scholarly journals GlassViz: Visualizing Automatically-Extracted Entry Points for Exploring Scientific Corpora in Problem-Driven Visualization Research

2020 ◽  
Author(s):  
Alejandro Benito-Santos ◽  
Roberto Theron
Keyword(s):  
Digital Humanities ◽  
Communication Channel ◽  
Interdisciplinary Communication ◽  
Cognitive Model ◽  
Proof Of Concept ◽  
Text Analytics ◽  
Research Papers ◽  
Proposed Model ◽  
Entry Points ◽  
Visual Text

In this paper, we report the development of a model and a proof-of-concept visual text analytics (VTA) tool to enhance document discovery in a problem-driven visualization research (PDVR) context. The proposed model captures the cognitive model followed by domain and visualization experts by analyzing the interdisciplinary communication channel as represented by keywords found in two disjoint collections of research papers. High distributional inter-collection similarities are employed to build informative keyword associations that serve as entry points to drive the exploration of a large document corpus. Our approach is demonstrated in the context of research on visualization for the digital humanities.

scholarly journals The aiming device for cervical distractor pin insertion: a proof-of-concept, feasibility study

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Torphong Bunmaprasert ◽  
Sittichai Luangkittikong ◽  
Menghong Tosinthiti ◽  
Supachoke Nivescharoenpisan ◽  
Raphi Raphitphan ◽  
...  
Keyword(s):  
Radiation Exposure ◽  
Feasibility Study ◽  
Vertebral Body ◽  
The Novel ◽  
Cervical Lordosis ◽  
Proof Of Concept ◽  
Entry Points ◽  
Free Hand Technique ◽  
Anterior Discectomy And Fusion ◽  
Vertebral Endplates

Abstract Background Restoration of cervical lordosis after anterior discectomy and fusion is a desirable goal. Proper insertion of the vertebral distraction or Caspar pin can assist lordotic restoration by either putting the tips divergently or parallel to the index vertebral endplates. With inexperienced surgeons, the traditional free-hand technique for Caspar pin insertion may require multiple insertion attempts that may compromise the vertebral body and increase radiation exposure during pin localization. Our purpose is to perform a proof-of-concept, feasibility study to evaluate the effectiveness of a pin insertion aiming device for vertebral distraction pin insertion. Methods A Smith-Robinson approach and anterior cervical discectomy were performed from C3 to C7 in 10 human cadaveric specimens. Caspar pins were inserted using a novel pin insertion aiming device at C3-4, C4-5, C5-6, and C6-7. The angles between the cervical endplate slope and Caspar pin alignment were measured with lateral cervical imaging. Results The average Superior Endplate-to-Caspar Pin angle (SE-CP) and the average Inferior Endplate-to-Caspar Pin angle (IE-CP) were 6.2 ± 2.0° and 6.3 ± 2.2° respectively. For the proximal pins, the SE-CP and the IE-CP were 4.0 ± 1.1°and 5.2 ± 2.4° respectively. For the distal pins, the SE-CP and the IE-CP were 7.7 ± 1.4° and 6.2 ± 2.0° respectively. No cervical endplate violations occurred. Conclusion The novel Caspar pin insertion aiming device can control the pin entry points and pin direction with the average SE-CP and average IE-CP of 6.2 ± 2.0° and 6.3 ± 2.2°, respectively. The study shows that the average different angles between the Caspar pin and cervical endplate are less than 7°.

Walking by Thinking: The Brainwaves Are Crucial, Not the Muscles!

2006 ◽  
Vol 15 (5) ◽  
pp. 500-514 ◽  
Author(s):  
Robert Leeb ◽  
Claudia Keinrath ◽  
Doron Friedman ◽  
Christoph Guger ◽  
Reinhold Scherer ◽  
...  
Keyword(s):  
Communication Channel ◽  
Muscular Activity ◽  
Proof Of Concept ◽  
Closed Loop System ◽  
Head Mounted Display ◽  
Eeg Recordings ◽  
Sensorimotor Rhythms ◽  
Electroencephalogram Eeg ◽  
Healthy Participants

Healthy participants are able to move forward within a virtual environment (VE) by the imagination of foot movement. This is achieved by using a brain-computer interface (BCI) that transforms thought-modulated electroencephalogram (EEG) recordings into a control signal. A BCI establishes a communication channel between the human brain and the computer. The basic principle of the Graz-BCI is the detection and classification of motor-imagery-related EEG patterns, whereby the dynamics of sensorimotor rhythms are analyzed. A BCI is a closed-loop system and information is visually fed back to the user about the success or failure of an intended movement imagination. Feedback can be realized in different ways, from a simple moving bar graph to navigation in VEs. The goals of this work are twofold: first, to show the influence of different feedback types on the same task, and second, to demonstrate that it is possible to move through a VE (e.g., a virtual street) without any muscular activity, using only the imagination of foot movement. In the presented work, data from BCI feedback displayed on a conventional monitor are compared with data from BCI feedback in VE experiments with a head-mounted display (HMD) and in a high immersive projection environment (Cave). Results of three participants are reported to demonstrate the proof-of-concept. The data indicate that the type of feedback has an influence on the task performance, but not on the BCI classification accuracy. The participants achieved their best performances viewing feedback in the Cave. Furthermore the VE feedback provided motivation for the subjects.

scholarly journals Research on Spatial Channel Model for Vehicle-to-Vehicle Communication Channel in Roadside Scattering Environment

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Xin Chen ◽  
Yong Fang ◽  
Weidong Xiang ◽  
Liang Zhou
Keyword(s):  
Communication Channel ◽  
Channel Model ◽  
Critical Distance ◽  
Traffic Density ◽  
Vehicle Speed ◽  
Angle Of Arrival ◽  
Vehicle To Vehicle ◽  
Proposed Model ◽  
Spatial Channel Model ◽  
The Impact

In this paper, an extension of spatial channel model (SCM) for vehicle-to-vehicle (V2V) communication channel in roadside scattering environment is investigated for the first time theoretically and by simulations. Subsequently, to efficiently describe the roadside scattering environment and reflect the nonstationary properties of V2V channels, the proposed SCM V2V model divides the scattering objects into three categories of clusters according to the location of effective scatterers by introducing critical distance. We derive general expressions for the most important statistical properties of V2V channels, such as channel impulse response, power spectral density, angular power density, autocorrelation function, and Doppler spread of the proposed model. The impact of vehicle speed, traffic density, and angle of departure, angle of arrival, and other statistical performances on the V2V channel model is thoroughly discussed. Numerical simulation results are presented to validate the accuracy and effectiveness of the proposed model.

Automatic Title Generation for Learning Resources and Pathways with Pre-trained Transformer Models

2021 ◽  
Vol 15 (04) ◽  
pp. 487-510
Author(s):  
Prakhar Mishra ◽  
Chaitali Diwan ◽  
Srinath Srinivasa ◽  
G. Srinivasaraghavan
Keyword(s):  
Online Learning ◽  
Language Model ◽  
Research Paper ◽  
Learning Resources ◽  
Research Papers ◽  
Medium Length ◽  
Proposed Model ◽  
Learning Pathways ◽  
Test Sets ◽  
Transformer Model

To create curiosity and interest for a topic in online learning is a challenging task. A good preview that outlines the contents of a learning pathway could help learners know the topic and get interested in it. Towards this end, we propose a hierarchical title generation approach to generate semantically relevant titles for the learning resources in a learning pathway and a title for the pathway itself. Our approach to Automatic Title Generation for a given text is based on pre-trained Transformer Language Model GPT-2. A pool of candidate titles are generated and an appropriate title is selected among them which is then refined or de-noised to get the final title. The model is trained on research paper abstracts from arXiv and evaluated on three different test sets. We show that it generates semantically and syntactically relevant titles as reflected in ROUGE, BLEU scores and human evaluations. We propose an optional abstractive Summarizer module based on pre-trained Transformer model T5 to shorten medium length documents. This module is also trained and evaluated on research papers from arXiv dataset. Finally, we show that the proposed model of hierarchical title generation for learning pathways has promising results.

scholarly journals Bimodal Emotion Recognition Model for Minnan Songs

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 145 ◽  
Author(s):  
Zhenglong Xiang ◽  
Xialei Dong ◽  
Yuanxiang Li ◽  
Fei Yu ◽  
Xing Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Short Term Memory ◽  
Music Appreciation ◽  
Research Papers ◽  
Audio Features ◽  
Analysis Theory ◽  
Proposed Model ◽  
Song Lyrics ◽  
Long Short Term Memory

Most of the existing research papers study the emotion recognition of Minnan songs from the perspectives of music analysis theory and music appreciation. However, these investigations do not explore any possibility of carrying out an automatic emotion recognition of Minnan songs. In this paper, we propose a model that consists of four main modules to classify the emotion of Minnan songs by using the bimodal data—song lyrics and audio. In the proposed model, an attention-based Long Short-Term Memory (LSTM) neural network is applied to extract lyrical features, and a Convolutional Neural Network (CNN) is used to extract the audio features from the spectrum. Then, two kinds of extracted features are concatenated by multimodal compact bilinear pooling, and finally, the concatenated features are input to the classifying module to determine the song emotion. We designed three experiment groups to investigate the classifying performance of combinations of the four main parts, the comparisons of proposed model with the current approaches and the influence of a few key parameters on the performance of emotion recognition. The results show that the proposed model exhibits better performance over all other experimental groups. The accuracy, precision and recall of the proposed model exceed 0.80 in a combination of appropriate parameters.

scholarly journals Towards a unified model for positive states, mindfulness and wellbeing

2020 ◽  
Author(s):  
Simon Bloomfield
Keyword(s):  
Emotional Expression ◽  
Emotional Experience ◽  
Geographical Area ◽  
Cognitive Model ◽  
Unified Model ◽  
Affective States ◽  
Neuronal Mechanisms ◽  
Proposed Model ◽  
The Relationship

<p>A unified model of positive emotional expression, based on existing research, is presented. The proposed model is broader in scope, than a purely cognitive model (see Fig. 1); incorporating pro-social states can be induced directly through adaptive perceptual processes such as mirror-neuronal mechanisms, but whereby state expressions are modulated by adaptive <i>fundamental cognitive evaluations</i> (FCEs). It is proposed that these FCEs work in cohort to elicit emotional experience and prime the expressional potentiality of related affective states that share FCE dimensions. So that an individual experiencing kindness would be more likely to be disposed to feeling compassion or muditā (vicarious joy) - enabling appropriate onward social interaction.</p> <p>It is proposed that the activation of FCEs are modulated by socio-cultural schema, including attitudinal scripts shared within a culture and reflected in heterogeneous trait patterns by cultural/geographical area. The role of mindful decentring from such schema, and the onward effect on FCE expression, is explored; specifically, in relation to states associated with Self-Determination Theory’s (SDT) motivational areas of competency, autonomy and relatedness. </p> <p>A speculative model exploring the relationship between SDT, positive states; key aspect of mindfulness and HEXACO traits is presented as a spur for future discussion and study (see Fig. 2).</p>

An Adaptable Secure Scheme in Mobile Ad hoc Network to Protect the Communication Channel From Malicious Behaviours

2021 ◽  
Vol 16 (3) ◽  
pp. 54-73
Author(s):  
Srilakshmi R. ◽  
Jaya Bhaskar M.
Keyword(s):  
Ad Hoc Network ◽  
Communication Channel ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Multipath Routing ◽  
Routing Overhead ◽  
Digital World ◽  
Proposed Model ◽  
Mobile Ad Hoc ◽  
Packet Drop

Mobile ad-hoc network (MANET) is a trending field in the smart digital world; it is effectively utilized for communication sharing purposes. Besides this communication, it has numerous advances like a personal computer. However, the packet drop and low throughput ratio became serious issues. Several algorithms are implemented to increase the throughput ratio by developing multipath routing. But in some cases, the multipath routing ends in routing overhead and takes more time to transfer the data because of data load in the same path. To end this problem, this research aimed to develop a novel temporary ordered route energy migration (TOREM). Here, the migration approach balanced the data load equally and enhanced the communication channel; also, the reference node creation strategy reduced the routing overhead and packet drop ratio. Finally, the outcome of the proposed model is validated with recent existing works and earned better results by minimizing packet drop and maximizing throughput ratio.

SAS Visual Text Analytics

2018 ◽  
pp. 263-282
Author(s):  
Murugan Anandarajan ◽  
Chelsey Hill ◽  
Thomas Nolan
Keyword(s):  
Text Analytics ◽  
Visual Text

scholarly journals Defragmenting Research Areas with Knowledge Visualization and Visual Text Analytics

2020 ◽  
Vol 10 (20) ◽  
pp. 7248
Author(s):  
Alejandro Benito-Santos ◽  
Roberto Therón Sánchez
Keyword(s):  
Computer Security ◽  
Communities Of Practice ◽  
Literature Analysis ◽  
Text Analytics ◽  
Sports Science ◽  
Research Areas ◽  
Wide Range ◽  
Common Interests ◽  
Visual Text ◽  
Visualization Techniques

The increasing specialization of science is motivating the fragmentation of traditional and well-established research areas into interdisciplinary communities of practice that focus on cooperation between experts to solve problems in a wide range of domains. This is the case of problem-driven visualization research (PDVR), in which groups of scholars use visualization techniques in different application domains such as the digital humanities, bioinformatics, sports science, or computer security. In this paper, we employ the findings obtained during the development of a novel visual text analytics tool we built in previous studies, GlassViz, to automatically detect interesting knowledge associations and groups of common interests between these communities of practice. Our proposed method relies on the statistical modeling of author-assigned keywords to make its findings, which are demonstrated in two use cases. The results show that it is possible to propose interactive, semisupervised visual approaches that aim at defragmenting a body of research using text-based, automatic literature analysis methods.

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