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
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.co¬m-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.
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.co¬m-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
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Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Welcome to the first live UiPath Community Day Dubai! Join us for this unique occasion to meet our local and global UiPath Community and leaders. You will get a full view of the MEA region's automation landscape and the AI Powered automation technology capabilities of UiPath. Also, hosted by our local partners Marc Ellis, you will enjoy a half-day packed with industry insights and automation peers networking.
📕 Curious on our agenda? Wait no more!
10:00 Welcome note - UiPath Community in Dubai
Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
10:35: Customer Success Journey
Deepthi Deepak, Head of Intelligent Automation CoE, First Abu Dhabi Bank
11:15 The UiPath approach to GenAI with our three principles: improve accuracy, supercharge productivity, and automate more
Boris Krumrey, Global VP, Automation Innovation, UiPath
12:15 To discover how Marc Ellis leverages tech-driven solutions in recruitment and managed services.
Brendan Lingam, Director of Sales and Business Development, Marc Ellis
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...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: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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
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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