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检索条件"任意字段=2020 International Conference on Data Processing Algorithms and Models, ICDPAM 2020"

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Estimation Of Visual Contents Based On Question Answering From Human Brain Activity

Estimation Of Visual Contents Based On Question Answering Fr...

引用

IEEE international conference on Image processing

作者： Saya Takada Ren Togo Takahiro Ogawa Miki Haseyama School of Engineering Hokkaido University Education and Research Center for Mathematical and Data Science Hokkaido University Faculty of Information Science and Technology Hokkaido University

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

We propose an estimation method for free-form Visual Question Answering (VQA) from human brain activity, brain decoding VQA. The task of VQA in the field of computer vision is generating an answer given an image and a question about its contents. The proposed method can realize answering arbitrary visual questions about images from brain activity measured by functional Magnetic Resonance Imaging (fMRI) while viewing the same images. We enable estimating various information from brain activity via a unique VQA model, which can realize a more detailed understanding of images and complex reasoning. In addition, we newly make use of un-labeled images not used in the training phase to improve the performance of the transformation, since fMRI datasets are generally small. The proposed method can answer a visual question from a little amount of fMRI data measured while subjects are viewing images.

关键词： Functional magnetic resonance imaging Visualization Feature extraction Decoding Brain Training data models

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Intrusion Detection and Attack Classification Leveraging Machine Learning Technique

Intrusion Detection and Attack Classification Leveraging Mac...

引用

international conference on Innovations in Information Technology

作者： Shahtaj Shaukat Arshid Ali Amreen Batool Fehaid Alqahtani Jan Sher Khan Arshad Jawad Ahmad HITEC University Taxila Pakistan University of Engineering and Technology Peshawar Pakistan Tiangong University Tianjin China King Fahad Naval Academy Al Jubail Saudi Arabia University of Gaziantep Gaziantep Turkey Institute for Energy and Environment University of Strathclyde United Kingdom School of Computing Edinburgh Napier University United Kingdom

ISBN: (数字)9781728181844

ISBN: (纸本)9781728181851

Due to the advancement in information exchange over the Internet and mobile technologies, malicious network attacks have significantly increased. Machine learning algorithms can play a vital role in network security and attacks classification. This paper compares two different types of classifiers (Naive Bayes and Decision Tree) for the intrusion detection system on the publicly available dataset. Simulations are carried out using the WEKA machine learning tool and experimentation is performed on full data and selected features using subset evaluator algorithm. The classifier performance is evaluated in terms of accuracy, specificity, recall, precision, f1-score, error rates and response time. Naive Bayes classifier performance was better in terms of computational time, however, the accuracy, error rate, f1-score, and recall values of Decision Tree were better than Naive Bayes.

关键词： Machine learning Feature extraction Computer crime Machine learning algorithms data models Intrusion detection Support vector machines

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Assessing Block-Sparsity-Based Spectrum Sensing Approaches for Cognitive Radar on Measured data

Assessing Block-Sparsity-Based Spectrum Sensing Approaches f...

引用

IEEE international conference on Radar

作者： A. Aubry V. Carotenuto A. De Maio M. A. Govoni A. Farina University of Naples Federico II Naples Italy U.S. Army Research Laboratory MD USA Selex ES Rome Italy

ISBN: (数字)9781728168135

ISBN: (纸本)9781728168142

Due to increasing demands for spectral resources in both communication and radar systems, the Radio Frequency (RF) electromagnetic spectrum is becoming more and more crowded with interfering nuisances. In order to tackle the scarcity of available spectral intervals, in recent years a multitude of spectrum sensing algorithms have been developed for improving spectrum sharing. Among these, two-dimensional (2-D) spectrum sensing can be used to obtain real time space-frequency electromagnetic spectrum awareness. Specifically, this approach makes it possible to optimize the spectrum usage of certain spectrum portions whose occupancy varies both temporally and spatially. In this paper, we evaluate the effectiveness of certain space-frequency map recovery algorithms relying on the use of commercially-available hardware. To this end, we employ an inexpensive four channel coherent receiver using Software Defined Radio (SDR) components for emitter localization. Hence, after proper calibration of the receiving system, the acquired samples are used to evaluate the effectiveness of different signal processing strategies which exploit the inherent block-sparsity of the overall profile. At the analysis stage, results reveal the effectiveness of such algorithms.

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Classification Based Load Balancing in Content Delivery Networks

Classification Based Load Balancing in Content Delivery Netw...

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international conference on Telecommunications and Signal processing (TSP)

作者： Peter Bakonyi Tomáš Boros Ivan Kotuliak Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Bratislava Slovakia

ISBN: (数字)9781728163765

ISBN: (纸本)9781728163772

The use of content delivery networks is emerging with the increasing population with access to the Internet. Content Delivery Networks bring content closer to the end-user lowering operational expenses and increasing end-user experience. End users consume content from the cache servers using redirection. Optimising the load distributed to these cache servers is crucial to maintain a sustainable network. In this article, we created and evaluated a new routing method with the help of artificial intelligence exploiting Software-defined networking. The new routing methods aim to distribute the load to the delivery servers optimally and maximises utilisation of the network, avoiding bottlenecks along the way. The proposed approach distinguishes and routes the flows in the network on the session layer rather than just following standard routing rules based on prefix matching. Concluding with the evaluation of achieved results and the gained performance increase in this article.

关键词： Servers Machine learning Bandwidth Routing Load modeling data models Protocols

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Automatic detection of infeasible ALL-DU paths in the data flow test using an evolutionary approach

Automatic detection of infeasible ALL-DU paths in the data f...

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international conference of Signal processing and Intelligent Systems (ICSPIS)

作者： Azam Cheraghi Seyed Mohammad Hossein Hasheminejad Alzahra University Tehran Iran

ISBN: (数字)9781728186290

ISBN: (纸本)9781728186306

The infeasible path indicates that no input can execute the code anyway. The infeasible path is a kind of defect that is easily visible in software development and is also one of the most important defects in the software's white box test. And the main reason for the program's infeasible paths is the correlation between some conditional *** correlation of conditional expressions plays an important role in identifying infeasible paths. In the data flow test, the pair of "def-uses" along the infeasible paths must be removed from the set of requirements for coverage by the test cases. As a result, the reduction of infeasible pairs increases the reliability of the regression test and the integration test. In this paper, we propose a new method to detect infeasible all-du paths in the data flow test using the concept of correlation tree and Particle Swarm Optimization algorithm, and we have used the concept of popularity and unpopularity in the new fitness function. we validate the performance of the proposed method through a group of experiments. To validate the proposed method, empirical analysis is performed on some of the most widely used standard programs. We use 10 subject programs written in C language for experiments. Experimental results showed that the proposed approach can detect effectively infeasible all-du paths.

关键词： Correlation C languages Signal processing algorithms Production Software Standards Testing

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Commodity sales forecast based on ARIMA model residual optimization

Commodity sales forecast based on ARIMA model residual optim...

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Communication, Image and Signal processing (CCISP), international conference on

作者： Wen Hu Xinchao Zhang School of Computer and Information Engineering Harbin University of Commerce Harbin China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin China

ISBN: (数字)9781728185897

ISBN: (纸本)9781728185903

Accurate merchandise sales forecasts increase the turnover rate for merchants' benign inventory stocking, formulate cost budget items, formulate effective strategies according to the time and local conditions, and provide theoretical basis for reasonable merchandise sales strategies. However, the traditional methods of forecasting are not comprehensive and the actual effect is not ideal. Use the ARIMA model to fit the original product sales time series, extract the main deterministic information in the product sales time series through the deterministic factor decomposition method, and subtract or remove the trend effect fitting polynomial and seasonal effect fitting from the sales time series The remaining part of the polynomial obtains the residual sequence. The similarity distance function of the comparison layer in the adaptive resonance theory neural network is improved to measure the degree of similarity between a subsequence of the residual sequence and another subsequence, and the weight is adjusted according to the similarity. Each neuron in the competition layer of the network compares the input subsequences accordingly to generate the most similar subsequences of each subsequence, and iteratively eliminates all sequences related to the total residual sequence to obtain the final residual sequence that meets the requirements. Experimental comparison shows that the model after optimization by residual error can reflect the sales law more accurately and comprehensively, and improve the accuracy of product sales forecast.

关键词： Predictive models Time series analysis Neurons Correlation data models Adaptation models Market research

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Multilevel Interaction Reasoning For Complex Event Recognition

Multilevel Interaction Reasoning For Complex Event Recogniti...

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IEEE international conference on Image processing

作者： Shicheng Li Hua Yang Jun Sun Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Event as a complex process contains many factors. Objects, environments, and their interactions vary with time. Recognizing event remains a challenging task in computer vision. In this paper, a multilevel interaction reasoning framework is proposed for complex event recognition. Firstly, we construct a 3D ConvNet to extract the spatial-temporal feature to present global scene. Then a graph ConvNet is built to reasoning about the multilevel interaction: object-object and object-environment interaction, via graphs that contain object feature in video, and projection of global scene feature in 3D ConvNet. The proposed method effectively explore the nature of events occurring and developing, and experimental results on challenging UCF-Crime dataset achieve state-of-the-art with a 3.5% gain over other models.

关键词： Feature extraction Cognition Convolution Three-dimensional displays data mining Semantics Training

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Prediction of Septic Shock Onset in ICU by Instantaneous Monitoring of Vital Signs*

Prediction of Septic Shock Onset in ICU by Instantaneous Mon...

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Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Maximiliano Mollura Stefano Romano Giulio Mantoan Li-wei Lehman Riccardo Barbieri Department of Electronic Information and Biomedical Engineering Politecnico di Milano Laboratory for Computational Physiology Massachusetts Institute of Technology

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Septic Shock is a critical pathological state that affects patients entering the intensive care unit (ICU). Many studies have been directed to characterize and predict the onset of the septic shock, both in ICU and in the Emergency Department employing data extracted from the Electronic Health Records. Recently, machine learning algorithms have been successfully employed to help characterize septic shock in a more objective and automatic fashion. Only a few of these studies employ information contained in the continuously recorded vital signs such as electrocardiogram and arterial blood pressure. In particular, we have devised a novel feature estimation procedure able to consider instantaneous dynamics related to cardiovascular control. This work aims at developing a short-term prediction algorithm for identifying patients experiencing septic shock among a population of 100 septic patients extracted from the MIMIC-III clinical and waveform database. Among all the results obtained from several trained machine learning models, the best performance reached an AUC on the test set equal to 0.93 (Accuracy=0.85, Sensitivity=0.89 and Specificity=0.82).

关键词： Electric shock Feature extraction Blood pressure data mining Time series analysis Monitoring Machine learning algorithms

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Traffic Classification of QoS Types Based on Machine Learning Combined with IP Query and Deep Packet Inspection

Traffic Classification of QoS Types Based on Machine Learnin...

引用

international conference on Signal processing and Communication Systems, ICSPCS

作者： Yung-Fa Huang Chien-Min Chung Chuan-Bi Lin Yan-Bo Peng Shing-Hong Liu Honda Chen Chaoyang University of Technology Taichung Taiwan

ISBN: (数字)9781728199726

ISBN: (纸本)9781728199733

Due to many network services using encryption protocols, it poses a massive challenge to accurately identify traffic and provide quality of service (QoS). Although deep packet inspection (DPI) technology can identify most traffic, there are still many disadvantages to identifying encrypted traffic. In this paper, we apply the IP (Internet Protocol) autonomous system information query with machine learning (ML) and deep packet detection technology for traffic identification. By combining these methods, it can quickly provide good QoS for different types of encrypted traffic. The experimental results show that different encrypted traffic can be effectively identified. The services with different QoS requirements can be effectively identified even if they use the same protocol.

关键词： Machine learning IP networks Protocols Training Quality of service data models Streaming media

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Towards Incorporating Contextual Knowledge into the Prediction of Driving Behavior

Towards Incorporating Contextual Knowledge into the Predicti...

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international conference on Intelligent Transportation

作者： Florian Wirthmüller Julian Schlechtriemen Jochen Hipp Manfred Reichert Mercedes-Benz AG Böblingen Germany Institute of Databases and Information Systems (DBIS) Ulm University Ulm Germany Institute of Real-time Learning Systems University of Siegen Siegen Germany

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

Predicting the behavior of surrounding traffic participants is crucial for advanced driver assistance systems and autonomous driving. Most researchers however do not consider contextual knowledge when predicting vehicle motion. Extending former studies, we investigate how predictions are affected by external conditions. To do so, we categorize different kinds of contextual information and provide a carefully chosen definition as well as examples for external conditions. More precisely, we investigate how a state-of-the-art approach for lateral motion prediction is influenced by one selected external condition, namely the traffic density. Our investigations demonstrate that this kind of information is highly relevant in order to improve the performance of prediction algorithms. Therefore, this study constitutes the first step towards the integration of such information into automated vehicles. Moreover, our motion prediction approach is evaluated based on the public highD data set showing a maneuver prediction performance with areas under the ROC curve above 97% and a median lateral prediction error of only 0. 18m on a prediction horizon of 5s.

关键词： Meteorology Roads Training Prediction algorithms Multilayer perceptrons Focusing data models

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