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The document describes a new method called SITAd for scalable similarity search of molecular descriptors in large databases. SITAd uses two techniques: 1) database partitioning to limit the search space, and 2) converting similarity search to inner product search. It builds a wavelet tree to efficiently solve the inner product search problem. Experiments on a database of 42 million compounds showed that SITAd was over 100 times faster than alternative inverted index methods while using less memory.

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Masters Thesis Defense Presentation

This document presents a methodology for applying text mining techniques to SQL query logs from the Sloan Digital Sky Survey (SDSS) SkyServer database. The methodology involves parsing, cleaning, and tokenizing SQL queries to represent them as feature vectors that can be analyzed using data mining algorithms. Experimental results demonstrate clustering SQL queries using fuzzy c-means clustering and visualizing relationships between queries using self-organizing maps. The methodology is intended to provide insights into database usage patterns from analysis of the SQL query logs.

Fast Single-pass K-means Clusterting at Oxford

This document describes fast single-pass k-means clustering algorithms. It discusses the rationale for using k-means clustering to enable fast search over large datasets. The document outlines ball k-means and surrogate clustering algorithms that can cluster data in a single pass. It discusses how these algorithms work and their implementation, including using locality sensitive hashing and projection searches to speed up clustering over high-dimensional data. Evaluation results show these algorithms can accurately cluster data much faster than traditional k-means approaches. The applications of these fast clustering algorithms include enabling fast nearest neighbor searches over large customer datasets for applications like marketing and fraud prevention.

Comparative Analysis of Algorithms for Single Source Shortest Path Problem

The single source shortest path problem is one of the most studied problem in algorithmic graph theory. Single Source Shortest Path is the problem in which we have to find shortest paths from a source vertex v to all other vertices in the graph. A number of algorithms have been proposed for this problem. Most of the algorithms for this problem have evolved around the Dijkstra’s algorithm. In this paper, we are going to do comparative analysis of some of the algorithms to solve this problem. The algorithms discussed in this paper are- Thorup’s algorithm, augmented shortest path, adjacent node algorithm, a heuristic genetic algorithm, an improved faster version of the Dijkstra’s algorithm and a graph partitioning based algorithm.

Dmdw1

This document contains four exam papers for a Data Warehousing and Data Mining course. Each paper contains 8 questions with sub-questions worth varying points. The questions cover topics such as data mining processes, differences between operational databases and data warehouses, data transformation techniques, data mining query languages, classification algorithms like naive Bayes and decision trees, clustering methods, and mining time-series, text and web data.

Representing and Querying Geospatial Information in the Semantic Web

The document discusses representing and querying geospatial information in the semantic web. It introduces stRDF, an extension of RDF that adds spatial literals and valid time to triples. It also introduces stSPARQL, an extension of SPARQL with functions for querying spatial data based on Open Geospatial Consortium standards. The document describes the Strabon system, which uses stRDF and supports both stSPARQL and the OGC standard GeoSPARQL for querying geospatial data stored in RDF graphs.

Building Scalable Semantic Geospatial RDF Stores

This document outlines a model called stRDF for representing geospatial and temporal data in RDF, along with a query language called stSPARQL. It also describes Strabon, a scalable geospatial RDF store for storing and querying stRDF data. Strabon extends the Semantic Web toolkit Sesame and uses PostGIS for geospatial indexing and functions. The document evaluates Strabon's performance against Sesame on geospatial linked data and synthetic datasets. Finally, it discusses other extensions like the RDFi framework for representing data with incomplete information.

Applications of datastructures

This document discusses data structures for priority queues and binomial heaps. It begins with an overview of priority queue structures like heaps and their common operations. It then discusses implementing a binary heap using an array, with operations like insert, delete, and change in O(log n) time. Binary heaps also enable heapsort in O(n log n) time. The document next covers binomial trees and binomial heaps, which support union in O(log n) time through merging trees of the same order. Overall, the document provides an in-depth overview of priority queue data structures and their applications.

Encoding survey

This document discusses encoding data structures to answer range maximum queries (RMQs) in an optimal way. It describes how the shape of the Cartesian tree of an array A can be encoded in 2n bits to answer RMQ queries, returning the index of the maximum element rather than its value. It also discusses encodings for other problems like nearest larger values, range selection, and others. Many of these encodings use asymptotically optimal space of roughly n log k bits for an input of size n with parameter k.

Masters Thesis Defense Presentation

This document presents a methodology for applying text mining techniques to SQL query logs from the Sloan Digital Sky Survey (SDSS) SkyServer database. The methodology involves parsing, cleaning, and tokenizing SQL queries to represent them as feature vectors that can be analyzed using data mining algorithms. Experimental results demonstrate clustering SQL queries using fuzzy c-means clustering and visualizing relationships between queries using self-organizing maps. The methodology is intended to provide insights into database usage patterns from analysis of the SQL query logs.

Fast Single-pass K-means Clusterting at Oxford

This document describes fast single-pass k-means clustering algorithms. It discusses the rationale for using k-means clustering to enable fast search over large datasets. The document outlines ball k-means and surrogate clustering algorithms that can cluster data in a single pass. It discusses how these algorithms work and their implementation, including using locality sensitive hashing and projection searches to speed up clustering over high-dimensional data. Evaluation results show these algorithms can accurately cluster data much faster than traditional k-means approaches. The applications of these fast clustering algorithms include enabling fast nearest neighbor searches over large customer datasets for applications like marketing and fraud prevention.

Comparative Analysis of Algorithms for Single Source Shortest Path Problem

The single source shortest path problem is one of the most studied problem in algorithmic graph theory. Single Source Shortest Path is the problem in which we have to find shortest paths from a source vertex v to all other vertices in the graph. A number of algorithms have been proposed for this problem. Most of the algorithms for this problem have evolved around the Dijkstra’s algorithm. In this paper, we are going to do comparative analysis of some of the algorithms to solve this problem. The algorithms discussed in this paper are- Thorup’s algorithm, augmented shortest path, adjacent node algorithm, a heuristic genetic algorithm, an improved faster version of the Dijkstra’s algorithm and a graph partitioning based algorithm.

Dmdw1

This document contains four exam papers for a Data Warehousing and Data Mining course. Each paper contains 8 questions with sub-questions worth varying points. The questions cover topics such as data mining processes, differences between operational databases and data warehouses, data transformation techniques, data mining query languages, classification algorithms like naive Bayes and decision trees, clustering methods, and mining time-series, text and web data.

Representing and Querying Geospatial Information in the Semantic Web

The document discusses representing and querying geospatial information in the semantic web. It introduces stRDF, an extension of RDF that adds spatial literals and valid time to triples. It also introduces stSPARQL, an extension of SPARQL with functions for querying spatial data based on Open Geospatial Consortium standards. The document describes the Strabon system, which uses stRDF and supports both stSPARQL and the OGC standard GeoSPARQL for querying geospatial data stored in RDF graphs.

Building Scalable Semantic Geospatial RDF Stores

This document outlines a model called stRDF for representing geospatial and temporal data in RDF, along with a query language called stSPARQL. It also describes Strabon, a scalable geospatial RDF store for storing and querying stRDF data. Strabon extends the Semantic Web toolkit Sesame and uses PostGIS for geospatial indexing and functions. The document evaluates Strabon's performance against Sesame on geospatial linked data and synthetic datasets. Finally, it discusses other extensions like the RDFi framework for representing data with incomplete information.

Applications of datastructures

This document discusses data structures for priority queues and binomial heaps. It begins with an overview of priority queue structures like heaps and their common operations. It then discusses implementing a binary heap using an array, with operations like insert, delete, and change in O(log n) time. Binary heaps also enable heapsort in O(n log n) time. The document next covers binomial trees and binomial heaps, which support union in O(log n) time through merging trees of the same order. Overall, the document provides an in-depth overview of priority queue data structures and their applications.

Encoding survey

This document discusses encoding data structures to answer range maximum queries (RMQs) in an optimal way. It describes how the shape of the Cartesian tree of an array A can be encoded in 2n bits to answer RMQ queries, returning the index of the maximum element rather than its value. It also discusses encodings for other problems like nearest larger values, range selection, and others. Many of these encodings use asymptotically optimal space of roughly n log k bits for an input of size n with parameter k.

LAP2009 c&p101-vector2 d.5ht

This document contains 6 practice problems about vectors in R2 from a Calculus & Physics 101 course. The problems cover topics like finding the sum and scalar multiples of vectors, sketching triangles defined by vectors, calculating work done using dot products of force and displacement vectors, and using differentiation and integration to calculate work as a function of time or position when force is variable. The document provides space for showing work and includes teacher notes on vectors in R2 and the dot product from a PreCalculus textbook.

Introduction to Ultra-succinct representation of ordered trees with applications

The document summarizes a paper on ultra-succinct representations of ordered trees. It introduces tree degree entropy, a new measure of information in trees. It presents a succinct data structure that uses nH*(T) + O(n log log n / log n) bits to represent an ordered tree T with n nodes, where H*(T) is the tree degree entropy. This representation supports computing consecutive bits of the tree's DFUDS representation in constant time. It also supports computing operations like lowest common ancestor, depth, and level-ancestor in constant time using an auxiliary structure of O(n(log log n)2 / log n) bits.

Exploring temporal graph data with Python:
a study on tensor decomposition o...

Tensor decompositions have gained a steadily increasing popularity in data mining applications. Data sources from sensor networks and Internet-of-Things applications promise a wealth of interaction data that can be naturally represented as multidimensional structures such as tensors. For example, time-varying social networks collected from wearable proximity sensors can be represented as 3-way tensors. By representing this data as tensors, we can use tensor decomposition to extract community structures with their structural and temporal signatures.
The current standard framework for working with tensors, however, is Matlab. We will show how tensor decompositions can be carried out using Python, how to obtain latent components and how they can be interpreted, and what are some applications of this technique in the academy and industry. We will see a use case where a Python implementation of tensor decomposition is applied to a dataset that describes social interactions of people, collected using the SocioPatterns platform. This platform was deployed in different settings such as conferences, schools and hospitals, in order to support mathematical modelling and simulation of airborne infectious diseases. Tensor decomposition has been used in these scenarios to solve different types of problems: it can be used for data cleaning, where time-varying graph anomalies can be identified and removed from data; it can also be used to assess the impact of latent components in the spreading of a disease, and to devise intervention strategies that are able to reduce the number of infection cases in a school or hospital. These are just a few examples that show the potential of this technique in data mining and machine learning applications.

Scalable Link Discovery for Modern Data-Driven Applications

"Scalable Link Discovery for Modern Data-Driven Applications" as presented in the 15th International Semantic Web Conference ISWC, Doctoral Consortium, October 18th, 2016, held in Kobe, Japan
This work was supported by grants from the EU H2020 Framework Programme provided for the project HOBBIT (GA no. 688227).

Some fixed point and common fixed point theorems of integral

The International Institute for Science, Technology and Education (IISTE). Science, Technology and Medicine Journals Call for Academic Manuscripts

Document clustering for forensic analysis

This document presents an approach for using document clustering algorithms to improve forensic analysis of seized computers. It discusses the limitations of existing approaches and proposes using algorithms like K-means and hierarchical clustering to group related documents without predefining the number of clusters. The system architecture involves preprocessing documents, calculating similarity, forming clusters, and evaluating results. Modules include preprocessing, calculating the number of clusters, clustering techniques, and removing outliers. The approach aims to enhance computer inspection by grouping relevant documents for experts to examine.

Clustering techniques

The document discusses clustering techniques and provides details about the k-means clustering algorithm. It begins with an introduction to clustering and lists different clustering techniques. It then describes the k-means algorithm in detail, including how it works, the steps involved, and provides an example illustration. Finally, it discusses comments on the k-means algorithm, focusing on aspects like choosing the value of k, initializing cluster centroids, and different distance measurement methods.

ThreeTen

This document discusses the ThreeTen library, which provides a replacement for the Java date and time API. It notes issues with the existing Calendar and Date classes, such as mutability and difficulty testing. ThreeTen addresses these by providing immutable classes like LocalDate and LocalTime, avoiding nulls, and making testing easier. The document outlines ThreeTen's API, how to convert between it and Date, and how to integrate it with Kotlin using operator overloading and extensions. It emphasizes conventions like using plus and minus for addition/subtraction of temporal amounts.

Representing Documents and Queries as Sets of Word Embedded Vectors for Infor...

1) The document presents a method to represent documents and queries as sets of word embeddings for information retrieval. It uses word embeddings to create a "Bag of Vectors" representation of documents and queries.
2) Documents are modeled as mixtures of Gaussian distributions centered around the word embeddings. Queries are represented as posterior likelihoods over these Gaussian mixtures.
3) The method is evaluated on several TREC datasets, showing improved retrieval performance over the standard language modeling approach on some datasets, particularly when using k-means clustering to assign words to Gaussian mixtures. The best performance was achieved with 100 Gaussian mixtures.

From Changes to Dynamics: Dynamics Analysis of Linked Open Data Sources

Presentation at PROFILES 2014 workshop (co-located with ESWC) on measuring the dynamics of linked data sources.

Applicationof datastructures

The document discusses various data structures used to implement priority queues, including binary heaps and binomial heaps. It describes how each structure can be implemented using an array and the time complexities of common operations like insertion, deletion, finding the minimum element, etc. It also provides an example of how binary heaps can be used to implement Dijkstra's algorithm for finding the shortest paths from a single source vertex in a graph.

Document clustering and classification

محاضرة ألقيتها ضمن برنامج السيمينار الذي نفذه قسم علوم الحاسوب وتكنولوجيا المعلومات في الكلية الجامعية للعلوم والتكنولوجيا عام 2012

Sortsearch

This document discusses algorithms for sorting and searching data. It introduces basic data structures like arrays and linked lists. Different sorting algorithms are described like insertion sort, shell sort, and quicksort. Dictionaries that allow efficient insertion, search and deletion are also covered, including hash tables, binary search trees, red-black trees, and skip lists. The document provides pseudocode for the algorithms and estimates their time complexity using Big O notation. Source code implementations of the algorithms in C and Visual Basic are available for download.

On clusteredsteinertree slide-ver 1.1

This document describes a genetic algorithm called PGA for solving the clustered Steiner tree problem (CluSteiner). The CluSteiner problem involves finding the minimum cost tree that connects target vertices while satisfying constraints that trees within each cluster are disjoint. PGA uses a two-level approach, first finding local trees for each cluster and then linking the trees. It represents solutions as an ordering of clusters and applies crossover and mutation genetic operators. Computational experiments show PGA improves on previous algorithms by up to 83% on test instances.

Au4201315330

International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.

Graph Based Clustering

The document discusses graph-based clustering methods. It describes how graphs can be used to represent real-world networks from domains like biology, technology, social networks, and economics. It introduces the idea of using minimal spanning trees and hierarchical clustering to identify clusters in graph data. Two common algorithms for finding minimal spanning trees are described: Prim's algorithm and Kruskal's algorithm. Different strategies for iteratively deleting branches from the minimal spanning tree are also summarized to form clusters, such as deleting the branch with the maximum weight or inconsistent branches based on a reference value.

Clustering on database systems rkm

This document discusses clustering algorithms for large datasets that do not fit into main memory. It introduces the Relational K-Means (RKM) algorithm, which limits disk I/O by assigning data points in batches and updating cluster centroids after only 3 iterations. RKM stores cluster assignment and centroid data in matrices on disk and minimizes I/O by accessing matrix rows sequentially. An evaluation shows RKM outperforms standard K-means on large datasets due to its ability to handle data that does not fit in memory through efficient disk access. However, RKM does not address all limitations of K-means clustering.

Pengantar dasar matematika 4 (TURUNAAN FUNGSI)

Integral fungsi tersebut adalah:
a. ∫(1-2x)dx = x - x^2 + C
b. ∫(2x)dx = x^2 + C
Penjelasan:
a. Fungsi (1-2x) merupakan fungsi polinomial derajat satu. Integral fungsi polinomial derajat satu adalah fungsi polinomial derajat dua dibagi dua, ditambah konstanta.
b. Fungsi 2x merupakan fungsi polinomial derajat satu. Integral fungsi polinomial derajat satu

IR-ranking

The document proposes an automated approach for ranking tuples in the results of SQL queries over databases. It computes global and conditional scores for tuples based on attribute correlations learned from past query workloads and data statistics. At query time, it merges pre-computed ranked lists corresponding to the query attributes to efficiently retrieve the top-k results without a full table scan. Experiments on real datasets show the approach is efficient and provides high quality rankings preferred by users over alternative methods.

Profiling in Python

Profiling in Python provides concise summaries of key profiling tools in 3 sentences:
cProfile and line_profiler profile execution time and identify slow lines of code. memory_profiler profiles memory usage with line-by-line or time-based outputs. YEP extends profiling to compiled C/C++ extensions like Cython modules, which are not covered by the standard Python profilers.

Gwt presen alsip-20111201

The document describes using a wavelet tree data structure to enable fast similarity searches of massive graph databases. A Weisfeiler-Lehman procedure is used to represent graphs as bags-of-words. The wavelet tree indexes these bags-of-words and allows semi-conjunctive queries to find graphs sharing a minimum number of words with a query graph in sublinear time. Experiments on 25 million molecular graphs showed the approach significantly outperformed inverted indexes in search time and memory usage.

Gwt sdm public

(1) The document describes a method for efficient similarity search in massive graph databases using wavelet trees. (2) It converts graphs into bags-of-words representations using the Weisfeiler-Lehman procedure and indexes the words with a wavelet tree to enable fast semi-conjunctive queries. (3) Experiments on 25 million chemical compounds showed the method was significantly faster than alternative approaches while using less memory.

LAP2009 c&p101-vector2 d.5ht

This document contains 6 practice problems about vectors in R2 from a Calculus & Physics 101 course. The problems cover topics like finding the sum and scalar multiples of vectors, sketching triangles defined by vectors, calculating work done using dot products of force and displacement vectors, and using differentiation and integration to calculate work as a function of time or position when force is variable. The document provides space for showing work and includes teacher notes on vectors in R2 and the dot product from a PreCalculus textbook.

Introduction to Ultra-succinct representation of ordered trees with applications

The document summarizes a paper on ultra-succinct representations of ordered trees. It introduces tree degree entropy, a new measure of information in trees. It presents a succinct data structure that uses nH*(T) + O(n log log n / log n) bits to represent an ordered tree T with n nodes, where H*(T) is the tree degree entropy. This representation supports computing consecutive bits of the tree's DFUDS representation in constant time. It also supports computing operations like lowest common ancestor, depth, and level-ancestor in constant time using an auxiliary structure of O(n(log log n)2 / log n) bits.

Exploring temporal graph data with Python:
a study on tensor decomposition o...

Tensor decompositions have gained a steadily increasing popularity in data mining applications. Data sources from sensor networks and Internet-of-Things applications promise a wealth of interaction data that can be naturally represented as multidimensional structures such as tensors. For example, time-varying social networks collected from wearable proximity sensors can be represented as 3-way tensors. By representing this data as tensors, we can use tensor decomposition to extract community structures with their structural and temporal signatures.
The current standard framework for working with tensors, however, is Matlab. We will show how tensor decompositions can be carried out using Python, how to obtain latent components and how they can be interpreted, and what are some applications of this technique in the academy and industry. We will see a use case where a Python implementation of tensor decomposition is applied to a dataset that describes social interactions of people, collected using the SocioPatterns platform. This platform was deployed in different settings such as conferences, schools and hospitals, in order to support mathematical modelling and simulation of airborne infectious diseases. Tensor decomposition has been used in these scenarios to solve different types of problems: it can be used for data cleaning, where time-varying graph anomalies can be identified and removed from data; it can also be used to assess the impact of latent components in the spreading of a disease, and to devise intervention strategies that are able to reduce the number of infection cases in a school or hospital. These are just a few examples that show the potential of this technique in data mining and machine learning applications.

Scalable Link Discovery for Modern Data-Driven Applications

"Scalable Link Discovery for Modern Data-Driven Applications" as presented in the 15th International Semantic Web Conference ISWC, Doctoral Consortium, October 18th, 2016, held in Kobe, Japan
This work was supported by grants from the EU H2020 Framework Programme provided for the project HOBBIT (GA no. 688227).

Some fixed point and common fixed point theorems of integral

The International Institute for Science, Technology and Education (IISTE). Science, Technology and Medicine Journals Call for Academic Manuscripts

Document clustering for forensic analysis

This document presents an approach for using document clustering algorithms to improve forensic analysis of seized computers. It discusses the limitations of existing approaches and proposes using algorithms like K-means and hierarchical clustering to group related documents without predefining the number of clusters. The system architecture involves preprocessing documents, calculating similarity, forming clusters, and evaluating results. Modules include preprocessing, calculating the number of clusters, clustering techniques, and removing outliers. The approach aims to enhance computer inspection by grouping relevant documents for experts to examine.

Clustering techniques

The document discusses clustering techniques and provides details about the k-means clustering algorithm. It begins with an introduction to clustering and lists different clustering techniques. It then describes the k-means algorithm in detail, including how it works, the steps involved, and provides an example illustration. Finally, it discusses comments on the k-means algorithm, focusing on aspects like choosing the value of k, initializing cluster centroids, and different distance measurement methods.

ThreeTen

This document discusses the ThreeTen library, which provides a replacement for the Java date and time API. It notes issues with the existing Calendar and Date classes, such as mutability and difficulty testing. ThreeTen addresses these by providing immutable classes like LocalDate and LocalTime, avoiding nulls, and making testing easier. The document outlines ThreeTen's API, how to convert between it and Date, and how to integrate it with Kotlin using operator overloading and extensions. It emphasizes conventions like using plus and minus for addition/subtraction of temporal amounts.

Representing Documents and Queries as Sets of Word Embedded Vectors for Infor...

1) The document presents a method to represent documents and queries as sets of word embeddings for information retrieval. It uses word embeddings to create a "Bag of Vectors" representation of documents and queries.
2) Documents are modeled as mixtures of Gaussian distributions centered around the word embeddings. Queries are represented as posterior likelihoods over these Gaussian mixtures.
3) The method is evaluated on several TREC datasets, showing improved retrieval performance over the standard language modeling approach on some datasets, particularly when using k-means clustering to assign words to Gaussian mixtures. The best performance was achieved with 100 Gaussian mixtures.

From Changes to Dynamics: Dynamics Analysis of Linked Open Data Sources

Presentation at PROFILES 2014 workshop (co-located with ESWC) on measuring the dynamics of linked data sources.

Applicationof datastructures

The document discusses various data structures used to implement priority queues, including binary heaps and binomial heaps. It describes how each structure can be implemented using an array and the time complexities of common operations like insertion, deletion, finding the minimum element, etc. It also provides an example of how binary heaps can be used to implement Dijkstra's algorithm for finding the shortest paths from a single source vertex in a graph.

Document clustering and classification

محاضرة ألقيتها ضمن برنامج السيمينار الذي نفذه قسم علوم الحاسوب وتكنولوجيا المعلومات في الكلية الجامعية للعلوم والتكنولوجيا عام 2012

Sortsearch

This document discusses algorithms for sorting and searching data. It introduces basic data structures like arrays and linked lists. Different sorting algorithms are described like insertion sort, shell sort, and quicksort. Dictionaries that allow efficient insertion, search and deletion are also covered, including hash tables, binary search trees, red-black trees, and skip lists. The document provides pseudocode for the algorithms and estimates their time complexity using Big O notation. Source code implementations of the algorithms in C and Visual Basic are available for download.

On clusteredsteinertree slide-ver 1.1

This document describes a genetic algorithm called PGA for solving the clustered Steiner tree problem (CluSteiner). The CluSteiner problem involves finding the minimum cost tree that connects target vertices while satisfying constraints that trees within each cluster are disjoint. PGA uses a two-level approach, first finding local trees for each cluster and then linking the trees. It represents solutions as an ordering of clusters and applies crossover and mutation genetic operators. Computational experiments show PGA improves on previous algorithms by up to 83% on test instances.

Au4201315330

International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.

Graph Based Clustering

The document discusses graph-based clustering methods. It describes how graphs can be used to represent real-world networks from domains like biology, technology, social networks, and economics. It introduces the idea of using minimal spanning trees and hierarchical clustering to identify clusters in graph data. Two common algorithms for finding minimal spanning trees are described: Prim's algorithm and Kruskal's algorithm. Different strategies for iteratively deleting branches from the minimal spanning tree are also summarized to form clusters, such as deleting the branch with the maximum weight or inconsistent branches based on a reference value.

Clustering on database systems rkm

This document discusses clustering algorithms for large datasets that do not fit into main memory. It introduces the Relational K-Means (RKM) algorithm, which limits disk I/O by assigning data points in batches and updating cluster centroids after only 3 iterations. RKM stores cluster assignment and centroid data in matrices on disk and minimizes I/O by accessing matrix rows sequentially. An evaluation shows RKM outperforms standard K-means on large datasets due to its ability to handle data that does not fit in memory through efficient disk access. However, RKM does not address all limitations of K-means clustering.

Pengantar dasar matematika 4 (TURUNAAN FUNGSI)

Integral fungsi tersebut adalah:
a. ∫(1-2x)dx = x - x^2 + C
b. ∫(2x)dx = x^2 + C
Penjelasan:
a. Fungsi (1-2x) merupakan fungsi polinomial derajat satu. Integral fungsi polinomial derajat satu adalah fungsi polinomial derajat dua dibagi dua, ditambah konstanta.
b. Fungsi 2x merupakan fungsi polinomial derajat satu. Integral fungsi polinomial derajat satu

IR-ranking

The document proposes an automated approach for ranking tuples in the results of SQL queries over databases. It computes global and conditional scores for tuples based on attribute correlations learned from past query workloads and data statistics. At query time, it merges pre-computed ranked lists corresponding to the query attributes to efficiently retrieve the top-k results without a full table scan. Experiments on real datasets show the approach is efficient and provides high quality rankings preferred by users over alternative methods.

Profiling in Python

Profiling in Python provides concise summaries of key profiling tools in 3 sentences:
cProfile and line_profiler profile execution time and identify slow lines of code. memory_profiler profiles memory usage with line-by-line or time-based outputs. YEP extends profiling to compiled C/C++ extensions like Cython modules, which are not covered by the standard Python profilers.

LAP2009 c&p101-vector2 d.5ht

LAP2009 c&p101-vector2 d.5ht

Introduction to Ultra-succinct representation of ordered trees with applications

Introduction to Ultra-succinct representation of ordered trees with applications

Exploring temporal graph data with Python:
a study on tensor decomposition o...

Exploring temporal graph data with Python:
a study on tensor decomposition o...

Scalable Link Discovery for Modern Data-Driven Applications

Scalable Link Discovery for Modern Data-Driven Applications

Some fixed point and common fixed point theorems of integral

Some fixed point and common fixed point theorems of integral

Document clustering for forensic analysis

Document clustering for forensic analysis

Clustering techniques

Clustering techniques

ThreeTen

ThreeTen

Representing Documents and Queries as Sets of Word Embedded Vectors for Infor...

Representing Documents and Queries as Sets of Word Embedded Vectors for Infor...

From Changes to Dynamics: Dynamics Analysis of Linked Open Data Sources

From Changes to Dynamics: Dynamics Analysis of Linked Open Data Sources

Applicationof datastructures

Applicationof datastructures

Document clustering and classification

Document clustering and classification

Sortsearch

Sortsearch

On clusteredsteinertree slide-ver 1.1

On clusteredsteinertree slide-ver 1.1

Au4201315330

Au4201315330

Graph Based Clustering

Graph Based Clustering

Clustering on database systems rkm

Clustering on database systems rkm

Pengantar dasar matematika 4 (TURUNAAN FUNGSI)

Pengantar dasar matematika 4 (TURUNAAN FUNGSI)

IR-ranking

IR-ranking

Profiling in Python

Profiling in Python

Gwt presen alsip-20111201

The document describes using a wavelet tree data structure to enable fast similarity searches of massive graph databases. A Weisfeiler-Lehman procedure is used to represent graphs as bags-of-words. The wavelet tree indexes these bags-of-words and allows semi-conjunctive queries to find graphs sharing a minimum number of words with a query graph in sublinear time. Experiments on 25 million molecular graphs showed the approach significantly outperformed inverted indexes in search time and memory usage.

Gwt sdm public

(1) The document describes a method for efficient similarity search in massive graph databases using wavelet trees. (2) It converts graphs into bags-of-words representations using the Weisfeiler-Lehman procedure and indexes the words with a wavelet tree to enable fast semi-conjunctive queries. (3) Experiments on 25 million chemical compounds showed the method was significantly faster than alternative approaches while using less memory.

Network analysis lecture

This document discusses network analysis. It defines what a network is and describes common network features like nodes, edges, and centrality measures. It also covers network representations, using the NetworkX library to analyze networks, detecting communities within networks, and analyzing how information spreads through networks. A variety of network analysis tools are also listed.

multiscale_tutorial.pdf

Multiscale Entropy Analysis (MSE) is a method for measuring the complexity of time series data across multiple temporal scales. It involves coarse-graining the time series into multiple scales and calculating a sample entropy value at each scale to quantify the regularity. When applied to physiological signals, MSE reveals greater complexity in original data versus surrogate data, unlike single-scale entropy analyses. The software provided calculates MSE for physiological time series and outputs sample entropy values over a range of scales. Outliers can impact results by changing the time series variance, and filtering can alter MSE curves.

Meow Hagedorn

The document discusses using topic modeling techniques to cluster and classify records from multiple OAI repositories to enhance metadata and subject descriptions. Key steps included preprocessing records, building a vocabulary, running topic modeling to generate 500 topics, organizing topics into broad topical categories, and developing a browser to explore topics and records. Evaluation of the techniques found it worked well for English repositories but requires more testing on other languages and repository types. Potential products and services are proposed like integrating the topics into OAIster for subject search and browse.

Faster Practical Block Compression for Rank/Select Dictionaries

We present faster practical encoding and decoding procedures for block compression. Such encoding and decoding procedures are important to efficiently support rank/select queries on compressed bit vectors. This paper was presented at the 24th International Symposium on String Processing and Information Retrieval (SPIRE 2017) in Palermo, Italy.

Efficient matching of multiple chemical subgraphs

This document discusses efficient matching of multiple chemical subgraphs. It describes:
1) Previous work on efficient single pattern matching and optimizations for scanning large databases to match multiple patterns.
2) The performance of different cheminformatics toolkits in matching a radioactive substructure query against large datasets, with compiled C++ code showing significant speedups over interpreted languages.
3) Techniques for generating code from chemical pattern specifications to allow pre-compilation and faster matching, including examples for OpenEye OEChem and ChemAxon JChem.

Text clustering

Text clustering involves grouping text documents into clusters such that documents within a cluster are similar to each other and dissimilar to documents in other clusters. Common text clustering methods include bisecting k-means clustering, which recursively partitions clusters, and agglomerative hierarchical clustering, which iteratively merges clusters. Text clustering is used to automatically organize large document collections and improve search by returning related groups of documents.

19. algorithms and-complexity

The document discusses algorithms complexity and data structures efficiency, explaining that algorithm complexity can be measured using asymptotic notation like O(n) or O(n^2) to represent operations scaling linearly or quadratically with input size, and different data structures have varying time efficiency for operations like add, find, and delete.

Ch07 linearspacealignment

This document outlines divide and conquer algorithms for linear space sequence alignment. It discusses MergeSort as an example divide and conquer algorithm, and describes using a divide and conquer approach to solve the longest common subsequence (LCS) problem. It explains how to find the "middle vertex" between the source and sink for the LCS problem by dividing the problem space in half at each step. The document also covers using block alignment and the Four Russians speedup technique to solve sequence alignment problems in sub-quadratic time.

Creating a Custom Serialization Format (Gophercon 2017)

The document describes a custom serialization format for querying JSON documents. It discusses motivations for a new format, including supporting queries directly on serialized data without needing to deserialize it first. The format uses bytes to represent scalar values like integers and strings, and variable-length headers and entries to represent composites like arrays and maps. Performance tests show it can serialize and deserialize efficiently at scale, and support fast common queries like getting a single value or slicing an array. Future work may expand the possible operations and add compression support.

Introduction to Bayesian phylogenetics and BEAST

This document provides an overview of a course on Bayesian phylogenetics and the BEAST software package. The course covers introductory topics on Bayesian analysis and BEAST, as well as more advanced analyses including incorporating temporal and trait data. The document outlines the organization and topics to be covered in lectures, including why Bayesian methods are well-suited for pathogen evolution analysis and an introduction to Markov chain Monte Carlo sampling. It also provides information on setting up BEAST analyses using BEAUti, evaluating runs in Tracer, and summarizing runs using LogCombiner and TreeAnnotator.

Data structures

Data structures allow for efficient representation of data and solutions to real-world problems like insertion, deletion, search, and sort. Common data structures include arrays, linked lists, stacks, queues, trees, and hashes. Arrays use contiguous memory allocation while linked lists connect elements using pointers. Trees and hashes are useful for modeling hierarchical and associative data respectively. Recursion and traversal algorithms like breadth-first and depth-first are used to process tree and graph structures.

Learning multifractal structure in large networks (Purdue ML Seminar)

This document discusses methods for modeling networks using multifractal network generators (MFNG). MFNG is a recursive model that samples nodes into categories at different levels to generate graphs. The document outlines techniques for estimating MFNG parameters from real networks using method of moments, describes challenges in sampling from MFNG efficiently, and shows MFNG can match properties of Twitter and citation networks.

Optimizing Set-Similarity Join and Search with Different Prefix Schemes

As part of the 2018 HPCC Systems Summit Community Day event:
Up first, Zhe Yu, NC State University briefly discusses his poster, How to Be Rich: A Study of Monsters and Mice of American Industry
Following, Fabian Fier, presents his breakout session in the Documentation & Training Track.
Finding duplicate textual content is crucial for many applications, especially plagiarism detection. When dealing with millions of documents finding duplicate content becomes very time-consuming. Thus it needs scalable and efficient data structures and algorithms that solve this task in seconds rather than hours. In my talk, I present an optimization of a common filter-and-verification set-similarity join and search approach. Filter-and-verification means that we only consider such pairs of objects which share a common word or token in a prefix. Such pairs are potentially similar and are verified in a subsequent step. The candidate set is usually orders of magnitudes smaller than the cross product over an input set. We optimizied this approach by regarding overlaps larger than 1, which reduces the candidate set further and makes the verification faster. On the other hand this requires larger prefixes, which use more memory. Our experiments using HPCC Systems show that we can usually optimize the runtime by choosing an overlap different from the standard overlap 1.
Fabian Fier is a PhD student at the database research group of Johann-Christoph Freytag. He holds a diploma in computer science from Humboldt-Universität. His research interest is similarity search on web-scale data. He uses techniques from textual similarity joins on Big Data and adapts them to similiarity search.

Clojure for Data Science

This document provides an overview of using Clojure for data science. It discusses why Clojure is suitable for data science due to its functional programming capabilities, performance on the JVM, and rich library ecosystem. It introduces core.matrix, a Clojure library that provides multi-dimensional array programming functionality through Clojure protocols. The document covers core.matrix concepts like array creation and manipulation, element-wise operations, broadcasting, and optional support for mutability. It also discusses core.matrix implementation details like the performance benefits of using Clojure protocols.

Python for Chemistry

This document summarizes a talk given by Dr. Noel O'Boyle on using Python for chemistry. It discusses what Python is, why it is useful for chemistry, and how it can be used. Specific examples are given of popular Python modules for tasks like data analysis, visualization, cheminformatics, and interfacing with other languages like R and Java. The document provides an overview of the capabilities of Python for scientific computing and highlights its growing adoption in the chemistry community.

Python for Chemistry

This document summarizes a talk given by Dr. Noel O'Boyle on using Python for chemistry. It discusses what Python is, why it is useful for chemistry, and how it can be used. Specific examples are given of popular Python modules for tasks like data analysis, visualization, cheminformatics, and interfacing with other languages like R and Java. The document provides an overview of the capabilities of Python for scientific computing and highlights its growing adoption in the chemistry community.

Tree representation in map reduce world

It discussed how to represent a tree-like data structure in MapReduce world and applied on applications like Hieratical Clustering.

Don't optimize my queries, organize my data!

Your queries won't run fast if your data is not organized right. Apache Calcite optimizes queries, but can we make it optimize data? We had to solve several challenges. Users are too busy to tell us the structure of their database, and the query load changes daily, so Calcite has to learn and adapt. We talk about new algorithms we developed for gathering statistics on massive database, and how we infer and evolve the data model based on the queries.

Gwt presen alsip-20111201

Gwt presen alsip-20111201

Gwt sdm public

Gwt sdm public

Network analysis lecture

Network analysis lecture

multiscale_tutorial.pdf

multiscale_tutorial.pdf

Meow Hagedorn

Meow Hagedorn

Faster Practical Block Compression for Rank/Select Dictionaries

Faster Practical Block Compression for Rank/Select Dictionaries

Efficient matching of multiple chemical subgraphs

Efficient matching of multiple chemical subgraphs

Text clustering

Text clustering

19. algorithms and-complexity

19. algorithms and-complexity

Ch07 linearspacealignment

Ch07 linearspacealignment

Creating a Custom Serialization Format (Gophercon 2017)

Creating a Custom Serialization Format (Gophercon 2017)

Introduction to Bayesian phylogenetics and BEAST

Introduction to Bayesian phylogenetics and BEAST

Data structures

Data structures

Learning multifractal structure in large networks (Purdue ML Seminar)

Learning multifractal structure in large networks (Purdue ML Seminar)

Optimizing Set-Similarity Join and Search with Different Prefix Schemes

Optimizing Set-Similarity Join and Search with Different Prefix Schemes

Clojure for Data Science

Clojure for Data Science

Python for Chemistry

Python for Chemistry

Python for Chemistry

Python for Chemistry

Tree representation in map reduce world

Tree representation in map reduce world

Don't optimize my queries, organize my data!

Don't optimize my queries, organize my data!

Space-efficient Feature Maps for String Alignment Kernels

This document proposes space-efficient feature maps for approximating string alignment kernels. It introduces edit-sensitive parsing (ESP) to map strings to integer vectors, and then uses feature maps to map the integer vectors to compact feature vectors. Linear SVMs trained on these feature vectors can achieve similar performance as non-linear SVMs using alignment kernels, with greatly improved scalability. Experimental results on real-world string datasets show the proposed method significantly reduces training time and memory usage compared to state-of-the-art string kernel methods, while maintaining high classification accuracy.

Scalable Partial Least Squares Regression on Grammar-Compressed Data Matrices

河原林ERATO感謝祭IIIの発表資料です。

Kdd2015reading-tabei

KDD2015読み会発表資料

DCC2014 - Fully Online Grammar Compression in Constant Space

FREQ_FOLCA and LOSSY_FOLCA are variants of FOLCA that work in constant space by removing infrequent production rules from the hash table. FREQ_FOLCA divides text into blocks and removes the lowest frequency rules each time the hash table reaches a size limit. LOSSY_FOLCA divides text into blocks and keeps rules for successive blocks based on frequency. Experiments show they can compress 100 human genomes totaling 306GB in about one day while using only a few dozen megabytes of working space.

GIW2013

The document summarizes research on developing a scalable method for predicting compound-protein interactions using minwise hashing. Key points:
- Minwise hashing is used to build compact fingerprints from high-dimensional fingerprints of compound-protein pairs, reducing memory and training time compared to previous methods.
- Linear support vector machines trained on the compact fingerprints achieve similar prediction accuracy as previous nonlinear methods, while requiring less memory and training faster, especially on large datasets of 216 million compound-protein pairs.
- Experiments show the proposed method, MH-L1SVM and MH-L2SVM, outperform baselines in training time while maintaining predictive performance, and it can extract important predictive features.

CPM2013-tabei201306

This document summarizes research presented at the 24th Annual Symposium on Combinatorial Pattern Matching. It discusses three open problems in optimally encoding Straight Line Programs (SLPs), which are compressed representations of strings. The document presents information theoretic lower bounds on SLP size and describes novel techniques for building optimal encodings of SLPs in close to minimal space. It also proposes a space-efficient data structure for the reverse dictionary of an SLP.

SPIRE2013-tabei20131009

FOLCA is a fully-online grammar compression method that builds a partial parse tree in an online manner and directly encodes it into a succinct representation using just nlgn+2n+o(n) bits of space. This is asymptotically optimal. It achieves small working space of (1+α)nlgn+n(3+lg(αn)) bits using a compressed hash table. It can extract substrings in O(l+h) time using extra space of nlg(N/n)+3n+o(n) bits. Experiments show it compresses and extracts faster than LZend while using less space.

WABI2012-SuccinctMultibitTree

This document summarizes a presentation on succinct representations of multibit trees for efficient chemical fingerprint searches. It describes:
1) Using succinct data structures like rank/select dictionaries and LOUDS representations to compactly encode multibit trees and fingerprint databases in memory.
2) Two approaches for compactly representing fingerprint databases - a variable-length array and succinct trie.
3) How the succinct representations allow fast similarity searches on large chemical fingerprint datasets while using less memory than pointer-based representations.

Mlab2012 tabei 20120806

The document describes a workshop on machine learning and applications to biology held in Sapporo, Japan in August 2012. It focuses on presenting space-efficient data structures for large-scale chemical fingerprint searches, including multibit trees and succinct representations of trees and tries. The goal is fast similarity searches of chemical fingerprints while using less memory than pointer-based representations.

Lgm pakdd2011 public

LGM is an algorithm that efficiently mines frequent subgraphs from a set of linear graphs. It uses a reverse search approach to enumerate all subgraphs without duplication, defining a search tree with a reduction map. By inverting the reduction map, it can extend patterns from parent to children nodes. Experiments apply LGM to mine motifs from protein structures, finding statistically significant patterns associated with thermophilic or mesophilic functions.

Dmss2011 public

This document summarizes a method for performing kernel-based similarity search in massive graph databases using wavelet trees. It introduces the need for efficient graph similarity search as graph databases grow large. It describes representing graphs as bags-of-words and using a semi-conjunctive query to relax cosine similarity searches. The method replaces inverted indexes with a wavelet tree to enable fast top-down search while using less memory than traditional inverted indexes. Experiments on a dataset of 25 million chemical compounds demonstrate the method's ability to perform similarity search efficiently in large graph databases.

Lgm saarbrucken

The document summarizes a method for mining frequent subgraphs from linear graphs. It describes:
1) Representing data like proteins, RNA and texts as linear graphs and the need for algorithms to mine frequent patterns from such graphs.
2) A method called LGM that can efficiently enumerate and mine both connected and disconnected subgraphs from linear graphs using reverse search techniques.
3) Experiments applying LGM to mine motifs from protein structures and phrases from texts, achieving better performance than existing methods.

Sketch sort sugiyamalab-20101026 - public

- The document describes a multiple sorting method called SketchSort for efficiently finding all pairs of similar items in large-scale datasets.
- SketchSort maps high-dimensional vector data to binary sketches while preserving distances. It then performs multiple sorting on the sketches to enumerate similar item pairs.
- Experiments show SketchSort can efficiently find neighbor pairs in large image and genetic datasets, outperforming other state-of-the-art methods. It enables applications like clustering and information retrieval in big data domains.

Sketch sort ochadai20101015-public

The document summarizes a multiple sorting method called SketchSort for performing all pairs similarity search on large-scale datasets. It maps vector data to binary sketches to reduce memory usage, then applies locality sensitive hashing and multiple sorting to efficiently find all pairs of data points within a given distance threshold. The method is evaluated on large image, chemical compound, and genome sequence datasets and is shown to outperform other state-of-the-art similarity search methods.

Space-efficient Feature Maps for String Alignment Kernels

Space-efficient Feature Maps for String Alignment Kernels

Scalable Partial Least Squares Regression on Grammar-Compressed Data Matrices

Scalable Partial Least Squares Regression on Grammar-Compressed Data Matrices

Kdd2015reading-tabei

Kdd2015reading-tabei

DCC2014 - Fully Online Grammar Compression in Constant Space

DCC2014 - Fully Online Grammar Compression in Constant Space

NIPS2013読み会: Scalable kernels for graphs with continuous attributes

NIPS2013読み会: Scalable kernels for graphs with continuous attributes

GIW2013

GIW2013

CPM2013-tabei201306

CPM2013-tabei201306

SPIRE2013-tabei20131009

SPIRE2013-tabei20131009

WABI2012-SuccinctMultibitTree

WABI2012-SuccinctMultibitTree

Mlab2012 tabei 20120806

Mlab2012 tabei 20120806

Ibisml2011 06-20

Ibisml2011 06-20

Lgm pakdd2011 public

Lgm pakdd2011 public

Dmss2011 public

Dmss2011 public

Lgm saarbrucken

Lgm saarbrucken

Sketch sort sugiyamalab-20101026 - public

Sketch sort sugiyamalab-20101026 - public

Sketch sort ochadai20101015-public

Sketch sort ochadai20101015-public

Lp Boost

Lp Boost

Design and optimization of ion propulsion drone

Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.

4. Mosca vol I -Fisica-Tipler-5ta-Edicion-Vol-1.pdf

Conceptos basicos de fisica

原版制作(Humboldt毕业证书)柏林大学毕业证学位证一模一样

原件一模一样【微信：bwp0011】《(Humboldt毕业证书)柏林大学毕业证学位证》【微信：bwp0011】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问微bwp0011
【主营项目】
一.毕业证【微bwp0011】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微bwp0011】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才

132/33KV substation case study Presentation

132/33Kv substation case study ppt

Software Engineering and Project Management - Introduction, Modeling Concepts...

Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.

Object Oriented Analysis and Design - OOAD

This ppt gives detailed description of Object Oriented Analysis and design.

AI + Data Community Tour - Build the Next Generation of Apps with the Einstei...

AI + Data Community Tour - Build the Next Generation of Apps with the Einstei...Paris Salesforce Developer Group

Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.Supermarket Management System Project Report.pdf

Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.

Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024

Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.

一比一原版(uofo毕业证书)美国俄勒冈大学毕业证如何办理

原版一模一样【微信：741003700 】【(uofo毕业证书)美国俄勒冈大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

Call For Paper -3rd International Conference on Artificial Intelligence Advan...

* Registration is currently open *
Call for Research Papers!!!
Free – Extended Paper will be published as free of cost.
3rd International Conference on Artificial Intelligence Advances (AIAD 2024)
July 13 ~ 14, 2024, Virtual Conference
Webpage URL: https://aiad2024.org/index
Submission Deadline: June 22, 2024
Submission System URL:
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Null Bangalore | Pentesters Approach to AWS IAM

#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)

smart pill dispenser is designed to improve medication adherence and safety f...

Smart Pill Dispenser that boosts medication adherence, empowers patients, enables remote monitoring, enhances safety, reduces healthcare costs, and contributes to data-driven healthcare improvements

Mechatronics material . Mechanical engineering

Mechatronics is a multidisciplinary field that refers to the skill sets needed in the contemporary, advanced automated manufacturing industry. At the intersection of mechanics, electronics, and computing, mechatronics specialists create simpler, smarter systems. Mechatronics is an essential foundation for the expected growth in automation and manufacturing.
Mechatronics deals with robotics, control systems, and electro-mechanical systems.

Applications of artificial Intelligence in Mechanical Engineering.pdf

Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.

DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL

As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.

一比一原版(osu毕业证书)美国俄勒冈州立大学毕业证如何办理

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一比一原版(uofo毕业证书)美国俄勒冈大学毕业证如何办理

一比一原版(uofo毕业证书)美国俄勒冈大学毕业证如何办理

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Null Bangalore | Pentesters Approach to AWS IAM

Null Bangalore | Pentesters Approach to AWS IAM

smart pill dispenser is designed to improve medication adherence and safety f...

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Mechatronics material . Mechanical engineering

Mechatronics material . Mechanical engineering

Applications of artificial Intelligence in Mechanical Engineering.pdf

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UNIT 4 LINEAR INTEGRATED CIRCUITS-DIGITAL ICS

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DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL

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一比一原版(osu毕业证书)美国俄勒冈州立大学毕业证如何办理

一比一原版(osu毕业证书)美国俄勒冈州立大学毕业证如何办理

- 1. Scalable Similarity Search for Molecular Descriptors Yasuo Tabei RIKEN Center for Advanced Intelligent Project (AIP), Japan Joint work with Simon J. Puglisi University of Helsinki, Finland SISAP’17, Oct. 6, 2017
- 2. Similarity search in chemoinformatics • Similarity search of chemical compounds is an important task for novel drug discoveries • Important fact: similar molecules tend to have a similar molecular functions • Can find functions of a query by searching databases of compounds • Whole chemical space is said to be approximately 1060 • There are large databases storing tens of millions of compounds, e.g., PubChem, ChEMBL • Scalable similarity searches of chemical compounds are required
- 3. Chemical fingerprint • Binary vector representation of molecule E.g.: x=(1, 0, 0, 1, 0) Ø Each dimension indicates the presence/absence of a substructure – Representative fingerprints: Dragon, PubChem et al. • Jaccard (a.k.a Tanimoto) similarity is used • Many methods has been proposed – Multibit tree, XOR-based, b-bit minhashing et al.
- 4. Molecular descriptor (NEW) • Integer vector representation of molecules – x=(3, 1, 0, 0, 2), equivalent to set W=(1:3, 2:1, 5:2) – Each dimension indicates a chemical property • Descriptors: RINGO [Vida et al.,05] and KCF-S [Kotera et al., 13] • Generalized Jaccard: • Similarity search of molecular descriptors is in a n infancy stage • Problem: Find all Wi similar to query Q (≧ε)
- 5. Similarity search using inverted index [Nasr’12] • Inverted index: associative array – Key = feature id, value= weight • Similarity search for query Q • Look up inverted index for each query element and compute similarities i Wi 1 (1:3) 2 (5:3) 3 (2:3) 4 (1:1,2:2,4:2) 5 (4:3) 6 (2:2,3:1,5:2) 7 (3:3) 8 (2:3) 1 (1:3) (4:1) 2 (3:3) (4:2) (6:2) (8:3) 3 (6:1) (7:3) 4 (4:2) (5:3) 5 (2:3) (6:2) (i) Descriptors (ii) Inverted index (iii) Similarity search for query Q=(1:3, 4:1) (1:3) (4:1) (4:2) (5:3) Liner time for the total length of lists
- 6. Drawback • Scanning lists takes much time, especially for long lists • Huge memory: – N: number of descriptors – M: maximum weight • One can compress inverted index by using compression methods, e.g., variable-byte codes and PForDelta • Decompression is time-consuming • Challenge: developing a fast and space-efficient similarity search for molecular descriptors
- 7. SITAd: Scalable similarity search for molecular descriptors • Two techniques 1. Database partitioning 2. Conversion to inner product search • Build wavelet tree on the notion of two techniques • Solve inner product search on wavelet tree
- 8. Database partitioning W1=(1:1, 3:1) W2=(2:1) W3=(2:2, 4:1) W4=(2:1, 4:1) W5=(3:1) W6=(1:2) W7=(1:1, 4:1) W8=(1:1, 2:2) W2=(2:1) W5=(3:1) W1=(1:1, 3:1) W4=(2:1, 4:1) W7=(1:1, 4:1) W6=(1:2) W3=(2:2, 4:1) W8=(1:1, 2:2) Theorem1 • Classify each descriptor Wi into block Bc • Search space can be limited to blocks satisfying Theorem1 for given query q and ε
- 9. Conversion to inner product search • Similarity search using generalized Jaccard similarity can be converted to inner product search • How to solve inner product search efficiently? • Suppose a simple case that all weights are one Ex) x = (3, 2, 0, 4, 2) ➞ x’= (1, 1, 0, 0, 1) ➞ W’=(1,2,5) • Can be solved as a semi conjunctive query Constant Inner product Set ≧ε
- 10. 2 Conjunctive Query § Query with k keywords § (Word 2, Word 4) § Identify the set intersection by sorting merged id list § It takes O(|A|+|B|) time § Can that be any faster? Word Document ids Word 1 1,3 Word 2 2,6,8 Word 3 1,5,7 Word 4 2,7 6 8 2 7 2 2 6 7 8 A B
- 11. Alternation α § Number of switches after sorting § There exists a data structure that allows to find set intersection in O(α log m) time (Barbay/Kenyon, 2002) § m : maximum value α = 2 2 6 8 2 7 2 2 6 7 8
- 12. Range intersection on array n Concatenate all rows of inverted index n Array A of length n, values 1 ≤ A[i] ≤ m n Query word = Interval n Range intersection: rint(A,[i,j],[k,l]) • Find set intersection of A[i,j] and A[k,l] n O(α log m) time using wavelet tree ! A 1 3 2 6 8 1 5 7 2 7 4 5 i j k l
- 13. Definition of Wavelet Tree
- 14. Tree of subarrays: Lower half = left, Higher half=right [1,4] [5,8] [1,8] 1 3 2 6 8 5 7 1 2 7 4 5 1 3 2 1 2 4 6 8 5 7 7 5 1 2 1 2 3 4 6 5 5 8 7 7 1 1 2 2 3 4 5 5 6 7 7 8 [1,2] [3,4] [5,6] [7,8]
- 15. Remember if each element is either in lower half (0) or higher half (1) [1,4] [5,8] [1,8] 0 0 0 1 1 1 1 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1 1 0 0 1 0 1 0 1 1 0 0 1 0 0 [1,2] [3,4] [5,6] [7,8] 1 2 3 4 5 6 7 8
- 16. Index each bit array with a rank dictionary n With rank dictionary, the rank operation can be done in O(1) time • rankc(B,i): return the number of c∈{0,1} in B[1…i] Ex) B=0110011100 i 1 2 3 4 5 6 7 8 9 10 0 1 1 0 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 0 rank1(B,8)=5 rank0(B,5)=3
- 17. Wavelet Tree = Collection of bit arrays indexed by rank dictionaries [1,4] [5,8] [1,8] 0 1 0 0 0 1 0 1 0 1 1 0 0 1 0 1 0 1 1 0 0 1 0 0 [1,2] [3,4] [5,6] [7,8] 1 2 3 4 5 6 7 8 0 0 0 1 1 1 1 0 0 1 0 1
- 18. Memory Usage n (1+γ) n log m bits l n: Number of all words in the database l m: Number of unique words l γ: Overhead for rank dictionary (around 0.6) l Not so different from simply storing the array (n log m bit)
- 19. Solving range intersection using wavelet tree
- 20. Range intersection: recap n Array A of length n, values 1 ≤ A[i] ≤ m n Query word = Interval n Range intersection: rint(A,[i,j],[k,l]) • Find set intersection of A[i,j] and A[k,l] n O(α log m) time using wavelet tree A 1 3 2 6 8 1 5 7 2 7 4 5 i j k l
- 21. O(1)-time division of an interval n Using the rank operations, the division of an interval can be done in constant time • rank0 for left child and rank1 for right child • Naïve = linear time to the total number of elements [1,4] [1,8] Aroot 1 3 2 6 8 1 5 7 2 7 4 5 Aleft 1 3 2 1 2 4 Aright 6 8 5 7 7 5 [5,8]
- 22. Fast computation of range intersection on wavelet tree [1,4] [5,8] [1,8] 1 3 2 6 8 1 5 7 2 7 4 5 1 3 2 1 2 4 6 8 5 7 7 5 1 2 1 2 3 4 6 5 5 8 7 7 1 1 2 2 3 4 5 5 6 7 7 8 [1,2] [3,4] [5,6] [6,8] Pruned solution!!
- 23. Fast computation of range intersection on wavelet tree [1,4] [5,8] [1,8] 1 3 2 6 8 1 5 7 2 7 4 5 1 3 2 1 2 4 6 8 5 7 7 5 1 2 1 2 3 4 6 5 5 8 7 7 1 1 2 2 3 4 5 5 6 7 7 8 [1,2] [3,4] [5,6] [6,8] Height log m
- 24. Solve inner product search using wavelet tree
- 25. • Inverted index 1 (1:3) (4:1) 2 (3:3) (4:2) (6:2) (8:3) 3 (6:1) (7:3) 4 (4:2) (5:3) 5 (2:3) (6:2) 1 4 3 4 6 8 6 7 4 5 2 6 § Array of ids➞wavelet tree 3 1 3 2 2 3 1 3 2 3 3 2 § Array of weights➞RMQ data structure § Build two arrays by concatenating feature ids and weights separately in each row § RMQ data structure - compute max B[t,s] in O(1)time and |B|log|B|/2 + |B|logM + 2n bits of space § Query = multiple interval extensions of range intersection § Find ids whose sum-of- products for weights is at least threshold Solve inner product search A B
- 26. Computing upper bound of inner product in O(1) time • Using RMQ data structure, upper bound of inner product can be computed in O(1)-time • We compute max B[t,s] for each interval on wavelet tree and compute upper bound 1 4 3 4 6 8 6 7 4 5 2 6 3 4 3 2 2 3 1 3 2 3 3 2 1 4 3 4 4 2 6 8 6 7 5 6 A B Ex)Q=(2:2,4:1) 3・2 + 3・1 =9 Wavelet Tree RMQ
- 27. Experiments • 42,971,672 compounds in PubChem database • Use KCF-S descriptors, 642,297 dimension • Use search time and memory as evaluation measures • Compare SITAd (proposed) to – OVA: compute similarity one-by-one – INV (state-of the-art): similarity search using inverted index – INV+VBYTE: INV compressed by variable byte codes – INV+PD: INV compressed by PForDelta
- 28. Search time for the number of compounds ●●●●● ● ● ● ● 0e+00 1e+07 2e+07 3e+07 4e+07 0123456 # of descriptors Searchtime(sec) ●●● ● ● ● ● ● ● ●●●●● ● ● ● ● ● ● SITAd epsilon=0.9 SITAd epsilon=0.95 SITAd epsilon=0.98 inverted index inverted index(varbyte) inverted index(pfordelta)
- 29. Search time and memory (MB) on 42 million compounds 0 5000 10000 15000 20000 25000 30000 35000 0246810 Memory (mega byte) searchtime(sec) SITAd epsilon=0.98 SITAd epsilon=0.95 SITAd epsilon=0.9 INV INV−VBYTE INV−PD OVA 2,400 0.23 0.61 1.54 33,012 5.24 9.58 8,171
- 30. Construction time ●●●●● ● ● ● ● 0e+00 1e+07 2e+07 3e+07 4e+07 0100200300400 # of descriptors Constructiontime(sec) ●●● ● ● ● ● ● ● ● SITAd INV INV-VBYTE INV-PD
- 31. Summary • Present SITAd, scalable similarity search for molecular descriptors • Use two data structures: wavelet tree, RMQ • Takes around 1 sec and use 2.5GB memory for searching 42 million compounds • Future work: develop similarity search methods using ANN
- 32. Software for similarity search is available https://sites.google.com/site/yasuotabei/ • All softwares are applicable to high dimension and hundreds of millions of data • All pairs similarity search (similarity join) - SketchSort for cosine similarity - SketchSortj for Jaccard similarity - SketchSort-minmax for minmax similarity • Similarity search - SMBT for Jaccard similarity • Graph similarity search - gWT