This document summarizes an academic paper titled "Item-Based Collaborative Filtering Recommendation Algorithms" presented at WWW 2001. It discusses item-based collaborative filtering, an improvement over traditional user-based collaborative filtering algorithms. Item-based CF involves precomputing item-to-item similarities offline which improves scalability. It then makes recommendations online by looking at similar items instead of similar users, reducing computation time from O(m^2n) to O(n) where m is the number of users and n is the number of items. The document outlines the key aspects of item-based CF including similarity metrics, prediction methods, and complexity analysis.
This document summarizes Takashi Umeda's summer seminar presentation on item-based collaborative filtering recommendation algorithms. The presentation introduced item-based CF as an improvement over traditional user-based CF algorithms. It evaluated item-based CF using movie rating data, finding it provided better prediction quality than nearest neighbor approaches, with higher online performance due to pre-computing static item similarities offline. While quality gains over nearest neighbor were small, item-based CF was shown to retain good prediction quality even when using only a subset of item similarities.
IOTA 2016 Social Recomender System Presentation.ASHISH JAGTAP
In today’s age of ever increasing use of internet, there are around 74% active internet users out of which 60% users contribute to social networking and most of them are students from the age group 16-30. If this young generation is targeted specifically towards educational activities keeping the same social networking environment in the background would create interest in students for educational activities and also yield productive results. This can be implemented by creating a social-cum-educational portal with recommender systems. Specific information to specific student can be provided. Use of such technology can reduce the gap between students and the information which can lead to their inherent development and success! However, most of the existing Social Recommender systems do not have good scalabilities which are unable to process huge volumes of data. Aiming to this problem we can design a social recommender system based on Hadoop and its parallel computing platform.
SOAR: SENSOR ORIENTED MOBILE AUGMENTED REALITY FOR URBAN LANDSCAPE ASSESSMENTTomohiro Fukuda
This slide is presented in CAADRIA2012 (The 17th International Conference on Computer Aided Architectural Design Research in Asia).
Abstract. This research presents the development of a sensor oriented mobile AR system which realizes geometric consistency using GPS, a gyroscope and a video camera which are mounted in a smartphone for urban landscape assessment. A low cost AR system with high flexibility is realized. Consistency of the viewing angle of a video camera and a CG virtual camera, and geometric consistency between a video image and 3DCG are verified. In conclusion, the proposed system was evaluated as feasible and effective.
DISTRIBUTED AND SYNCHRONISED VR MEETING USING CLOUD COMPUTING: Availability a...Tomohiro Fukuda
This slide is presented in CAADRIA2012 (The 17th International Conference on Computer Aided Architectural Design Research in Asia).
Abstract. The mobility of people's activities, and cloud computing technologies are becoming advanced in the modern age of information and globalisation. This study describes the availability of discussing spatial design while sharing a 3-dimensional virtual space with stakeholders in a distributed and synchronised environment. First of all, a townscape design support system based on a cloud computing type VR system is constructed. Next, an experiment of a distributed and synchronised discussion of townscape design is executed with subjects who are specialists in the townscape design field. After the experiment, both qualitative mental evaluation and quantitative evaluation were carried out. The conclusions are as follows: 1. Users who use VR frequently and who use videoconferencing consider that the difference with face-to-face discussion is small. 2. A Moiré pattern may occur in a gradation picture. 3. The availability of distributed and synchronised discussions with cloud computing type VR is high.
A STUDY OF VARIATION OF NORMAL OF POLY-GONS CREATED BY POINT CLOUD DATA FOR A...Tomohiro Fukuda
This slide is presented in CAADRIA2011 (The 16th International Conference on Computer Aided Architectural Design Research in Asia).
Abstracts: Acquiring current 3D space data of cities, buildings, and rooms rapidly and in detail has become indispensable. When the point cloud data of an object or space scanned by a 3D laser scanner is converted into polygons, it is an accumulation of small polygons. When object or space is a closed flat plane, it is necessary to merge small polygons to reduce the volume of data, and to convert them into one polygon. When an object or space is a closed flat plane, each normal vector of small polygons theoretically has the same angle. However, in practise, these angles are not the same. Therefore, the purpose of this study is to clarify the variation of the angle of a small polygon group that should become one polygon based on actual data. As a result of experimentation, no small polygons are converted by the point cloud data scanned with the 3D laser scanner even if the group of small polygons is a closed flat plane lying in the same plane. When the standard deviation of the extracted number of polygons is assumed to be less than 100, the variation of the angle of the normal vector is roughly 7 degrees.
GOAR: GIS Oriented Mobile Augmented Reality for Urban Landscape AssessmentTomohiro Fukuda
This slide is presented in CMC2012 (2012 4th International Conference on
Communications, Mobility, and Computing).
Abstract. This research presents the development of a mobile AR system which realizes geometric consistency
using GIS, a gyroscope and a video camera which are mounted in a smartphone for urban landscape assessment. A low cost AR system with high flexibility is developed.
Geometric consistency between a video image and 3DCG are verified. In conclusion, the proposed system was evaluated as feasible and effective.
Availability of Mobile Augmented Reality System for Urban Landscape SimulationTomohiro Fukuda
This slide is presented in CDVE2012 (The 9th International Conference on Cooperative Design, Visualization, and Engineering).
Abstract. This research presents the availability of a landscape simulation method for a mobile AR (Augmented Reality), comparing it with photo montage and VR (Virtual Reality) which are the main existing methods. After a pilot experiment with 28 subjects in Kobe city, a questionnaire about three landscape simulation methods was implemented. In the results of the questionnaire, the mobile AR method was well evaluated for reproducibility of a landscape, operability, and cost. An evaluation rated as better than equivalent was obtained in comparison with the existing methods. The suitability of mobile augmented reality for landscape simulation was found to be high.
This slide explains a simple Android library called Debot.
Debot offers features that are useful to debug Android applications. Those features can be added to any activity that has the toolbar menu. Also, developers can easily add their own custom debugging features with simple steps.
https://github.com/tomoima525/debot
This document summarizes Takashi Umeda's summer seminar presentation on item-based collaborative filtering recommendation algorithms. The presentation introduced item-based CF as an improvement over traditional user-based CF algorithms. It evaluated item-based CF using movie rating data, finding it provided better prediction quality than nearest neighbor approaches, with higher online performance due to pre-computing static item similarities offline. While quality gains over nearest neighbor were small, item-based CF was shown to retain good prediction quality even when using only a subset of item similarities.
IOTA 2016 Social Recomender System Presentation.ASHISH JAGTAP
In today’s age of ever increasing use of internet, there are around 74% active internet users out of which 60% users contribute to social networking and most of them are students from the age group 16-30. If this young generation is targeted specifically towards educational activities keeping the same social networking environment in the background would create interest in students for educational activities and also yield productive results. This can be implemented by creating a social-cum-educational portal with recommender systems. Specific information to specific student can be provided. Use of such technology can reduce the gap between students and the information which can lead to their inherent development and success! However, most of the existing Social Recommender systems do not have good scalabilities which are unable to process huge volumes of data. Aiming to this problem we can design a social recommender system based on Hadoop and its parallel computing platform.
SOAR: SENSOR ORIENTED MOBILE AUGMENTED REALITY FOR URBAN LANDSCAPE ASSESSMENTTomohiro Fukuda
This slide is presented in CAADRIA2012 (The 17th International Conference on Computer Aided Architectural Design Research in Asia).
Abstract. This research presents the development of a sensor oriented mobile AR system which realizes geometric consistency using GPS, a gyroscope and a video camera which are mounted in a smartphone for urban landscape assessment. A low cost AR system with high flexibility is realized. Consistency of the viewing angle of a video camera and a CG virtual camera, and geometric consistency between a video image and 3DCG are verified. In conclusion, the proposed system was evaluated as feasible and effective.
DISTRIBUTED AND SYNCHRONISED VR MEETING USING CLOUD COMPUTING: Availability a...Tomohiro Fukuda
This slide is presented in CAADRIA2012 (The 17th International Conference on Computer Aided Architectural Design Research in Asia).
Abstract. The mobility of people's activities, and cloud computing technologies are becoming advanced in the modern age of information and globalisation. This study describes the availability of discussing spatial design while sharing a 3-dimensional virtual space with stakeholders in a distributed and synchronised environment. First of all, a townscape design support system based on a cloud computing type VR system is constructed. Next, an experiment of a distributed and synchronised discussion of townscape design is executed with subjects who are specialists in the townscape design field. After the experiment, both qualitative mental evaluation and quantitative evaluation were carried out. The conclusions are as follows: 1. Users who use VR frequently and who use videoconferencing consider that the difference with face-to-face discussion is small. 2. A Moiré pattern may occur in a gradation picture. 3. The availability of distributed and synchronised discussions with cloud computing type VR is high.
A STUDY OF VARIATION OF NORMAL OF POLY-GONS CREATED BY POINT CLOUD DATA FOR A...Tomohiro Fukuda
This slide is presented in CAADRIA2011 (The 16th International Conference on Computer Aided Architectural Design Research in Asia).
Abstracts: Acquiring current 3D space data of cities, buildings, and rooms rapidly and in detail has become indispensable. When the point cloud data of an object or space scanned by a 3D laser scanner is converted into polygons, it is an accumulation of small polygons. When object or space is a closed flat plane, it is necessary to merge small polygons to reduce the volume of data, and to convert them into one polygon. When an object or space is a closed flat plane, each normal vector of small polygons theoretically has the same angle. However, in practise, these angles are not the same. Therefore, the purpose of this study is to clarify the variation of the angle of a small polygon group that should become one polygon based on actual data. As a result of experimentation, no small polygons are converted by the point cloud data scanned with the 3D laser scanner even if the group of small polygons is a closed flat plane lying in the same plane. When the standard deviation of the extracted number of polygons is assumed to be less than 100, the variation of the angle of the normal vector is roughly 7 degrees.
GOAR: GIS Oriented Mobile Augmented Reality for Urban Landscape AssessmentTomohiro Fukuda
This slide is presented in CMC2012 (2012 4th International Conference on
Communications, Mobility, and Computing).
Abstract. This research presents the development of a mobile AR system which realizes geometric consistency
using GIS, a gyroscope and a video camera which are mounted in a smartphone for urban landscape assessment. A low cost AR system with high flexibility is developed.
Geometric consistency between a video image and 3DCG are verified. In conclusion, the proposed system was evaluated as feasible and effective.
Availability of Mobile Augmented Reality System for Urban Landscape SimulationTomohiro Fukuda
This slide is presented in CDVE2012 (The 9th International Conference on Cooperative Design, Visualization, and Engineering).
Abstract. This research presents the availability of a landscape simulation method for a mobile AR (Augmented Reality), comparing it with photo montage and VR (Virtual Reality) which are the main existing methods. After a pilot experiment with 28 subjects in Kobe city, a questionnaire about three landscape simulation methods was implemented. In the results of the questionnaire, the mobile AR method was well evaluated for reproducibility of a landscape, operability, and cost. An evaluation rated as better than equivalent was obtained in comparison with the existing methods. The suitability of mobile augmented reality for landscape simulation was found to be high.
This slide explains a simple Android library called Debot.
Debot offers features that are useful to debug Android applications. Those features can be added to any activity that has the toolbar menu. Also, developers can easily add their own custom debugging features with simple steps.
https://github.com/tomoima525/debot
Takashi Umeda is a researcher interested in recommendation algorithms, information diffusion on the internet, and consumer behavior in electronic commerce. He was born in Gifu Prefecture in 1985 and studied at Shizuoka University from 2003-2007 and the Tokyo Institute of Technology from 2007-2009. His bachelor's thesis examined direct financing in Silicon Valley using agent-based modeling and his master's thesis evaluated collaborative filtering using agent-based simulation in light of market environments. His research aims to design recommendation algorithms for electronic commerce by modeling interaction with market environments and evaluating collaborative filtering techniques.
The document discusses adaptive learning environments and adaptive systems. It covers topics such as the need for adaptation, user modeling, adaptation of presentation and navigation, and the GRAPPLE architecture. Adaptive systems can adapt content, information, and processes like navigation based on attributes of the user like knowledge, goals, preferences, and context. User modeling involves representing these attributes in a user model, such as with an overlay model to represent a user's knowledge. The document also discusses adaptation techniques, application areas of adaptive systems, and issues to consider in designing adaptive systems.
Using Grids to support Information Filtering SystemsLeandro Ciuffo
The document discusses running a movie recommendation system using collaborative filtering on grid computing middleware. It describes implementing a Movielens-like recommender system that collects explicit movie ratings from users to generate recommendations. The system was run on the gLite-based EGEE and EELA grids to take advantage of their computational resources. Future work proposed expanding the experiments to other datasets and cloud platforms like Amazon EC2.
Cumulus is a filesystem backup utility that uses cloud storage on Amazon S3. It addresses the scalability issues of tape drives for backup. Cumulus segments files to reduce costs from small file sizes and uses sub-file incrementals to only store changed portions of files between snapshots. Evaluation of Cumulus showed it significantly reduced storage costs and backup time compared to traditional methods.
IRJET- Criminal Recognization in CCTV Surveillance VideoIRJET Journal
This document presents research on criminal recognition in CCTV surveillance videos using deep learning. It proposes a method where a user can upload faces of known criminals. When CCTV footage is recorded, the application will monitor for these faces. If a face is recognized, the CCTV camera will track the identified person through multiple cameras by alerting other cameras. The system segments video into images, acquires images, recognizes human faces, constructs motion flows between cameras to track individuals. Experimental results on a dataset show the system's ability to extract patterns from faces and cluster images of different angled faces. The system aims to identify criminals across surveillance camera networks.
Cumulus is a filesystem backup utility that uses cloud storage on Amazon S3. It addresses the scalability issues of tape drives for backup. Cumulus segments files to reduce costs from small file sizes and uses sub-file incrementals to only store changed portions of files between snapshots. Evaluation of Cumulus showed it significantly reduced storage costs and backup time compared to traditional methods.
This document describes a book recommendation system that uses collaborative filtering and content-based filtering techniques. It provides an overview of how collaborative filtering, content-based filtering, and hybrid recommendation systems work. For collaborative filtering, it discusses user-based and item-based nearest neighbor algorithms. It also outlines some of the advantages and limitations of collaborative filtering, content-based filtering, and hybrid recommendation approaches. The document then presents the architecture of a book recommendation system and describes how it would use a k-nearest neighbors algorithm and weighted rating formulas to make recommendations based on a user's book ratings and similar users' ratings.
Matrix Factorization Techniques For Recommender SystemsLei Guo
The document discusses matrix factorization techniques for recommender systems. It begins by describing common recommender system strategies like content-based and collaborative filtering approaches. It then introduces matrix factorization methods, which characterize both users and items by vectors of latent factors inferred from rating patterns. The basic matrix factorization model approximates user ratings as the inner product of user and item vectors in the joint latent factor space. Learning algorithms like stochastic gradient descent and alternating least squares are used to compute the user and item vectors by minimizing a regularized error function on known ratings.
This document proposes an online course recommendation system that uses machine learning algorithms like K-nearest neighbor (KNN), K-means clustering, and collaborative filtering to recommend courses to students. It extracts student data like marks, attendance, and teacher ratings to classify students and identify lacking skills. It then generates personalized course recommendations and study material links for each student cluster. Finally, it provides recommendations to students using collaborative filtering by rating previously recommended links. The system aims to provide more effective recommendations than solely using collaborative filtering by integrating multiple student attributes.
[CB20] -U25 Ethereum 2.0 Security by Naoya OkanamiCODE BLUE
Ethereum 2.0 is a major upgrade to improve the performance of Ethereum. It will increase transaction processing capacity and alleviate the problem of fees, which have been rising to the point where millions of dollars are spent every day.
In this talk, I explain how Ethereum 2.0 can be secured as the next generation of decentralized application platforms and present the research we are working on as part of that effort. First, I talk about technologies to improve security, including client diversity, fuzzing using multiple clients, and the fee market protocol EIP-1559. Second, I introduce Shargri-La, a protocol development support software we are developing (*), a simulator that helps researchers and developers quickly test protocol hypotheses to improve the performance and security of Ethereum 2.0. It simulates state transitions at the transaction level of granularity in "sharding," a technique that divides the blockchain into multiple pieces. Finally, I present the results of multi-agent simulations under EIP-1559 by modeling users' selfish behaviors and show possible future problems and mitigation solutions.
REAL-TIME OBJECT DETECTION USING OPEN COMPUTER VISIONIRJET Journal
This document discusses real-time object detection using open computer vision. It reviews various object detection techniques like YOLO, OpenCV, and SVM. The proposed system uses YOLO as a supporting module with OpenCV for real-time object detection in a video or image. It analyzes the performance of algorithms in detecting and recognizing three construction vehicles on a scaled construction site. The paper also reviews and compares various object recognition models like R-CNN, YOLO, and SSD.
Gridify your Spring application with Grid Gain @ Spring Italian Meeting 2008Sergio Bossa
Cheaper hardware and highly demanding applications make nowadays scalability a strong requirement: what will you say when your Boss will complain about more and more users waiting for that long task to complete before committing their transaction?
So take your application and make it scale with the Spring Framework, the leading full-stack solution for your Java applications, and Grid Gain, the most powerful Open Source production-ready grid computing framework!
In this talk you will learn about scalability principles, the
Map/Reduce pattern and how they\'re applied in Grid Gain for scaling out your Spring application.
Skovsgaard.2011.evaluation of a remote webcam based eye trackermrgazer
This paper evaluates the performance of a remote webcam-based eye tracker compared to two commercial eye trackers. An experiment with 5 subjects performing targeting tasks found that the open-source eye tracker had significantly higher accuracy than one commercial system but a higher error rate than the other. The webcam solution may be a lower-cost alternative for some, though it was less accurate than the higher-end commercial tracker.
Utilizing Marginal Net Utility for Recommendation in E-commerceLiangjie Hong
The document discusses utilizing marginal net utility for recommendation in e-commerce. It proposes modeling user behavior based on marginal net utility to maximize the net utility for each user. It introduces a Cobb-Douglas utility function that captures diminishing returns. It then revamps existing SVD recommendation algorithms to estimate utility based on this new function. Experiments on a real e-commerce dataset show the new approach significantly outperforms baselines in recommending re-purchases and new products.
IRJET- Sketch-Verse: Sketch Image Inversion using DCNNIRJET Journal
The document describes a system that uses deep convolutional neural networks to convert face sketch images to photorealistic images. It first constructs a semi-simulated dataset from a large dataset containing face sketches and corresponding photos. It then trains a model using techniques like deep residual learning and perceptual losses. The trained model is able to take face sketches as input and generate photorealistic images as output. An evaluation of the system found a conversion rate of around 70% for test images. The authors aim to improve the model's robustness through additional data augmentation and training.
The document discusses various workflows for modeling net-to-gross (NTG) and associated properties in reservoir models, highlighting potential issues that can arise. It recommends upscaling binary NTG logs to non-binary values and modeling NTG and properties conditionally to facies models while allowing values in all facies, rather than assigning zeros, to avoid underestimating reservoir volumes. The document provides examples of how discrepancies between facies and layering schemes can impact calculated hydrocarbon volumes if not properly accounted for.
The document summarizes a webinar presented by UserZoom, an international online market research firm specializing in user experience and usability testing. The webinar provided an overview of UserZoom's unmoderated remote UX research capabilities, including its self-serve online tool for defining studies, collecting data, and analyzing results. It also included a case study of how Monster used UserZoom's international usability studies to evaluate a website redesign across multiple markets.
San Agustin Evaluation Of A Low Cost Open Source Gaze TrackerKalle
This document evaluates a low-cost open-source gaze tracking system using an off-the-shelf webcam. The performance of the gaze tracker was tested in an eye-typing task using two different typing applications. Participants were able to type between 3.56-6.78 words per minute depending on the application. A pilot study was also conducted with a user with severe motor impairments who was able to successfully type on a wall-projected interface using eye movements. The document describes the hardware and software components of the low-cost gaze tracking system and provides details on the experimental procedure and results of the eye-typing evaluation.
Partial Object Detection in Inclined Weather ConditionsIRJET Journal
This document provides a comprehensive analysis of imbalance problems in object detection. It presents a taxonomy to classify different types of imbalances and discusses solutions proposed in literature. The analysis highlights significant gaps including existing imbalances that require further attention, as well as entirely new imbalances that have never been addressed before. A survey of imbalance problems caused by weather conditions and common object imbalances is conducted. Methods for addressing imbalances include data augmentation using GANs and balancing training based on class performance.
Sustainable Development using Green ProgrammingIRJET Journal
The document discusses sustainable development using green programming. It notes that programmers typically receive training on programming languages and methodologies but not on software energy consumption. Modern technologies like mobile apps and cloud computing require increased awareness of energy usage. The document outlines various functions that are associated with high energy consumption like graphics, computation, algorithms, memory usage, and networking. It then discusses methods to improve energy efficiency such as using better algorithms, caching, multithreading, and native code. A survey found that programmers have limited knowledge of energy efficiency and best practices for reducing software energy usage. The document argues for educating programmers on the importance of creating energy-efficient software.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Takashi Umeda is a researcher interested in recommendation algorithms, information diffusion on the internet, and consumer behavior in electronic commerce. He was born in Gifu Prefecture in 1985 and studied at Shizuoka University from 2003-2007 and the Tokyo Institute of Technology from 2007-2009. His bachelor's thesis examined direct financing in Silicon Valley using agent-based modeling and his master's thesis evaluated collaborative filtering using agent-based simulation in light of market environments. His research aims to design recommendation algorithms for electronic commerce by modeling interaction with market environments and evaluating collaborative filtering techniques.
The document discusses adaptive learning environments and adaptive systems. It covers topics such as the need for adaptation, user modeling, adaptation of presentation and navigation, and the GRAPPLE architecture. Adaptive systems can adapt content, information, and processes like navigation based on attributes of the user like knowledge, goals, preferences, and context. User modeling involves representing these attributes in a user model, such as with an overlay model to represent a user's knowledge. The document also discusses adaptation techniques, application areas of adaptive systems, and issues to consider in designing adaptive systems.
Using Grids to support Information Filtering SystemsLeandro Ciuffo
The document discusses running a movie recommendation system using collaborative filtering on grid computing middleware. It describes implementing a Movielens-like recommender system that collects explicit movie ratings from users to generate recommendations. The system was run on the gLite-based EGEE and EELA grids to take advantage of their computational resources. Future work proposed expanding the experiments to other datasets and cloud platforms like Amazon EC2.
Cumulus is a filesystem backup utility that uses cloud storage on Amazon S3. It addresses the scalability issues of tape drives for backup. Cumulus segments files to reduce costs from small file sizes and uses sub-file incrementals to only store changed portions of files between snapshots. Evaluation of Cumulus showed it significantly reduced storage costs and backup time compared to traditional methods.
IRJET- Criminal Recognization in CCTV Surveillance VideoIRJET Journal
This document presents research on criminal recognition in CCTV surveillance videos using deep learning. It proposes a method where a user can upload faces of known criminals. When CCTV footage is recorded, the application will monitor for these faces. If a face is recognized, the CCTV camera will track the identified person through multiple cameras by alerting other cameras. The system segments video into images, acquires images, recognizes human faces, constructs motion flows between cameras to track individuals. Experimental results on a dataset show the system's ability to extract patterns from faces and cluster images of different angled faces. The system aims to identify criminals across surveillance camera networks.
Cumulus is a filesystem backup utility that uses cloud storage on Amazon S3. It addresses the scalability issues of tape drives for backup. Cumulus segments files to reduce costs from small file sizes and uses sub-file incrementals to only store changed portions of files between snapshots. Evaluation of Cumulus showed it significantly reduced storage costs and backup time compared to traditional methods.
This document describes a book recommendation system that uses collaborative filtering and content-based filtering techniques. It provides an overview of how collaborative filtering, content-based filtering, and hybrid recommendation systems work. For collaborative filtering, it discusses user-based and item-based nearest neighbor algorithms. It also outlines some of the advantages and limitations of collaborative filtering, content-based filtering, and hybrid recommendation approaches. The document then presents the architecture of a book recommendation system and describes how it would use a k-nearest neighbors algorithm and weighted rating formulas to make recommendations based on a user's book ratings and similar users' ratings.
Matrix Factorization Techniques For Recommender SystemsLei Guo
The document discusses matrix factorization techniques for recommender systems. It begins by describing common recommender system strategies like content-based and collaborative filtering approaches. It then introduces matrix factorization methods, which characterize both users and items by vectors of latent factors inferred from rating patterns. The basic matrix factorization model approximates user ratings as the inner product of user and item vectors in the joint latent factor space. Learning algorithms like stochastic gradient descent and alternating least squares are used to compute the user and item vectors by minimizing a regularized error function on known ratings.
This document proposes an online course recommendation system that uses machine learning algorithms like K-nearest neighbor (KNN), K-means clustering, and collaborative filtering to recommend courses to students. It extracts student data like marks, attendance, and teacher ratings to classify students and identify lacking skills. It then generates personalized course recommendations and study material links for each student cluster. Finally, it provides recommendations to students using collaborative filtering by rating previously recommended links. The system aims to provide more effective recommendations than solely using collaborative filtering by integrating multiple student attributes.
[CB20] -U25 Ethereum 2.0 Security by Naoya OkanamiCODE BLUE
Ethereum 2.0 is a major upgrade to improve the performance of Ethereum. It will increase transaction processing capacity and alleviate the problem of fees, which have been rising to the point where millions of dollars are spent every day.
In this talk, I explain how Ethereum 2.0 can be secured as the next generation of decentralized application platforms and present the research we are working on as part of that effort. First, I talk about technologies to improve security, including client diversity, fuzzing using multiple clients, and the fee market protocol EIP-1559. Second, I introduce Shargri-La, a protocol development support software we are developing (*), a simulator that helps researchers and developers quickly test protocol hypotheses to improve the performance and security of Ethereum 2.0. It simulates state transitions at the transaction level of granularity in "sharding," a technique that divides the blockchain into multiple pieces. Finally, I present the results of multi-agent simulations under EIP-1559 by modeling users' selfish behaviors and show possible future problems and mitigation solutions.
REAL-TIME OBJECT DETECTION USING OPEN COMPUTER VISIONIRJET Journal
This document discusses real-time object detection using open computer vision. It reviews various object detection techniques like YOLO, OpenCV, and SVM. The proposed system uses YOLO as a supporting module with OpenCV for real-time object detection in a video or image. It analyzes the performance of algorithms in detecting and recognizing three construction vehicles on a scaled construction site. The paper also reviews and compares various object recognition models like R-CNN, YOLO, and SSD.
Gridify your Spring application with Grid Gain @ Spring Italian Meeting 2008Sergio Bossa
Cheaper hardware and highly demanding applications make nowadays scalability a strong requirement: what will you say when your Boss will complain about more and more users waiting for that long task to complete before committing their transaction?
So take your application and make it scale with the Spring Framework, the leading full-stack solution for your Java applications, and Grid Gain, the most powerful Open Source production-ready grid computing framework!
In this talk you will learn about scalability principles, the
Map/Reduce pattern and how they\'re applied in Grid Gain for scaling out your Spring application.
Skovsgaard.2011.evaluation of a remote webcam based eye trackermrgazer
This paper evaluates the performance of a remote webcam-based eye tracker compared to two commercial eye trackers. An experiment with 5 subjects performing targeting tasks found that the open-source eye tracker had significantly higher accuracy than one commercial system but a higher error rate than the other. The webcam solution may be a lower-cost alternative for some, though it was less accurate than the higher-end commercial tracker.
Utilizing Marginal Net Utility for Recommendation in E-commerceLiangjie Hong
The document discusses utilizing marginal net utility for recommendation in e-commerce. It proposes modeling user behavior based on marginal net utility to maximize the net utility for each user. It introduces a Cobb-Douglas utility function that captures diminishing returns. It then revamps existing SVD recommendation algorithms to estimate utility based on this new function. Experiments on a real e-commerce dataset show the new approach significantly outperforms baselines in recommending re-purchases and new products.
IRJET- Sketch-Verse: Sketch Image Inversion using DCNNIRJET Journal
The document describes a system that uses deep convolutional neural networks to convert face sketch images to photorealistic images. It first constructs a semi-simulated dataset from a large dataset containing face sketches and corresponding photos. It then trains a model using techniques like deep residual learning and perceptual losses. The trained model is able to take face sketches as input and generate photorealistic images as output. An evaluation of the system found a conversion rate of around 70% for test images. The authors aim to improve the model's robustness through additional data augmentation and training.
The document discusses various workflows for modeling net-to-gross (NTG) and associated properties in reservoir models, highlighting potential issues that can arise. It recommends upscaling binary NTG logs to non-binary values and modeling NTG and properties conditionally to facies models while allowing values in all facies, rather than assigning zeros, to avoid underestimating reservoir volumes. The document provides examples of how discrepancies between facies and layering schemes can impact calculated hydrocarbon volumes if not properly accounted for.
The document summarizes a webinar presented by UserZoom, an international online market research firm specializing in user experience and usability testing. The webinar provided an overview of UserZoom's unmoderated remote UX research capabilities, including its self-serve online tool for defining studies, collecting data, and analyzing results. It also included a case study of how Monster used UserZoom's international usability studies to evaluate a website redesign across multiple markets.
San Agustin Evaluation Of A Low Cost Open Source Gaze TrackerKalle
This document evaluates a low-cost open-source gaze tracking system using an off-the-shelf webcam. The performance of the gaze tracker was tested in an eye-typing task using two different typing applications. Participants were able to type between 3.56-6.78 words per minute depending on the application. A pilot study was also conducted with a user with severe motor impairments who was able to successfully type on a wall-projected interface using eye movements. The document describes the hardware and software components of the low-cost gaze tracking system and provides details on the experimental procedure and results of the eye-typing evaluation.
Partial Object Detection in Inclined Weather ConditionsIRJET Journal
This document provides a comprehensive analysis of imbalance problems in object detection. It presents a taxonomy to classify different types of imbalances and discusses solutions proposed in literature. The analysis highlights significant gaps including existing imbalances that require further attention, as well as entirely new imbalances that have never been addressed before. A survey of imbalance problems caused by weather conditions and common object imbalances is conducted. Methods for addressing imbalances include data augmentation using GANs and balancing training based on class performance.
Sustainable Development using Green ProgrammingIRJET Journal
The document discusses sustainable development using green programming. It notes that programmers typically receive training on programming languages and methodologies but not on software energy consumption. Modern technologies like mobile apps and cloud computing require increased awareness of energy usage. The document outlines various functions that are associated with high energy consumption like graphics, computation, algorithms, memory usage, and networking. It then discusses methods to improve energy efficiency such as using better algorithms, caching, multithreading, and native code. A survey found that programmers have limited knowledge of energy efficiency and best practices for reducing software energy usage. The document argues for educating programmers on the importance of creating energy-efficient software.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
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This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
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UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
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1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
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Test Automation with generative AI and Open AI.
UiPath integration with generative AI
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Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Project Management Semester Long Project - Acuityjpupo2018
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AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
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Webinar: Designing a schema for a Data WarehouseFederico Razzoli
Are you new to data warehouses (DWH)? Do you need to check whether your data warehouse follows the best practices for a good design? In both cases, this webinar is for you.
A data warehouse is a central relational database that contains all measurements about a business or an organisation. This data comes from a variety of heterogeneous data sources, which includes databases of any type that back the applications used by the company, data files exported by some applications, or APIs provided by internal or external services.
But designing a data warehouse correctly is a hard task, which requires gathering information about the business processes that need to be analysed in the first place. These processes must be translated into so-called star schemas, which means, denormalised databases where each table represents a dimension or facts.
We will discuss these topics:
- How to gather information about a business;
- Understanding dictionaries and how to identify business entities;
- Dimensions and facts;
- Setting a table granularity;
- Types of facts;
- Types of dimensions;
- Snowflakes and how to avoid them;
- Expanding existing dimensions and facts.
4. 1-1. My Research Domain
• Evaluating recommendation Algorithms by ABM
– Recommendation:
• Rule Based Approach
• Contents Based Approach
• Collaborative Filtering(CF)
• Bayesian Network
– Why CF?
• It’s mainly used in many websites
– Why ABM?
• To use ABM, Algorithms are optimized according to the
market environment
Summer Seminar 2008 @Susukakedai http://umekoumeda.net/
5. 1-2. What’s CF? (1/2)
• Have you used Amazon.com ?
Summer Seminar 2008 @Susukakedai http://umekoumeda.net/
6. 1-3. What’s CF 2/2
Collaborative Filtering Algorithms(CF) is commonly
used in EC WebSite.
Recommendation
Summer Seminar 2008 @Susukakedai http://umekoumeda.net/
7. 1-4. What’s CF 3/3
Book List
CF will
recommend
Prof Deguchi
Follow book,
Prof. Kizima Based on people
that are similar
with him
Book List
They have same books
Prof. Deguchi ↓
They have similar
preference
Summer Seminar 2008 @Susukakedai http://umekoumeda.net/
8. 1-5. Contribution of this paper
• Problem of the Basic CF Algorithms
– Basic CF : Nearest Neighbors
– Scalability(Performance)
• High Scalability : In many users, a system recommend for
them quickly
– Accuracy(Quality)
• High Accuracy : if the data were sparse, a system recommend
the item that a user may like
• In this paper, the Author proposed new
Algorithms
– Item-Based CF
– Performance & Quality can be improved
Summer Seminar 2008 @Susukakedai http://umekoumeda.net/
9. 1-6. Collaborative Filtering Process
Input Data CF-Algorithm Output Interface
i1 i2 ・・ in
Pa,j
u1 a 1,2
• Predicted the degree of
u2 Prediction
u3 User – Item Matrices likeness of item ij by the
: user ua
um
• Ir ∩Iua = Φ
•U ={ u1,u2,..,um}
• I ={i1,i2,..,in} A list of N-items
• Iui : item where user ui that the user will
evalues, Iui ⊆ I Recommendation
(Top-N Recommendation) like the most(Ir⊂I)
• ai,j : evaluation of item ij
by user ui •Ir ∩Iua = Φ
Summer Seminar 2008 @Susukakedai http://umekoumeda.net/
10. 1-7.Variation of the CF-Algorithm
CF- Algorithm
Memory Based Approach Model Based Approach
• Procedure
•Procedure(Nearest Neighbor) 1. The system develops a
1. The system defines a set of model of user ratings at off-
users known as neighbors line
at on-line 2. By using the model, the
2. The system produces a system produce a
prediction or top-n prediction or top n
recommendation recommendation
• How developing the mode ?
• Bayesian Network
• clustering
Summer Seminar 2008 @Susukakedai http://umekoumeda.net/
11. 1-8.What ‘s online and offline ?
Off-line Computation On-line Computation
At a suitable interval, When a user used the
offline computation is system, online
performed automatically Computation is
performed quickly
• Indexing If you input a query, the
EX: • Crowling search engine output the
Google • Ranking result.
Summer Seminar 2008 @Susukakedai http://umekoumeda.net/
12. 1-9.the problem of the basic CF
Sparsity of user-item matrices:
many users may have purchased
Accuracy well under 1% of the all items →
accuracy of Nearest Neighbor
Weakness of algorithm may be poor
the Nearest
Neighbor With millions of users and
items, Nearest Neighbor
Scalability algorithm may suffer serious
scalability problem
We need new CF-Algorithms………..
Summer Seminar 2008 @Susukakedai http://umekoumeda.net/
14. 2-1. Overview of Item base CF
Off-line Computation On-line Computation
Item Similarity Computation Prediction Computation
Si,j : Similarity between item ii and ij •Pu,i is the degree of the
likeness item-i by user-
i1 i2 ・・ in u ,based on the similarity
u1 R 1,2 between items,S
u2
u3
:
um
S2n
Summer Seminar 2008 @Susukakedai http://umekoumeda.net/
15. 2-2. Item Similarity Computation
• Cosine-Based Similarity
• Correlation-based Similarity The Difference
in rating scale
between
defferent users
• Adjusted Cosine Similarity
Summer Seminar 2008 @Susukakedai http://umekoumeda.net/
16. 2-3.Prediction Computation
• Weighted Sum •N is the set of item that is very
similar with item I
• |N| : neighbor size
normalization coefficient
• Regression
– Ru,n is calculated by Regression model
– Ri: Target item’s rating(explaining variable)
– Rn: Similar item’s rating (explained variable)
Summer Seminar 2008 @Susukakedai http://umekoumeda.net/
17. 2-4. Time Complexity(1/2)
Time complexity of Nearest Neibhor is…..
On-line Computation
User Similarity
Action Prediction Computation
Computation
•Computing 1 user-user similarity,
Recommend System scan n scores.
→ O(n) • Computing 1 Pi,j-Value,
Time • Recommend System must Recommend System scan m
Compl computing m × m user-user user-user similarity → O(m)
exity similarity. →O(m×m)
O(m2n) + O(m)
Summer Seminar 2008 @Susukakedai http://umekoumeda.net/
18. 2-4. Time Complexity(2/2)
Time complexity of Item-Based CF is better Performance
than Neaest Neighbor
Off-line Computation On-line Computation
Item Similarity
Action Prediction Computation
Computation
Item-Item Similarity is static as
Computing 1 Pi,j-Value,
opposed the User Similarity → It
Time Recommend System scan n
It’s possible to precompute item
item similarity → O(n)
Compl Similarity ( = model )
exity
O(n)
Summer Seminar 2008 @Susukakedai http://umekoumeda.net/
20. 3-1. Experimental Procedure
the data set is divided into a train and a test portion
1.Data Dividing user item rating
u1 i2 3
u2 Test
i1 2 Evaluation
u6
Train
i3 3 Parameter Learning
2.To fix the
optimal values The Follow parameters is decided.
of a parameter • Similarity Algorithms
• Train/ Test Ratio(x) : Sparsity level in data
• neighborhood size
3.Full Experiment To evalue Item based CF, the follow value is measured
• Performance
• Quality
Summer Seminar 2008 @Susukakedai http://umekoumeda.net/
21. 3-2. Data Sets
• Data Sets
– Data from website “ MovieLens”
– MovieLens is web based recommender system
– Hundreds of users visit MovieLens to rate and
receive recommendations for movies.
– A data set was converted into a user-item
matrix( 943user × 1682 columns )
Summer Seminar 2008 @Susukakedai http://umekoumeda.net/
22. 3-3. Evaluation Metrics
• To evaluating the quality of a recomender system,
we use MAE as evaluation metrics.
• MAE: Mean Absolute Error
– pi: Predicted Rating for item I (predicted based on a
train data)
– qi: true Rating for item I (from a test data)
– The lower the MAE, the more accurately the
recommendation engine predicts user ratings.
Summer Seminar 2008 @Susukakedai http://umekoumeda.net/
24. 4-1.Optimal Values of a parameter(1/2)
Item-Similarity Algorithms =
Train-test ratio (x) = 0.8 as an
Adjusted cosine is the best
optimum value
quality
Summer Seminar 2008 @Susukakedai http://umekoumeda.net/
25. 4-1.Optimal Values of a parameter(2/2)
In Full Experiment, basic
parameter is as follows.
• Similarity Algorithms:
Adjusted Cosine
Considering both trends, • test/train ratio: 0.8
Optimal choise of
Neighborhood Size
Is 30 • neighborhood size : 30
Summer Seminar 2008 @Susukakedai http://umekoumeda.net/
26. 4-2. Quality
• Quality
• Item-Based CF ( weighted sum ) out perform the nearest-neighbor
• Item-Based CF (regression ) out perform the other two cases at low values
of x and at low neighborhood size
Summer Seminar 2008 @Susukakedai http://umekoumeda.net/
27. 4-3. Performance(1/2)
• model size:
– Full model: At item similarity computation,
all item – item similarity(1682×1682) is
computed .
– Model size = 200: At item similarity
computation, 200 item – 200 item similarity
(200×200 ) is computated .
• If model size is small , Good quality is
consistent ?
– Other model based Approach is consistent
– If it is consistent, online performance is
higher than full- model case
• Result:
– if model size is 100 ~ 200, it’s possible to
obtain resonably good prediction quality
In the case of not using all item-item similarity , the accurarcy of
prediction don’t down and the performance improve.
Summer Seminar 2008 @Susukakedai http://umekoumeda.net/
29. 5. Conclusion
• Quality
– Item-based CF provides better quality of predictions
than nearest neighbor Algorithms.
• Independent of Neighborhood size and train/test ratio
– The improvement in quality is not large
• Performance
– Item-Similarity Computation can be pre-computed
• Item-similarity is static
– High online Performance
– It is possible to retain only a small subset of items and
produce good prediction quality& high Performance
Summer Seminar 2008 @Susukakedai http://umekoumeda.net/