This document presents research on improving the detection of low quality posts on Stack Overflow. The researchers analyzed metrics related to post quality, readability, and popularity to build a classification model. They applied the model to refine the review queue, reducing its size by up to 44% while increasing the percentage of low quality posts it contained. Their best approach significantly enhanced the prioritization of posts actually requiring human review.
Towards Discovering the Role of Emotions in Stack OverflowNicole Novielli
N. Novielli, F. Calefato, F. Lanubile. “Towards Discovering the Role of Emotions in Stack Overflow” – In Proceedings of the 6th International Workshop on Social Software Engineering pp. 33-36, ACM 2014
************************************************************************************************************
Today, people increasingly try to solve domain-specific problems through interaction on online Question and Answer (Q&A) sites, such as Stack Overflow. The growing success of the Stack Overflow community largely depends on the will of their members to answer others’ questions. Recent research has shown that the factors that push members of online communities encompass both social and technical aspects. Yet, we argue that also the emotional style of a technical question does influence the probability of promptly obtaining a satisfying answer. In this presentation, we describe the design of an empirical study aimed to investigate the role of affective lexicon on the questions posted in Stack Overflow.
- Study the architecture and design
- Compare Old & New Technology stack
- Analyze evolution of architecture and scalability
- Lessons learned over time
In this talk we briefly discuss some of our recent studies of Stack Overflow, a popular Q&A site targeting software developers. As opposed to studies of software artefacts discussed at Stack Overflow (e.g., APIs or programming examples), we focus on studying individuals active on Stack Overflow---who are they, what motivates them, and what affects their participation in Stack Overflow discussions.
Our findings indicate that Stack Overflow is no different from other communities of software developers in terms of gender representation but is significantly different from them in terms of gender engagement: controlling for engagement duration women and men ask and answer comparable number of questions, but women disengage faster. We conjecture that faster disengagement of women is the less pretty consequence of gamification mechanisms embedded in Stack Overflow, the same gamification mechanisms that provide developers with faster answers than ever before, attract numerous contributors and ultimately catalyse software development.
As an additional contribution we present genderComputer, a tool inferring gender of an individual based on her/his name and location.
The talk is based on the following papers:
* Gender, representation and online participation: A quantitative study, Vasilescu, B., Capiluppi, A. and Serebrenik, A., Interacting with Computers. 2013, Oxford University Press.
* How social Q&A sites are changing knowledge sharing in open source software communities, Vasilescu, B., Serebrenik, A., Devanbu, P. T. and Filkov, V., In CSCW, 2014, ACM.
* StackOverflow and GitHub: Associations between software development and crowdsourced knowledge, Vasilescu, B., Filkov, V. and Serebrenik, A., In Social Computing, 2013, IEEE.
This presentation describes the approach that I developed for Kaggle's WISE 2014 challenge. The challenge was about multi-label classification of printed media articles to topics. The main ingredients of my solution was a plug-in rule approach for F1 maximization, feature selection using a chi squared based criterion, feature normalization and a multi-view ensemble scheme.
Towards Discovering the Role of Emotions in Stack OverflowNicole Novielli
N. Novielli, F. Calefato, F. Lanubile. “Towards Discovering the Role of Emotions in Stack Overflow” – In Proceedings of the 6th International Workshop on Social Software Engineering pp. 33-36, ACM 2014
************************************************************************************************************
Today, people increasingly try to solve domain-specific problems through interaction on online Question and Answer (Q&A) sites, such as Stack Overflow. The growing success of the Stack Overflow community largely depends on the will of their members to answer others’ questions. Recent research has shown that the factors that push members of online communities encompass both social and technical aspects. Yet, we argue that also the emotional style of a technical question does influence the probability of promptly obtaining a satisfying answer. In this presentation, we describe the design of an empirical study aimed to investigate the role of affective lexicon on the questions posted in Stack Overflow.
- Study the architecture and design
- Compare Old & New Technology stack
- Analyze evolution of architecture and scalability
- Lessons learned over time
In this talk we briefly discuss some of our recent studies of Stack Overflow, a popular Q&A site targeting software developers. As opposed to studies of software artefacts discussed at Stack Overflow (e.g., APIs or programming examples), we focus on studying individuals active on Stack Overflow---who are they, what motivates them, and what affects their participation in Stack Overflow discussions.
Our findings indicate that Stack Overflow is no different from other communities of software developers in terms of gender representation but is significantly different from them in terms of gender engagement: controlling for engagement duration women and men ask and answer comparable number of questions, but women disengage faster. We conjecture that faster disengagement of women is the less pretty consequence of gamification mechanisms embedded in Stack Overflow, the same gamification mechanisms that provide developers with faster answers than ever before, attract numerous contributors and ultimately catalyse software development.
As an additional contribution we present genderComputer, a tool inferring gender of an individual based on her/his name and location.
The talk is based on the following papers:
* Gender, representation and online participation: A quantitative study, Vasilescu, B., Capiluppi, A. and Serebrenik, A., Interacting with Computers. 2013, Oxford University Press.
* How social Q&A sites are changing knowledge sharing in open source software communities, Vasilescu, B., Serebrenik, A., Devanbu, P. T. and Filkov, V., In CSCW, 2014, ACM.
* StackOverflow and GitHub: Associations between software development and crowdsourced knowledge, Vasilescu, B., Filkov, V. and Serebrenik, A., In Social Computing, 2013, IEEE.
This presentation describes the approach that I developed for Kaggle's WISE 2014 challenge. The challenge was about multi-label classification of printed media articles to topics. The main ingredients of my solution was a plug-in rule approach for F1 maximization, feature selection using a chi squared based criterion, feature normalization and a multi-view ensemble scheme.
Transferring Software Testing Tools to PracticeTao Xie
ACM SIGSOFT Webinar co-presented by Nikolai Tillmann (Microsoft), Judith Bishop (Microsoft Research), Pratap Lakshman (Microsoft), Tao Xie (University of Illinois at Urbana-Champaign) http://www.sigsoft.org/resources/webinars.html
In this talk, I consider various channels of social media and consider how they impact software engineering. I then focus on what the channels enable (e.g. peer production, social programmer) and how these may change the laws and assumptions of software evolution.
"The (R)evolution of Social Media in Software Engineering",
Margaret-Anne (Peggy) Storey
Leif Singer
Brendan Cleary
Fernando Figueira Filho
Alexey Zagalsky
Presented at ICSE 2014, Future of Software Engineering Track, Hyderabad, June 4, 2014.
A preprint of the paper can be found here: http://chiselgroup.files.wordpress.com/2014/01/fose14main-storey-submitted.pdf
Summary of ICSE 2011 Panel on "What Industry wants from Research". This is a summary of all the presentations from that panel that I presented in an invited talk at the CSER meeting in Toronto, November, 2011.
A companion blogpost is available here: http://margaretstorey.com/blog/2016/12/01/fse2016panel/
The panel is available on YouTube: https://youtu.be/sE_jX92jJr8
Abstract: As software becomes more ubiquitous and pervasive in today’s interconnected and instrumented world, software engineering—as a practice and as a research topic—is having a hard time keeping up. In this panel, we invite FSE 2016’s participants to engage with five prominent software engineering researchers as they reflect on the state of current software engineering research and share how they each believe our work impacts (or should impact) science, society and industry. Our panelists will discuss whether our community as a whole is achieving the right balance of science, engineering and design in its combined research efforts. This lively and interactive panel discussion will also highlight new areas of research that our community should pay more attention to, as well as suggest new ways of conducting research that could improve the impact of software engineering research in the near and distant future.
Panelists:
Lionel Briand, University of Luxembourg
Prem Devanbu, University of California at Davis
Peri Tarr, IBM Research
Laurie Williams, North Carolina State University
Tao Xie, University of Illinois at Urbana-Champaign
Moderator:
Margaret-Anne Storey, University of Victoria
SLE 2012 Keynote: Cognitive and Social Challenges of Ontology Use in the Biom...Margaret-Anne Storey
ABSTRACT: Ontologies can provide a conceptualization of a domain leading to a common vocabulary for communities of researchers and important standards to facilitate computation, software interoperability and data reuse. Most successful ontologies, especially those that have been developed by diverse communities over long periods of time, are typically large and complex. To address this complexity, ontology authoring and browsing tools must provide cognitive support to improve comprehension of the many concepts and relationships in ontologies. Also, ontology tools must support collaboration as the heart of ontology design and use is centered on community consensus.
In this talk, I will describe how standardized ontologies are developed and used in the biomedical and clinical domains to aid in scientific and medical discoveries. Specifically, I will present how the US National Center for Biomedical Ontology has designed the BioPortal ontology library (and associated technologies) to promote the use of standardized ontologies and tools. I will review how BioPortal and other ontology tools use established and novel visualization and collaboration approaches to improve ontology authoring and data curation activities. I will also discuss an ambitious project by the World Health Organization that leverages the use of social media to broaden participation in the development of the next version of the International Classification of Diseases. To conclude, I will discuss the challenges and opportunities that arise from using ontologies to bridge communities that manage and curate important information resources.
Analysis of StackOverflow posts/user data trend analysis. Predicting time to answer (classification) using Weka. CSCI599 final project on Social media data analytics
To Bot or Not: How Bots can Support Collaboration in Software Engineering (I...Margaret-Anne Storey
Abstract and video link below)
Presented at ICGSE 2016: Conference on Global Software Engineering (http://www.ics.uci.edu/~icgse2016/2_0cfp.html)
Video link: https://www.youtube.com/watch?v=BsgnLwPMqWM&feature=youtu.be&list=PLcm9UtazJCOLBwPaaHNn_htAjPAXIdRGr
Abstract:
Software development stakeholders require a constellation of tools to support their communication, collaboration and coordination activities. But poor tool integration can lead to gaps in knowledge flow, or worse, to an overabundance of shared communication and information. The software development community is witnessing the rise of "social bots" to integrate diverse development and communication tools and to address the challenge of information overload. A bot is a conversational user interface that can automate rote or tedious tasks. It may fetch or share information, extract and analyze data, detect and monitor events and activities in communication and social media, connect developers with each other or with other tools, or it may provide feedback on individual and collaborative development tasks. Some bots are emerging as important team members, providing support for individual and team task management and for the automation of dev-ops and customer support. However, the rapid adoption of bots and the platforms that support them brings possible drawbacks. Designing effective platforms for bots is challenging and bots may introduce alienation among stakeholders or lead to other technical challenges. In this talk, I will discuss the emerging role of bots in software development and describe some of the advantages and challenges that may lie ahead.
According to Altimeter Group research, the average enterprise-class company owns 178 social media accounts, while 13 departments—from marketing to customer support to legal-- actively engage in social media.
Yet social media— and as a result, social data— are still largely isolated from business-critical enterprise data sourced from platforms such as Customer Relationship Management, Business Intelligence and market research.
This lack of a holistic view of social signals in the context of other enterprise and external data can lead to partially-informed decisions, missed opportunity, and increased risk and cost, as the organization makes decisions without the benefit of critical input from external constituencies.
In this Altimeter Group research report reflecting input from 35 enterprise-class organizations and technology ecosystem contributors, industry analyst Susan Etlinger lays out an imperative for Social Data Intelligence, identifying key dimensions that organizations must understand, pragmatic steps they can take toward mature integration, and how successful businesses are already using social data in the context of other critical enterprise data to drive measurable value throughout the organization.
CINF 17: Comparing Cahn-Ingold-Prelog Rule Implementations: The need for an o...NextMove Software
The Cahn-Ingold-Prelog (CIP) priority rules have been the corner stone in written communication of stereo-chemical configuration for more than half a century. The rules rank ligands around a stereocentre allowing an atom order and layout invariant stereo-descriptor to be assigned, for example R (right) or S (left) for tetrahedral atoms. Despite their widespread daily use, many chemists may be surprised to find that beyond trivial cases, different software may assign different labels to the same structure diagram.
There have been several attempts to either replace or amend the CIP rules. This talk will highlight the more challenging aspects of the ranking and present a comparison of software that provide CIP labels and where they disagree. Providing an IUPAC verified free and open source CIP implementation would allow software maintainers and vendors to validate and improve their implementations. Ultimately this would improve the accuracy in exchange of written chemical information for all.
Without Self-Service Operations, the Cloud is Just Expensive Hosting 2.0 - (a...dev2ops
Damon Edwards (DTO Solutions) presentation at Cloud Expo 2014 Santa Clara.
We are all here because we are sold on the transformative promise of The Cloud. But what good is all of this ephemeral, on-demand infrastructure if your usage doesn't actually improve the agility and speed of your business? How must Operations adapt in order to avoid stifling your Cloud initiative?
Transferring Software Testing Tools to PracticeTao Xie
ACM SIGSOFT Webinar co-presented by Nikolai Tillmann (Microsoft), Judith Bishop (Microsoft Research), Pratap Lakshman (Microsoft), Tao Xie (University of Illinois at Urbana-Champaign) http://www.sigsoft.org/resources/webinars.html
In this talk, I consider various channels of social media and consider how they impact software engineering. I then focus on what the channels enable (e.g. peer production, social programmer) and how these may change the laws and assumptions of software evolution.
"The (R)evolution of Social Media in Software Engineering",
Margaret-Anne (Peggy) Storey
Leif Singer
Brendan Cleary
Fernando Figueira Filho
Alexey Zagalsky
Presented at ICSE 2014, Future of Software Engineering Track, Hyderabad, June 4, 2014.
A preprint of the paper can be found here: http://chiselgroup.files.wordpress.com/2014/01/fose14main-storey-submitted.pdf
Summary of ICSE 2011 Panel on "What Industry wants from Research". This is a summary of all the presentations from that panel that I presented in an invited talk at the CSER meeting in Toronto, November, 2011.
A companion blogpost is available here: http://margaretstorey.com/blog/2016/12/01/fse2016panel/
The panel is available on YouTube: https://youtu.be/sE_jX92jJr8
Abstract: As software becomes more ubiquitous and pervasive in today’s interconnected and instrumented world, software engineering—as a practice and as a research topic—is having a hard time keeping up. In this panel, we invite FSE 2016’s participants to engage with five prominent software engineering researchers as they reflect on the state of current software engineering research and share how they each believe our work impacts (or should impact) science, society and industry. Our panelists will discuss whether our community as a whole is achieving the right balance of science, engineering and design in its combined research efforts. This lively and interactive panel discussion will also highlight new areas of research that our community should pay more attention to, as well as suggest new ways of conducting research that could improve the impact of software engineering research in the near and distant future.
Panelists:
Lionel Briand, University of Luxembourg
Prem Devanbu, University of California at Davis
Peri Tarr, IBM Research
Laurie Williams, North Carolina State University
Tao Xie, University of Illinois at Urbana-Champaign
Moderator:
Margaret-Anne Storey, University of Victoria
SLE 2012 Keynote: Cognitive and Social Challenges of Ontology Use in the Biom...Margaret-Anne Storey
ABSTRACT: Ontologies can provide a conceptualization of a domain leading to a common vocabulary for communities of researchers and important standards to facilitate computation, software interoperability and data reuse. Most successful ontologies, especially those that have been developed by diverse communities over long periods of time, are typically large and complex. To address this complexity, ontology authoring and browsing tools must provide cognitive support to improve comprehension of the many concepts and relationships in ontologies. Also, ontology tools must support collaboration as the heart of ontology design and use is centered on community consensus.
In this talk, I will describe how standardized ontologies are developed and used in the biomedical and clinical domains to aid in scientific and medical discoveries. Specifically, I will present how the US National Center for Biomedical Ontology has designed the BioPortal ontology library (and associated technologies) to promote the use of standardized ontologies and tools. I will review how BioPortal and other ontology tools use established and novel visualization and collaboration approaches to improve ontology authoring and data curation activities. I will also discuss an ambitious project by the World Health Organization that leverages the use of social media to broaden participation in the development of the next version of the International Classification of Diseases. To conclude, I will discuss the challenges and opportunities that arise from using ontologies to bridge communities that manage and curate important information resources.
Analysis of StackOverflow posts/user data trend analysis. Predicting time to answer (classification) using Weka. CSCI599 final project on Social media data analytics
To Bot or Not: How Bots can Support Collaboration in Software Engineering (I...Margaret-Anne Storey
Abstract and video link below)
Presented at ICGSE 2016: Conference on Global Software Engineering (http://www.ics.uci.edu/~icgse2016/2_0cfp.html)
Video link: https://www.youtube.com/watch?v=BsgnLwPMqWM&feature=youtu.be&list=PLcm9UtazJCOLBwPaaHNn_htAjPAXIdRGr
Abstract:
Software development stakeholders require a constellation of tools to support their communication, collaboration and coordination activities. But poor tool integration can lead to gaps in knowledge flow, or worse, to an overabundance of shared communication and information. The software development community is witnessing the rise of "social bots" to integrate diverse development and communication tools and to address the challenge of information overload. A bot is a conversational user interface that can automate rote or tedious tasks. It may fetch or share information, extract and analyze data, detect and monitor events and activities in communication and social media, connect developers with each other or with other tools, or it may provide feedback on individual and collaborative development tasks. Some bots are emerging as important team members, providing support for individual and team task management and for the automation of dev-ops and customer support. However, the rapid adoption of bots and the platforms that support them brings possible drawbacks. Designing effective platforms for bots is challenging and bots may introduce alienation among stakeholders or lead to other technical challenges. In this talk, I will discuss the emerging role of bots in software development and describe some of the advantages and challenges that may lie ahead.
According to Altimeter Group research, the average enterprise-class company owns 178 social media accounts, while 13 departments—from marketing to customer support to legal-- actively engage in social media.
Yet social media— and as a result, social data— are still largely isolated from business-critical enterprise data sourced from platforms such as Customer Relationship Management, Business Intelligence and market research.
This lack of a holistic view of social signals in the context of other enterprise and external data can lead to partially-informed decisions, missed opportunity, and increased risk and cost, as the organization makes decisions without the benefit of critical input from external constituencies.
In this Altimeter Group research report reflecting input from 35 enterprise-class organizations and technology ecosystem contributors, industry analyst Susan Etlinger lays out an imperative for Social Data Intelligence, identifying key dimensions that organizations must understand, pragmatic steps they can take toward mature integration, and how successful businesses are already using social data in the context of other critical enterprise data to drive measurable value throughout the organization.
CINF 17: Comparing Cahn-Ingold-Prelog Rule Implementations: The need for an o...NextMove Software
The Cahn-Ingold-Prelog (CIP) priority rules have been the corner stone in written communication of stereo-chemical configuration for more than half a century. The rules rank ligands around a stereocentre allowing an atom order and layout invariant stereo-descriptor to be assigned, for example R (right) or S (left) for tetrahedral atoms. Despite their widespread daily use, many chemists may be surprised to find that beyond trivial cases, different software may assign different labels to the same structure diagram.
There have been several attempts to either replace or amend the CIP rules. This talk will highlight the more challenging aspects of the ranking and present a comparison of software that provide CIP labels and where they disagree. Providing an IUPAC verified free and open source CIP implementation would allow software maintainers and vendors to validate and improve their implementations. Ultimately this would improve the accuracy in exchange of written chemical information for all.
Without Self-Service Operations, the Cloud is Just Expensive Hosting 2.0 - (a...dev2ops
Damon Edwards (DTO Solutions) presentation at Cloud Expo 2014 Santa Clara.
We are all here because we are sold on the transformative promise of The Cloud. But what good is all of this ephemeral, on-demand infrastructure if your usage doesn't actually improve the agility and speed of your business? How must Operations adapt in order to avoid stifling your Cloud initiative?
Hedge Fund IT Challenges Financial SurveyAvere Systems
This survey highlights results of a recent Avere Systems Survey capturing challenges that hedge fund IT managers are experiencing in an era of constant and rapid change.
Predicting Helpfulness of User-Generated Product Reviews Through Analytical M...Ankita Kaul
Customer reviews are an important feature on Amazon’s vast array of products. Many customers rely heavily on the honest reviews of past users during purchasing decisions. Currently, the only way to regulate the quality of these reviews is for other users to voluntarily thumbs up/down a review as ‘helpful’ or ‘not helpful’. It is in the best interest of Amazon (and potential customers) to be shown the most helpful reviews first and de-prioritize (or flag) useless reviews. Thus, we wanted to try and create a model that could successfully predict whether or not customers would find user product reviews helpful. With such a model, Amazon would be able to better prioritize user reviews displayed on product pages from the moment a review is posted.
Why Reinvent the Wheel: Let's Build Question Answering Systems TogetherKuldeep Singh
component-oriented, a dynamically composable question answering system.
(presented in the Web Conference 2018 in research track)
https://lnkd.in/dPWfc4x
The success of developer forums like Stack Overflow (SO) depends on the participation of users and the quality of shared knowledge. SO allows its users to suggest edits to improve the quality of the posts (e.g., questions and answers). Such posts can be rolled back to an earlier version when the current version of the post with the suggested edit does not satisfy the user. However, subjectivity bias in deciding either an edit is satisfactory or not could introduce inconsistencies in the rollback edits. For example, while a user may accept the formatting of a method name (e.g., getActivity()) as a code term, another user may reject it. Such bias in rollback edits could be detrimental and demotivating to the users whose suggested edits were rolled back. This problem is compounded due to the absence of specific guidelines and tools to support consistency across users on their rollback actions. To mitigate this problem, we investigate the inconsistencies in the rollback editing process of SO and make three contributions. First, we identify eight inconsistency types in rollback edits through a qualitative analysis of 777 rollback edits in 382 questions and 395 answers. Second, we determine the impact of the eight rollback inconsistencies by surveying 44 software developers. More than 80% of the study participants find our produced catalogue of rollback inconsistencies to be detrimental to the post quality. Third, we develop a suite of algorithms to detect the eight rollback inconsistencies. The algorithms offer more than 95% accuracy and thus can be used to automatically but reliably inform users in SO of the prevalence of inconsistencies in their suggested edits and rollback actions.
A Set of Heuristics to Support Early Identification of Conflicting RequirementsAlejandro Salado
This paper presents a set of heuristics that could support identifying conflicting requirements or objectives by structuring non-normative methods such as subject matter experts.
DOI: http://dx.doi.org/10.1002/j.2334-5837.2015.00062.x
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Improving Low Quality Stack Overflow Post Detection
1. Improving Low Quality
StackOverflow Post Detection
Luca Ponzanelli David Fullerton
Andrea Mocci
University Of Lugano
Switzerland
Alberto Bacchelli
Delft University of Technology
Netherlands
StackExchange Inc.
New York, USA
Michele Lanza
5. Q
Q
Q
Q
StackOverflow
Review Process
Q
Q
Moderator
System
6. Q
Q
Q
Q
StackOverflow
Review Process
Q
Q
Moderator
System
7. Suggested Edits
Late Answers and
StackOverflow
Review Process
First Posts
Low Quality Posts
8. Low Quality Posts
Identified by the system
StackOverflow
Review Process
9. Low Quality Posts
an inefficient approach
increases the review
StackOverflow
Review Process
queue size
10. Low Quality Posts
an efficient approach
saves time to reviewers
StackOverflow
Review Process
11. Low Quality Post
Refine the review queue to
remove misclassified posts
StackOverflow
Review Process
12. Body Length
Capital Title
Emails Count
Lowercase Percentage
Spaces Count
StackOverflow
Tags Count
Text Speak Count
Title Body Similarity
Title Length
Uppercase Percentage
Quality Metrics
13. Body Length
Capital Title
Emails Count
Lowercase Percentage
Spaces Count
Pure Textual Metrics
StackOverflow
Tags Count
Text Speak Count
Title Body Similarity
Title Length
Uppercase Percentage
Quality Metrics
15. Average Term Entropy
Automated Reading Index
Coleman Liau Index
Flesch Kincaid Grade Level
Flesch Reading Ease Score
Gunning Fox Index
LOC Percentage
Metric Entropy
Sentences Count
SMOG Grade
Words Count
Readability Metrics
16. Average Term Entropy
Automated Reading Index
Coleman Liau Index
Flesch Kincaid Grade Level
Flesch Reading Ease Score
Gunning Fox Index
Readab
ility
LOC Percentage
Metric Entropy
Sentences Count
SMOG Grade
Words Count
Readability Metrics
17. Average Term Entropy
Automated Reading Index
Coleman Liau Index
Flesch Kincaid Grade Level
Flesch Reading Ease Score
Gunning Fox Index
Readab
ility
LOC Percentage
Metric Entropy
Sentences Count
SMOG Grade
Words Count
Readability Metrics
18. Accepted by Originator Votes
Approved Edit Suggestion
Answer Badges Count
Badges-Tags Coverage
Bounty Start (End) Votes
Close Votes
Deletion Votes
Down Votes
Favorite Votes
Moderator Review Votes
Offensive Votes
Reopen Votes
Question Badges Count
Spam Votes
Total Badges
Undeletion Votes
Up Votes
Popularity Metrics
21. StackOverflow
Public Dump
Very Good (A)
Good (B)
Bad (C)
Very Bad (D)
Classification
Approach
L. Ponzanelli, A. Mocci, A. Bacchelli, M. Lanza
Understanding and Classifying the Quality of Technical Forum Questions
In Proceedings of QSIC 2014 (14th International Conference on Quality Software)
22. Very Good (A)
Good (B)
Bad (C)
Very Bad (D)
Classification
Approach
L. Ponzanelli, A. Mocci, A. Bacchelli, M. Lanza
Understanding and Classifying the Quality of Technical Forum Questions
In Proceedings of QSIC 2014 (14th International Conference on Quality Software)
23. Neither Closed nor Deleted
With an Accepted Answer
Score > 7
Very Good (A)
Good (B)
Bad (C)
Very Bad (D)
Classification
Approach
L. Ponzanelli, A. Mocci, A. Bacchelli, M. Lanza
Understanding and Classifying the Quality of Technical Forum Questions
In Proceedings of QSIC 2014 (14th International Conference on Quality Software)
24. Neither Closed nor Deleted
With an Accepted Answer
1 < Score < 6
Very Good (A)
Good (B)
Bad (C)
Very Bad (D)
Classification
Approach
L. Ponzanelli, A. Mocci, A. Bacchelli, M. Lanza
Understanding and Classifying the Quality of Technical Forum Questions
In Proceedings of QSIC 2014 (14th International Conference on Quality Software)
25. Neither Closed nor Deleted
With an Accepted Answer
Score < 0
Very Good (A)
Good (B)
Bad (C)
Very Bad (D)
Classification
Approach
L. Ponzanelli, A. Mocci, A. Bacchelli, M. Lanza
Understanding and Classifying the Quality of Technical Forum Questions
In Proceedings of QSIC 2014 (14th International Conference on Quality Software)
26. Closed or Deleted
Very Good (A)
Good (B)
Bad (C)
Very Bad (D)
Classification
Approach
L. Ponzanelli, A. Mocci, A. Bacchelli, M. Lanza
Understanding and Classifying the Quality of Technical Forum Questions
In Proceedings of QSIC 2014 (14th International Conference on Quality Software)
28. Genetic Algorithm
QF =
Xn
i=1
wi · mi
wi 2 [−1, 1] mi 2 [0, 1]
Classification
Function
29. Data Metrics
L. Ponzanelli, A. Mocci, A. Bacchelli, M. Lanza
Classification
Function
Understanding and Classifying the Quality of Technical Forum Questions
In Proceedings of QSIC 2014 (14th International Conference on Quality Software)
30. Metrics
L. Ponzanelli, A. Mocci, A. Bacchelli, M. Lanza
Classification
Function
Understanding and Classifying the Quality of Technical Forum Questions
In Proceedings of QSIC 2014 (14th International Conference on Quality Software)
Data
31. A function assigns
Positive Value if Good
Negative Value if Bad
L. Ponzanelli, A. Mocci, A. Bacchelli, M. Lanza
Classification
Function
Understanding and Classifying the Quality of Technical Forum Questions
In Proceedings of QSIC 2014 (14th International Conference on Quality Software)
32. quantiles
q = 0.25 q = 0.25
25%
25%
-1 0 1
x = QF(post)
y = freq(x)
D C B A
Classification
Function
33. 10% 10%
q = 0.25 q = 0.25
D C B A
-1 0 1
x = QF(post)
y = freq(x)
Classification
Function
34. q = 0.25 q = 0.25
D C B A
-1 0 1
x = QF(post)
y = freq(x)
40% 40%
Classification
Function
39. Review Queue (RQ)
D D D D C C B B A A A A
A
q=0.25
D C C B A A A A A
Review Queue
Refinement
40. Review Queue (RQ)
D D D C B B A
A
q=0.25
D C C B A A A A A
Review Queue
Refinement
41. Review Queue (RQ)
D D D D C C B B A A A A
∩
D D D C C C B A A A A A
D
q=0.1
Review Queue
Refinement
42. Review Queue (RQ)
D D B
∩
D D D C C C B A A A A A
D
q=0.1
Review Queue
Refinement
43. Review Queue (RQ)
D D D D C C B B A A A A
A A
q=0.25
D C C B A A A A A
q=0.1
U
Review Queue
Refinement
44. Review Queue (RQ)
D D D D C B A
D C C B A A A A A
A A
U
q=0.25 q=0.1
Review Queue
Refinement
45. Hard Precision (HP)
The percentage of posts in the review
queue belonging to the class D
Soft Precision (SP)
The percentage of posts in the review
queue belonging to the class D and C
Review Queue
Refinement
46. Hard Precision (HP)
41.90%
Soft Precision (SP)
64.31%
Review Queue (RQ) Size
3,416
Without
Refinement
Review Queue
Refinement
54. Readability and Popularity Metrics
are the most effective
for queue refinement
Tradeoff between review queue
reduction and bad post reduction
Lessons Learned