The Impact of Segmentation on the Accuracy and Sensitivity of a Melanoma Classifier Based on Skin Lesion Images
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Jack Burdick, Oge Marques, Borko Furht, Janet Weinthal (FAU, Boca Raton, FL, USA), Adria Romero Lopez, and Xavier Giro-i-Nieto (UPC, Barcelona). Presented at SIIM 2017 Annual Meeting: http://siim.org/page/17s_analytics3
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Avihu Efrat's Viola and Jones face detection slides
KaoNet: Face Recognition and Generation App using Deep Learning
KaoNet: Face Recognition and Generation App using Deep Learning
THE EFFECT OF PHYSICAL BASED FEATURES FOR RECOGNITION OF RECAPTURED IMAGES
THE EFFECT OF PHYSICAL BASED FEATURES FOR RECOGNITION OF RECAPTURED IMAGES
Similar to The Impact of Segmentation on the Accuracy and Sensitivity of a Melanoma Classifier Based on Skin Lesion Images
Face recognition
The document discusses face recognition using principal components analysis (PCA). It provides three key points:
1. PCA is used to reduce the dimensionality of face image data to 2D or 3D by finding patterns in high-dimensional data and visualizing it. This allows for face recognition by representing each face as a set of weights of significant eigenvectors.
2. A training set is used to form the PCA coordinate system and represent each training face as weights of eigenvectors. A test face is then recognized as the closest training face based on Euclidean distance between their representations in the PCA space.
3. PCA allows for data compression, noise reduction, and classification of faces by projecting high-dimensional image data onto
Detection and recognition of face using neural network
This document describes research on face detection and recognition using neural networks. It discusses using the Viola-Jones algorithm for face detection and a backpropagation neural network for face recognition. The Viola-Jones algorithm uses haar features, integral images, AdaBoost training, and cascading classifiers for real-time face detection. A backpropagation network with sigmoid activation functions is trained on facial images for recognition. Results show the network can accurately recognize faces after training. The document concludes the approach allows face recognition from an input image and discusses limitations and potential improvements.
ACET.pptx
This document provides an overview of facial recognition technology and its applications. It discusses how facial recognition systems work by using nodal points on faces to recognize individuals. The objectives are to design software that can accurately detect faces from images without physical interaction and allow for high identification and verification rates. The research methodology involves a workflow for the facial recognition system. Potential applications mentioned include using facial recognition for computer and border security, voting verification, and commercial uses like residential security and banking.
Face recognition: A Comparison of Appearance Based Approaches
Face recognition approaches can be divided into three main categories: direct correlation, eigenfaces, and fisherfaces. Direct correlation directly compares pixel intensity values between images. Eigenfaces uses principal component analysis to project faces into a face space defined by eigenvectors. Fisherfaces aims to maximize between-class variations while minimizing within-class variations to better account for differences in lighting and expressions. Pre-processing techniques like color normalization, histogram equalization, and edge detection can improve the accuracy of face recognition systems by reducing the effects of lighting variations. Testing various pre-processing techniques on different approaches found that the fisherfaces method combined with SLBC preprocessing achieved the lowest error rate of 17.8%, followed closely by direct correlation with intensity normalization at 18.
IP%20FACE%20RECOGNITION%20ATTENDANCE%20SYSTEM.pptx
facial recognition system is a technology capable of matching a human face from a digital image or a video frame against a database of faces, typically employed to authenticate users through ID verification services, works by pinpointing and measuring facial features from a given image.[1]
Development began on similar systems in the 1960s, beginning as a form of computer application. Since their inception, facial recognition systems have seen wider uses in recent times on smartphones and in other forms of technology, such as robotics. Because computerized facial recognition involves the measurement of a human's physiological characteristics, facial recognition systems are categorized as biometrics. Although the accuracy of facial recognition systems as a biometric technology is lower than iris recognition and fingerprint recognition, it is widely adopted due to its contactless process.[2] Facial recognition systems have been deployed in advanced human–computer interaction, video surveillance and automatic indexing of images.[3]
Facial recognition systems are employed throughout the world today by governments and private companies.[4] Their effectiveness varies, and some systems have previously been scrapped because of their ineffectiveness. The use of facial recognition systems has also raised controversy, with claims that the systems violate citizens' privacy, commonly make incorrect identifications, encourage gender norms and racial profiling, and do not protect important biometric data. The appearance of synthetic media such as deepfakes has also raised concerns about its security.[5] These claims have led to the ban of facial recognition systems in several cities in the United States.[6] As a result of growing societal concerns, Meta announced[7] that it plans to shut down Facebook facial recognition system, deleting the face scan data of more than one billion users.[8] This change will represent one of the largest shifts in facial recognition usage in the technology's history Facial recognition systems are employed throughout the world today by governments and private companies.[4] Their effectiveness varies, and some systems have previously been scrapped because of their ineffectiveness. The use of facial recognition systems has also raised controversy, with claims that the systems violate citizens' privacy, commonly make incorrect identifications, encourage gender norms and racial profiling, and do not protect important biometric data. The appearance of synthetic media such as deepfakes has also raised concerns about its security.[5] These claims have led to the ban of facial recognition systems in several cities in the United States.[6] As a result of growing societal concerns, Meta announced[7] that it plans to shut down Facebook facial recognition system, deleting the face scan data of more than one billion users.[8] This change will represent one of the largest shifts in facial recognition usage in the technology's history. Pleasure.
face recognition
This document outlines a project that uses face recognition from face motion manifolds. It proposes an information-theoretic approach using Resistor-Average Distance (RAD) as a dissimilarity measure between distributions of face images. A kernel-based algorithm is introduced that allows modeling of complex, nonlinear manifolds while retaining the closed-form RAD expression between normal distributions. Recognition rates of 90-100% can be achieved on databases of 10-100 people by modeling errors in face registration. The algorithm uses kernel PCA to nonlinearly map data and computes RAD on the mapped data as the dissimilarity measure between face image distributions.
dt.ppt
This document presents a method for face detection and gender recognition using data science. It introduces the importance of estimating age and gender from facial images for applications like access control and surveillance. The method uses mean absolute error and cumulative score to measure age estimation accuracy. It then describes the steps of the method which include segmenting the input image pixels, removing non-face objects, separating connected components, identifying components as faces or non-faces, refining face positions, finding faces through template matching, and detecting gender based on mean intensity and template matching. Test results on sample images are presented showing high accuracy of face detection and low false positive rates.
Face and Eye Detection Varying Scenarios With Haar Classifier_2015
The document presents a face and eye detection system. It discusses challenges in face detection like image quality, pose variation, and facial expressions. It describes the history of face detection and various methods like knowledge-based, feature-invariant, template matching, and appearance-based. The methodology section explains the Viola-Jones algorithm using Haar-like features, integral images, AdaBoost, and cascade classifiers. The implementation uses OpenCV for detection. Experiments showed high detection rates for single faces but lower rates for group faces and detecting eyes with pose variations. Future work involves improving classifiers and detecting side faces in real-time.
braintumordetectionusingimagesegmentationppt-210830184640 (1).pdf
This document describes a system for detecting brain tumors in MRI images using image segmentation. It begins with an introduction and abstract. It then describes the existing manual tumor detection system and its limitations. The proposed system applies preprocessing like noise removal, image segmentation to detect tumor edges, feature extraction, and classification to automatically detect tumors. The system is implemented in MATLAB and aims to help doctors detect tumors faster and earlier.
Brain tumor detection using image segmentation ppt
This document describes a system for detecting brain tumors in MRI images using image segmentation. It discusses how existing manual detection of tumors is difficult due to noise and requires many days. The proposed system applies preprocessing like filtering and grayscale conversion. It then uses image segmentation techniques to detect tumor edges and boundaries. Features are extracted and classification is used to differentiate between normal and tumor images, helping doctors detect tumors earlier. The system is implemented in MATLAB and aims to overcome difficulties in early tumor detection.
braintumordetectionusingimagesegmentationppt-210830184640.pdf
This document describes a system for detecting brain tumors in MRI images using image segmentation. It begins with an introduction and abstract. It then describes the existing manual tumor detection system and its limitations. The proposed system applies preprocessing like noise removal, image segmentation to detect tumor edges, feature extraction, and classification to automatically detect tumors. The system is implemented in MATLAB and aims to help doctors detect tumors faster and earlier.
braintumordetectionusingimagesegmentationppt-210830184640.pdf
This document describes a system for detecting brain tumors in MRI images using image segmentation. It begins with an introduction and abstract. It then describes the existing manual tumor detection system and its limitations. The proposed system applies preprocessing like noise removal, image segmentation to detect tumor edges, feature extraction, and classification to automatically detect tumors. The system is implemented in MATLAB and segmented images are used for accurate diagnosis. It concludes the segmentation method can help physicians with improved diagnosis and treatment.
Cross Pose Facial Recognition Method for Tracking any Person's Location an Ap...
In todays world, there are number of existing methods for facial recognition. These methods are based on frontal view face data. There are few methods which are based on non-frontal view face recognition method. In most of the face recognition algorithm, œFeature space approach is used. In this approach, different feature vectors are extracted from face. These distances are compared to determine matches. In this paper, it is proposed that how any person can be located in a campus or in a city using a cross pose face recognition method. This paper is focusing on three parts 1) generation of multi-view images 2) comparison of images 3) showing the actual location of a person. Sanjay D. Sawaitul"Cross Pose Facial Recognition Method for Tracking any Persons Location an Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7186.pdf http://www.ijtsrd.com/computer-science/data-processing/7186/cross-pose-facial-recognition-method-for--tracking-any-persons-location-an-approach/sanjay-d-sawaitul
Report Face Detection
This document discusses face detection techniques. It begins with an introduction that defines face detection and discusses why it is important and challenging. It then covers topics like image segmentation, face detection approaches, morphological image processing, and skin color-based face detection. The document analyzes literature on face detection methods and provides descriptions of techniques like thresholding, edge detection, region-based segmentation, and template matching. It also includes a case study on specific face detection software applications and concludes by summarizing the discussed techniques.
FACE DETECTION USING PRINCIPAL COMPONENT ANALYSIS
This document describes a face detection method using principal component analysis. It first preprocesses images using histogram equalization to address illumination issues. It then detects faces using skin segmentation to identify skin regions. Finally, it recognizes the extracted facial features using principal component analysis and a neural network, which reduces the dimensionality of the images for efficient recognition.
Face Recognition Technology
Humans often use faces to recognize individuals, and advancements in computing capability over the past few decades now enable similar recognitions automatically. Early facial recognition algorithms used simple geometric models, but the recognition process has now matured into a science of sophisticated mathematical representations and matching processes. Major advancements and initiatives in the past 10 to 15 years have propelled facial recognition technology into the spotlight. Facial recognition can be used for both verification and identification.
Facial recognition
Facial recognition is a type of biometric system that identifies individuals by analyzing patterns in images of their faces. The presentation summarizes how facial recognition systems work by detecting faces, normalizing them, extracting distinguishing features to create a template, and then matching templates to identify individuals. It notes advantages like convenience but also challenges like difficulty with changes in appearance over time. Applications discussed include security, banking, and voter verification.
IRJET- Survey on Face Detection Methods
The document reviews 15 papers on various face detection methods published between 2013 and 2018. It finds that the most popular feature extraction method is skin color segmentation, which achieves detection rates of 88-98%. The Viola-Jones method typically detects face regions as well as other body parts at a rate of 80-90%. Common face detection methods reviewed include skin color segmentation, Viola-Jones, Haar features, 3D mean shift, and Cascaded Head and Shoulder Detection. OpenCV, Python or MATLAB are typically used to implement real-time face detection systems.
Face Recognition Based Attendance System with Auto Alert to Guardian using Ca...
This document presents a face recognition based attendance system that automatically marks student attendance using image processing techniques. It uses the Viola-Jones face detection algorithm to detect faces in images and then performs face recognition using algorithms like PCA to identify students and mark their attendance in a database. It also provides alerts to guardians if a student is marked absent by sending SMS or making phone calls. The system aims to automate the manual attendance marking process which is time-consuming and error-prone. It discusses the architecture of the system and the face detection and recognition algorithms used in detail. The paper concludes that the automatic attendance system replaces the manual process and is faster, more efficient and saves time and costs.
Anits dip
This document provides an overview of digital image processing (DIP) and discusses various topics related to it. It begins with welcoming remarks and introductions. It then discusses key areas of application for image processing like optical character recognition, security, compression, and medical imaging. Some main techniques covered include image acquisition, pre-processing, enhancement, segmentation, feature extraction, classification, and understanding. Application areas like remote sensing, astronomy, security, and OCR are also summarized. The document provides examples and illustrations of different image processing concepts.
Similar to The Impact of Segmentation on the Accuracy and Sensitivity of a Melanoma Classifier Based on Skin Lesion Images (20)
Detection and recognition of face using neural network
Detection and recognition of face using neural network
Face recognition: A Comparison of Appearance Based Approaches
Face recognition: A Comparison of Appearance Based Approaches
IP%20FACE%20RECOGNITION%20ATTENDANCE%20SYSTEM.pptx
IP%20FACE%20RECOGNITION%20ATTENDANCE%20SYSTEM.pptx
Face and Eye Detection Varying Scenarios With Haar Classifier_2015
Face and Eye Detection Varying Scenarios With Haar Classifier_2015
braintumordetectionusingimagesegmentationppt-210830184640 (1).pdf
braintumordetectionusingimagesegmentationppt-210830184640 (1).pdf
Brain tumor detection using image segmentation ppt
Brain tumor detection using image segmentation ppt
braintumordetectionusingimagesegmentationppt-210830184640.pdf
braintumordetectionusingimagesegmentationppt-210830184640.pdf
braintumordetectionusingimagesegmentationppt-210830184640.pdf
braintumordetectionusingimagesegmentationppt-210830184640.pdf
Cross Pose Facial Recognition Method for Tracking any Person's Location an Ap...
Cross Pose Facial Recognition Method for Tracking any Person's Location an Ap...
Face Recognition Based Attendance System with Auto Alert to Guardian using Ca...
Face Recognition Based Attendance System with Auto Alert to Guardian using Ca...
More from Oge Marques
Visual Information Retrieval: Advances, Challenges and Opportunities
Visual Information Retrieval: Advances, Challenges and Opportunities discusses advances and challenges in visual information retrieval. Key points include:
- Visual information retrieval aims to find relevant images/videos based on visual and text queries, addressing the "semantic gap" between low-level features and high-level meanings.
- Advances include improved text-based, content-based, and mixed search methods, as well applications in medical image retrieval and mobile visual search.
- Ongoing challenges include capturing image similarity, addressing various representation gaps, understanding user intentions, and developing broad domain solutions.
Image Processing and Computer Vision in iOS
- Image processing and computer vision applications are becoming more common on mobile devices like the iPhone and iPad. There are many opportunities to build successful apps that can improve how users work with images and videos.
- The talk provided an overview of developing image and computer vision apps for iOS, including recommended tools like Core Image and OpenCV. It also offered advice on focusing an app idea on solving a specific problem and being aware of competition and market timing.
- Mobile image processing and computer vision have a promising future, and there is a need for good solutions to specific problems in this area that developers can work on building.
Using games to improve computer vision solutions
Dr. Oge Marques discusses using games to improve computer vision solutions. Specifically, Dr. Marques describes a two-player web-based guessing game called Ask'nSeek that helps solve the computer vision problems of object detection, labeling, and semantic scene segmentation. Ask'nSeek logs spatial relationships and labels from a small number of games per image to train machine learning models for these tasks.
Image retrieval: challenges and opportunities
Google Goggles is a mobile visual search system that allows users to search for information by taking photos with their smartphone cameras. It uses computer vision techniques like interest point detection, feature extraction and indexing to match query images to images in large online databases. This kind of visual search is relevant because of the rise of powerful mobile devices with cameras and popular image-sharing apps. It enables new commercial opportunities for visual search and discovery on mobile phones.
Advances and Challenges in Visual Information Search and Retrieval (WVC 2012 ...
Part I – Concepts, challenges, and state of the art
Part II – Medical image retrieval
Part III – Mobile visual search
Part IV – Where is image search headed?
Mobile Visual Search
Mobile Visual Search (MVS) is a fascinating research field that has the potential to impact how visual data is organized, annotated, and retrieved using mobile devices. The document outlines opportunities in MVS, basic concepts, and technical aspects of MVS systems. It discusses the MVS pipeline including descriptor extraction, interest point detection, feature descriptor computation, feature indexing/matching, and geometric verification. Challenges of MVS like low latency, robust recognition, and handling broad/narrow domains are also covered. The Compressed Histogram of Gradients (CHoG) descriptor is presented as an example of a compact descriptor designed for MVS.
Advances in Image Search and Retrieval
This document discusses advances in image search and retrieval. It begins with an overview of visual information retrieval and its challenges, including the semantic gap between low-level visual features and high-level semantics. It then covers recent techniques like Google image search and similarity search. The document outlines core concepts like capturing similarity, large datasets, and user needs. It also revisits a 2000 paper on the challenges still facing the field, including the unsolved semantic gap and need for standardized evaluation benchmarks.
Image Processing and Computer Vision in iPhone and iPad
This document provides an overview of image processing and computer vision applications for the iPhone and iPad. It discusses the growing market for mobile apps in this field and the technical capabilities of iPhone devices. The document outlines a mini-course on developing iPhone and iPad apps for image processing and computer vision. It covers fundamentals of iOS development like Xcode, Objective-C, classes and objects, and the model-view-controller design pattern. It also discusses OpenCV and examples of commercial apps and student projects.
Recent advances in visual information retrieval marques klu june 2010
The document summarizes key points from a 2010 presentation on visual information retrieval (VIR). It revisits conclusions from a 2000 paper on challenges facing content-based image retrieval (CBIR). While some predictions were accurate, like increased data sizes and interaction options, others were not, like solving image understanding. Significant progress was made on benchmarks and datasets but less on similarity metrics. Medical image retrieval poses new challenges to understand but offers opportunities if VIR methods can adapt to new domains.
Oge Marques (FAU) - invited talk at WISMA 2010 (Barcelona, May 2010)
- Image search and retrieval remains a challenging problem with many open issues even 10 years after it was deemed to be past its early years.
- While progress has been made in areas like datasets, benchmarks and interfaces, core problems around similarity, semantics, and bridging the semantic gap between low-level visual features and high-level concepts remain largely unsolved.
- Narrowing domains and combining content-based techniques with metadata and user involvement through tagging and feedback may provide more successful solutions going forward.
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Visual Information Retrieval: Advances, Challenges and Opportunities
Visual Information Retrieval: Advances, Challenges and Opportunities
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Image Processing and Computer Vision in iPhone and iPad
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Recent advances in visual information retrieval marques klu june 2010
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Oge Marques (FAU) - invited talk at WISMA 2010 (Barcelona, May 2010)
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一比一原版(UO毕业证)渥太华大学毕业证如何办理
UO毕业证录取书【微信95270640】购买（渥太华大学毕业证成绩单硕士学历）Q微信95270640代办UO学历认证留信网伪造渥太华大学学位证书精仿渥太华大学本科/硕士文凭证书补办渥太华大学 diplomaoffer,Transcript购买渥太华大学毕业证成绩单购买UO假毕业证学位证书购买伪造渥太华大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
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留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
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查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时多
How To Control IO Usage using Resource Manager
How To Control IO Usage using Resource Manager
In Multitenant and Exadata architecture
DSSML24_tspann_CodelessGenerativeAIPipelines
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
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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
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版爱尔兰都柏林大学毕业证(本硕）ucd学位证书如何办理
原版一模一样【微信：741003700 】【爱尔兰都柏林大学毕业证(本硕）ucd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
办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【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。
办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【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。
办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【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，办理爱尔兰都柏林大学毕业证(本硕）ucd学位证书【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。
一比一原版(lbs毕业证书)伦敦商学院毕业证如何办理
原版一模一样【微信：741003700 】【(lbs毕业证书)伦敦商学院毕业证成绩单】【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(lbs毕业证书)伦敦商学院毕业证【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(lbs毕业证书)伦敦商学院毕业证【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(lbs毕业证书)伦敦商学院毕业证【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(lbs毕业证书)伦敦商学院毕业证【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。
UofT毕业证如何办理
原件一模一样【微信：95270640】【多伦多大学毕业证UofT学位证成绩单】【微信：95270640】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，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）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，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：95270640】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：95270640】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份【微信：95270640】
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
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办理多伦多大学毕业证硕士毕业证成绩单UofT学位证【微信：95270640 】外观非常精致，由特殊纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理多伦多大学毕业证UofT学位证硕士毕业证成绩单【微信：95270640 】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。
办理多伦多大学毕业证硕士毕业证成绩单UofT学位证【微信：95270640 】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，办理多伦多大学毕业证硕士毕业证成绩单UofT学位证【微信：95270640 】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。
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
学校原件一模一样【微信：741003700 】《(英国UCA毕业证书)创意艺术大学毕业证》【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
一比一原版(Sheffield毕业证书)谢菲尔德大学毕业证如何办理
原版制作【微信:41543339】【(Sheffield毕业证书)谢菲尔德大学毕业证】【微信:41543339】《成绩单、外壳、雅思、offer、留信学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，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）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，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器一制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
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
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版卡尔加里大学毕业证（uc毕业证）如何办理
原版一模一样【微信：741003700 】【卡尔加里大学毕业证（uc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
办理卡尔加里大学毕业证（uc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。
办理卡尔加里大学毕业证（uc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。
办理卡尔加里大学毕业证（uc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，办理卡尔加里大学毕业证（uc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。
End-to-end pipeline agility - Berlin Buzzwords 2024
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
一比一原版(Unimelb毕业证书)墨尔本大学毕业证如何办理
原版制作【微信:41543339】【(Unimelb毕业证书)墨尔本大学毕业证】【微信:41543339】《成绩单、外壳、雅思、offer、留信学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，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）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，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同进口机器一比一制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
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
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
社内勉強会資料_Hallucination of LLMs .
社内エンジニア・リサーチャー勉強会の発表資料「Hallucination of LLMs」を公開しました！
大規模言語モデル(LLM)に起こるハルシネーションのメカニズムと、それらを軽減する方法について紹介しています。
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
by
Timothy Spann
Principal Developer Advocate
https://budapestdata.hu/2024/en/
https://budapestml.hu/2024/en/
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://www.youtube.com/@flank-stack
milvus
vector database
gen ai
generative ai
deep learning
machine learning
apache nifi
apache pulsar
apache kafka
apache flink
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Jio cinema Retention & Engagement Strategy.pdf
Introduction to Jio Cinema**:
- Brief overview of Jio Cinema as a streaming platform.
- Its significance in the Indian market.
- Introduction to retention and engagement strategies in the streaming industry.
2. **Understanding Retention and Engagement**:
- Define retention and engagement in the context of streaming platforms.
- Importance of retaining users in a competitive market.
- Key metrics used to measure retention and engagement.
3. **Jio Cinema's Content Strategy**:
- Analysis of the content library offered by Jio Cinema.
- Focus on exclusive content, originals, and partnerships.
- Catering to diverse audience preferences (regional, genre-specific, etc.).
- User-generated content and interactive features.
4. **Personalization and Recommendation Algorithms**:
- How Jio Cinema leverages user data for personalized recommendations.
- Algorithmic strategies for suggesting content based on user preferences, viewing history, and behavior.
- Dynamic content curation to keep users engaged.
5. **User Experience and Interface Design**:
- Evaluation of Jio Cinema's user interface (UI) and user experience (UX).
- Accessibility features and device compatibility.
- Seamless navigation and search functionality.
- Integration with other Jio services.
6. **Community Building and Social Features**:
- Strategies for fostering a sense of community among users.
- User reviews, ratings, and comments.
- Social sharing and engagement features.
- Interactive events and campaigns.
7. **Retention through Loyalty Programs and Incentives**:
- Overview of loyalty programs and rewards offered by Jio Cinema.
- Subscription plans and benefits.
- Promotional offers, discounts, and partnerships.
- Gamification elements to encourage continued usage.
8. **Customer Support and Feedback Mechanisms**:
- Analysis of Jio Cinema's customer support infrastructure.
- Channels for user feedback and suggestions.
- Handling of user complaints and queries.
- Continuous improvement based on user feedback.
9. **Multichannel Engagement Strategies**:
- Utilization of multiple channels for user engagement (email, push notifications, SMS, etc.).
- Targeted marketing campaigns and promotions.
- Cross-promotion with other Jio services and partnerships.
- Integration with social media platforms.
10. **Data Analytics and Iterative Improvement**:
- Role of data analytics in understanding user behavior and preferences.
- A/B testing and experimentation to optimize engagement strategies.
- Iterative improvement based on data-driven insights.
一比一原版英国赫特福德大学毕业证（hertfordshire毕业证书）如何办理
原版一模一样【微信：741003700 】【英国赫特福德大学毕业证（hertfordshire毕业证书）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
办理英国赫特福德大学毕业证（hertfordshire毕业证书）【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。
办理英国赫特福德大学毕业证（hertfordshire毕业证书）【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。
办理英国赫特福德大学毕业证（hertfordshire毕业证书）【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。
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The Impact of Segmentation on the Accuracy and Sensitivity of a Melanoma Classifier Based on Skin Lesion Images
- 1. #SIIM17 The Impact of Segmentation on the Accuracy and Sensitivity of a Melanoma Classifier Based on Skin Lesion Images Oge Marques, PhD Professor College of Engineering and Computer Science Florida Atlantic University
- 2. #SIIM17 Our Team Adrià Romero López Xavier Giró-i.Nieto Image Processing Group Signal Theory and Communications Department MIDDLE Research Group Oge Marques Borko Furht Jack Burdick Janet Weinthal NSF Award No. 1464537, I/UCRC Phase II under NSF 13-542
- 3. #SIIM17 Outline • Motivation • Context • Scope and goals • Challenges • State of the art • Our work • Hypothesis • Methodology • Experimental results • Ongoing and future work • Concluding remarks
- 4. #SIIM17 Motivation [Source: https://seer.cancer.gov/statfacts/html/melan.html] Melanoma of the skin
- 5. #SIIM17 Context • This is not a typical SIIM presentation • No specialized imaging equipment • No PACS • No DICOM • No metadata • No workflows • Instead... • Regular photographs • Unstructured (and purely visual) data • Minimal ground truth
- 6. #SIIM17 Scope • Skin Disease: An Illustrated Taxonomy • Our focus: skin lesion analysis for (early) melanoma detection [Source: Esteva et al., Nature (2017)]
- 7. #SIIM17 Scope and Goals • Scope: • Help physicians to detect melanoma (a 2-class classifier) • Goals: • Design an intelligent medical imaging skin lesion diagnosis system using deep learning techniques • Achieve (or improve upon) state-of-the-art results for skin lesion classification
- 9. #SIIM17 Success Rate - Sensitivity Physicians, as low as: • 43 % - naked eye • 79 % - dermoscopy (Vestergaard et al, 2008) Deep learning based methods, as high as: • 94.83 % (Jafari et al, 2016; Premaladha and Ravichandran 2016)
- 10. #SIIM17 Classical Techniques [Source: Center For Excellence In Dermatology - Kennewick, WA] CASH (Henning et al., 2007) Color Architecture Symmetry Homogeneity
- 11. #SIIM17 Typical workflow (pre-DL) Source: [Glaister2013] 11
- 12. #SIIM17 Challenges • Segmentation • Artifacts, such as: freckles, hair, shading, and noise. • Irregular, fuzzy lesion border • Low contrast between lesion and surrounding skin • Multi-fragment lesions • Classification • Manual feature extraction is not effective • False positives and false negatives have different impact
- 13. #SIIM17 State of the art • Esteva et al. Nature 2017
- 14. #SIIM17 Our Approach • Pretrained VGG16 (Simonyan & Zisserman, 2014) • Transfer Learning [Source:Blier 2016]
- 16. #SIIM17 Transfer Learning 1. Train on Imagenet 3. Medium dataset: finetuning more data = retrain more of the network (or all of it) 2. Small dataset: feature extractor Freeze these Train this Freeze these Train this Slide credit: Bay Area Deep Learning School Presentation by A. Karpathy Medical Imaging case
- 17. #SIIM17 Prediction Examples: Getting It Right True Positives True Negatives
- 18. #SIIM17 Prediction Examples: Getting It Wrong False Positives False Negatives
- 19. #SIIM17 Our Hypothesis • Image segmentation improves the performance of skin lesion classifiers using convolutional neural networks. [Source:International Skin Imaging Collaboration Archive] Not segmented Perfectly segmented Partially segmented
- 20. #SIIM17 Methods • ISBI 2016 Challenge dataset • Skin Lesion Analysis towards melanoma detection • 1279 RGB images • Labeled as either benign or malignant Class Benign Malignant Total Images Training subset 727 173 900 Testing subset 304 75 379
- 21. #SIIM17 Methods • Dataset balancing through downsampling. • Dataset split: 70-30% training/testing • Input images: • Unsegmented images: straight from the dataset. • Perfectly segmented images: bitwise AND operation of the unaltered images and its corresponding binary mask provided by the ISIC dataset. • Partially segmented images: original binary masks morphologically dilated with a disk-shaped structuring element (50 pixel radius). • Additional preprocessing methods (resizing and normalization) were also performed to match the input size expected by the VGG16 architecture.
- 22. #SIIM17 Results
- 23. #SIIM17 Further Investigation • What if we vary the degree of border expansion? Sensitivity Accuracy AUC Perfect Segmentation 45.3% 58.7% 62.2% +25 53.3% 61.3% 64.2% +50 56.0% 60.7% 62.6% +75 57.3% 59.3% 60.8% +100 34.7% 55.3% 57.9% Unsegmented 24.0% 51.3% 53.2%
- 24. #SIIM17 Ongoing and Future Work •Additional / larger / more challenging datasets •Other CNN architectures •Better image preprocessing •Partnerships and collaborations •Mobile app
- 25. #SIIM17 Concluding remarks • Challenges • Difficulty in acquiring datasets and reproducing / benchmarking results • The “black box” aspect of DL-based solutions • Hard to tell positives from negatives • Learning curve: TensorFlow, Keras, HPC, DL concepts and best practices, etc. • Opportunities • Many variations of the basic classification problem • Mobile app market • Tech-minded dermatology practices
- 26. #SIIM17