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International conference on Computer Graphics, Imaging and visualization, CGIV

作者： S. M. Azam Z.A. Mansoor M. Shahzad Mughal S. Mohsin COMSATS Institute of Information Technology Pakistan

Neural networks have found profound success in the area of pattern recognition. In the recent years there has been use of neural network for speech recognition. In this paper backpropagation neural network has been used for isolated spoken Urdu digits recognition. Mel frequency cepstral coefficients (MFCC) has been used to represent speech signal. Dimensions of speech features were reduced to a vector of 39 values. Only 39 values from MFCC features speech are fed to the neural network having more than one hidden layers with varying number of neurons, for training and recognition An analysis has been made between different number of hidden layers and different number of neurons on hidden layers. It has been found that results for these 39 values are similar to that obtained using complete MFCC features that range from 804 to 67x39. With the use of 39 values on input layer, computational complexity and time for training and recognition of neural network is reduced. In order to evaluate the significance of the proposed method on data other than Urdu digits, 30 English words have been trained and recognized that gave 98% results. All the implementation has been done inMATLAB.

关键词： Mel frequency cepstral coefficient Neural networks Speech recognition Neurons Feature extraction Pattern recognition Speech analysis Artificial neural networks Information technology Computational complexity

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SNTS: sensor network troubleshooting suite 07

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Proceedings of the 3rd IEEE international conference on Distributed computing in sensor systems

作者： Mohammad Maifi Hasan Khan Liqian Luo Chengdu Huang Tarek Abdelzaher University of Illinois at Urbana Chamapign Department of Computer Science

ISBN: (纸本)9783540730897

Sensor network troubleshooting is a notoriously difficult task, further exacerbated by resource constraints, unreliable components, unpredictable natural phenomena, and experimental programming paradigms. This paper presents SNTS (Sensor Network Troubleshooting Suite), a tool that performs automated failure diagnosis in sensor networks. SNTS can be used to monitor network conditions using simple visualization techniques as well as to troubleshoot deployed distributed sensor systems using data mining approaches. It is composed of (i) a data collection front-end that records events internal to the network and (ii) a data processing back-end for subsequent analysis. We use data mining techniques to automate failure diagnosis on the back-end. The assumption is that the occurrence of execution conditions that cause failures (e.g., traversal of an execution path that contains a "bug" or occurrence of a sequence of events that a protocol was not designed to handle) will have a measurable correlation (by causality) with the resulting failure itself. Hence, by mining for network conditions that correlate with failure states the root causes of failure are revealed with high probability. To evaluate the effectiveness of the tool, we have used it to troubleshoot a tracking system called EnviroTrack [4], which, although performs well most of the time, occasionally fails to track targets correctly. Results show that SNTS can identify the major causes of the problem and give developers useful hints on improving the performance of the tracking system.

关键词： sensor network data mining distributed troubleshooting

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Improvement of Collision Detection Performance of Hierarchies by Using Dynamic-Density of 3D Objects Based on LOD (Level-of-Detail)

Improvement of Collision Detection Performance of Hierarchie...

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International conference on Computer Graphics, Imaging and visualization, CGIV

作者： ChunHo Yi WonSeop Kim TaeYong Kim Research Institute for Networks and Communications Engineering Radio and Optical Communications Laboratory Chung-Ang University Seoul South Korea

This paper is many studies have been conducted focusing on standard objects in 3D space for quick and exact collision detection in 3D graphics. A great deal of these took interest in the improvement of performance speed of collision detection of 3D objects, and in realization of realistic expressions along with the marvelous advancement of 3D graphics hardware. In this paper, features of various collision detection algorithms for standard 3D objects among 3D graphic algorithms were analyzed, and a 'dynamic density' that utilizes the center value and corner points of standard objects in simple hierarchic structures of existing 3D objects is suggested. By using this algorithm, an LOD (level-of-detail) algorithm can be applied to 3D space to improve collision detection of 3D objects in a 3D space. Also, through suggesting efficient rendering of 3D dimensional spaces as an important element of 3D graphics and a realistic expression method, the realism and efficiency of 3D games, which regards the real-time element as an important one, can be greatly improved.

关键词： Object detection Rendering (computer graphics) Computer graphics Hardware Conductive films Detection algorithms Algorithm design and analysis Engines data structures Real time systems

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Real-Time Gender Detection in the Wild Using Deep Neural Networks

Real-Time Gender Detection in the Wild Using Deep Neural Net...

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Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI)

作者： Luis Felipe de Araujo Zeni Claudio Rosito Jung Informatics Institute Federal University of Rio Grande do Sul Porto Alegre Brazil

Gender recognition can be used in many applications, such as video surveillance, human-computer interaction and customized advertisement. Current state-of-the-art gender recognition methods are detector-dependent or region-dependent, focusing mostly on facial features (a face detector is typically required). These limitations do not allow an end-to-end training pipeline, and many features used in the detection phase must be re-learned in the classification step. Furthermore, the use of facial features limits the application of such methods in the wild, where the face might not be present. This paper presents a real-time end-to-end gender detector based on deep neural networks. The proposed method detects and recognizes the gender of persons in the wild, meaning in images with a high variability in pose, illumination an occlusions. To train and evaluate the results a new annotation set of Pascal VOC 2007 and CelebA were created. Our experimental results indicate that combining both datasets during training can increase the mAp of our gender detector. We also visually analyze which parts leads our network to make mistakes and the bias introduced by the training data.

关键词： Face Feature extraction Detectors Training visualization Facial features Task analysis

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The simulation of ship oscillatory motions in irregular waves

The simulation of ship oscillatory motions in irregular wave...

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The 2011 International conference on Mechanical Materials and Manufacturing Engineering(ICMMME 2011)

作者： Yang Song Yan Lin Mingxia Zhang Pinle Qin State Key Laboratory of Structural Analysis for Industrial Equipment Dalian University of Technology School of Electronics and Comuputer Science and Technology North University of China

Based on the theory of nonlinear random dynamic and ship oscillatory motions, by analyzing the spectrum of the random ocean wave and the force of the ship, the roll and pitch motion equations were erected respectively, then the irregular waves were created according to the superposition theory. Meanwhile, the coupling motion between rolling and heaving was studied, then the time-domain responses were the driving data of the motions simulation. Using Visual Basic, the interaction interface was established. Using MATLAB, the motions were solved. Using Open GL the 3-D model of ship was described. We just need input the ship and environment parameters then the results will be displayed with the 2-D figures and the 3-D model quickly, then the visualization of ship motions simulation is achieved. The program developed can simulate 3-D ships and can be used for designers to analyze the properties of ship intuitively.

关键词： Ship Simulation Oscillatory Irregular waves

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GeoHashViz: interactive analytics for mapping spatiotemporal diffusion of Twitter hashtags 15

GeoHashViz: interactive analytics for mapping spatiotemporal...

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Proceedings of the 2015 XSEDE conference: Scientific Advancements Enabled by Enhanced Cyberinfrastructure

作者： Kiumars Soltani Aditya Parameswaran Shaowen Wang University of Illinois at Urbana-Champaign Champaign IL

ISBN: (纸本)9781450337205

Since its birth in 2006, Twitter has evolved to a multi-purpose social media that attracts hundreds of millions of users to share their activities and ideas on a daily basis. The potential of capturing fine-grained activity log of users, combined with ever increasing geographical information derived from GPS-enabled devices, has made Twitter data a valuable source for spatiotemporal analysis of human activities. One of the early innovations of Twitter is the use of hashtag as a unique tagging mechanism to provide additional information about a user post. From its emergence in late 2007, hashtags have been used extensively to express ideas, group tweets and report events among Twitter users. The increasing popularity of hashtags, in addition to their simple and concise structure, has inspired multiple recent studies to propose hashtag as a medium to assess diffusion of ideas in a virtual world. Studying collective effort of users in making a hashtag go viral can shed light on the complex process of idea diffusion that involves psychological, sociological and geographical *** most of the previous research on idea diffusion in virtual world purely focuses on the users social graph, recent studies have confirmed that the spatial relationship among users and regions also play a crucial role in its adoption patterns [1]. This comes back to First Law of Geography that was formulated by Waldo Tobler more than 40 years ago, as "everything is related to everything else, but near things are more related than distant things". However, previous work on designing an interactive visual analytical framework for hashtag diffusion (http://***/, http://***/, https://***/), lack in-depth spatial analysis capabilities, hence not well-suited to be used for studying diffusion patterns. This research aims to fill this gap by providing an interactive framework to offer visual analytics on geographical diffusion of hashtags over time. Our framework,

关键词： GeoHashViz CyberGIS interactive visualization Hadoop social media

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Exploring the Performance of You Only Look Once Model for Object Detection in VQA Images

Exploring the Performance of You Only Look Once Model for Ob...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International conference on

作者： Deepak Suresh Asudani Naresh Kumar Nagwani Pradeep Singh Department of Computer Science and Engineering National Institute of Technology Raipur Chhattisgarh India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

Identifying objects is among the most crucial and complex problems in computer vision. The effectiveness of object detection tasks has significantly increased due to advancements in deep learning architectures. Object detectors are classified into single-stage (SSD) and two-stage (TSD). SSD model architecture evaluates the spatial region proposals in a single pass. TSDs employ a more complex architecture to focus on selective region proposals. Object detection is an essential component of visual question answering (VQA), which requires algorithms to respond to text-based inquiries about images. While many object detection algorithms are available, real-time application is still somewhat difficult. This paper explores the object detection approaches for VQA systems through a comparative analysis of various YOLO variants. The study compares the effectiveness of YOLOv8s and YOLOv8n architectures against TSD approaches in detecting object positions with bounding boxes. Experiments were conducted on the MS-COCO and two versions of the PASCAL-VOC (2007 and 2012) datasets. The paper outlines the data preprocessing steps required to convert source datasets to the YOLO model format. The experimental evaluations on the PASCAL-VOC dataset indicate that the TSD Faster-RCNN model, using ResNet101 pretrained architecture, achieves the highest mAP of 78.80. In contrast, the SSD YOLOv8s model achieves the highest mAP of 89.63 using the CSPDarkNet53 model architecture. YOLO is essential for real-time object detection, allowing VQA systems to process images in a single forward pass. The study concludes that the SSD YOLOv8s model efficiently identifies objects and generates bounding boxes with higher accuracy compared to other models.

关键词： YOLO Computer vision visualization Accuracy Computational modeling Computer architecture Detectors Real-time systems Object recognition Proposals

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A user credit assessment model based on swarm intelligence for broadband network new media service supervision

A user credit assessment model based on swarm intelligence f...

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2010 2nd International conference on Intellectual Technology in Industrial Practice (ITIP2010)

作者： Liu Fang1, Lu Rui1,2, Cao San-xing1, Su Zhi-wu1 1School of Information Engineering, Communication University of China, 100024, Beijing, China 2Xinhua News Agency, 100803, Beijing, China

This paper proposes a user credit assessment model based on swarm intelligence aiming to solve the problem that users illegally spread pirated and pornographic media contents within the user self-service oriented broadband network new media platforms. Its idea is to do the new media user credit assessment by establishing indices system based on user credit behaviors, and the illegal users could be found according to the credit assessment results, thus to curb the bad videos and audios transmitted on the *** user credit assessment model based on swarm intelligence proposed by this paper which integrates the advantages that swarm intelligence clustering is suitable for user credit behavior analysis and K-means clustering could eliminate the scattered users existed in the result of swarm intelligence clustering, thus to realize all the users' credit classification automatically. The results of model's verification experiments based on standard credit application data set of UCI machine learning repository show that this model has good credit classification ability and its visualization features facilitate to observe and locate illegal users, and compared with the results of Logistic regression under the same conditions, this model is robustness and has better prediction accuracy.

关键词： swarm intelligence, credit behavior analysis, service contents regulation, new media, clustering analysis

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Hotspots and Frontiers of Foreign Consumers' Online Purchase Intention—visualization analysis of Bibliometrics Based on WOS (2007-2019)

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Journal of Physics: conference Series 2020年第1期1574卷

作者： Qingxin Gou Renji Duan Yunfeng Guo Cong Wang Shenglan Yang Xu Zu Business School Sichuan Agricultural University Chengdu China

In order to objectively reflect the development status and hotspots of foreign consumers'online purchase intention research, this article uses the core journal literature included in the core collection retrieval platform SSCI database of WOS as the data source, and uses Citespace software to visualize the research results of foreign consumers' online purchase intention analysis. The study found that foreign consumers'online purchase intention research focuses on hot topics such as trust, consumer behavior, information systems, and e-commerce models, focusing on cutting-edge content such as online shopping product development and e-commerce marketing models.

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analysis of the socio-economic impact of renewable energy hybrid electricity utilization for rural community development (case study: Pantai Anyar, Yogyakarta special region, Indonesia)

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IOP conference Series: Earth and Environmental Science 2019年第1期383卷

作者： D Rachmawatie E Rustiadi Aad Fauzi Bambang Juanda Universitas Muhammadiyah Yogyakarta Department of Regal Development Faculty of Agriculture Bogor Agricultural University Department of Resource Economics and Environment Faculty of Economy and Management Bogor Agricultural University Department of Economic Science Faculty of Economy and Management Bogor Agricultutal University

The aim of this research were to: (1) study on socio-economics from Renewable Energy Hybrid Power Plant (PLTH) electricity development for rural communities, (2) find out information related to average households beneficiaries of electricity (pre and post the utilization of electrical energy hybrid electricity at Baru Beach. The research was conducted at Baru Beach, Yogyakarta Special Region on November – December, 2017. The data used were primary and secondary data. The research was conducted by surveying 68 respondents who are direct beneficiaries of hybrid electric energy utilization activities in Baru Beach. The analytical method used in this part of the study is the visualization analysis of the Baru Beach map area for 20072017, analysis of household income, and Paired Sample T-test. The results showed that: (1) based on changes in the map of Pantai Baru area in 2007-2017, changes in road changes, the access and connectivity of this region to other regions have become much better. In the analysis of income obtained, post PLTH was developed at Baru Beach. The income analysis was found that, post PLTH was developed in Baru Beach, there was an increase in the average income in the non-agricultural sector and household income for PLTH electricity beneficiaries, but there was a decrease in the average income value in the agricultural sector, (2) based on Paired Sample T-Test analysis show there is a significant difference the average household income post PLTH developed at Baru Beach with a significance level of 0.041. Thus the results of this research conclude that there is any a socio-economic impact from the utilization of renewable electricity not only for the beneficiaries of electricity in rural communities, but also for the development of local area.

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