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Oracle Bone Inscription Detector Based on SSD 2nd

Oracle Bone Inscription Detector Based on SSD

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2nd international Workshop on Recent advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international Workshop on Pattern Recognition for Cultural Heritage, PatReCH 2019, 1st international Workshop eHealth in the Big Data and Deep Learning Era, e-BADLE 2019, international Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international conference on Image Analysis and Processing, ICIAP 2019

作者： Meng, Lin Lyu, Bing Zhang, Zhiyu Aravinda, C.V. Kamitoku, Naoto Yamazaki, Katsuhiro Department of Electronic and Computer Engineering Ritsumeikan University KusatsuShiga525-8577 Japan Department of Computer Science and Engineering NMAM Institute of Technology Nitte Karkala TalukKarnataka India

ISBN: (纸本)9783030307530

this paper introduces Oracle Bone Inscription Detector which based on Single Shot Multibox Detector, for segmenting and recognizing Oracle Bone Inscriptions from rubbing images. Oracle Bone Inscription which is the one of the oldest and most mysterious ancient characters, used about 3000 years ago in china, and lots of these literature are stored by rubbing images. Because that only few of specialists understand the Oracle Bone Inscriptions, lots of Oracle Bone Inscriptions are waiting for be understood for helping researchers know the history, culture, economy etc. Currently, deep learning method of single shot multibox detector achieves a good performance for segmentation and recognition, and may achieve a good performance for Oracle Bone Inscription detection. However, we fond the Single Shot Multibox Detector is weak at small object detection. this research equips and extends Single Shot Multibox Detector for Oracle Bone Inscription detection, and analyzes the mis-detection for achieving a better accuracy. the experimental results shows that Precision, Recall and F value achieve 0.95, 0.83 and 0.88 respectively, and proves the effectiveness of extended Single Shot Multibox Detector in Oracle Bone Inscription detection. © 2019, Springer Nature Switzerland AG.

关键词： Object detection

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Is Stress State an Important Factor in the BCI-P300 Speller Performance? 15th

Is Stress State an Important Factor in the BCI-P300 Speller ...

引用

15th international Work-conference on Artificial Neural Networks (IWANN)

作者： Garcia, Liliana Zak, Maud Grenier, Celestin Hanrio, Solene Henry, Dorine Randriamanantena, Romain Semal, Catherine Andre, Jean Marc Lespinet-Najib, Veronique Ron-Angevin, Ricardo Bordeaux Univ CNRS IMS Lab Bordeaux INP Talence France Bordeaux INP ENSC Talence France CNRS UMR 5287 INCIA Lab Bordeaux France Univ Malaga Dept Tecnol Elect Malaga Spain

ISBN: (纸本)9783030205218;9783030205201

Brain-computer Interface (BCI) is an advanced human-machine interaction technology requiring higher-order cognitive functions for an efficient task execution. the relation between cognition, human performance and psychological state has been studied for many years. Nevertheless, the effect of acute stress on cognitive performance involving BCI systems has never been studied. Nowadays, people are more and more affected by stressful situations. Stress is an important human factor which can impact the ability to appropriately process cognitive information related to language, working memory, attention, or executive control. Individuals are continuously interacting with technology to execute daily actions. BCI represent an alternative way to allow any individual, even with motor disabilities, to interact with that technology. BCI-P300 Speller is driven by EEG signals and enables communication without physical intervention. It is used in both clinical investigations and fundamental research. In this work, we study the impact of acute stress effects on cognitive ability to control a BCI-P300 speller. Although we have observed a broad spectrum of response to stress, analyses show a correlation between BCI-speller performance and user's stress state. We have also noted that BCI performance seems to be improved if users have a good cognitive engagement in the task and if they showed an ability to develop efficient strategies, such as selective attention or increased effort, in order to cope with the stressful situations. In conclusion, these preliminary results performed on a small sample (n = 7) show that BCI-P300 Speller is a robust and reliable tool and suggest that an optimal utilization of BCI systems could be assured despite the fluctuations of users' state. We assume that neural mechanisms involved in the BCI task, may set the brain in an adequate level of generalized arousal, which allows establishment of compensatory mechanisms in stressful states.

关键词： Brain-computer Interface (BCI) P300-Speller Acute stress User's state

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Constant Angular Velocity Regulation for Visually Guided Terrain Following 15th

Constant Angular Velocity Regulation for Visually Guided Ter...

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15th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2019

作者： Wang, Huatian Fu, Qinbing Wang, Hongxin Peng, Jigen Yue, Shigang School of Computer Science University of Lincoln LincolnLN6 7TS United Kingdom School of Mathematics and Information Science Guangzhou University Guangzhou510006 China Machine Life and Intelligence Research Center Guangzhou University Guangzhou510006 China

ISBN: (纸本)9783030198220

Insects use visual cues to control their flight behaviours. By estimating the angular velocity of the visual stimuli and regulating it to a constant value, honeybees can perform a terrain following task which keeps the certain height above the undulated ground. For mimicking this behaviour in a bio-plausible computation structure, this paper presents a new angular velocity decoding model based on the honeybee’s behavioural experiments. the model consists of three parts, the texture estimation layer for spatial information extraction, the motion detection layer for temporal information extraction and the decoding layer combining information from pervious layers to estimate the angular velocity. Compared to previous methods on this field, the proposed model produces responses largely independent of the spatial frequency and contrast in grating experiments. the angular velocity based control scheme is proposed to implement the model into a bee simulated by the game engine Unity. the perfect terrain following above patterned ground and successfully flying over irregular textured terrain show its potential for micro unmanned aerial vehicles’ terrain following. © 2019, IFIP international Federation for Information Processing.

关键词： Antennas

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the Power of the "Pursuit" Learning Paradigm in the Partitioning of Data 15th

The Power of the "Pursuit" Learning Paradigm in the Partitio...

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15th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2019

作者： Shirvani, Abdolreza John Oommen, B. School of Computer Science Carleton University Ottawa Canada Centre for Artificial Intelligence Research University of Agder Grimstad Norway

ISBN: (纸本)9783030198220

Traditional Learning Automata (LA) work with the understanding that the actions are chosen purely based on the "state" in which the machine is. this modus operandus completely ignores any estimation of the Random Environment’s (RE’s) (specified as E reward/penalty probabilities. To take these into consideration, Estimator/Pursuit LA utilize "cheap" estimates of the Environment’s reward probabilities to make them converge by an order of magnitude faster. this concept is quite simply the following: Inexpensive estimates of the reward probabilities can be used to rank the actions. thereafter, when the action probability vector has to be updated, it is done not on the basis of the Environment’s response alone, but also based on the ranking of these estimates. While this phenomenon has been utilized in the field of LA, until recently, it has not been incorporated into solutions that solve partitioning problems. In this paper, we will submit a complete survey of how the "Pursuit" learning paradigm can be and has been used in Object Partitioning. the results demonstrate that incorporating this paradigm can hasten the partitioning by a order of magnitude. © 2019, IFIP international Federation for Information Processing.

关键词： Probability

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Localization of Epileptic Foci by Using Convolutional Neural Network Based on iEEG 15th

Localization of Epileptic Foci by Using Convolutional Neural...

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15th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2019

作者： Sui, Linfeng Zhao, Xuyang Zhao, Qibin Tanaka, Toshihisa Cao, Jianting Graduate School of Engineering Saitama Institute of Technology Fukaya Japan Tokyo Japan Department of Electronic and Information Engineering Tokyo University of Agriculture and Technology Tokyo Japan Faculty of Medicine Juntendo University Tokyo Japan Rhythm-Based Brain Information Processing Unit RIKEN CBS Wako Japan School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9783030198220

Epileptic focus localization is a critical factor for successful surgical therapy of resection of epileptogenic tissues. the key challenging problem of focus localization lies in the accurate classification of focal and non-focal intracranial electroencephalogram (iEEG). In this paper, we introduce a new method based on short time Fourier transform (STFT) and convolutional neural networks (CNN) to improve the classification accuracy. More specifically, STFT is employed to obtain the time-frequency spectrograms of iEEG signals, from which CNN is applied to extract features and perform classification. the time-frequency spectrograms are normalized with Z-score normalization before putting into this network. Experimental results show that our method is able to differentiate the focal from non-focal iEEG signals with an average classification accuracy of 91.8%. © 2019, IFIP international Federation for Information Processing.

关键词： Convolution

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Motion Segmentation Based on Structure-Texture Decomposition and Improved three Frame Differencing 15th

Motion Segmentation Based on Structure-Texture Decomposition...

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15th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2019

作者： Sengar, Sandeep Singh Department of Computer Science and Engineering SRM University-AP Amaravati522 502 India

ISBN: (纸本)9783030198220

Motion segmentation from the video datasets has several important applications like traffic monitoring, action recognition, visual object tracking, and video surveillance. the proposed technique combines the structure-texture decomposition and the improved three frames differencing for motion segmentation. First, the Osher and Vese approach is employed to decompose the video frame into two components, viz., structure and texture/noise. Now, to eliminate the noise, only the structure components are employed for further steps. Subsequently, the difference between (i) the current frame and the previous frame as well as (ii) the current frame and the next frame are estimated. Next, both the difference frames are combined using pixel-wise maximum operation. Each combined difference frame is then partitioned into non-overlapping blocks, and the intensity sum as well as median of each block is computed. Successively, target objects are detected with the help of threshold and intensity median. Finally, post-processing in the form of morphology operation and connected component analysis is carried out to accurately find the foreground. Our technique has been formulated, implemented and tested on publicly available standard benchmark datasets and it is proved from performance analysis that our technique exhibit efficient outcomes than existing approaches. © 2019, IFIP international Federation for Information Processing.

关键词： Morphology

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Diversity Regularized Adversarial Deep Learning 15th

Diversity Regularized Adversarial Deep Learning

引用

15th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2019

作者： Ayinde, Babajide O. Nishihama, Keishin Zurada, Jacek M. Electrical and Computer Engineering Department University of Louisville LouisvilleKY United States Information Technology Institute University of Social Science Lodz90-113 Poland

ISBN: (纸本)9783030198220

the two key players in Generative Adversarial Networks (GANs), the discriminator and generator, are usually parameterized as deep neural networks (DNNs). On many generative tasks, GANs achieve state-of-the-art performance but are often unstable to train and sometimes miss modes. A typical failure mode is the collapse of the generator to a single parameter configuration where its outputs are identical. When this collapse occurs, the gradient of the discriminator may point in similar directions for many similar points. We hypothesize that some of these shortcomings are in part due to primitive and redundant features extracted by discriminator and this can easily make the training stuck. We present a novel approach for regularizing adversarial models by enforcing diverse feature learning. In order to do this, both generator and discriminator are regularized by penalizing both negatively and positively correlated features according to their differentiation and based on their relative cosine distances. In addition to the gradient information from the adversarial loss made available by the discriminator, diversity regularization also ensures that a more stable gradient is provided to update both the generator and discriminator. Results indicate our regularizer enforces diverse features, stabilizes training, and improves image synthesis. © 2019, IFIP international Federation for Information Processing.

关键词： Generative adversarial networks

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Tools for Semi-automatic Preparation of Training Data for OCR 15th

Tools for Semi-automatic Preparation of Training Data for OC...

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15th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2019

作者： Lenc, Ladislav Martínek, Jiří Král, Pavel Department of Computer Science and Engineering Faculty of Applied Sciences University of West Bohemia Plzeň Czech Republic NTIS - New Technologies for the Information Society Faculty of Applied Sciences University of West Bohemia Plzeň Czech Republic

ISBN: (纸本)9783030198220

this work aims at data preparation for OCR systems based on recurrent neural networks. Precisely annotated data are necessary for training a network as well as for evaluation of OCR methods. It is possible to synthesize the data, however such data are not that realistic as the real ones. Manual annotation is thus still needed in many cases, especially in the case of historical documents we are focusing on. Although there are several complex systems for historical document processing, to the best of our knowledge, a simple annotation tool for OCR data is completely missing. therefore, we propose and implement a set of tools utilizing artificial intelligence that simplify the annotation process. these tools create ground truths for line images that are used for training of nowadays OCR systems. Another contribution of this paper is making these tools freely available for research purposes. © 2019, IFIP international Federation for Information Processing.

关键词： Optical character recognition

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Performance Evaluation of Augmented Reality based 3D Modelling Furniture Application 7

Performance Evaluation of Augmented Reality based 3D Modelli...

引用

7th international conference on advances in Computing, Communications and Informatics, ICACCI 2018

作者： Ahmed, T. Tahir Vijaya Shetty, S. Samirasimha, R. Sushmitha Bedere, J. Department of Computer Science and Engineering NitteMeenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9781538653142

the advances in information and computer technology allows humans to interact with both real and virtual world objects. Augmented Reality (AR) is one such technology that integrates digital information with the users' environment in real time. It superimposes virtual objects such as information, computer generated images etc. on the users' view of the real world. In this paper, we propose to develop and evaluate an android app with augmented reality based system 'AR Furniture App';which makes use of Kudan SDK to put the AR content onto natural features detected from the surroundings. Using this application the user can visualize how the furniture would look in the real world, offering preview of furniture in one's real environment. By the usage of this application, the furniture sellers can win competitive edge in the market. the system also prevents revenue loss to the business, spoilt brand image of the service provider, customer attrition and deterioration of stakeholders' interests as the customers can try visualizations of furniture placements in the available space before purchasing them. © 2018 IEEE.

关键词： Augmented reality

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GLEnc Algorithm to Secure Data in Public Cloud Environment 7

GLEnc Algorithm to Secure Data in Public Cloud Environment

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7th international conference on advances in Computing, Communications and Informatics, ICACCI 2018

作者： George, Lalu P. George Amalarethinam, D.I. Chandran, Anjana S. Jamal Mohamed College Tiruchirapalli620 020 India Computer Science Tiruchirapalli620 020 India SCMS School of Technology and Management Cochin683501 India

ISBN: (纸本)9781538653142

From Electronic Numerical Integrator And computer (ENIAC) through desktops to mobile devices, technology has evolved throughout the years. Data storage has become transparent after the commencement of the internet. the emergence of cloud computing allows data stored at different locations with different servers using internet and central remote servers. the advantage of cloud computing has transformed the method distributed computing used in the past. Data security is totally maintained by means of encryption. Encryption and decryption are the key elements where hidden data is transformed into an undecipherable code, while decryption is the exact opposite resulting in the original data. the proposed algorithm is performed prior to the arrival of data in the cloud storage. Here the real plain text undergoes different types of splitting and dividing techniques. three keys are employed at different stages to get highly encrypted data. this paper presents to protect data in the Public Cloud Environment. © 2018 IEEE.

关键词： Cloud security

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