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international conference on Information Science and Technology (ICIST)

作者： Zhongqing Wang Yan Yang Yingli Zhong School of Computer Science Technology Heilongjiang University Harbin China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

Graph similarity search is an important research problem in many applications, such as finding result graphs that have a similar structure to a given entity in biochemistry, data mining, and pattern recognition. Top-k graph similarity search is one of graph similarity search tasks, which aims to find the top-k graphs that are most similar to the query graph in a given graph database. In this paper, the top-k similarity search problem based on the graph edit distance is studied according to the corollary of the partitioned similarity theorem. Firstly, in order to speed up the online search process and avoid scanning each graph in the database one by one, an offline hierarchical inverted index is constructed to satisfy top-k search. Secondly, the offline hierarchical inverted index is used to filter the candidate graphs online and verify them, which reduces the time of searching the graphs. Finally, the good performance of the algorithm in running time and scalability is verified by running the similarity algorithm on real dataset and synthetic dataset.

关键词： Information science Databases Scalability Filtering algorithms Search problems Partitioning algorithms Indexes

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Stage-Wise Neural Architecture Search

Stage-Wise Neural Architecture Search

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international conference on pattern recognition

作者： Artur Jordao Fernando Akio Maiko Lie William Robson Schwartz Smart Sense Laboratory Federal University of Minas Gerais Brazil

Modern convolutional networks such as ResNet and NASNet have achieved state-of-the-art results in many computer vision applications. these architectures consist of stages, which are sets of layers that operate on representations in the same resolution. It has been demonstrated that increasing the number of layers in each stage improves the prediction ability of the network. However, the resulting architecture becomes computationally expensive in terms of floating point operations, memory requirements and inference time. thus, significant human effort is necessary to evaluate different trade-offs between depth and performance. To handle this problem, recent works have proposed to automatically design high-performance architectures, mainly by means of neural architecture search (NAS). Current NAS strategies analyze a large set of possible candidate architectures and, hence, require vast computational resources and take many GPUs days. Motivated by this, we propose a NAS approach to efficiently design accurate and low-cost convolutional architectures and demonstrate that an efficient strategy for designing these architectures is to learn the depth stage-by-stage. For this purpose, our approach increases depth incrementally in each stage taking into account its importance, such that stages with low importance are kept shallow while stages with high importance become deeper. We conduct experiments on the CIFAR and different versions of ImageNet datasets, where we show that architectures discovered by our approach achieve better accuracy and efficiency than human-designed architectures. Additionally, we show that architectures discovered on CIFAR-10 can be successfully transferred to large datasets. Compared to previous NAS approaches, our method is substantially more efficient, as it evaluates one order of magnitude fewer models and yields architectures on par with the state-of-the-art.

关键词： Computer vision Design methodology Memory management Computer architecture Search problems pattern recognition

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LiDAR system benchmarking for VRU detection in heavy goods vehicle blind spots

LiDAR system benchmarking for VRU detection in heavy goods v...

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IEEE international conference on Intelligent Computer Communication and Processing (ICCP)

作者： Topi Miekkala Pasi Pyykönen Matti Kutila Arto Kyytinen VTT Technical Research Center of Finland Ltd. Tampere Finland TTS Kehitys Ltd. Finland

ISBN: (纸本)9781665409773

this article is related to using modern LiDARs and neural networks based algorithms for vulnerable road users (VRU) detection. the problem is obvious especially when considering blind spots of heavy goods vehicles. LiDARs have developed a lot recently and the results indicate that adults can be detected up-to 75 m distance from the sensor even thought that pattern recognition requires sufficient point cloud-resolution. Two different LiDAR brands have been compared to understand costbenefits between LiDAR technologies. the results have been conducted using an automated passenger car while considering feasibility to big trucks. Due to automotive requirements, the processing rate is also considered since usually, the main bottleneck is computation power, which is limited in automotive products. the used neural network algorithm is Yolo based and has been designed for VRU detection.

关键词： Cloud computing Laser radar three-dimensional displays Roads conferences Neural networks Benchmark testing

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Auditory Evoked Potential Detection During Pure-Tone Audiometry

Auditory Evoked Potential Detection During Pure-Tone Audiome...

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international IEEE/EMBS conference on Neural Engineering, CNE

作者： George Langroudi Ramaswamy Palaniappan Ian McLoughlin George Langroudi and Ramaswamy Palaniappan School of Computing University of Kent UK ICT Cluster Singapore Institute of Technology Singapore

Modern audiometry is largely a behavioural task, with the pure-tone audiogram (PTA) being the gold standard for evaluating frequency-specific hearing thresholds in adults. the nature of behavioural audiometry makes estimating accurate hearing thresholds difficult in infants and people with disabilities, where following instructions or interacting with the test may be difficult or impossible. We propose a method in which Auditory Evoked Potentials (AEPs) are used as an alternative to behavioural audiometry for detecting frequency-specific thresholds. Specifically, P300 responses elicited by the tones of a PTA are automatically detected from electroencephalogram (EEG) data, to evaluate hearing acuity. To assess the effectiveness of this method, we created a dataset of EEG recordings from participants presented with a series of pure tones at 6 different frequencies with steadily decreasing volumes, during a PTA test. this dataset was used to train a support vector machine (SVM) to identify when a participant was played a tone, whether they perceived it or not using their EEG. Results demonstrate that detecting hearing events can be very accurate for participants for whom the classifier has been trained apriori. However, accuracy drops significantly for unseen participants - when the classifier has not been trained on any prior data from a given participant before classifying their EEG. However, by establishing that AEP response-based audiometry is viable for detecting tones, future work will explore the ability of more powerful deep neural networks to accurately estimate for unseen participants.

关键词： Support vector machines Training Electric potential Neural networks Neural engineering Auditory system Electroencephalography

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Sand Body Microfacies Types and Single Sand Body Division in Sandy Braided River Reservoirs: A Case Study from Yan10 Member in the H303-6 Well Area, Southwestern Ordos Basin, China 10th

Sand Body Microfacies Types and Single Sand Body Division in...

引用

10th international Field Exploration and Development conference, IFEDC 2020

作者： Zhao, Yong-gang Li, Lei Shi, Liu-qi Wang, Zheng-jie Yang, Lu-yan Wang, Zhi-wei School of Earth Sciences and Engineering Xi’an Shiyou University Xi’an710065 China Shaanxi Key Laboratory of Petroleum Accumulation Geology Xi’an Shiyou University Xi’an Shaanxi710065 China Exploration and Development Technology Research Center Yanchang Oil Field Co. Ltd. Yan’an716000 China

ISBN: (纸本)9789811607622

this paper takes lower Jurassic Yan10 Member of the H303-6 well area as an example in southwestern Ordos Basin. Beginning with core, well logging, logging and other data, mainly using lithology and sedimentary structure and other sedimentary facies indices, combined with logging curve characteristics, it is firstly considered that the Yan10 Member belongs to sandy braided river facies, and main sand body microfacies types of sandy braided river of Yan10 reservoirs are recognized in the study area. the key point of this paper is to identify together single sand body by core, logging curve and sand body microfacies. By using "microfacies profile genetic analysis method", sand body is mainly divided into "central bar single sand body", "channel sand single sand body", "channel margin single sand body", "river flat single sand body" and so on in Yan10 reservoirs. Depending on the characteristics of well logging response, the vertical contact style of single sand body is mainly consist of "isolated style", "overlap style" and "overlay cutting style", while the lateral contact relation mainly includes "butt-joint type" and "side shear type". the distribution characteristics of single sand body are analyzed by contact relation pattern of sand body. According to well pattern type, injection-production well groups are divided, and the injection-production correspondence between water injection well and oil well is judged. By identifying the connectivity of single sand body, injection and production relationship types are divided into "injection and production type", "injection and no production type", "production and no injection type" and "no production and no injection type". Based on the distribution characteristics and connectivity of single sand body, specific measures of injection and production improvement are proposed to provide a reliable basis for timely adjustment of reservoirs development plan. the division of single sand body is a very important work of this res

关键词： Lithology

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Perceptually Constrained Fast Adversarial Audio Attacks

Perceptually Constrained Fast Adversarial Audio Attacks

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international conference on Machine Learning and Applications (ICMLA)

作者： Jason Henry Mehmet Ergezer Marko Orescanin Naval Postgraduate School Monterey CA School of Computing and Data Science Wentworth Institute of Technology Boston MA

ISBN: (纸本)9781665443388

Audio adversarial attacks on deep learning models are of great interest given the commercial success and proliferation of these technologies. these types of attacks have been successfully demonstrated, however, artifacts introduced in the adversarial audio are easily detectable by a human observer. In this work, an expansion of the fast audio adversarial perturbation framework is proposed that can produce an adversarial attack that is imperceptible to a human observer in near-real time using black-box attacks. this is achieved by proposing a perceptually motivated penalty function. We propose a perceptual fast audio adversarial perturbation generator (PFAPG) that employs a loudness constrained loss function, in lieu of a conventional L-2 norm, between the adversarial example and original audio signal. We compare the performance of PFAPG against the conventional constraint based on the MSE on three audio recognition datasets: speaker recognition, speech command, and the Ryerson audiovisual database of emotional speech and song. Our results indicate that, on average, PFAPG equipped with the loudness-constrained loss function yields a 11% higher success rate, while reducing the undesirable distortion artifacts in adversarial audio by 10% dB compared to the prevalent MSE constraints.

关键词： Training Measurement Psychoacoustics Perturbation methods Speech recognition Observers Distortion

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Brain Tumor Segmentation and Tumor Prediction Using 2D-VNet Deep Learning Architecture

Brain Tumor Segmentation and Tumor Prediction Using 2D-VNet ...

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Deependra Rastogi Prashant Johri Varun Tiwari School of Computing Science and Engineering Galgotias University Greater Noida

ISBN: (纸本)9781665439718

Segmentation of brain tumors is a difficult task because of the enormous variation in the intensity and size of gliomas. the tumor type Glioma is the highly prevalent malignant tumor in the brain, with a high death rate and a morbidity rate of more than 3%. In the clinic, MRI is the most common way of detecting brain cancers. Automatic segmentation is difficult because of the overlapping area between the intensity distributions of healthy, enhancing, non-enhancing and edema regions. Segmenting brain tumour areas utilising multi-modal MRI scan pictures can help with treatment observation, post-diagnosis monitoring, and patient impacts evaluation. Manual segmentation, on the other hand, is still the most common procedure in clinical brain tumour segmentation, which takes time and results in significant performance variations across operators. For this reason, the development of accurate and consistent automatic segmentation technology is required. Convolutional neural networks (CNNs), have shown promise in brain tumor segmentation due to their powerful learning capacity. this article suggests an 2D-VNet model for brain tumor segmentation and enhancing the prediction. the presented model was successfully segmented brain tumors and predict the result in enhancing tumor and real enhancing tumor. Experiment with BRATS2020 benchmarks dataset, we found that Loss (for Training: .0025, Testing: .0032 and Validation: .0031), Dice Coefficient (for Training: .9974, Testing: .9967 and Validation: .9968) and Accuracy (for Training: .9971 Testing: .9963 and Validation: .9964).

关键词： Training Image segmentation Magnetic resonance imaging Predictive models Brain modeling Lesions Task analysis

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Speculative prefetching of induction pointers 10th

Speculative prefetching of induction pointers

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10th international conference on Compiler Construction, CC 2001 Held as Part of the Joint European conferences on theory and Practice of Software, ETAPS 2001

作者： Stoutchinin, Artour Amaral, José Nelson Gao, Guang R. Dehnert, James C. Jain, Suneel Douillet, Alban STMicroelectronics Grenoble France Department of Computing Science University of Alberta EdmontonAB Canada Computer Architecture and Parallel System Laboratory University of Delaware NewarkDE United States Transmeta Co Santa ClaraCA United States Hewlett-Packard Co CupertinoCA United States

ISBN: (纸本)354041861X

We present an automatic approach for prefetching data for linked list data structures. the main idea is based on the observation that linked list elements are frequently allocated at constant distance from one another in the heap. When linked lists are traversed, a regular pattern of memory accesses with constant stride emerges. this regularity in the memory footprint of linked lists enables the development of a prefetching framework where the address of the element accessed in one of the future iterations of the loop is dynamically predicted based on its previous regular behavior. We automatically identify pointer-chasing recurrences in loops that access linked lists. this identification uses a surprisingly simple method that looks for induction pointers | pointers that are updated in each loop iteration by a load with a constant offset. We integrate induction pointer prefetching with loop scheduling. A key intuition incorporated in our framework is to insert prefetches only if there are processor resources and memory bandwidth available. In order to estimate available memory bandwidth we calculate the number of potential cache misses in one loop iteration. Our estimation algorithm is based on an application of graph coloring on a memory access interference graph derived from the control flow graph. We implemented the prefetching framework in an industry-strength production compiler, and performed experiments on ten benchmark programs with linked lists. We observed performance improvements between 15% and 35% in three of them. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Iterative methods

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Artificial Neural Networks Applied to Reliability and Well-Being Assessment of Composite Power Systems

Artificial Neural Networks Applied to Reliability and Well-B...

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international conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Armando M. Leite da Silva Leonidas C. de Resende Luiz A. da Fonseca Manso V. Miranda Institute of Electric System and Energy Federal University of Itajubá-UNIFEI Brazil Electrical Engineering Department Federal University of São João del Rei Brazil Faculty of Engineering University of Porto FEUP Portugal

ISBN: (纸本)9781934325216;9781934325407;1934325406

this paper presents a new methodology for assessing both reliability and well-being indices for composite generation and transmission systems. Firstly, a transmission network reduction is applied to find an equivalent for assessing composite reliability for practical large power systems. After that, in order to classify the operating states, Artificial Neural Networks (ANNs) based on Group Method Data Handling (GMDH) techniques are used to capture the patterns of the operating states, during the beginning of the non-sequential Monte Carlo simulation (MCS). the idea is to provide the simulation process with an intelligent memory, based only on polynomial parameters, to speed up the evaluation of the operating states. For the conventional reliability assessment, the ANNs are used to classify the operating states into success and failure. However, for the well-being analysis, only success states are classified into healthy and marginal by the ANNs. the proposed methodology is applied to the IEEE Reliability Test System 1996 and to a configuration of the Brazilian South- Southeastern System.

关键词： Power system reliability Artificial neural networks Power system simulation Power system analysis computing Data handling Polynomials System testing pattern analysis Failure analysis State estimation

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Text recognition in UAV Aerial Images 21

Text Recognition in UAV Aerial Images

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Proceedings of the 2021 4th international conference on Artificial Intelligence and pattern recognition

作者： Shu Wang Dianwei Wang Pengfei Han Xincheng Ren ZHIJIE XU Xi'an University of Posts&Telecommunications China School of Physics and Electronic Information Yan'an University China School of Computing and Engineering University of HuddersfieldHuddersfieldHD1 3DH China

ISBN: (纸本)9781450384087

Text recognition in unmanned aerial vehicle (UAV) aerial images is an important branch in the field of machine intelligence, which can provide important discriminative information for subsequent applications. At this stage, text recognition methods have made breakthrough progress, but the recognition of distorted and slanted text is still a challenge. In this case, we construct a text recognition network model with correction module, and propose a new type of UAV aerial image text recognition method. Specifically, the model mainly includes two parts: rectification network and recognition network. the rectification network can be optimized without manual annotation, and it can regularize various distorted and inclined UAV image texts. the recognition network introduces the attention mechanism and improves the decoder to perform bidirectional recognition of the rectified UAV image text. In addition, we verify the effectiveness of the rectification network through a large number of experiments, and prove that the model composed of the rectification network and the recognition network can achieve the optimal recognition performance.

关键词： Aerial images Rectification network Text recognition UAV

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