Simseer - A Software Similarity Web Service
•Download as PPT, PDF•
1 like•1,040 views
This document summarizes an overview talk on software similarity. It introduces the speaker and their research focus on malware detection and vulnerability detection. It then provides an overview of the core topics of software similarity, how it is approached in academia, and introduces a new web service that identifies software similarity. It discusses how software similarity can be used for malware detection, software theft detection, plagiarism detection, and software clone detection. It also provides taxonomy of different program features that can be analyzed and examples of how features like ASTs and control flow can be represented. Finally, it introduces resources like a wiki, book, and new web service called Simseer for software similarity.
Report
Share
Report
Share
![Silvio Cesare Deakin University [email_address]](https://image.slidesharecdn.com/ruxmon-softwaresimilarity-120224155335-phpapp02/85/Simseer-A-Software-Similarity-Web-Service-1-320.jpg)
![Who am I and where did this talk come from? ,[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Ppt
This document describes a multilingual faceted search system for tweets. It features content tagging, faceted search, sentiment analysis, and cross-lingual information retrieval. The system architecture includes modules for handling user queries, facets, tweets, and sentiment analysis. It explains how the various features are implemented, including using APIs for content tagging, sentiment analysis, and language detection. Demographic views of positive and negative tweets are also described. The document concludes with potential areas for future work, such as analyzing live twitter data and other social media sources.
Sanmitra Ijeri Resume
Sanmitra Ijeri is a second year Masters student in Computer Science specializing in Machine Learning at UC San Diego. During an internship at Salesforce, she built prototypes for lead scoring and opportunity scoring models using algorithms like Naive Bayes, Logistic Regression, N-grams, and neural networks. Previously, she worked as a Senior Software Developer at D. E. Shaw & Co where she developed various applications and tools using technologies like Python, Java, and machine learning algorithms.
Основы теории графов 04: метрики на деревьях
Доказываем теорему Зарецкого о существовании дерева с заданными расстояниями между вершинами.
[Слайды курса, прочитанного в МФТИ в 2013 году.]
csmalware_malware
This document describes CrowdSource, a system that uses natural language processing to infer high-level malware capabilities based on low-level strings extracted from malware binaries. It trains a machine learning model on millions of technical documents from StackExchange to map low-level strings to high-level capabilities. The system was evaluated on 1,457 malware samples and shown to detect 14 capabilities with an average F1-score of 0.86 and can analyze tens of thousands of samples per day.
Nikhil CV
Nikhil Sharma has a Master of Science in Data Informatics from USC and a Bachelor of Engineering in Electronics and Communication from M.S. Ramaiah Institute of Technology in India. He has work experience as a Software Engineer Intern at Salesforce where he designed applications for database performance analysis using Python. Previously he was a Senior Systems Engineer at Infosys where he implemented IT infrastructure for banks in various countries. His academic projects involve machine learning, data mining, and information retrieval using technologies like Python, Solr, and Caffe.
Knowledge and Data Engineering IEEE 2015 Projects
List of Knowledge and Data Engineering IEEE 2015 Projects. It Contains the IEEE Projects in the Domain Knowledge and Data Engineering for the year 2015
The Relevance of the Apache Solr Semantic Knowledge Graph
The Semantic Knowledge Graph is an Apache Solr plugin that can be used to discover and rank the relationships between any arbitrary queries or terms within the search index. It is a relevancy swiss army knife, able to discover related terms and concepts, disambiguate different meanings of terms given their context, cleanup noise in datasets, discover previously unknown relationships between entities across documents and fields, rank lists of keywords based upon conceptual cohesion to reduce noise, summarize documents by extracting their most significant terms, generate recommendations and personalized search, and power numerous other applications involving anomaly detection, significance/relationship discovery, and semantic search. This talk will walk you through how to setup and use this plugin in concert with other open source tools (probabilistic query parser, SolrTextTagger for entity extraction) to parse, interpret, and much more correctly model the true intent of user searches than traditional keyword-based search approaches.
A Fast Flowgraph Based Classification System for Packed and Polymorphic Malwa...
We propose an algorithm to identify malware variants by determining program similarity through estimating isomorphic control flow graphs. We implement this approach in a prototype system that demonstrates its ability to detect real malware variants with low false positives and logarithmic performance scalability, making it suitable for endhost adoption. Control flow graphs provide a more invariant characteristic than traditional static features like byte sequences for identifying polymorphic malware variants. Our system generates signatures for control flow graphs to efficiently compare programs and classify unknown samples.
Recommended
Ppt
This document describes a multilingual faceted search system for tweets. It features content tagging, faceted search, sentiment analysis, and cross-lingual information retrieval. The system architecture includes modules for handling user queries, facets, tweets, and sentiment analysis. It explains how the various features are implemented, including using APIs for content tagging, sentiment analysis, and language detection. Demographic views of positive and negative tweets are also described. The document concludes with potential areas for future work, such as analyzing live twitter data and other social media sources.
Sanmitra Ijeri Resume
Sanmitra Ijeri is a second year Masters student in Computer Science specializing in Machine Learning at UC San Diego. During an internship at Salesforce, she built prototypes for lead scoring and opportunity scoring models using algorithms like Naive Bayes, Logistic Regression, N-grams, and neural networks. Previously, she worked as a Senior Software Developer at D. E. Shaw & Co where she developed various applications and tools using technologies like Python, Java, and machine learning algorithms.
Основы теории графов 04: метрики на деревьях
Доказываем теорему Зарецкого о существовании дерева с заданными расстояниями между вершинами.
[Слайды курса, прочитанного в МФТИ в 2013 году.]
csmalware_malware
This document describes CrowdSource, a system that uses natural language processing to infer high-level malware capabilities based on low-level strings extracted from malware binaries. It trains a machine learning model on millions of technical documents from StackExchange to map low-level strings to high-level capabilities. The system was evaluated on 1,457 malware samples and shown to detect 14 capabilities with an average F1-score of 0.86 and can analyze tens of thousands of samples per day.
Nikhil CV
Nikhil Sharma has a Master of Science in Data Informatics from USC and a Bachelor of Engineering in Electronics and Communication from M.S. Ramaiah Institute of Technology in India. He has work experience as a Software Engineer Intern at Salesforce where he designed applications for database performance analysis using Python. Previously he was a Senior Systems Engineer at Infosys where he implemented IT infrastructure for banks in various countries. His academic projects involve machine learning, data mining, and information retrieval using technologies like Python, Solr, and Caffe.
Knowledge and Data Engineering IEEE 2015 Projects
List of Knowledge and Data Engineering IEEE 2015 Projects. It Contains the IEEE Projects in the Domain Knowledge and Data Engineering for the year 2015
The Relevance of the Apache Solr Semantic Knowledge Graph
The Semantic Knowledge Graph is an Apache Solr plugin that can be used to discover and rank the relationships between any arbitrary queries or terms within the search index. It is a relevancy swiss army knife, able to discover related terms and concepts, disambiguate different meanings of terms given their context, cleanup noise in datasets, discover previously unknown relationships between entities across documents and fields, rank lists of keywords based upon conceptual cohesion to reduce noise, summarize documents by extracting their most significant terms, generate recommendations and personalized search, and power numerous other applications involving anomaly detection, significance/relationship discovery, and semantic search. This talk will walk you through how to setup and use this plugin in concert with other open source tools (probabilistic query parser, SolrTextTagger for entity extraction) to parse, interpret, and much more correctly model the true intent of user searches than traditional keyword-based search approaches.
A Fast Flowgraph Based Classification System for Packed and Polymorphic Malwa...
We propose an algorithm to identify malware variants by determining program similarity through estimating isomorphic control flow graphs. We implement this approach in a prototype system that demonstrates its ability to detect real malware variants with low false positives and logarithmic performance scalability, making it suitable for endhost adoption. Control flow graphs provide a more invariant characteristic than traditional static features like byte sequences for identifying polymorphic malware variants. Our system generates signatures for control flow graphs to efficiently compare programs and classify unknown samples.
Knowledge and Data Engineering IEEE 2015 Projects
List of Knowledge and Data Engineering IEEE 2015 Projects. It Contains the IEEE Projects in the Domain Knowledge and Data Engineering for the year 2015
The Apache Solr Semantic Knowledge Graph
What if instead of a query returning documents, you could alternatively return other keywords most related to the query: i.e. given a search for "data science", return me back results like "machine learning", "predictive modeling", "artificial neural networks", etc.? Solr’s Semantic Knowledge Graph does just that. It leverages the inverted index to automatically model the significance of relationships between every term in the inverted index (even across multiple fields) allowing real-time traversal and ranking of any relationship within your documents. Use cases for the Semantic Knowledge Graph include disambiguation of multiple meanings of terms (does "driver" mean truck driver, printer driver, a type of golf club, etc.), searching on vectors of related keywords to form a conceptual search (versus just a text match), powering recommendation algorithms, ranking lists of keywords based upon conceptual cohesion to reduce noise, summarizing documents by extracting their most significant terms, and numerous other applications involving anomaly detection, significance/relationship discovery, and semantic search. In this talk, we'll do a deep dive into the internals of how the Semantic Knowledge Graph works and will walk you through how to get up and running with an example dataset to explore the meaningful relationships hidden within your data.
Application Security at DevOps Speed and Portfolio Scale
Published on Nov 26, 2013
AppSec at DevOps Speed and Portfolio Scale - Jeff Williams
Watch this talk on YouTube: https://www.youtube.com/watch?v=cIvOth0fxmI
Software development is moving much faster than application security with new platforms, languages, frameworks, paradigms, and methodologies like Agile and Devops.
Unfortunately, software assurance hasn't kept up with the times. For the most part, our security techniques were built to work with the way software was built in 2002. Here are some of the technologies and practices that today's best software assurance techniques *can't*handle: JavaScript, Ajax, inversion of control, aspect-oriented programming, frameworks, libraries, SOAP, REST, web services, XML, JSON, raw sockets, HTML5, Agile, DevOps, WebSocket, Cloud, and more. All of these rest pretty much at the core of modern software development.
Although we're making progress in application security, the gains are much slower than the stunning advances in software development. After 10 years of getting further behind every day, software *assurance* is now largely incompatible with modern software *development*. It's not just security tools -- application security processes are largely incompatible as well. And the result is that security has very little influence on the software trajectory at all.
Unless the application security community figures out how to be a relevant part of software development, we will continue to lag behind and effect minimal change. In this talk, I will explore a radically different approach based on instrumenting an entire IT organization with passive sensors to collect realtime data that can be used to identify vulnerabilities, enhance security architecture, and (most importantly) enable application security to generate value. The goal is unprecedented real-time visibility into application security across an organization's entire application portfolio, allowing all the stakeholders in security to collaborate and finally become proactive.
Speaker
Jeff Williams
CEO, Aspect Security
Jeff is a founder and CEO of Aspect Security and recently launched Contrast Security, a new approach to application security analysis. Jeff was an OWASP Founder and served as Global Chairman from 2004 to 2012, contributing many projects including the OWASP Top Ten, WebGoat, ESAPI, ASVS, and more. Jeff is passionate about making it possible for anyone to do their own continuous application security in real time.
YamenTrace: Requirements Traceability - Recovering and Visualizing Traceabili...
This paper introduces YamenTrace, an automatic approach to recover and visualize requirement-to-code traceability links (RtC-TLs) in object-oriented software systems. YamenTrace uses latent semantic indexing (LSI) to measure similarity between requirements and source code, and formal concept analysis (FCA) to cluster similar elements and identify trace links. The approach was applied to three case studies and successfully recovered most RtC-TLs, validating its importance and performance. YamenTrace recovers links without manual effort by exploiting code identifiers, comments, and relations during analysis of the software requirements and source code.
Software Analytics: Data Analytics for Software Engineering and Security
Frodo Baggins presents on software analytics for software engineering and security tasks. The presentation discusses how software and how it is built and used is changing, with data now being ubiquitous and software having continuous development and release. Software analytics aims to enable software practitioners to perform data exploration and analysis to obtain useful insights. Examples of software analytics techniques discussed include XIAO for scalable code clone analysis, and SAS for incident management of online services. The presentation then shifts to discussing software analytics techniques for mobile app security, including WHYPER for natural language processing on app descriptions to link permissions to functionality, and AppContext for machine learning to classify malware.
Pattern-Based Software Diagnostics
The slides from Software Diagnostics Services seminar (14th of January, 2013) about how pattern-based software diagnostics, troubleshooting, and debugging address software post-construction problem solving pattern life cycle: from the discovery of a new pattern through its integration into an existing pattern catalogue and pattern language, testing, packaging and delivering to pattern consumers with subsequent usage, refactoring and writing case studies. Topics include: Definitions of Software Diagnostics, Diagnostics Pattern, Pattern Orientation, Pattern Cloud, Pattern Catalogs, Pattern Catalog Classification, Pattern Catalog Partition, Pattern Implementation, Pattern Forms, Pattern Networks, The Relationship between Software Construction Patterns and Software Diagnostics Patterns, Pattern Sequences, Pattern-Driven vs. Pattern-Based, Pattern Catalog Evolution, Pattern Packaging and Delivery, Economic Benefits of Pattern-Oriented Software Diagnostics.
Software Birthmark Based Theft/Similarity Comparisons of JavaScript Programs
A birthmark is a set of characteristic possessed by a program that uniquely recognizes a program. Birthmark of the software is based on Heap Graph. It is generated by using Google Chrome Developer Tools when the program is in execution. Software’s behavioural structure is demonstrated in the heap graph. It describes how the objects are related to each other to deliver the desired functionality of the website. Our aim is to develop and evaluate a system that can find theft/similarity between websites by using Agglomerative Clustering and Improved Frequent Subgraph Mining. To identify if a website is using the original program’s code or its module, birthmark of the original program is explored in the suspected program’s heap graph.
Predicting Fault-Prone Files using Machine Learning
This document summarizes a study that used machine learning algorithms to predict fault-prone files in nine open source Java projects containing over 18,000 files and 3 million lines of code. Six machine learning algorithms (Naive Bayes, Bayesian network, decision tree, radial basis function, simple logistic, and zeroR) were evaluated based on their ability to rank files by probability of being buggy, as determined by the FindBugs tool. The results showed that decision trees and Bayesian networks performed best at ranking the files, with decision trees outperforming other methods in previous studies. Lift curves were used to evaluate the performance of the models by plotting the number of files examined against the number of buggy files found.
MyCityInfoProjectReport.docx
MyCityInfo is a project report which explores the city details (Hotels, Theaters, Hospitals, Temples, Schools, NGO)
Simon Brown: Software Architecture as Code at I T.A.K.E. Unconference 2015
This document discusses software architecture and how it relates to code. It suggests that software architecture should be more accessible to developers and embodied in the code through architecturally evident coding styles. Components can be extracted from code if naming conventions, packaging, and other patterns are used. Both diagrams and code should reflect the architectural abstractions. Software architecture models can be maintained as code to keep them in sync with implementation changes.
Design Decisions For Understanding Software Architecture
This document discusses the "browser wars" between Netscape and Microsoft in the 1990s. Netscape initially dominated the browser market with its Netscape Navigator. However, Microsoft was able to gain majority market share by bundling its Internet Explorer browser with Windows. As a result, Netscape struggled and was eventually acquired by America Online. The summary examines the key events in the browser war between the two companies over browser market control in 3 sentences or less.
Fishing Graphs in a Hadoop Data Lake
Hadoop clusters can store nearly everything in a cheap and blazingly fast way to your data lake. Answering questions and gaining insights out of this ever growing stream becomes the decisive part for many businesses. Increasingly data has a natural structure as a graph, with vertices linked by edges, and many questions arising about the data involve graph traversals or other complex queries, for which one does not have an a priori given bound on the length of paths.
Fast Automated Unpacking and Classification of Malware
This document summarizes Silvio Cesare's research presentation on fast automated unpacking and classification of malware. The research aims to efficiently and effectively detect and classify malware using static analysis. It involves developing an automated unpacker using emulation and entropy analysis to unpack malware. It then extracts control flow graphs from unpacked malware and uses graph matching techniques to classify malware and identify variants by similarity. The techniques were evaluated on real malware samples and shown to accurately unpack and classify malware with low processing times suitable for real-time systems.
Software Horticulture
Presentation I gave at Nitro about thinking about using analogies to think about software development process.
Web Page Ranking using Machine Learning
This document discusses techniques for page ranking using machine learning, including k-nearest means and decision tree induction. It provides information on topics like PageRank, static ranking, supervised learning algorithms like KNN ranking, and compares the performance of PageRank versus fRank models. Decision trees are constructed based on values for different page optimization metrics to learn the most important URLs in the education domain.
Session2-Application Threat Modeling
The document discusses application threat modeling for a college library website. It describes decomposing the application into external dependencies, entry points, assets, and trust levels. It then covers determining and ranking threats using STRIDE and ASF categorizations. The document outlines identifying security controls and countermeasures to address vulnerabilities. It provides steps for threat analysis and defining mitigation strategies.
Simseer and Bugwise - Web Services for Binary-level Software Similarity and D...
This document describes two web services called Simseer and Bugwise for software defect detection and similarity analysis. Simseer performs malware variant and plagiarism detection by generating control flow signatures and comparing similarities. Bugwise detects bugs like double frees through decompilation and data flow analysis. The services are implemented through a PHP frontend and C++ backend called Malwise that performs analysis through plugins. Initial results found the web services had minimal overhead compared to command line usage.
Android malware detection through online learning
Android malware constantly evolves so as to evade detection. The entire malware population to be nonstationary.
Contrary to this fact, most of the prior works on machine learning based android malware detection have
assumed that the distribution of the observed malware characteristics (i.e., features) does not change over time. The
problem of malware population drift and propose a novel online learning based framework to detect malware, named
CASANDRA (Context-aware, Adaptive and Scalable Android malware detector). In order to perform accurate
detection, a novel graph kernel that facilitates capturing apps security-sensitive behaviours along with their context
information from dependence graphs is proposed. Besides being accurate and scalable, CASANDRA has specific
advantages: first, being adaptive to the evolution in malware features over time; second, explaining the significant
features that led to an apps classification as being malicious or benign.
Software Birthmark for Theft Detection of JavaScript Programs: A Survey
The document discusses software birthmarks, which are characteristics of a program that uniquely identify it. A birthmark can be used to detect software theft by searching for the birthmark of a plaintiff program in a suspected program. Specifically, the document discusses heap graph-based birthmarks, which are generated from a program's runtime heap structure and object references. A subgraph of the heap graph forms the birthmark. Subgraph monomorphism is used to search for the birthmark in a suspected program's heap graph to detect copying of code. Heap graph-based birthmarks are robust against attacks like code obfuscation that aim to disguise stolen code.
Coping with Data Variety in the Big Data Era: The Semantic Computing Approach
Big Data is based on the vision of providing users and applications with a more complete picture of the reality supported and mediated by data. This vision comes with the inherent price of data variety, i.e. data which is semantically heterogeneous, poorly structured, complex and with data quality issues. Despite the hype on technologies targeting data volume and velocity, solutions for coping with data variety remain fragmented and with limited adoption. In this talk we will focus on emerging data management approaches, supported by semantic technologies, to cope with data variety. We will provide a broad overview of semantic computing approaches and how they can be applied to data management challenges within organizations today. This talk will allow the audience to have a glimpse into the next-generation, Big Data-driven information systems.
A BEGINNER’S JOURNEY INTO THE WORLD OF HARDWARE HACKING
This document provides an introduction to hardware hacking for software engineers. It outlines several beginner hardware hacking projects, including interfacing with UART to gain serial console access on devices, ripping firmware from chips to analyze code and find passwords/strings, manipulating IR alarm systems by learning codes and repurposing remotes, and building an Arduino-controlled backyard irrigation system networked to a PC. The document explains how to identify important chips, interfaces, and voltages, and techniques for reading serial flash and desoldering chips to extract firmware. It presents hardware hacking as an accessible new hobby that can build skills in electronics and low-level programming.
A WHIRLWIND TOUR OF ACADEMIC TECHNIQUES FOR REAL-WORLD SECURITY RESEARCHERS
This document provides an overview of various academic techniques that can be useful for security researchers, including mathematical objects, comparing objects, similarity searching, classification, clustering, and program analysis. It discusses representing problems as different objects like strings, vectors, and graphs and using techniques like n-grams, vector distances, and graph decomposition. Case studies of projects that applied these techniques are also summarized.
More Related Content
Similar to Simseer - A Software Similarity Web Service
Knowledge and Data Engineering IEEE 2015 Projects
List of Knowledge and Data Engineering IEEE 2015 Projects. It Contains the IEEE Projects in the Domain Knowledge and Data Engineering for the year 2015
The Apache Solr Semantic Knowledge Graph
What if instead of a query returning documents, you could alternatively return other keywords most related to the query: i.e. given a search for "data science", return me back results like "machine learning", "predictive modeling", "artificial neural networks", etc.? Solr’s Semantic Knowledge Graph does just that. It leverages the inverted index to automatically model the significance of relationships between every term in the inverted index (even across multiple fields) allowing real-time traversal and ranking of any relationship within your documents. Use cases for the Semantic Knowledge Graph include disambiguation of multiple meanings of terms (does "driver" mean truck driver, printer driver, a type of golf club, etc.), searching on vectors of related keywords to form a conceptual search (versus just a text match), powering recommendation algorithms, ranking lists of keywords based upon conceptual cohesion to reduce noise, summarizing documents by extracting their most significant terms, and numerous other applications involving anomaly detection, significance/relationship discovery, and semantic search. In this talk, we'll do a deep dive into the internals of how the Semantic Knowledge Graph works and will walk you through how to get up and running with an example dataset to explore the meaningful relationships hidden within your data.
Application Security at DevOps Speed and Portfolio Scale
Published on Nov 26, 2013
AppSec at DevOps Speed and Portfolio Scale - Jeff Williams
Watch this talk on YouTube: https://www.youtube.com/watch?v=cIvOth0fxmI
Software development is moving much faster than application security with new platforms, languages, frameworks, paradigms, and methodologies like Agile and Devops.
Unfortunately, software assurance hasn't kept up with the times. For the most part, our security techniques were built to work with the way software was built in 2002. Here are some of the technologies and practices that today's best software assurance techniques *can't*handle: JavaScript, Ajax, inversion of control, aspect-oriented programming, frameworks, libraries, SOAP, REST, web services, XML, JSON, raw sockets, HTML5, Agile, DevOps, WebSocket, Cloud, and more. All of these rest pretty much at the core of modern software development.
Although we're making progress in application security, the gains are much slower than the stunning advances in software development. After 10 years of getting further behind every day, software *assurance* is now largely incompatible with modern software *development*. It's not just security tools -- application security processes are largely incompatible as well. And the result is that security has very little influence on the software trajectory at all.
Unless the application security community figures out how to be a relevant part of software development, we will continue to lag behind and effect minimal change. In this talk, I will explore a radically different approach based on instrumenting an entire IT organization with passive sensors to collect realtime data that can be used to identify vulnerabilities, enhance security architecture, and (most importantly) enable application security to generate value. The goal is unprecedented real-time visibility into application security across an organization's entire application portfolio, allowing all the stakeholders in security to collaborate and finally become proactive.
Speaker
Jeff Williams
CEO, Aspect Security
Jeff is a founder and CEO of Aspect Security and recently launched Contrast Security, a new approach to application security analysis. Jeff was an OWASP Founder and served as Global Chairman from 2004 to 2012, contributing many projects including the OWASP Top Ten, WebGoat, ESAPI, ASVS, and more. Jeff is passionate about making it possible for anyone to do their own continuous application security in real time.
YamenTrace: Requirements Traceability - Recovering and Visualizing Traceabili...
This paper introduces YamenTrace, an automatic approach to recover and visualize requirement-to-code traceability links (RtC-TLs) in object-oriented software systems. YamenTrace uses latent semantic indexing (LSI) to measure similarity between requirements and source code, and formal concept analysis (FCA) to cluster similar elements and identify trace links. The approach was applied to three case studies and successfully recovered most RtC-TLs, validating its importance and performance. YamenTrace recovers links without manual effort by exploiting code identifiers, comments, and relations during analysis of the software requirements and source code.
Software Analytics: Data Analytics for Software Engineering and Security
Frodo Baggins presents on software analytics for software engineering and security tasks. The presentation discusses how software and how it is built and used is changing, with data now being ubiquitous and software having continuous development and release. Software analytics aims to enable software practitioners to perform data exploration and analysis to obtain useful insights. Examples of software analytics techniques discussed include XIAO for scalable code clone analysis, and SAS for incident management of online services. The presentation then shifts to discussing software analytics techniques for mobile app security, including WHYPER for natural language processing on app descriptions to link permissions to functionality, and AppContext for machine learning to classify malware.
Pattern-Based Software Diagnostics
The slides from Software Diagnostics Services seminar (14th of January, 2013) about how pattern-based software diagnostics, troubleshooting, and debugging address software post-construction problem solving pattern life cycle: from the discovery of a new pattern through its integration into an existing pattern catalogue and pattern language, testing, packaging and delivering to pattern consumers with subsequent usage, refactoring and writing case studies. Topics include: Definitions of Software Diagnostics, Diagnostics Pattern, Pattern Orientation, Pattern Cloud, Pattern Catalogs, Pattern Catalog Classification, Pattern Catalog Partition, Pattern Implementation, Pattern Forms, Pattern Networks, The Relationship between Software Construction Patterns and Software Diagnostics Patterns, Pattern Sequences, Pattern-Driven vs. Pattern-Based, Pattern Catalog Evolution, Pattern Packaging and Delivery, Economic Benefits of Pattern-Oriented Software Diagnostics.
Software Birthmark Based Theft/Similarity Comparisons of JavaScript Programs
A birthmark is a set of characteristic possessed by a program that uniquely recognizes a program. Birthmark of the software is based on Heap Graph. It is generated by using Google Chrome Developer Tools when the program is in execution. Software’s behavioural structure is demonstrated in the heap graph. It describes how the objects are related to each other to deliver the desired functionality of the website. Our aim is to develop and evaluate a system that can find theft/similarity between websites by using Agglomerative Clustering and Improved Frequent Subgraph Mining. To identify if a website is using the original program’s code or its module, birthmark of the original program is explored in the suspected program’s heap graph.
Predicting Fault-Prone Files using Machine Learning
This document summarizes a study that used machine learning algorithms to predict fault-prone files in nine open source Java projects containing over 18,000 files and 3 million lines of code. Six machine learning algorithms (Naive Bayes, Bayesian network, decision tree, radial basis function, simple logistic, and zeroR) were evaluated based on their ability to rank files by probability of being buggy, as determined by the FindBugs tool. The results showed that decision trees and Bayesian networks performed best at ranking the files, with decision trees outperforming other methods in previous studies. Lift curves were used to evaluate the performance of the models by plotting the number of files examined against the number of buggy files found.
MyCityInfoProjectReport.docx
MyCityInfo is a project report which explores the city details (Hotels, Theaters, Hospitals, Temples, Schools, NGO)
Simon Brown: Software Architecture as Code at I T.A.K.E. Unconference 2015
This document discusses software architecture and how it relates to code. It suggests that software architecture should be more accessible to developers and embodied in the code through architecturally evident coding styles. Components can be extracted from code if naming conventions, packaging, and other patterns are used. Both diagrams and code should reflect the architectural abstractions. Software architecture models can be maintained as code to keep them in sync with implementation changes.
Design Decisions For Understanding Software Architecture
This document discusses the "browser wars" between Netscape and Microsoft in the 1990s. Netscape initially dominated the browser market with its Netscape Navigator. However, Microsoft was able to gain majority market share by bundling its Internet Explorer browser with Windows. As a result, Netscape struggled and was eventually acquired by America Online. The summary examines the key events in the browser war between the two companies over browser market control in 3 sentences or less.
Fishing Graphs in a Hadoop Data Lake
Hadoop clusters can store nearly everything in a cheap and blazingly fast way to your data lake. Answering questions and gaining insights out of this ever growing stream becomes the decisive part for many businesses. Increasingly data has a natural structure as a graph, with vertices linked by edges, and many questions arising about the data involve graph traversals or other complex queries, for which one does not have an a priori given bound on the length of paths.
Fast Automated Unpacking and Classification of Malware
This document summarizes Silvio Cesare's research presentation on fast automated unpacking and classification of malware. The research aims to efficiently and effectively detect and classify malware using static analysis. It involves developing an automated unpacker using emulation and entropy analysis to unpack malware. It then extracts control flow graphs from unpacked malware and uses graph matching techniques to classify malware and identify variants by similarity. The techniques were evaluated on real malware samples and shown to accurately unpack and classify malware with low processing times suitable for real-time systems.
Software Horticulture
Presentation I gave at Nitro about thinking about using analogies to think about software development process.
Web Page Ranking using Machine Learning
This document discusses techniques for page ranking using machine learning, including k-nearest means and decision tree induction. It provides information on topics like PageRank, static ranking, supervised learning algorithms like KNN ranking, and compares the performance of PageRank versus fRank models. Decision trees are constructed based on values for different page optimization metrics to learn the most important URLs in the education domain.
Session2-Application Threat Modeling
The document discusses application threat modeling for a college library website. It describes decomposing the application into external dependencies, entry points, assets, and trust levels. It then covers determining and ranking threats using STRIDE and ASF categorizations. The document outlines identifying security controls and countermeasures to address vulnerabilities. It provides steps for threat analysis and defining mitigation strategies.
Simseer and Bugwise - Web Services for Binary-level Software Similarity and D...
This document describes two web services called Simseer and Bugwise for software defect detection and similarity analysis. Simseer performs malware variant and plagiarism detection by generating control flow signatures and comparing similarities. Bugwise detects bugs like double frees through decompilation and data flow analysis. The services are implemented through a PHP frontend and C++ backend called Malwise that performs analysis through plugins. Initial results found the web services had minimal overhead compared to command line usage.
Android malware detection through online learning
Android malware constantly evolves so as to evade detection. The entire malware population to be nonstationary.
Contrary to this fact, most of the prior works on machine learning based android malware detection have
assumed that the distribution of the observed malware characteristics (i.e., features) does not change over time. The
problem of malware population drift and propose a novel online learning based framework to detect malware, named
CASANDRA (Context-aware, Adaptive and Scalable Android malware detector). In order to perform accurate
detection, a novel graph kernel that facilitates capturing apps security-sensitive behaviours along with their context
information from dependence graphs is proposed. Besides being accurate and scalable, CASANDRA has specific
advantages: first, being adaptive to the evolution in malware features over time; second, explaining the significant
features that led to an apps classification as being malicious or benign.
Software Birthmark for Theft Detection of JavaScript Programs: A Survey
The document discusses software birthmarks, which are characteristics of a program that uniquely identify it. A birthmark can be used to detect software theft by searching for the birthmark of a plaintiff program in a suspected program. Specifically, the document discusses heap graph-based birthmarks, which are generated from a program's runtime heap structure and object references. A subgraph of the heap graph forms the birthmark. Subgraph monomorphism is used to search for the birthmark in a suspected program's heap graph to detect copying of code. Heap graph-based birthmarks are robust against attacks like code obfuscation that aim to disguise stolen code.
Coping with Data Variety in the Big Data Era: The Semantic Computing Approach
Big Data is based on the vision of providing users and applications with a more complete picture of the reality supported and mediated by data. This vision comes with the inherent price of data variety, i.e. data which is semantically heterogeneous, poorly structured, complex and with data quality issues. Despite the hype on technologies targeting data volume and velocity, solutions for coping with data variety remain fragmented and with limited adoption. In this talk we will focus on emerging data management approaches, supported by semantic technologies, to cope with data variety. We will provide a broad overview of semantic computing approaches and how they can be applied to data management challenges within organizations today. This talk will allow the audience to have a glimpse into the next-generation, Big Data-driven information systems.
Similar to Simseer - A Software Similarity Web Service (20)
Application Security at DevOps Speed and Portfolio Scale
Application Security at DevOps Speed and Portfolio Scale
YamenTrace: Requirements Traceability - Recovering and Visualizing Traceabili...
YamenTrace: Requirements Traceability - Recovering and Visualizing Traceabili...
Software Analytics: Data Analytics for Software Engineering and Security
Software Analytics: Data Analytics for Software Engineering and Security
Software Birthmark Based Theft/Similarity Comparisons of JavaScript Programs
Software Birthmark Based Theft/Similarity Comparisons of JavaScript Programs
Predicting Fault-Prone Files using Machine Learning
Predicting Fault-Prone Files using Machine Learning
Simon Brown: Software Architecture as Code at I T.A.K.E. Unconference 2015
Simon Brown: Software Architecture as Code at I T.A.K.E. Unconference 2015
Design Decisions For Understanding Software Architecture
Design Decisions For Understanding Software Architecture
Fast Automated Unpacking and Classification of Malware
Fast Automated Unpacking and Classification of Malware
Simseer and Bugwise - Web Services for Binary-level Software Similarity and D...
Simseer and Bugwise - Web Services for Binary-level Software Similarity and D...
Software Birthmark for Theft Detection of JavaScript Programs: A Survey
Software Birthmark for Theft Detection of JavaScript Programs: A Survey
Coping with Data Variety in the Big Data Era: The Semantic Computing Approach
Coping with Data Variety in the Big Data Era: The Semantic Computing Approach
More from Silvio Cesare
A BEGINNER’S JOURNEY INTO THE WORLD OF HARDWARE HACKING
This document provides an introduction to hardware hacking for software engineers. It outlines several beginner hardware hacking projects, including interfacing with UART to gain serial console access on devices, ripping firmware from chips to analyze code and find passwords/strings, manipulating IR alarm systems by learning codes and repurposing remotes, and building an Arduino-controlled backyard irrigation system networked to a PC. The document explains how to identify important chips, interfaces, and voltages, and techniques for reading serial flash and desoldering chips to extract firmware. It presents hardware hacking as an accessible new hobby that can build skills in electronics and low-level programming.
A WHIRLWIND TOUR OF ACADEMIC TECHNIQUES FOR REAL-WORLD SECURITY RESEARCHERS
This document provides an overview of various academic techniques that can be useful for security researchers, including mathematical objects, comparing objects, similarity searching, classification, clustering, and program analysis. It discusses representing problems as different objects like strings, vectors, and graphs and using techniques like n-grams, vector distances, and graph decomposition. Case studies of projects that applied these techniques are also summarized.
Simseer.com - Malware Similarity and Clustering Made Easy
Simseer.com provides free web services to analyze malware using program structure as a signature. The services include Simseer, which compares malware samples and visualizes relationships; SimseerCluster, which groups samples into clusters identifying potential families; and SimseerSearch, which finds similar samples to a query. The services leverage control flow graph signatures and machine learning to provide robust malware analysis without traditional string signatures.
FooCodeChu - Services for Software Analysis, Malware Detection, and Vulnerabi...
Bugwise is a tool that detects bugs in binaries using decompilation and data flow analysis. It detects issues like use-after-free bugs, double free bugs, and unsafe calls to getenv(). It has scanned over 123,000 Debian binaries and reported 85 getenv() related bugs across 47 packages. The probability of a binary having a vulnerability is 0.00067, and the probability of a package having at least one vulnerable binary is 0.00255. Bugwise is based on strong theoretical underpinnings like data flow analysis and is extensible to detect more bug classes. The presenter aims to make more of their research public and get more people using their tools via their website.
Detecting Bugs in Binaries Using Decompilation and Data Flow Analysis
The document discusses using static analysis techniques like data flow analysis and decompilation to detect bugs in binary files. It describes decompiling binaries into an intermediate representation and then performing intraprocedural and interprocedural data flow analysis on the representation. This allows detecting bugs involving unsafe functions like getenv() and memory issues like use-after-free and double free errors. The approach involves lifting x86 into a RISC-like intermediate language, inferring stack pointers, and decompiling locals and arguments to perform analysis and optimization.
Clonewise - Automatically Detecting Package Clones and Inferring Security Vu...
This document discusses automatically detecting package clones and inferring security vulnerabilities. It proposes using statistical classification techniques to identify cloned code between software packages. Features like common filenames, hashes, and fuzzy content would be used for classification. Packages found to share code could then be checked against known vulnerabilities to see if any vulnerabilities may affect the cloned code. The approach aims to scale the analysis to thousands of packages and help identify vulnerabilities in packages with cloned code that may not otherwise be tracked.
Wire - A Formal Intermediate Language for Binary Analysis
The document describes an intermediate representation (IR) for representing low-level machine code instructions. It defines the syntax and semantics for the IR, including instruction operations, register operands, memory addresses, and condition codes. It also provides examples of x86 assembly instructions translated to the defined IR format.
Effective flowgraph-based malware variant detection
Silvio Cesare presented an effective approach for flowgraph-based malware variant detection. The approach transforms control flow graphs into strings that are then compared using an assignment problem dissimilarity metric for sets of strings. Evaluation on Roron malware variants showed the approach was more effective at detecting variants than previous exact matching approaches. The system was also shown to have low false positive rates and efficient processing times for malware detection. The techniques developed have also been applied to other software analysis tools for similarity detection, bug finding, and more.
Faster, More Effective Flowgraph-based Malware Classification
Silvio Cesare is a PhD candidate at Deakin University researching malware detection and automated vulnerability discovery. His current work extends his Masters research on fast automated unpacking and classification of malware. He presented this work last year at Ruxcon 2010. His system uses control flow graphs and q-grams of decompiled code as "birthmarks" to detect unknown malware samples that are suspiciously similar to known malware, reducing the need for signatures. He evaluated the system on 10,000 malware samples with only 10 false positives. The system provides improved effectiveness and efficiency over his previous work in 2010.
Automated Detection of Software Bugs and Vulnerabilities in Linux
This document summarizes the key points from a technical presentation about detecting software defects and vulnerabilities. It identifies that the presenter is a PhD student researching malware classification and bug detection. Their approach involves combining decompilation with static analysis to find bugs in Linux binaries. They have found previously unknown bugs and vulnerabilities. Their ongoing work aims to automatically identify embedded third-party packages within software distributions in order to detect shared vulnerabilities.
Malware Variant Detection Using Similarity Search over Sets of Control Flow G...
The document discusses techniques for detecting software similarity, including control flow graphs, birthmarks, and algorithms like q-grams and optimal distance. It also evaluates these techniques on malware samples, showing detection rates and false positives for different algorithms and similarity thresholds. Processing times for analyzing benign and malicious files are presented.
Simple Bugs and Vulnerabilities in Linux Distributions
This talk discusses automated techniques for finding bugs and vulnerabilities in Linux software packages. The techniques were able to find:
- 27+ bug reports submitted to Debian after scanning for memset function bugs
- 741 programs that crashed when passed a null argv[0] parameter in Debian (27% crash rate)
- 3 segmentation faults when fuzzing most SUID/SGID programs in Debian
- 16 vulnerabilities found in Debian packages and 15 in Fedora packages after scanning for signatures of embedded vulnerable libraries
Linux distributions are using the results to improve security testing and patch vulnerabilities.
Malware Classification Using Structured Control Flow
This document summarizes a system for classifying malware using control flow graph signatures. It discusses:
1) Using entropy analysis to identify and unpack packed malware through application-level emulation.
2) Generating control flow graph signatures using a "structuring" technique and calculating similarities to signatures in a malware database.
3) Evaluating the system on real malware, showing high similarities between variants and low similarities between unrelated programs.
Security Applications For Emulation
The document discusses using emulation for security applications like reverse engineering Cisco IOS's heap management, tracing program execution to evaluate binaries, implementing dynamic taint analysis, and developing automated unpacking tools. It describes how emulation allows intercepting program execution at the instruction level and adding instrumentation to perform these dynamic analyses, avoiding detection by anti-debugging techniques. Specific tools mentioned include Dynamips, TTAnalyze, Argos, Pandora's Bochs, and the author's own unpacker and emulator.
Auditing the Opensource Kernels
The document provides an overview of a presentation on kernel auditing research, including:
- Three parts to the presentation covering kernel auditing research, exploitable bugs found, and kernel exploitation.
- Audits were conducted on several open source kernels, finding over 100 vulnerabilities across them.
- A sample of exploitable bugs is then presented from the audited kernels to provide evidence that kernels are not bug-free and vulnerabilities can be relatively simple to find and exploit.
More from Silvio Cesare (15)
A BEGINNER’S JOURNEY INTO THE WORLD OF HARDWARE HACKING
A BEGINNER’S JOURNEY INTO THE WORLD OF HARDWARE HACKING
A WHIRLWIND TOUR OF ACADEMIC TECHNIQUES FOR REAL-WORLD SECURITY RESEARCHERS
A WHIRLWIND TOUR OF ACADEMIC TECHNIQUES FOR REAL-WORLD SECURITY RESEARCHERS
Simseer.com - Malware Similarity and Clustering Made Easy
Simseer.com - Malware Similarity and Clustering Made Easy
FooCodeChu - Services for Software Analysis, Malware Detection, and Vulnerabi...
FooCodeChu - Services for Software Analysis, Malware Detection, and Vulnerabi...
Detecting Bugs in Binaries Using Decompilation and Data Flow Analysis
Detecting Bugs in Binaries Using Decompilation and Data Flow Analysis
Clonewise - Automatically Detecting Package Clones and Inferring Security Vu...
Clonewise - Automatically Detecting Package Clones and Inferring Security Vu...
Wire - A Formal Intermediate Language for Binary Analysis
Wire - A Formal Intermediate Language for Binary Analysis
Effective flowgraph-based malware variant detection
Effective flowgraph-based malware variant detection
Faster, More Effective Flowgraph-based Malware Classification
Faster, More Effective Flowgraph-based Malware Classification
Automated Detection of Software Bugs and Vulnerabilities in Linux
Automated Detection of Software Bugs and Vulnerabilities in Linux
Malware Variant Detection Using Similarity Search over Sets of Control Flow G...
Malware Variant Detection Using Similarity Search over Sets of Control Flow G...
Simple Bugs and Vulnerabilities in Linux Distributions
Simple Bugs and Vulnerabilities in Linux Distributions
Malware Classification Using Structured Control Flow
Malware Classification Using Structured Control Flow
Recently uploaded
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Essentials of Automations: The Art of Triggers and Actions in FME
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!
Microsoft - Power Platform_G.Aspiotis.pdf
Revolutionizing Application Development
with AI-powered low-code, presentation by George Aspiotis, Sr. Partner Development Manager, Microsoft
20 Comprehensive Checklist of Designing and Developing a Website
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
“I’m still / I’m still / Chaining from the Block”
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
UiPath Test Automation using UiPath Test Suite series, part 5
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Pushing the limits of ePRTC: 100ns holdover for 100 days
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Communications Mining Series - Zero to Hero - Session 1
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentationsVideo Streaming: Then, Now, and in the Future
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
Recently uploaded (20)
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
20 Comprehensive Checklist of Designing and Developing a Website
20 Comprehensive Checklist of Designing and Developing a Website
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Pushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 days
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
Simseer - A Software Similarity Web Service
- 1. Silvio Cesare Deakin University [email_address]
- 7. Program Features Examples AST (left) and Control Flow (right)
- 14. rNN (Range Nearest Neighbour)