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2020 24th ISPRS congress - Technical Commission V (TC-V) on Education and Outreach - Youth Forum

作者： Li, J. Sheng, J. Chen, Y. Ke, L. Yao, N. Miao, Z. Zeng, X. Hu, L. Wang, Q. College of Resource and Environmental Engineering Wuhan University of Science and Technology Wuhan China

Remote sensing course is a general disciplinary required course of human geography and urban-rural planning major. Its class hour is 48, including theoretical classes and experimental classes. Rapid technological developments is remote sensing area demand quick and steady changes in the education programme and its realization, especially in experimental classes. Experimental classes include: introduction to remote sensing software and basic operations, remote sensing data pre-processing (input, output, 2D and 3D terrain display, image cut, image mosaic, and projection transformation), remote sensing image enhancement, remote sensing image transformation, computer aided classification, image interpretation, and remote sensing image terrain analysis. There are two difficulties in the remote sensing experimental classes. First, it cost a lot of time to prepare the remote sensing software and the remote sensing images. Second, some students just want to use the remote sensing as a tool to investigate environment changing, some other students may want to study more remote sensing image processing technologies. A web-based learning environment of remote sensing is developed to facilitate the application of remote sensing experimental teaching. To make the learning more effective, there are eight modules including four optional modules. The Python programming language is chosen to implement the web-based remote sensing learning environment. The web-based learning environment is implemented in a local network server, including the remote sensing data processing algorithms and many satellite image data. Students can easily exercise the remote sensing experimental courses by connecting to the local network server. It is developed mainly for remote sensing experimental course, and also can be adopted by digital image processing or other courses. The feature of web-based learning may be very useful as the online education adopted because of Corona Virus Disease 2019. The result

关键词： Python

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AP-STDP: A Novel Self-Organizing Mechanism for Efficient Reservoir Computing

AP-STDP: A Novel Self-Organizing Mechanism for Efficient Res...

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international Joint Conference on Neural Networks

作者： Yingyezhe Jin Peng Li Department of Electrical and Computer Engineering Texas A&M University College Station

ISBN: (纸本)9781509006212

The Liquid State Machine (LSM) exploits the computation capability of recurrent spiking neural networks by incorporating a randomly generated reservoir, which is often fixed. This standard choice relaxes the challenging need for training the complex recurrent reservoir. The fixed reservoir is used as a generic kernel to map the temporal input signals to the internal network dynamics, and a readout layer is trained to extract the information embedded in the network dynamics to facilitate pattern classification. However, the question of how to effectively tune the reservoir for given computational tasks remains to be answered. In this paper, we propose a novel Activity-based Probabilistic Spiking-Timing Dependent Plastic (AP-STDP) mechanism for self-organizing reservoirs. Compared to conventional STDP mechanisms, the proposed rule improves tuning efficiency, prevents the saturation of synaptic memory, and boosts performance. We assess the internal representation ability of the proposed self-organizing mechanism via principal component analysis (PCA) and show that the proposed method is advantageous over other STDP algorithms. Using the spoken English letters adopted from the TI46 speech corpus for performance benchmarking, we demonstrate that AP-STDP consistently outperforms other STDP mechanisms regardless of reservoir size, and is able to boost the performance of the isolated spoken English letter recognition by 2.7% with a small reservoir size.

关键词： reservoirs principal components analysis Pattern recognition recurrent neural nets letter

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Predicting taxi demand at high spatial resolution: Approaching the limit of predictability

Predicting taxi demand at high spatial resolution: Approachi...

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IEEE international Conference on Big Data

作者： Kai Zhao Denis Khryashchev Juliana Freire Cláudio Silva Huy Vo Center for Urban Science and Progress New York University Department of Computer Science and Engineering New York University Department of Computer Science The City College of New York

ISBN: (纸本)9781467390064

In big cities, taxi service is imbalanced. In some areas, passengers wait too long for a taxi, while in others, many taxis roam without passengers. Knowledge of where a taxi will become available can help us solve the taxi demand imbalance problem. In this paper, we employ a holistic approach to predict taxi demand at high spatial resolution. We showcase our techniques using two real-world data sets, yellow cabs and Uber trips in New York City, and perform an evaluation over 9,940 building blocks in Manhattan. Our approach consists of two key steps. First, we use entropy and the temporal correlation of human mobility to measure the demand uncertainty at the building block level. Second, to identify which predictive algorithm can approach the theoretical maximum predictability, we implement and compare three predictors: the Markov predictor (a probability-based predictive algorithm), the Lempel-Ziv-Welch predictor (a sequence-based predictive algorithm), and the Neural Network predictor (a predictive algorithm that uses machine learning). The results show that predictability varies by building block and, on average, the theoretical maximum predictability can be as high as 83%. The performance of the predictors also vary: the Neural Network predictor provides better accuracy for blocks with low predictability, and the Markov predictor provides better accuracy for blocks with high predictability. In blocks with high maximum predictability, the Markov predictor is able to predict the taxi demand with an 89% accuracy, 11% better than the Neural Network predictor, while requiring only 0.03% computation time. These findings indicate that the maximum predictability can be a good metric for selecting prediction algorithms.

关键词： Public transportation Entropy Prediction algorithms Correlation Markov processes Urban areas Neural networks

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Text sentiment computation for online forums hotspot detection

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international Journal of Information and Communication Technology 2016年第4期8卷 328-343页

作者： Nirmala Devi, K. Murali Bhaskaran, V. CSE Department Kongu Engineering College Perundurai ErodeTamil Nadu India Dhirajlal Gandhi College of Technology SalemTamil Nadu India

The user generated content on the web grows rapidly in this emergent information age. The tremendous growth of content available in forums, blogs, news reports, etc., are having large volume of public opinion information, it is essential to analyse in time and understands the trends of their opinion correctly. The evolutionary changes in technology make use of such information to capture only the user's essence and finally the useful information are exposed to information seekers. Most of the existing research on text information processing, focus on the factual domain rather than the opinion domain. In this paper, we detect online hotspot forums by computing sentiment analysis for text data available in each forum. This approach analyses the forum text data and computes value for each word of text. The proposed approach combines K-means clustering and support vector machine with PSO (SVM-PSO) classification algorithm that can be used to group the forums into two clusters forming hotspot forums and non-hotspot forums within the current time span. The proposed system accuracy is compared with the other classification algorithms such as Naïve Bayes, decision tree and SVM. The experiment helps to identify that K-means and SVM-PSO together achieve highly consistent results. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Sentiment analysis

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An extended K-Means algorithm using mapreduce framework for mixed datasets

International Journal of Database Theory and Application

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international Journal of Database Theory and Application 2016年第9期9卷 167-176页

作者： Chadha, Anupama Kumar, Suresh Faculty of Computer Applications MRIU Faridabad India Faculty of Engineering and Technology MRIU Faridabad India

K-Means is a famous partition based clustering algorithm. Various extensions of K-Means have been proposed depending on the type of datasets being handled. Popular ones include K-Modes for categorical data and K-Prototype for mixed numerical and categorical data. The K-Means and its extensions suffer from one major limitation that is dependency on prior input of number of clusters K. Sometimes it becomes practically impossible to correctly estimate the optimum number of clusters in advance. Various ways have been suggested in literature to overcome this limitation for numerical data. But for categorical and mixed data work is still in progress. In this paper, we introduce a new algorithm based on the K-Means that takes mixed dataset as an input and generates appropriate number of clusters on the run using MapReduce programming style. The new algorithm not only overcomes the limitation of providing the value of K initially but also reduces the computation time using MapReduce framework. © 2016 SERSC.

关键词： Clustering algorithms

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Otsu multi-thresholding based on DCG3A for medical image

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ICIC Express Letters, Part B: Applications 2016年第8期7卷 1651-1656页

作者： Fang, Zheng Shen, Xuanjing Chen, Haipeng Feng, Yuncong College of Computer Science and Technology Jilin University No. 2699 Qianjin Ave. Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University No. 2699 Qianjin Ave. Changchun130012 China

In order to solve the problem that multi-thresholding segmentation spends too much time finding the optimal solution in medical image segmentation, Otsu multi-thresholding based on dynamic combination of genetic algorithm and ant algorithm (abbreviated as DCG3A-OM) was proposed in this paper. Firstly, the algorithm uses genetic algorithm to generate preliminary partition results, and then converts them into the initial information of ant colony. Finally ant algorithm will approach to optimal values efficiently. While running genetic algorithm, the conditions of combination will be analyzed dynamically to avoid stopping genetic algorithm early or late. Besides, it increases the probability of variation and decreases the numbers of individuals which are calculated for the evolution rate of colony. The experimental results demonstrate that DCG3A-OM gets the optimal solution more quickly and efficiently than the others, and it can always get the optimal values mostly. © 2016 ICIC international.

关键词： Genetic algorithms

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Sharpness of Tire Angle Determination Based on Point Cloud Dataset

Sharpness of Tire Angle Determination Based on Point Cloud D...

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2018 3rd international Conference on Control, Automation and Artificial Intelligence (CAAI 2018)

作者： Xinrong Lyu College of Information and Control Engineering China University of Petroleum(East China)

In the tire production line, it is usually necessary to trim the blank tires to finished tires according to the sharpness of tire angle. If the determination of tire angle is made by mistakes, it will cause the tire to be a waste for choosing a wrong scraper. In order to determine the sharpness of tire angle accurately, point cloud acquisition camera was used to collect the data of tires in the production line. Based on the point cloud data of tires, a framework to detect the sharpness of tire angle was proposed. In the proposed framework, a locating method using image processing algorithms such as morphological operation, connected component labeling and corner point detection was used to locate the tire angle. A ridge detection algorithm including surface normal vector and principal curvature computation was used to determine the type of tire angle. A number of point cloud datasets of different tires experimental results show that the proposed framework has higher accuracy and efficiency to determine whether the tire angle is sharp or round.

关键词： point cloud sharpness tire angle

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Multi-Agent Finite Impulse Response Optimizer for Numerical Optimization Problems

Research Square

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Research Square 2020年

作者： Ab Rahman, Tasiransurini Ab Aziz, Nor Azlina Ibrahim, Zuwairie Aziz, Nor Hidayati Abdul Shapiai, Mohd Ibrahim Suri, Jasjit S. Department of Electronic Engineering Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Johor 86400 Malaysia Faculty of Engineering and Technology Multimedia University Melaka75450 Malaysia College of Engineering Universiti Malaysia Pahang Pahang 26300 Malaysia Malaysia Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia Advance Knowledge Engineering Center Global Biomedical Technologies Inc. RosevilleCA95661 United States

This paper investigates the potential of the ultimate iterative unbiased finite impulse response (UFIR) filter as a source of inspiration in a population-based metaheuristic algorithm. Here, a new algorithm inspired by the measurement and estimation procedures of the UFIR filter named the Multi-Agent Finite Impulse Response Optimizer (MAFIRO) for solving numerical optimization problems is proposed. MAFIRO works with a set of agents where each performs the measurement and estimation to find a solution. MAFIRO employs a random mutation of the best-so-far solution and the shrinking local neighborhood method to balance between the exploration and exploitation phases during the optimization process. Subsequently, the performance of MAFIRO is tested by solving the benchmark test suite of the IEEE congress on Evolutionary computation 2014. The benchmark is composed of 30 mathematical functions. The competency of MAFIRO is compared with the Particle Swarm Optimization algorithm, Genetic Algorithm, and Grey Wolf Optimizer. The results show that MAFIRO leads in 23 out of 30 functions and has the highest Friedman rank. MAFIRO performs significantly better than the other tested algorithms. Based on the findings, we show that the concept of the UFIR filter is a good inspiration for a population-based metaheuristic algorithm. © 2020, CC BY.

关键词： Estimation

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Drug target path discovery on semantic biomedical big data

Drug target path discovery on semantic biomedical big data

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IEEE international Conference on Big Data

作者： Fang Du Ting Li Yingjie Shi Lijuan Song Xiaojun Gu School of Information Engineering Ningxia University Yinchuan China School of mathematics and statistics Ningxia University Yinchuan China School of Information Engineering Beijing Institute Of Fashion Technology Beijing China

ISBN: (纸本)9781467390064

Systems chemical biology integrate chemistry, biology and computation tools as a whole system, which can help researchers to deeply study the interaction and relationship among small molecules, such as genes, proteins, targets, compounds and so on. With systems chemical biology, researchers can concentrate on new way of drug discovery, including drug target path discovery, which can not only help biomedical researchers to find evidences for existing disease associate genes, but also to design new effect medicine based on targets. Network based approaches are the state-of-art solutions for drug target path discovery, however, there are still some challenges: 1) The quality of the network dominate the efficiency and accuracy of the results, therefore a well designed network is quite important on drug target path discovery mission; 2) the existing network based approaches only work on small graph, it can not handle massive data well. In the paper, we designed a novel framework of systems chemical biology based on semantic big data. In the paper, we proposed a novel drug target path discovery approach. It can identify targets associated with specific medicines (disease) and the path of relationship based on a RDF semantic D-T network. The ranking of candidate targets is performed through an improved parallel random walk with restart algorithm. The experimental studies show that the proposed approaches can efficiently discover drug target relationship path, meanwhile, the approaches have good scalability which are suitable for big data analysis.

关键词： Decision support systems Big data Semantics Drugs Conferences Buildings Distributed algorithms

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Securing broker-less publisher/subscriber systems using cryptographic technique

Securing broker-less publisher/subscriber systems using cryp...

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international Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Shilpa Shitole A. D. Gujar TSSM'S Bhivarabai Sawant College of Engineering and Research Narhe Pune India TSSM'S Bhivarabai Sawant College of Engineering And Research Narhe Pune India

ISBN: (纸本)9781509032921

A publisher-subscriber framework is generally develop on join network architecture. This framework allows authorized data distribution among subscribers through publishers. This is an open overlay network. It has some security issues including integrity and confidentiality, authorization, validation etc. Also there is another problem regarding this network, such as Events secrecy and subscriptions clashes with content based routing. Such security issues are very difficult to predict and resolve. To increase the confirmation and secrecy of broker, this paper introduced the concept of broker less content based publish/subscribe system and also developed a novel technique to improve the same. In this proposed system, subscribers are allowed to view and publish the information if and only if publishers want to share their information. Additionally, to make secure information sharing, only data in encrypted format are shared among subscriber and publishers. For this purpose a more secure encryption algorithm i.e. ECC algorithm is used. Therefore this proposed system is more secure and safe. As ECC utilizes small key size for encryption and decryption, this system takes less computation time and consumes less memory. Different from existing system which can only publish the articles, this proposed system also support for dynamic news or article update with publishing it. After this, to increase the performance of system, cloud services are used. Because of this the performance of system is improved in terms of efficiency, security, data availability and low cost. Also system detect the man in middle attack and replace attack which occurred at the time of data upload and download in the cloud and improve the performance.

关键词： Indexes Elliptic curve cryptography Heuristic algorithms Encryption

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