To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Instance Space Analysis for Search Based Software EngineeringAldeida Aleti
Search-Based Software Engineering is now a mature area with numerous techniques developed to tackle some of the most challenging software engineering problems, from requirements to design, testing, fault localisation, and automated program repair. SBSE techniques have shown promising results, giving us hope that one day it will be possible for the tedious and labour intensive parts of software development to be completely automated, or at least semi-automated. In this talk, I will focus on the problem of objective performance evaluation of SBSE techniques. To this end, I will introduce Instance Space Analysis (ISA), which is an approach to identify features of SBSE problems that explain why a particular instance is difficult for an SBSE technique. ISA can be used to examine the diversity and quality of the benchmark datasets used by most researchers, and analyse the strengths and weaknesses of existing SBSE techniques. The instance space is constructed to reveal areas of hard and easy problems, and enables the strengths and weaknesses of the different SBSE techniques to be identified. I will present on how ISA enabled us to identify the strengths and weaknesses of SBSE techniques in two areas: Search-Based Software Testing and Automated Program Repair. Finally, I will end my talk with future directions of the objective assessment of SBSE techniques.
Amazon Product Review Sentiment Analysis with Machine Learningijtsrd
Users of Amazons online shopping service are allowed to leave feedback for the items they buy. Amazon makes no effort to monitor or limit the scope of these reviews. Although the amount of reviews for various items varies, the reviews provide easily accessible and abundant data for a variety of applications. This paper aims to apply and expand existing natural language processing and sentiment analysis research to data obtained from Amazon. The number of stars given to a product by a user is used as training data for supervised machine learning. Since more people are dependent on online products these days, the value of a review is increasing. Before making a purchase, a buyer must read thousands of reviews to fully comprehend a product. In this day and age of machine learning, however, sorting through thousands of comments and learning from them would be much easier if a model was used to polarize and learn from them. We used supervised learning to polarize a massive Amazon dataset and achieve satisfactory accuracy. Ravi Kumar Singh | Dr. Kamalraj Ramalingam "Amazon Product Review Sentiment Analysis with Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42372.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42372/amazon-product-review-sentiment-analysis-with-machine-learning/ravi-kumar-singh
Ilya Kuzovkin - Adaptive Interactive Learning for Brain-Computer InterfacesIlya Kuzovkin
This is a brief description of the approach and algorithm I proposed in my master's thesis. The core algorithm keeps track of both user's and computer's progress and gives them valuable feedback.
Instance Space Analysis for Search Based Software EngineeringAldeida Aleti
Search-Based Software Engineering is now a mature area with numerous techniques developed to tackle some of the most challenging software engineering problems, from requirements to design, testing, fault localisation, and automated program repair. SBSE techniques have shown promising results, giving us hope that one day it will be possible for the tedious and labour intensive parts of software development to be completely automated, or at least semi-automated. In this talk, I will focus on the problem of objective performance evaluation of SBSE techniques. To this end, I will introduce Instance Space Analysis (ISA), which is an approach to identify features of SBSE problems that explain why a particular instance is difficult for an SBSE technique. ISA can be used to examine the diversity and quality of the benchmark datasets used by most researchers, and analyse the strengths and weaknesses of existing SBSE techniques. The instance space is constructed to reveal areas of hard and easy problems, and enables the strengths and weaknesses of the different SBSE techniques to be identified. I will present on how ISA enabled us to identify the strengths and weaknesses of SBSE techniques in two areas: Search-Based Software Testing and Automated Program Repair. Finally, I will end my talk with future directions of the objective assessment of SBSE techniques.
Amazon Product Review Sentiment Analysis with Machine Learningijtsrd
Users of Amazons online shopping service are allowed to leave feedback for the items they buy. Amazon makes no effort to monitor or limit the scope of these reviews. Although the amount of reviews for various items varies, the reviews provide easily accessible and abundant data for a variety of applications. This paper aims to apply and expand existing natural language processing and sentiment analysis research to data obtained from Amazon. The number of stars given to a product by a user is used as training data for supervised machine learning. Since more people are dependent on online products these days, the value of a review is increasing. Before making a purchase, a buyer must read thousands of reviews to fully comprehend a product. In this day and age of machine learning, however, sorting through thousands of comments and learning from them would be much easier if a model was used to polarize and learn from them. We used supervised learning to polarize a massive Amazon dataset and achieve satisfactory accuracy. Ravi Kumar Singh | Dr. Kamalraj Ramalingam "Amazon Product Review Sentiment Analysis with Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42372.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42372/amazon-product-review-sentiment-analysis-with-machine-learning/ravi-kumar-singh
Ilya Kuzovkin - Adaptive Interactive Learning for Brain-Computer InterfacesIlya Kuzovkin
This is a brief description of the approach and algorithm I proposed in my master's thesis. The core algorithm keeps track of both user's and computer's progress and gives them valuable feedback.
Co-Extracting Opinions from Online ReviewsEditor IJCATR
Exclusion of opinion targets and words from online reviews is an important and challenging task in opinion mining. The
opinion mining is the use of natural language processing, text analysis and computational process to identify and recover the subjective
information in source materials. This paper propose a Supervised word alignment model, which identifying the opinion relation. Rather
than this paper focused on topical relation, in which to extract the relevant information or features only from a particular online reviews.
It is based on feature extraction algorithm to identify the potential features. Finally the items are ranked based on the frequency of
positive and negative reviews. Compared to previous methods, our model captures opinion relation and feature extraction more precisely.
One of the most advantages that our model obtain better precision because of supervised alignment model. In addition, an opinion
relation graph is used to refer the relationship between opinion targets and opinion words.
These are slides from a talk I gave at the British Computer Society's SIGIST Conference in June 2013. The talk attempts to provoke the audience into think beyond the current standard approaches for testing in the industry.
Qualitative Studies in Software Engineering - Interviews, Observation, Ground...alessio_ferrari
This
Lecture about qualitative data collection methods and qualitative data analysis in software engineering. Topics covered are:
1. Sampling
2. Interviews
3. Observation and Participant Observation
4. Archival Data Collection
5. Grounded theory, Coding, Thematic Analysis
6. Threats to validity in qualitative studies
Find the videos at: https://www.youtube.com/playlist?list=PLSKM4VZcJjV-P3fFJYMu2OhlTjEr9Bjl0
GTC 2021: Counterfactual Learning to Rank in E-commerceGrubhubTech
Many ecommerce companies have extensive logs of user behavior such as clicks and conversions. However, if supervised learning is naively applied, then systems can suffer from poor performance due to bias and feedback loops. Using techniques from counterfactual learning we can leverage log data in a principled manner in order to model user behaviour and build personalized recommender systems. At Grubhub, a user journey begins with recommendations and the vast majority of conversions are powered by recommendations. Our recommender policies can drive user behavior to increase orders and/or profit. Accordingly, the ability to rapidly iterate and experiment is very important. Because of our powerful GPU workflows, we can iterate 200% more rapidly than with counterpart CPU workflows. Developers iterate ideas with notebooks powered by GPUs. Hyperparameter spaces are explored up to 8x faster with multi-GPUs Ray clusters. Solutions are shipped from notebooks to production in half the time with nbdev. With our accelerated DS workflows and Deep Learning on GPUs, we were able to deliver a +12.6% conversion boost in just a few months. In this talk we hope to present modern techniques for industrial recommender systems powered by GPU workflows. First a small background on counterfactual learning techniques, then followed by practical information and data from our industrial application.
By Alex Egg, accepted to Nvidia GTC 2021 Conference
Unsupervised Anomaly Detection with Isolation Forest - Elena SharovaPyData
PyData London 2018
This talk will focus on the importance of correctly defining an anomaly when conducting anomaly detection using unsupervised machine learning. It will include a review of Isolation Forest algorithm (Liu et al. 2008), and a demonstration of how this algorithm can be applied to transaction monitoring, specifically to detect money laundering.
---
www.pydata.org
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Learning Vague Knowledge From Socially Generated Content in an Enterprise Fra...Panos Alexopoulos
The advent and wide proliferation of Social Web in the re-
cent years has promoted the concept of social interaction as an important influencing factor of the way enterprises and organizations conduct business. Among the fields influenced is that of Enterprise Knowledge Management, where adoption of social computing approaches aims at increasing and maintaining at high levels the active participation of users in the organization's knowledge management activities. An important challenge towards this is the achievement of the right balance between informalities of socially generated data and the required formality of enterprise knowledge. In this context, we focus on the problem of mining vague knowledge from social content generated within an enterprise framework and we propose a learning framework based on microblogging and fuzzy ontologies.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Reversible watermarking based on invariant image classification and dynamic h...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Co-Extracting Opinions from Online ReviewsEditor IJCATR
Exclusion of opinion targets and words from online reviews is an important and challenging task in opinion mining. The
opinion mining is the use of natural language processing, text analysis and computational process to identify and recover the subjective
information in source materials. This paper propose a Supervised word alignment model, which identifying the opinion relation. Rather
than this paper focused on topical relation, in which to extract the relevant information or features only from a particular online reviews.
It is based on feature extraction algorithm to identify the potential features. Finally the items are ranked based on the frequency of
positive and negative reviews. Compared to previous methods, our model captures opinion relation and feature extraction more precisely.
One of the most advantages that our model obtain better precision because of supervised alignment model. In addition, an opinion
relation graph is used to refer the relationship between opinion targets and opinion words.
These are slides from a talk I gave at the British Computer Society's SIGIST Conference in June 2013. The talk attempts to provoke the audience into think beyond the current standard approaches for testing in the industry.
Qualitative Studies in Software Engineering - Interviews, Observation, Ground...alessio_ferrari
This
Lecture about qualitative data collection methods and qualitative data analysis in software engineering. Topics covered are:
1. Sampling
2. Interviews
3. Observation and Participant Observation
4. Archival Data Collection
5. Grounded theory, Coding, Thematic Analysis
6. Threats to validity in qualitative studies
Find the videos at: https://www.youtube.com/playlist?list=PLSKM4VZcJjV-P3fFJYMu2OhlTjEr9Bjl0
GTC 2021: Counterfactual Learning to Rank in E-commerceGrubhubTech
Many ecommerce companies have extensive logs of user behavior such as clicks and conversions. However, if supervised learning is naively applied, then systems can suffer from poor performance due to bias and feedback loops. Using techniques from counterfactual learning we can leverage log data in a principled manner in order to model user behaviour and build personalized recommender systems. At Grubhub, a user journey begins with recommendations and the vast majority of conversions are powered by recommendations. Our recommender policies can drive user behavior to increase orders and/or profit. Accordingly, the ability to rapidly iterate and experiment is very important. Because of our powerful GPU workflows, we can iterate 200% more rapidly than with counterpart CPU workflows. Developers iterate ideas with notebooks powered by GPUs. Hyperparameter spaces are explored up to 8x faster with multi-GPUs Ray clusters. Solutions are shipped from notebooks to production in half the time with nbdev. With our accelerated DS workflows and Deep Learning on GPUs, we were able to deliver a +12.6% conversion boost in just a few months. In this talk we hope to present modern techniques for industrial recommender systems powered by GPU workflows. First a small background on counterfactual learning techniques, then followed by practical information and data from our industrial application.
By Alex Egg, accepted to Nvidia GTC 2021 Conference
Unsupervised Anomaly Detection with Isolation Forest - Elena SharovaPyData
PyData London 2018
This talk will focus on the importance of correctly defining an anomaly when conducting anomaly detection using unsupervised machine learning. It will include a review of Isolation Forest algorithm (Liu et al. 2008), and a demonstration of how this algorithm can be applied to transaction monitoring, specifically to detect money laundering.
---
www.pydata.org
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Learning Vague Knowledge From Socially Generated Content in an Enterprise Fra...Panos Alexopoulos
The advent and wide proliferation of Social Web in the re-
cent years has promoted the concept of social interaction as an important influencing factor of the way enterprises and organizations conduct business. Among the fields influenced is that of Enterprise Knowledge Management, where adoption of social computing approaches aims at increasing and maintaining at high levels the active participation of users in the organization's knowledge management activities. An important challenge towards this is the achievement of the right balance between informalities of socially generated data and the required formality of enterprise knowledge. In this context, we focus on the problem of mining vague knowledge from social content generated within an enterprise framework and we propose a learning framework based on microblogging and fuzzy ontologies.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Reversible watermarking based on invariant image classification and dynamic h...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Comparable Entity Mining from Comparative Questions Abstract 2017ioshean
Comparing one thing with another is a typical part of human decision making process. However, it is not always easy to know what to compare and what are the alternatives. To address this difficulty, we present a novel way to automatically mine comparable entities from comparative questions that users posted online.
Driver Analysis and Product Optimization with Bayesian NetworksBayesia USA
Market driver analysis and product optimization are one of the central tasks in Product Marketing and thus relevant to virtually all types of businesses. BayesiaLab provides a uni!ed software platform, which can, based on consumer data,
1. provide deep understanding of the market preference structure
2. directly generate recommendations for prioritized product actions.
The proposed approach utilizes Probabilistic Structural Equation Models (PSEM), based on machine-learned Bayesian networks. PSEMs provide an ef!cient alternative to Structural Equation Models (SEM), which have been used traditionally in market research.
Annotated Bibliography
.
Guidelines: Annotated Bibliography
Purpose: Explore current literature (collection of writing on a specific topic) to increase
knowledge of leadership in nursing practice.
The annotated bibliography assignment will help students prepare to design and present a poster presentation regarding nursing leadership in practice. The focus is building student knowledge of various leadership roles in nursing (current trends). The assignment also promotes student reflection on development of their own leadership skills.
Students will read the summary of the Institute of Medicine (IOM) “The Future of Nursing: Leading Change, Advancing Health” for baseline identification of leadership roles (posted in Blackboard).
Students will then search the literature to identify and select five (5) nurse leaders, who will be the topic of the annotated bibliography summaries (students must use credible sources when searching literature).
Students may also choose to submit 2 of the 5 annotated bibliography summaries on the following topics:
1. Student Nurse Leaders (2)
2. Current Trends in Nursing Leadership (3)
Each of the five annotated bibliography summaries should be no more than one page, typed, and must include the following:
1. The identified leader’s specific roles & responsibilities
2. The identified leader’s accomplishments
3. Barriers and facilitators to leader achievement of goals
4. Knowledge gained from reading content included in the annotated bibliography summary
Annotated Bibliography Grading Rubric
Criteria
Points Possible
Points Earned
Faculty Comments
Provides a clear description of the identified leader’s role (s) and responsibilities (related to nursing)
20
Provides examples of the leader’s
accomplishments (at least 2 examples)
10
Summarizes barriers inhibiting the leader’s achievement of goals
15
Summarizes facilitators enhancing the leader’s achievement of goals
15
Summary of leadership knowledge gained from reading content included in the annotated bibliography summary
20
Correct grammar/spelling
10
APA format
10
Total
100
[Type text]
30 February 2005 QUEUE rants: [email protected] DARNEDTesting large systems is a daunting task, but there are steps we can take to ease the pain.
T
he increasing size and complexity of software, coupled with concurrency and dis-
tributed systems, has made apparent the ineffectiveness of using only handcrafted
tests. The misuse of code coverage and avoidance of random testing has exacer-
bated the problem. We must start again, beginning with good design (including
dependency analysis), good static checking (including model property checking), and
good unit testing (including good input selection). Code coverage can help select and
prioritize tests to make you more effi cient, as can the all-pairs technique for controlling
the number of confi gurations. Finally, testers can use models to generate test coverage
and good stochastic.
Abstract—Combinatorial testing (also called interaction testing) is an effective specification-based test input generation technique. By now most of research work in combinatorial testing aims to propose novel approaches trying to generate test suites with minimum size that still cover all the pairwise, triple, or n-way combinations of factors. Since the difficulty of solving this problem is demonstrated to be NP-hard, existing approaches have been designed to generate optimal or near optimal combinatorial test suites in polynomial time. In this paper, we try to apply particle swarm optimization (PSO), a kind of meta-heuristic search technique, to pairwise testing (i.e. a special case of combinatorial testing aiming to cover all the pairwise combinations). To systematically build pairwise test suites, we propose two different PSO based algorithms. One algorithm is based on one-test-at-a-time strategy and the other is based on IPO-like strategy. In these two different algorithms, we use PSO to complete the construction of a single test. To successfully apply PSO to cover more uncovered pairwise combinations in this construction process, we provide a detailed description on how to formulate the search space, define the fitness function and set some heuristic settings. To verify the effectiveness of our approach, we implement these algorithms and choose some typical inputs. In our empirical study, we analyze the impact factors of our approach and compare our approach to other well-known approaches. Final empirical results show the effectiveness and efficiency of our approach.
Top 100+ Google Data Science Interview Questions.pdfDatacademy.ai
Data science interviews can be particularly difficult due to the many proficiencies that you'll have to demonstrate (technical skills, problem solving, communication) and the generally high bar to entry for the industry.we Provide Top 100+ Google Data Science Interview Questions : All You Need to know to Crack it
visit by :-https://www.datacademy.ai/google-data-science-interview-questions/
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Similar to Comparable entity mining from comparative questions (20)
Scalable face image retrieval using attribute enhanced sparse codewordsIEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Scalable face image retrieval using attribute enhanced sparse codewordsIEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Noise reduction based on partial reference, dual-tree complex wavelet transfo...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Local directional number pattern for face analysis face and expression recogn...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
An access point based fec mechanism for video transmission over wireless la nsIEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Spoc a secure and privacy preserving opportunistic computing framework for mo...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Secure and efficient data transmission for cluster based wireless sensor netw...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Privacy preserving back propagation neural network learning over arbitrarily ...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Geo community-based broadcasting for data dissemination in mobile social netw...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Enabling data dynamic and indirect mutual trust for cloud computing storage s...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Dynamic resource allocation using virtual machines for cloud computing enviro...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
A secure protocol for spontaneous wireless ad hoc networks creationIEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Utility privacy tradeoff in databases an information-theoretic approachIEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
When stars align: studies in data quality, knowledge graphs, and machine lear...
Comparable entity mining from comparative questions
1. Comparable Entity Mining from Comparative Questions
Abstract:
Comparing one thing with another is a typical part of human decision making process.
However, it is not always easy to know what to compare and what are the alternatives. To
address this difficulty, we present a novel way to automatically mine comparable entities from
comparative questions that users posted online.To ensure high precision and high recall, we
develop a weakly-supervised bootstrapping method for comparative question identification and
comparable entity extraction by leveraging a large online question archive. The experimental
results show our method achieves F1-measure of 82.5% in comparative question identification
and 83.3% in comparable entity extraction. Both significantly outperform an existing state-of-
the-art method.
GLOBALSOFT TECHNOLOGIES
IEEE PROJECTS & SOFTWARE DEVELOPMENTS
IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE
BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS
CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401
Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com
2. Architecture Diagram:
Existing system:
comparator mining is related to the research on entity and relation extraction in information
extraction Specifically, the most relevant work is mining comparative sentences and relations.
Their methods applied class sequential rules (CSR) and label sequential rules (LSR) learned
from annotated corpora to identify comparative sentences and extract comparative relations
respectively in the news and review domains. The same techniques can be applied to
comparative question identification and comparator mining from questions.
Disadvantages:
This methods typically can achieve high precision but suffer from low recall.
Proposed system:
we present a novel weakly supervised method to identify comparative questions and extract
comparator pairs simultaneously. We rely on the key insight that a good comparative question
identification pattern should extract good comparators, and a good comparator pair should occur
in good comparative questions to bootstrap the extraction and identification process. By
leveraging large amount of unlabeled data and the bootstrapping process with slight supervision
to determine four parameters.
3. Advantages:
To ensure high precision and high recall, we develop a weakly-supervised bootstrapping
method for comparative question identification and comparable entity extraction by leveraging
a large online question archive
Main Modules:
Pattern Generation(comparable Entity):
1. Lexical patterns
2. Generalized patterns
3. Specialized patterns
Pattern Evaluation(comparable questions):
Lexical patterns:
Lexical patterns indicate sequential patterns consisting of only words and symbols ($C, #start,
and #end). They are generated by suffix tree algorithm with two constraints: A pattern should
contain more than one $C, and its frequency in collection should be more than an empirically
determined number.
Generalized patterns:
A lexical pattern can be too specific. Thus, we generalize lexical patterns by replacing one or
more words with their POS tags. 2 − 1 generalized patterns can be produced from a lexical
pattern containing N words excluding $Cs.
Specialized patterns:
In some cases, a pattern can be too general. For example, although a question “ipod or zune?”
is comparative, the pattern “<$C or $C>” is too general, and there can be many non-
comparative questions matching the pattern, for instance, “true or false?”. For this reason, we
perform pattern specialization by adding POS tags to all comparator slots. For example ,from
the lexical pattern “<$C or $C>”and the question “ipod or zune?”, “<$C/NNor $C/NN?>” will
be produced as a specialized pattern.
4. Pattern Evaluation(comparable questions):
In complete knowledge about reliable comparator pairs. For example, very few reliable pairs are
generally discovered in early stage of bootstrapping. In this case, the value of might be
underestimated which could affect the effectiveness of on distinguishing IEPs from non-reliable
patterns. We mitigate this problem by a look ahead procedure. Let us denote the set of candidate
patterns at the iteration k by . We define the support for comparator pair which can be
extracted by and does not exist in the current reliable set.
System Configuration:
HARDWARE REQUIREMENTS:
Hardware - Pentium
Speed - 1.1 GHz
RAM - 1GB
Hard Disk - 20 GB
Floppy Drive - 1.44 MB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA
SOFTWARE REQUIREMENTS:
Operating System : Windows
Technology : Java and J2EE
Web Technologies : Html, JavaScript, CSS
IDE : My Eclipse
Web Server : Tomcat
5. Tool kit : Android Phone
Database : My SQL
Java Version : J2SDK1.5