cases, we use supervised machine learning to map entity attributes and relations to a known set of attributes and relations from appropriate background knowledge bases to predict instance entity types. We evaluated this approach in experiments on data from DBpedia, Freebase, and Arnetminer using DBpedia as the background knowledge base.

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