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21st international conference on Medical Image computing and Computer-Assisted Intervention (MICCAI) / 8th Eurographics Workshop on Visual computing for Biology and Medicine (VCBM) / international Workshop on Computational Diffusion MRI (CDMRI)

作者： Avila, Nicole Labra Lebenberg, Jessica Riviere, Denis Auzias, Guillaume Fischer, Clara Poupon, Fabrice Guevara, Pamela Poupon, Cyril Mangin, Jean-Francois CEA DRF Neurospin UNATI Gif Sur Yvette France Univ Paris Sud Paris Saclay France Univ Paris Diderot Sorbonne Paris Cite UMR S 1161 INSERM Paris France Aix Marseille Univ Inst Neurosci la Timone Marseille France Univ Concepcion Concepcion Chile CEA DRF Neurospin UNIRS Gif Sur Yvette France

ISBN: (纸本)9783030058319;9783030058302

We present a new framework for the creation of an extended atlas of short fiber bundles between 20 and 80 mm length. this method uses a Diffeomorphic inter-subject alignment procedure including information of cortical foldings and forces the accurate match of the sulci that have to be circumvented by the U-bundles. then, a clustering is performed to extract the most reproducible bundles across subjects. First results show an increased number of U-bundles consistently mapped in the general population compared with previous atlases created from the same database. Future analysis over this new extended Brain atlas may improve our understanding of the relationship between the folding pattern and the U-bundle variability. the ultimate aim will be the possibility to detect abnormal configurations induced by developmental issues.

关键词： White matter U-fiber U-bundles Short fiber bundles dMRI Diffusion MRI Brain atlas Bundle atlas Diffeomorphic alignment Sulcus base alignment Cortical folding pattern Sulci

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Contribution of temporal and multi-level body cues to emotion classification

Contribution of temporal and multi-level body cues to emotio...

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international conference on Affective computing and Intelligent Interaction and Workshops, ACII

作者： Nesrine Fourati Catherine Pelachaud Patrice Darmon UMANIS Research & Innovation Paris France CNRS ISIR Sorbonne University Paris France

the representation of expressive body movement is a critical step to automatically classify bodily expression of emotions. Using motion capture data, we study the contribution of different types of body features to the classification of emotions. In particular, we focus on the role played by temporal profiles of motion cues with regards to a set of multi-level body cues, including postural and dynamic ones.

关键词： Dynamics Machine learning three-dimensional displays Emotion recognition Magnetic heads End effectors Affective computing

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Anomaly Detection Approaches for Secure Cloud Reference Architectures in Legal Metrology 8

Anomaly Detection Approaches for Secure Cloud Reference Arch...

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8th international conference on Cloud computing and Services Science (CLOSER)

作者： Oppermann, Alexander Toro, Federico Grasso thiel, Florian Seifert, Jean-Pierre Phys Tech Bundesanstalt PTB Dept Metrol IT 8 5 Berlin Germany Tech Univ Berlin Secur Telecommun Berlin Germany

ISBN: (纸本)9789897582950

Securing Computer Systems against all kind of threats is an impossible challenge to fulfill. Although, in the field of Legal Metrology, it shall be assured that one can rely on the measurement carried out by a trusted computer system. In a distributed environment, a measurement instrument cannot be simply disconnected to gurantee its security. However, being able to monitor the computer systems constantly in order to deduce a normal system behaviour, can be a particular promising approach to secure such systems. In cases of detected anomalies, the system evaluates them to measure the severity of the detected incident and place it into three different categories: green, yellow and red. the presented Anomaly Detection Module can detect attacks against distributed applications in an cloud computing environment, using pattern recognition for clustering as well as statistical approaches. Both, inexperienced and experienced attacks have been tested and results are presented.

关键词： Anomaly Detection Continous Monitoring Secure Cloud computing Anomaly Detection as a Service Distributed computing Legal Metrology

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Multimodal and Temporal Perception of Audio-visual Cues for Emotion recognition

Multimodal and Temporal Perception of Audio-visual Cues for ...

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international conference on Affective computing and Intelligent Interaction and Workshops, ACII

作者： Esam Ghaleb Mirela Popa Stylianos Asteriadis University Maastricht Maastricht Netherlands

In Audio-Video Emotion recognition (AVER), the idea is to have a human-level understanding of emotions from video clips. there is a need to bring these two modalities into a unified framework, to effectively learn multimodal fusion for AVER. In addition, literature studies lack in-depth analysis and utilization of how emotions vary as a function of time. Psychological and neurological studies show that negative and positive emotions are not recognized at the same speed. In this paper, we propose a novel multimodal temporal deep network framework that embeds video clips using their audio-visual content, onto a metric space, where their gap is reduced and their complementary and supplementary information is explored. We address two research questions, (1) how audio-visual cues contribute to emotion recognition and (2) how temporal information impacts the recognition rate and speed of emotions. the proposed method is evaluated on two datasets, CREMA-D and RAVDESS. the study findings are promising, achieving the state-of-the-art performance on both datasets, and showing a significant impact of multimodal and temporal emotion perception.

关键词： Measurement Emotion recognition Visualization Task analysis Machine learning Affective computing Logic gates

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A Multi-pattern Matching Algorithm for Chinese-Hmong Mixed Strings 8th

A Multi-pattern Matching Algorithm for Chinese-Hmong Mixed S...

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8th CCF international conference on Natural Language Processing and Chinese computing, NLPCC 2019

作者： He, Shun-Ping Mo, Li-Ping Kang, Di-Wen College of Information Science & Engineering Jishou University JishouHunan416000 China

ISBN: (纸本)9783030322359

To solve the problem of rapid retrieval of Chinese-Hmong mixed text, a multi-pattern matching algorithm in double-bytes unit combined with the idea of AC algorithm and the mismatch processing strategy of Horspool algorithm is proposed for the Chinese-Hmong mixed strings. In this algorithm, a deterministic finite automaton is constructed based on the pattern-set according to the idea of AC algorithm, and the moving distance of the pattern is calculated by the bad-character rule of the Horspool algorithm, and the text is only traversed once to complete the quick search task of all patterns by using the finite automata. the experimental results show that the proposed algorithm has a good performance in multi-pattern matching for Chinese-Hmong mixed texts in different scale, even for the mixed texts containing more than 100,000 characters, the matching efficiency is also significantly higher than the AC algorithm. © 2019, Springer Nature Switzerland AG.

关键词： Natural language processing systems

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Multivariate pattern Analysis of Electroencephalography Data in a Demand-Selection Task 1

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8th international Work-conference on the Interplay Between Natural and Artificial Computation (IWINAC)

作者： Lopez-Garcia, David Sobrado, Alberto Gonzalez-Penalver, J. M. Manuel Gorriz, Juan Ruz, Maria Univ Granada Mind Brain & Behav Res Ctr CIMCYC Granada Spain Univ Granada Signal Theory Telemat & Commun Dept TSTC Granada Spain

ISBN: (数字)9783030195915

ISBN: (纸本)9783030195915;9783030195908

Cognitive effort is costly and partly aversive, and thus humans usually avoid it if given the chance. In Demand-Selection Tasks (DST), participants tend to choose the easy option over the hard one. the neural underpinnings of this effect, however, are not well understood. the current study is an initial approximation to adapt a DST to a format that allows measuring concurrent high-density electroencephalography. We used multivariate pattern analysis (MVPA) to decode conflict-related neural processes associated with congruent or incongruent events in a time-frequency resolved way and determined how different frequency bands contribute to the overall decoding accuracy. the decoding analysis involved the use of Support Vector Machines, a supervised learning algorithm that provides a theoretically elegant, computationally efficient, and very effective solution for many practical pattern recognition problems. Preliminary results show significant differences in activation patterns for congruent and incongruent trials, yielding 80% of decoding accuracy 400 ms after the stimulus onset. the results of frequency bands contribution analysis suggest that context-dependent proportion of congruency effect may rely on neural processes operating in Delta and theta-band frequencies.

关键词： Multivariate pattern analysis Electroencephalography Classification Support Vector Machine Demand-Selection Task

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Co-localized augmented human and X-ray observers in collaborative surgical ecosystem

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international JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2019年第9期14卷 1553-1563页

作者： Fotouhi, Javad Unberath, Mathias Song, Tianyu Hajek, Jonas Lee, Sing Chun Bier, Bastian Maier, Andreas Osgood, Greg Armand, Mehran Navab, Nassir Johns Hopkins Univ Comp Aided Med Procedures Baltimore MD 21218 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Friedrich Alexander Univ Erlangen Nurnberg Pattern Recognit Lab Erlangen Germany Johns Hopkins Univ Appl Phys Lab Baltimore MD 21218 USA Johns Hopkins Univ Hosp Dept Orthoped Surg Baltimore MD USA Tech Univ Munich Comp Aided Med Procedures Munich Germany

Purpose Image-guided percutaneous interventions are safer alternatives to conventional orthopedic and trauma surgeries. To advance surgical tools in complex bony structures during these procedures with confidence, a large number of images is acquired. While image-guidance is the de facto standard to guarantee acceptable outcome, when these images are presented on monitors far from the surgical site the information content cannot be associated easily with the 3D patient anatomy. Methods In this article, we propose a collaborative augmented reality (AR) surgical ecosystem to jointly co-localize the C-arm X-ray and surgeon viewer. the technical contributions of this work include (1) joint calibration of a visual tracker on a C-arm scanner and its X-ray source via a hand-eye calibration strategy, and (2) inside-out co-localization of human and X-ray observers in shared tracking and augmentation environments using vision-based simultaneous localization and mapping. Results We present a thorough evaluation of the hand-eye calibration procedure. Results suggest convergence when using 50 pose pairs or more. the mean translation and rotation errors at convergence are 5.7 mm and 0.26 circle, respectively. Further, user-in-the-loop studies were conducted to estimate the end-to-end target augmentation error. the mean distance between landmarks in real and virtual environment was 10.8 mm. Conclusions the proposed AR solution provides a shared augmented experience between the human and X-ray viewer. the collaborative surgical AR system has the potential to simplify hand-eye coordination for surgeons or intuitively inform C-arm technologists for prospective X-ray view-point planning.

关键词： Augmented reality Calibration C-arm X-ray Surgery

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A REVIEW OF IMPLEMENTATION OF DEEP LEARNING IN BIG DATA ANALYSIS 8

A REVIEW OF IMPLEMENTATION OF DEEP LEARNING IN BIG DATA ANAL...

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8th IEEE international conference on Cloud computing, Data Science and Engineering (Confluence) / Global Technology, Innovation and Enterpreneurship Summit

作者： Lekhrajani, Serena Samdani, Krishna MPSTME NMIMS Comp Engn Dept Mumbai Maharashtra India

ISBN: (纸本)9781538617199

Data is being generated very rapidly and at very high magnitudes. this data, termed as 'Big Data has found its use in many fields. the aim of this paper is to discuss the challenges posed by Big Data analysis and the techniques that can he used to solve these challenges. One of the most efficient techniques used to do so is Deep Learning. this paper also focuses on the models that have been implemented for big data analysis with deep learning and its applications.

关键词： Big Data Data Analysis Deep Learning Unsupervised Learning pattern Mining

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A Novel Random Forest and its Application on Classification of Air Quality

A Novel Random Forest and its Application on Classification ...

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IIAI international conference on Advanced Applied Informatics (IIAIAAI)

作者： Hualing Yi Qingyu Xiong Qinghong Zou Rui Xu Kai Wang Min Gao School of Big Data & Software Engineering Chongqing University Chongqing China School of Automation Chongqing University Chongqing China

ISBN: (纸本)9781728126289

Air pollution has a serious impact on daily life. It is necessary to inform the air quality in time to the public in order to take measures in advance. Machine learning methods such as random forest are good at evaluating grades of air quality. We find the distribution of air data is imbalance, which leads to negative effect on random forest classifiers. We propose a random forest method based on samples grouped bootstrap to solve this problem. then we design three sets of experiments to evaluate the performance of the proposed method. the results of experiments indicate that the proposed method presents an improvement of random forest when both apply on balance datasets. the improvement is very significant when they apply on imbalance datasets, where the new method is much better at classifying minority samples.

关键词： air pollution air quality environmental science computing pattern classification random forests sampling methods Air quality pattern recognition Air contamination sampling methods computing science flight data Random Forest Taxonomy

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A Hybrid Scheme Using PCA and ICA Based Statistical Feature for Epileptic Seizure recognition from EEG Signal

A Hybrid Scheme Using PCA and ICA Based Statistical Feature ...

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international conference on Informatics, Electronics & Vision (ICIEV)

作者： Abdul Matin Rasel Ahmed Bhuiyan Shafiur Raihan Shafi Amit Kumar Kundu Muhammad Usama Islam Department of Computer Science and Engineering Uttara University Bangladesh Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Bangladesh Department of Electrical and Electronic Engineering Uttara University Bangladesh School of Computing and Informatics University of Louisiana Lafayette USA

Epilepsy is commonly regarded as a neurological disorder which can be characterized by repetitive unprovoked seizures. Electroencephalogram (EEG) is the neuro-physiological measurement of the brain's electrical activity recorded by electrodes placed in the cerebral cortex. the EEG signals play an essential role in the diagnosis of epilepsy. this paper proposes an approach for classifying the epileptic seizure patterns that carry significant indications regarding chronic neurological disorders. In this regard, a dimensionality reduction scheme, hybrid in nature, utilizing Independent and Principal Component Analysis (ICA and PCA) is developed followed by the extraction of statistical features for epileptic seizure identification. At first, a particular number of sub-frames is extracted from the given EEG signal. the extracted sub-frames are considered as the input of PCA and ICA. After that, the significant components of ICA and PCA are utilized for statistical feature extraction. Lastly, the supervised support vector machine (SVM) classifier is employed for the seizure classification purpose. To evaluate the raised method, the publicly available EPILEPTIC dataset is used. According to the experiments and result analysis, the proposed scheme achieves convincing performance in terms of accuracy when the first components of ICA and PCA algorithms are used for feature extraction.

关键词： Feature extraction Principal component analysis Electroencephalography Support vector machines Brain modeling Time series analysis Dimensionality reduction

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