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Proving tree algorithms for succinct data structures 10

Proving tree algorithms for succinct data structures

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10th international Conference on Interactive theorem Proving, ITP 2019

作者： Affeldt, Reynald Garrigue, Jacques Qi, Xuanrui Tanaka, Kazunari National Institute of Advanced Industrial Science and Technology Tsukuba Japan Graduate School of Mathematics Nagoya University Japan Department of Computer Science Tufts University MedfordMA United States

ISBN: (纸本)9783959771221

Succinct data structures give space-efficient representations of large amounts of data without sacrificing performance. they rely on cleverly designed data representations and algorithms. We present here the formalization in Coq/SSReflect of two different tree-based succinct representations and their accompanying algorithms. One is the Level-Order Unary Degree Sequence, which encodes the structure of a tree in breadth-first order as a sequence of bits, where access operations can be defined in terms of Rank and Select, which work in constant time for static bit sequences. the other represents dynamic bit sequences as binary balanced trees, where Rank and Select present a low logarithmic overhead compared to their static versions, and with efficient insertion and deletion. the two can be stacked to provide a dynamic representation of dictionaries for instance. While both representations are well-known, we believe this to be their first formalization and a needed step towards provably-safe implementations of big data. © Reynald Affeldt, Jacques Garrigue, Xuanrui Qi, and Kazunari Tanaka.

关键词： Binary trees

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Research on Network Intrusion Detection Based on Support Vector Machine Optimized with Grasshopper Optimization Algorithm 10

Research on Network Intrusion Detection Based on Support Vec...

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10th IEEE international Conference on Intelligent data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Ye, Zhiwei Sun, Yiheng Sun, Shuang Zhan, Sikai Yu, Han Yao, Quanfeng Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China Fujian Prov Key Lab Data Intens Comp Quanzhou Peoples R China Fujian Prov Univ Key Lab Intelligent Comp & Informat Proc Fuzhou Peoples R China Wuhan FiberHome Tech Serv Co Ltd Wuhan Peoples R China

ISBN: (纸本)9781728140698

As one of the most important parts of network security, more significance is attached to intrusion detection system (IDS). Numerous techniques including support vector machine (SVM) have been applied to the intrusion detection. However, many methods are utilized to improve the original SVM whose performance is markedly depended on its kernel parameters. Evolutionary algorithms such as genetic algorithm (GA) and particle swarm algorithm (PSO) are also employed to search better kernel parameters while the traditional optimization methods are vulnerable to fall into local minima with slow speed of convergence. In order to improve the precision of SVM in intrusion detection, the support vector machine based on grasshopper optimization algorithm (GOA-SVM) is proposed in the paper. To verify the practicality of the proposed method, several contrast experiments have been carried out using tool of Matlab. the experimental results finally demonstrates the superior performance of the proposed method on intrusion detection.

关键词： component intrusion detection support vector machine grasshopper optimization algorithm

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Image Segmentation Using Fuzzy C-means Optimized by Ant Lion Optimization 10

Image Segmentation Using Fuzzy C-means Optimized by Ant Lion...

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10th IEEE international Conference on Intelligent data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Jin, Can Ye, Zhiwei Yan, Lingyu Cao, Ye Zhang, Aixin Ma, Lie Hu, Xiang Hu, Jiwei Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China Wuhan Jingzhi Technol Co Ltd Wuhan Hubei Peoples R China Wuhan FiberHome Tech Serv Co Ltd Wuhan Peoples R China Fujian Prov Univ Fujian Prov Key Lab Data Intens Comp Fuzhou Peoples R China Fujian Prov Univ Key Lab Intelligent Comp & Informat Proc Fuzhou Peoples R China

ISBN: (纸本)9781728140698

Image segmentation is the indispensable part in the field of computer vision. there are tremendous methods for handling this task such as Otsu-thresholding and Fuzzy C-means (FCM). However, the segmentation results of the images are occasionally unsatisfactory due to the presence of noise. In this paper, two kinds of spatial information consisting of the relative position information and the intensity information of the neighborhood pixels in an image are taken into consideration in constructing the objective function in FCM. Moreover, Ant Lion Optimization, one of the recently proposed optimization algorithms is utilized to optimize the relevant index. Bio-inspired ALO has the robust ability to find optimal parameters in search spaces. So the proposed approach to image segmentation based on Fuzzy C-Means (FCM) and Ant Lion Optimization (ALO) may alleviate this problem to a certain degree. A series of experimental validation has been implemented for demonstrating the performance of the proposed approach in the end of the paper.

关键词： Image segmentation Fuzzy c-means spatial information Ant lion optimization

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A Learnable-by-Design (LEAD) Model for Designing Experiments for Computer Science Labs 10

A Learnable-by-Design (LEAD) Model for Designing Experiments...

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10th IEEE international Conference on Technology for Education, T4E 2019

作者： Kumar, Mrityunjay Choppella, Venkatesh Sunil, Sanjana Syed, Sumaid IIIT Hyderabad Hyderabad India

ISBN: (纸本)9781728142272

India graduates 1.5 million engineering students every year, majority of them from its Tier 2 and Tier 3 colleges where there is severe shortage of qualified faculty and lab infrastructure, and where English is second or third language for most students. To provide affordable virtual laboratories to these engineering colleges, Government of India runs the Virtual Labs project with a select set of participating institutions to create virtual labs required for entire engineering student population of India. the program suffers from two problems: 1) lack of focus on pedagogy and learnability of labs being created, and 2) little ability to scale to more teachers and subject matter experts to produce labs aligned to local contexts. this paper presents a model (LEAD model) for the design of virtual labs for a data structures and algorithms course in computer science and is intended to address the problems stated above. the model encodes the principles of learning in the design and structure of an experiment so that labs can be learnable by design. the model is based on constructivist theory of learning. It uses Bloom's Taxonomy for defining learning objectives and applies Merrill's first principles and Gagne's 9 events of instruction as instructional design methodologies. To ensure tasks in an experiment indeed aid learning, the model requires a mapping between tasks and learning principles to be created and published along with the experiment. the model uses a pedagogy that lays strong emphasis on conceptual understanding without the use of code or program. To demonstrate the efficacy of this model, we present the data analysis of the feedback from early users of one the experiments. Even though limited in scope, data shows that experiments built using LEAD model can aid understanding for the students. © 2019 IEEE.

关键词： data structures

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Intellectual analysis and basic modeling of complex threats 10

Intellectual analysis and basic modeling of complex threats

引用

10th Anniversary international Scientific and Technical Conference on Secure Information Technologies, BIT 2019

作者： Korneev, Nikolai Merkulov, Vyacheslav Faculty of Integrated Security of Fuel and Energy Complex Gubkin Russian State University of Oil and Gas National Research University Moscow Russia Department of Data Analysis Decision-Making and Financial Technology Financial University under the Government of the Russian Federation Moscow Russia

the paper describes the basic principles of complex threats modeling, and the task of complex threats detection is formalized. the proposed modeling principles are based on the idea of identifying the links between elementary threats as part of a complex one. As an example, the process of constructing a complex threat model based on the proposed modeling rules is given. Based on the examples presented in the work, the paper includes the description of tasks while working with complex threats: the tasks of complex threats detection, the identification of their inner structure and purposes of the implementation. Based on the formulated principles of basic modeling, the paper also gives a formal statement of complex threats detection problem, which explains the possibility for applying data mining algorithms and big data processing technologies in the construction of protection systems against complex threats and developing the neurographic theory of complex security. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0)

关键词： Big data

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Estimation of the delamination size of carbon fiber panels from the impact sound 12

Estimation of the delamination size of carbon fiber panels f...

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12th international workshop on Structural Health Monitoring: Enabling Intelligent Life-Cycle Health Management for Industry Internet of things (IIOT), IWSHM 2019

作者： Uriarte, Patrick Shah, Kumar Y. Tansel, Ibrahim Etienne, Kayla Florida International University Mechanical and Materials Engineering Department 10555 W. Flagler St. MiamiFL33174 United States

ISBN: (纸本)9781605956015

Large composite panels of airplanes are regularly inspected, and delaminated sections are repaired. Experienced technicians can detect the delaminated sections of composite panels from the sound created from a small impact. In this study, performance of computerized analysis for impact sound was evaluated for composite panels at different delamination levels. the impact sound of carbon fiber panels without any defect and panels with simulated delaminated sections were recorded. the recorded audio data were processed and interpreted. Fast Fourier Transformation was used for converting the audio data into the frequency (spectrum) or time-frequency domain (spectrogram). Neural networks were used for the identification of carbon fiber panels. three different approaches were used for encoding the data before they were used for the training and testing of the neural networks. the simulated delamination sizes of the panels were 5mm X 5mm, 10 mm X 10 mm, 20 mm X 20 mm and 30 mm X 30 mm. the Levenberg-Marquardt neural network algorithm provided the most consistent estimation compared to the Gradient Descent with Momentum, Fletcher Reeves and Resilient Backpropagation algorithms. During test cases, the Levenberg-Marquardt neural network algorithm identified a carbon fiber panel's delamination level with lower than 10% error. the study indicated that the audio data from the sound of impact could be used for the identification of delamination severity in composite structures. © international workshop on Structural Health Monitoring. All rights reserved.

关键词： Structural health monitoring

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Clustering Alexa Internet data using Auto Encoder Network and Affinity Propagation

Clustering Alexa Internet Data using Auto Encoder Network an...

引用

international eConference on Computer and Knowledge Engineering (ICCKE)

作者： Ali Risheh Ashkan Goharfar Nastooh Taheri Javan Artificial Intelligence Laboratory AmirKabir university Tehran Iran

ISBN: (数字)9781728185668

ISBN: (纸本)9781728185675

Non-linear mapping is one of the most popular solutions for complex data structures and distinct patterns to cluster data. Auto encoder Networks (AENs) are widely used in clustering as they improve data representation. In this paper, we collect *** data by crawling popular websites profiles, where dataset has 84 columns with type number and array of words. Next, an AEN architecture is presented to identify specific websites with exceptional patterns and the encoded data expresses new feature space of our original data. (Our) Encoded data is clustered by Affinity Propagation which is a partitioning algorithm without the need for specifying the number of clusters. there are important results based on 194 clusters and exemplars which are filtered and analyzed. One remarkable fact about results is that the first 11 columns as raw data are not clustered by Affinity Propagation a which re considered all as outlier. the results are summarized by selecting the best option w.r.t. the statistics and charts. Some of the obtained results are useful for website owners and provide some suggestions and solutions for Search Engine Optimization (SEO). Finally, we propose our crawler application which crawls and records data over 4 days. It must be added that the proposed web crawler faces challenges and their solutions can be helpful in most commonly used web crawling algorithms and libraries.

关键词： Clustering algorithms Feature extraction Search engines Crawlers Optimization Internet Encoding

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Make evolutionary multiobjective algorithms scale better with advanced data structures: Van emde boas tree for non-dominated sorting 10th

Make evolutionary multiobjective algorithms scale better wit...

引用

10th international Conference on Evolutionary Multi-Criterion Optimization, EMO 2019

作者： Buzdalov, Maxim ITMO University 49 Kronverkskiy Avenue Saint Petersburg197101 Russia

ISBN: (纸本)9783030125974

We improve the worst-case time complexity of non-dominated sorting, an operation frequently used in evolutionary multiobjective algorithms, to O(n·(log n)k-2 log log n), where n is the number of solutions, k is the number of objectives, and the random-access memory computation model is assumed. this improvement was possible thanks to the van Emde Boas tree, an "advanced" data structure which stores a set of non-negative integers less than n and supports many queries in O(log log n). this is not only a theoretical improvement, as we also provide an efficient implementation of the van Emde Boas tree, which resulted in a competitive algorithm that scales better than other algorithms when n grows, at least for small numbers of objectives greater than two. © Springer Nature Switzerland AG 2019.

关键词： Forestry

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Research On Short-term Electric Load Forecast Based On Grey Neural Network and Snap-drift Cuckoo Search Algorithm 10

Research On Short-term Electric Load Forecast Based On Grey ...

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10th IEEE international Conference on Intelligent data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Chen, Feng Ye, Zhiwei Su, Jun Lang, Haofeng Shi, Xiaoxiao Wang, Shuqing Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China Fujian Prov Univ Fujian Prov Key Lab Data Intens Comp Fuzhou Fujian Peoples R China Fujian Prov Univ Key Lab Intelligent Comp & Informat Proc Fuzhou Fujian Peoples R China

ISBN: (纸本)9781728140698

In order to provide users with reliable and qualified power, it becomes an indispensable task to enhance the forecasting capability of the short-term power load. However, the existing approaches of short-term electric load forecasting are not proper enough. A short-term electric load forecasting method based on grey neural network based on snap-drift cuckoo search optimization algorithm(SDCS-GNN) is proposed in this paper. Parameters of gray neural network (GNN) are selected randomly which is similar to the initial spatial position of birds' eggs in the parasitic nest of cuckoo. the SDCS is utilized to search the better weight and threshold of the conventional gray neural network (GNN), which improves the stability and accuracy of the prediction model. To validate the superior performance of the proposed method, several well-known evolutionary algorithms such as particle swarm optimization (PSO), grey wolf optimization(GWO), moth-fire suppression optimization(MFO) and cuckoo search optimization (CS) are employed to constitute the contrast experiment of the prediction of short-term power load. the mean squared error predicted by the SDCS-GNN model is the smallest, which compared with GNN, PSO-GNN, GWO-GNN, MFO-GNN, and CS-GNN is 0.36, 1.79, 15.23, 4.53, 2.93, respectively. the Average prediction accuracy of SDCS-GNN model is better than other models which is 7.1592, 1.427, 15.1516, 11.5438, 10.5202, respectively. the simulation results show that the SDCS-GNN model has better approximation ability and higher prediction accuracy than the conventional GNN and other evolutionary algorithms in the short-term electric load forecasting. the experiments above indicates that the prediction method is effective and feasible.

关键词： Grey neural network(GNN) Snap-drift cuckoo search optimization algorithm(SDCS) Short-term power load prediction

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workshop on Microbiomics, Metagenomics, and Metabolomics 19

Workshop on Microbiomics, Metagenomics, and Metabolomics

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10th ACM international Conference on Bioinformatics, Computational Biology and Health Informatics (ACM-BCB)

作者： Hassoun, Soha El-Manzalawy, Yasser Gerber, Georg Koslicki, David Rosen, Gail Tufts Univ Dept Comp Sci Medford MA 02155 USA Geisinger Hlth Syst Dept Biomed & Translat Informat Danville PA USA Harvard Med Sch Dept Pathol Boston MA 02115 USA Penn State Univ Comp Sci & Engn Biol State Coll PA USA Drexel Univ Dept Elect & Comp Engn Philadelphia PA 19104 USA

ISBN: (纸本)9781450366663

the past decade has witnessed a revolution in microbiology through the advent microbiomics: investigating entire microbial ecosystems in and around us as opposed to studying single microbes in isolation. Recent technological advances in high throughput sequencing technology has enabled these studies, significantly improving our capacity to explore the composition and dynamics of these microbial communities. A key goal of microbiome research is defining the structure of and functional relationships between various organisms constituting microbiota under different conditions (e.g., health and disease states and/or environmental factors). these efforts stand to significantly improve human and environmental health since many physical processes are regulated by or interact with microbial organisms. Large scale collaborative efforts in this direction, such as the Human Microbiome Project and the Earth Microbiome Project, have generated unprecedented amounts of metagenomic data. To decipher this resulting data, novel computational algorithms and tools are rapidly being developed to identify organisms (taxonomic classification of microbiome sequences), assemble bacterial genomes from these in situ experiments (metagenome assembly), functionally and metabolically profile microbiomes, and identify taxonomic and functional biomarkers. Against this background, the main goal of this workshop is to highlight recent advances in computational methods for metagenomics and metabolomics and to discuss remaining key challenges and promising future research directions. Topics include methods to identify microbes and their functions, measure similarity of samples, identify biomarkers, provide functional profiling, and enable efficient real-time analysis of ubiquitous microbiome samples and studies.

关键词： Microbiomics Metagenomics Metabolomics

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