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machine learning assisted content delivery at edge of mobile social networks 4

Machine learning assisted content delivery at edge of mobile...

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4th IEEE international conference on Data Science in Cyberspace, DSC 2019

作者： Tao, Yu Bian, Kaigui Gao, Chengliang Zhang, Yuanxing Song, Lingyang Dong, Shaolin School of EECS Peking University Beijing China Fibonacci Data Consulting Services Inc Shenzhen China

ISBN: (纸本)9781728145280

the demand of content delivery (for video streaming, photos, mobile web pages) has explosively grown in recent years, leading to an excessive volume of network traffic at the edge of mobile networks. Mobile social networking (MSN) is widely recognized as the most popular mobile application, as it attracts the largest number of users, and consumes the highest daily time of each user on average. However, mobile users can be distantly located, and their preferences to content may vary over time, leading to highly dynamic distribution of network traffic. Hence, it is necessary to allocate network edge resources for meeting the growing user demand to different types of contents spatially and temporally. To study this problem, we establish a machine learning framework for predicting how users' location and their consumed content can affect the traffic distribution in the underlying content distribution network. Given the predicted distribution of content demands, the proposed smart content delivery scheme is able to instruct the edge servers to dynamically deliver the content to edge users. We evaluate the performance of the conventional regression models and the proposed content delivery scheme over a real-world content distribution network for MSNs. Results reveal that the neural network model outper-forms other approaches by increasing the hit ratio by 30%, and saving the network cost by 15%. © 2019 IEEE.

关键词： machine learning

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the development of an internet of things mobile application for tracking an electric bus in a campus 4

The development of an internet of things mobile application ...

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4th Joint international conference on Digital Arts, Media and Technology and 2nd ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT-NCON 2019

作者： Wongthai, Winai Khruahong, Sanya Srithong, Pasathon Samphao-Ngoen, Muthita Department of Computer Science and Information Technology and Research Center for Academic Excellence in Nonlinear Analysis and Optimization Faculty of Science Naresuan University Phitsanulok Thailand Chainatpittayakom School Chi Nat Thailand

ISBN: (纸本)9781538680728

To reach the idea of data science for the Internet of things (IoT) innovation, we believe that if we can collect data from our daily life activities such as our commuting data from a bus system based on IoT. We can add values to this collected data using, for example, machine learning or data analysis and statistics. However, we need to enable the system to be able to measure our daily life's activities. As an example of such a system, this research demonstrates how to apply the IoT technology to track an electrical bus status of a mass transit system in a university. We develop a mobile application to show the information of the bus on a smartphone. the information can be alerted to a user or passenger in real-time. this information includes an estimated waiting time until the bus reaches a user, the number of passengers on board, and the current bus's position on the route in Google Maps. the mobile application can help a passenger to decide and manage the right journeys to travel in the university. the application was developed on the Android platform with many technologies including an Arduino integrated development environment or IDE, Google Maps application programming interface or API, a global positioning system or GPS module, thingSpeak cloud, and infrared distance sensors. the 30 passengers evaluated the satisfaction survey. the survey mainly includes the accuracy of the tracking and alerting processes. the results are that the passengers were delighted with the processes. the evaluation related values are following. the mean is 4.91, and the standard deviation or SD is 0.29 or representing as 98.20%. To sum up, the application in this research can provide an example of mobile application development with current and available technologies such as IoT and cloud in the disruptive era to enhance applications in a transportation or others domain. © 2019 IEEE.

关键词： Internet of things

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Logic-based online complex event rule learning with weight optimization 4

Logic-based online complex event rule learning with weight o...

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4th IEEE international conference on Data Science in Cyberspace, DSC 2019

作者： Wang, Yongheng Zhu, Rong Wang, Zengwang Zhang, Xiaozan Zhang, Baoxing Research Center of Cyberspace Bigsearch Zhejiang Lab Hangzhou China College of Computer Science and Electronic Engineering Hunan University Changsha China PLA 305 Hospital Beijing China

ISBN: (纸本)9781728145280

Complex event recognition system process continuous data stream in real time and use the defined event rules to infer the occurrence of the event in time. the rules used by complex event recognition systems are generally defined by experts manually. However, the manual definition of rules is often complicated and error-prone. Logic-based event recognition system has been studied in recent years. Event calculus is a kind of temporal logic that has become a logic-based expression of event rules. It combines with statistical relationship learning to conduct reasoning and learning of events, effectively solving the complicated and error-prone problems of artificial construction rules. Currently, logic-based event rule learning is mostly combined with ILP for top-down search rules, and the training time is often long. the existing logic-based online rule learning algorithm has a long training time and a fast learning rate. We proposed a method based on root mean square prop (RMSprop), a weight learning method to improve the original algorithm. Moreover, we optimize the weight of the rule structure, and combine the existing rule structure learning method and weight learning method to learn rules. the optimized method has been obtained good results. Finally, we design a set of experiments to verify the feasibility and effectiveness of our method. © 2019 IEEE.

关键词： Calculations

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Image Analysis and Processing of Skin Cell Injury Based on OpenCV 4

Image Analysis and Processing of Skin Cell Injury Based on O...

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2019 4th international conference on Intelligent Computing and Signal Processing, ICSP 2019

作者： Li, Gaohe Zhang, Yanli Xu, Bo Li, Xinhao School of Economic Management Xi'An Shiyou University Xi'an710065 China International Business School Shaanxi Normal University Xi'an710119 China School of Petroleum Engineering Xi'An Shiyou University Xi'an710065 China

OpenCV is an open source computer vision and machine learning software library that provides a common infrastructure for computer vision applications and accelerates their use of machine awareness in commercial products[1].this paper uses the visual processing algorithm provided by OpenCV to analyze and process the image of skin cells in detail, and quantitatively determine the percentage of damaged cells in the image. First, dilation, opening operation, closing operation, coarsening, refinement, skeleton, tailoring, boundary extraction, holes filling, connected components and so on of a variety of morphological methods are used in the process of corrosion for the graphics pretreatment. then the watershed algorithm is adopted to image segmentation. this paper expounds the ridge algorithm to water in detail. Finally, in order to determine the proportion of damaged cells in the segmented cells, the polygonal fitting method in graphic image processing was creatively used, and the polygonal fitting method was described in detail, which successfully solved the task of the research topic. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： application programs

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7th EAI international conference on Context-Aware Systems and applications, ICCASA 2018 and 4th EAI international conference on Nature of Computation and and Communication, ICTCC 2018

7th EAI International Conference on Context-Aware Systems an...

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7th EAI international conference on Context-Aware Systems and applications, ICCASA 2018 and 4th EAI international conference on Nature of Computation and Communication, ICTCC 2018

ISBN: (纸本)9783030061517

the proceedings contain 20 papers. the special focus in this conference is on Context-Aware Systems and applications. the topics include: MVMO with opposite gradient initialization for single objective problems;context based algorithm for social influence measurement on Twitter;context-aware recommendation with objective interestingness measures;development of a peer-interaction programming learning system;dynamic measurement for detecting the road of an autonomous vehicle using the proximity sensor;post-quantum cryptoschemes: New finite non-commutative algebras for defining hidden logarithm problem;simulating the irrigation operations with cellular automata;modeling with words based on hedge algebra;a variable neighborhood search algorithm for solving the steiner minimal tree problem;transmission reordering in self-organizing network coordination framework;Handling missing values for the CN2 algorithm;context-aware parking systems in urban areas: A survey and early experiments;stream pseudo-probabilistic ciphers;jFAT: An automation framework for web application testing;on the compliance of access control policies in web applications;two-stage approach to classifying multidimensional cubes for visualization of multivariate data;proposing storage structures and interpolation algorithms of 3D spatial data;visually analyzing evolution of geographic objects at different levels of details over time.

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Applying a Multilayer Perceptron for Traffic Flow Prediction to Empower a Smart Ecosystem 19th

Applying a Multilayer Perceptron for Traffic Flow Prediction...

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19th international conference on Computational Science and Its applications (ICCSA)

作者： Ferreira, Yan Mendes Frank, Lucas Rodrigues Julio, Eduardo Pagani Ferreira, Francisco Henrique C. Dembogurski, Bruno Jose Silva, Edelberto Franco Univ Fed Juiz de Fora Juiz De Fora Brazil Univ Fed Rural Rio de Janeiro Rio De Janeiro Brazil

ISBN: (纸本)9783030242893

A direct impact of population density is more cities suffering from constant traffic jams. thinking this way, Intelligent Transportation Systems, a key area in smart cities, uses computational intelligence techniques and analyses to aid in traffic dimensioning solutions. In this context, accurate traffic prediction models are vital to creating a more autonomous and intelligent environment. With an increase in projects for intelligent cities, research in the area of computational intelligence becomes a necessity, since its models can address complex real-world problems, which are usually difficult for conventional methods. In this work, an application is introduced applying machine learning to empower a smart ecosystem. To validate it, an extensive evaluation was performed, comparing it with the state-of-the-art and, also, verifying the impact of parameter variation and activation functions on the model of traffic flow prediction. All evaluations were done using real data traffic of two very distinct scenarios. Firstly, a free traffic flow scenario was evaluated in a benchmark dataset. then, both models were evaluated in a complex traffic scenario where traffic flow is not continuous nor large. In both scenarios, the presented application, called SmartTraffic, outperforms the current state-of-the-art, with a performance gain of over 100% when compared in the first scenario and an improvement of approximately 31%, on average, in the second one.

关键词： Smart cities machine learning Traffic flow prediction Intelligent Transportation Systems Smart Campus

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Char-Level Neural Network for Network Anomaly Behavior Detection 1

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4th international conference on Human Centered Computing, HCC 2018

作者： Wang, Sheng Song, Jiaming Guo, Ruixu Northen Institute of Electronic Equipment of China Beijing 100191 China West Tucheng Road on the 10th Beijing China Chengdu Zhongke Hexun Science and Technology Co. Ltd. Chengdu China

ISBN: (数字)9783030151270

ISBN: (纸本)9783030151263

With the rapid development of the Internet, various attacks against network servers have been increasing. At present, most of the network protection measures are mainly aimed at attacks on the network layer and the transport layer. there is almost no protection against attacks at the application layer, but more and more attacks against the web are completed through the application layer. Traditional intrusion detection methods rely too much on rule matching, and there is a problem of high false positive rate. In view of the shortcomings of traditional network intrusion detection, this paper introduces char-level neural network method into the field of Network anomaly behavior detection, and the experimental data is the http requests parsed from the collected web logs. the experiment results show that, compared to traditional machine learning models, the char-level neural network performs better in term of detecting anomaly intrusion. © 2019, Springer Nature Switzerland AG.

关键词： HTTP

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Predicting the Early Stages of the Alzheimer's Disease via Combined Brain Multi-projections and Small Datasets 14

Predicting the Early Stages of the Alzheimer's Disease via C...

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14th international Joint conference on computer Vision, Imaging and computer Graphics theory and applications (VISAPP)

作者： Duarte, Kaue T. N. de Paiva, Pedro V. V. Martins, Paulo S. Carvalho, Marco A. G. Univ Campinas UNICAMP Sch Technol Limeira Brazil

ISBN: (纸本)9789897583544

Alzheimer is a neurodegenerative disease that usually affects the elderly. It compromises a patient's memory, his/her cognition, and perception of the environment. Alzheimer's Disease detection in its initial stage, known as Mild Cognitive Impairment, attracts special efforts from experts due to the possibility of using drugs to delay the progression of the disease. this paper aims to provide a method for the detection of this impairment condition via the classification of brain images using Transfer learning - Deep Features and Support Vector machine. the small number of images used in this work justifies the application of Transfer learning, which employs weights from VGG19 initial layers used for ImageNet classification as deep features extractor, and then applies Support Vector machines. Majority Voting, False-Positive Priori, and Super Learner were applied to combine previous classifiers predictions. the final step was a detection to assign a label to the previous voting outcomes, determining the presence or absence of an Alzheimers pre-condition. the OASIS-1 database was used with a total of 196 images (axial, coronal, and sagittal). Our method showed a promising performance in terms of accuracy, recall and specificity.

关键词： Classification Transfer learning Mild Cognitive Impairment Clinical Dementia Rating Support Vector machines

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NetML: An NFV Platform with Efficient Support for machine learning applications 5

NetML: An NFV Platform with Efficient Support for Machine Le...

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5th IEEE conference on Network Softwarization (IEEE NetSoft) / 2nd international Workshop on Advances in Slicing for Softwarized Infrastructures (S4SI)

作者： Dhakal, Aditya Ramakrishnan, K. K. Univ Calif Riverside Dept Comp Sci & Engn Riverside CA 92521 USA

ISBN: (纸本)9781538693766

Real-time applications such as autonomous and connected cars, surveillance, and online learning applications have to train on streaming data. they require low-latency, high throughput machine learning (ML) functions resident in the network and in the cloud to perform learning and inference. NFV on edge cloud platforms can provide support for these applications by having heterogeneous computing including GPUs and other accelerators to offload ML-related computation. GPUs provide the necessary speedup for performing learning and inference to meet the needs of these latency sensitive real-time applications. Supporting ML inference and learning efficiently for streaming data in NFV platforms has several challenges. In this paper, we present a framework, NetML, that runs existing ML applications on an heterogeneous NFV platform that includes both CPUs and GPUs. NetML efficiently transfers the appropriate packet payload to the GPU, minimizing overheads, avoiding locks, and avoiding CPU-based data copies. Additionally, NetML minimizes latency by maximizing overlap between the data movement and GPU computation. We evaluate the efficiency of our approach for training and inference using popular object detection algorithms on our platform. NetML reduces the latency for inferring images by more than 20% and increases the training throughput by 30% while reducing CPU utilization compared to other state-of-the-art alternatives.

关键词： Graphics processing units Servers Noise measurement Computational modeling Instruction sets throughput Cloud computing instruction sets Noise measurement Nelfinavir throughput Servers Graphics Processing Unit Computational modeling Cloud Computing Latency real-time application program Platform central processing units

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Library Automation System: Book cover recognition using deep learning 4

Library Automation System: Book cover recognition using deep...

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4th international conference on Computational Systems and Information Technology for Sustainable Solution, CSITSS 2019

作者： Parikh, Jainil Viren Natarajan, Abhiram Sathish Babu, B. Computer Science and Engineering RV College of Engineering Bangalore India

ISBN: (纸本)9781728126197

With thousands of books across hundreds of disciplines present in a library, manual allocation of books is both time consuming, human intensive, as well as inefficient in terms of monetary costs. the presence of manual labour while providing employment to a section of society tends to slow down the process of lending in addition to increasing the probability of errors. the proposed research work attempts at automating the library book management system there by reducing latency and long queues as well as the potential of making mistakes in the distribution of books. Recent advancements of deep neural networks have improved textual recognition in natural scenes. the system proposed employs a neural network model for text detection and recognition, having the potential to reduce and even eliminate manual labour performed by the staff. this paper attempts to investigate neural networks for the application of library management by proposing our own Optical Character Recognition (OCR) and text matching algorithms. this task further introduces challenges such as getting the exact name of the book as well as dealing with distorted images and varied backgrounds. the system thus developed performs very well with book covers producing results over 60% in accuracy against current models that provide about 40% thereby making the system achieve state of the art performance on book cover information retrieval. © 2019 IEEE.

关键词： Face recognition

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