Cartic Ramakrishnan's dissertation defense
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The document summarizes Cartic Ramakrishnan's dissertation on extracting semantic metadata from text to facilitate knowledge discovery in biomedicine. It defines knowledge discovery as opportunistic search over an ill-defined space leading to surprising but useful knowledge. It discusses using ontologies and text mining to extract semantic relationships from unstructured text and represent them as structured semantic metadata to enable knowledge exploration and discovery. It presents preliminary work on automating some of Swanson's biomedical discoveries by extracting relationships between concepts from parsed sentences in publications.
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Video can be found at: http://www.youtube.com/watch?v=NeQomGsJvDk
PhD thesis defense of Ajith Ranabahu
Description - Ajith defended his thesis on application and data portability in cloud
computing. More details on Ajith's research and publications can be
found at http://knoesis.wright.edu/researchers/ajith/
Video can be found at : http://www.youtube.com/watch?v=oDBeBIIFmHc&list=UUORqXk1ZV44MOwpCorAROyQ&index=1&feature=plpp_video
A Semantics-based Approach to Machine Perception
Cory Henson defended his thesis on "A Semantics-based Approach to Machine Perception".
Video can be found at: http://www.youtube.com/watch?v=L8M7eoGKtSE
Automatic Emotion Identification from Text
Video: https://www.youtube.com/watch?v=ZCToaDgxnAs
Abstract:
People's emotions can be gleaned from their text using machine learning techniques to build models that exploit large self-labeled emotion data from social media. Further, the self-labeled emotion data can be effectively adapted to train emotion classifiers in different target domains where training data are sparse.
Emotions are both prevalent in and essential to most aspects of our lives. They influence our decision-making, affect our social relationships and shape our daily behavior. With the rapid growth of emotion-rich textual content, such as microblog posts, blog posts, and forum discussions, there is a growing need to develop algorithms and techniques for identifying people's emotions expressed in text. It has valuable implications for the studies of suicide prevention, employee productivity, well-being of people, customer relationship management, etc. However, emotion identification is quite challenging partly due to the following reasons: i) It is a multi-class classification problem that usually involves at least six basic emotions. Text describing an event or situation that causes the emotion can be devoid of explicit emotion-bearing words, thus the distinction between different emotions can be very subtle, which makes it difficult to glean emotions purely by keywords. ii) Manual annotation of emotion data by human experts is very labor-intensive and error-prone. iii) Existing labeled emotion datasets are relatively small, which fails to provide a comprehensive coverage of emotion-triggering events and situations.
Mining and Analyzing Subjective Experiences in User-generated Content
Mining and Analyzing Subjective Experiences in User-generated ContentArtificial Intelligence Institute at UofSC
Dissertation Defense:
" Mining and Analyzing Subjective Experiences in User Generated Content "
By Lu Chen
Tuesday, April 9, 2016
Dissertation Committee: Dr. Amit Sheth, Advisor, Dr. T. K. Prasad, Dr. Keke Chen, Dr. Ingmar Weber, and Dr. Justin Martineau,
Pictures: https://www.facebook.com/Kno.e.sis/photos/?tab=album&album_id=1225911137443732
Video: https://youtu.be/tzLEUB-hggQ
Lu's Home page: http://knoesis.wright.edu/researchers/luchen/
ABSTRACT
Web 2.0 and social media enable people to create, share and discover information instantly anywhere, anytime. A great amount of this information is subjective information -- the information about people's subjective experiences, ranging from feelings of what is happening in our daily lives to opinions on a wide variety of topics. Subjective information is useful to individuals, businesses, and government agencies to support decision making in areas such as product purchase, marketing strategy, and policy making. However, much useful subjective information is buried in ever-growing user generated data on social media platforms, it is still difficult to extract high quality subjective information and make full use of it with current technologies.
Current subjectivity and sentiment analysis research has largely focused on classifying the text polarity -- whether the expressed opinion regarding a specific topic in a given text is positive, negative, or neutral. This narrow definition does not take into account the other types of subjective information such as emotion, intent, and preference, which may prevent their exploitation from reaching its full potential. This dissertation extends the definition and introduces a unified framework for mining and analyzing diverse types of subjective information. We have identified four components of a subjective experience: an individual who holds it, a target that elicits it (e.g., a movie, or an event), a set of expressions that describe it (e.g., "excellent", "exciting"), and a classification or assessment that characterize it (e.g., positive vs. negative). Accordingly, this dissertation makes contributions in developing novel and general techniques for the tasks of identifying and extracting these components.
We first explore the task of extracting sentiment expressions from social media posts. We propose an optimization-based approach that extracts a diverse set of sentiment-bearing expressions, including formal and slang words/phrases, for a given target from an unlabeled corpus. Instead of associating the overall sentiment with a given text, this method assesses the more fine-grained target-dependent polarity of each sentiment expression. Unlike pattern-based approaches which often fail to capture the diversity of sentiment expressions due to the informal nature of language usage and writing style in social media posts, the proposed approach is capable of identifying sentiment phrasePersonalized and Adaptive Semantic Information Filtering for Social Media - P...
Personalized and Adaptive Semantic Information Filtering for Social Media - P...Artificial Intelligence Institute at UofSC
Video of the talk: https://www.youtube.com/watch?v=7k-u_TUew3o
Abstract: Social media has experienced immense growth in recent times. These platforms are becoming increasingly common for information seeking and consumption, and as part of its growing popularity, information overload pose a significant challenge to users. For instance, Twitter alone generates around 500 million tweets per day and it is impractical for users to have to parse through such an enormous stream to find information that are interesting to them. This situation necessitates efficient personalized filtering mechanisms for users to consume relevant, interesting information from social media.
Building a personalized filtering system involves understanding users interests and utilizing these interests to deliver relevant information to users. These tasks primarily include analyzing and processing social media text which is challenging due to its shortness in length, and the real-time nature of the medium. The challenges include: (1) Lack of semantic context: Social Media posts are on an average short in length, which provides limited semantic context to perform textual analysis. This is particularly detrimental for topic identification which is a necessary task for mining users interests; (2) Dynamically changing vocabulary: Most social media websites such as Twitter and Facebook generate posts that are of current (timely) interests to the users. Due to this real-time nature, information relevant to dynamic topics of interest evolve reflecting the changes in the real world. This in turn changes the vocabulary associated with these dynamic topics of interest making it harder to filter relevant information; (3) Scalability: The number of users on social media platforms are significantly large, which is difficult for centralized systems to scale to deliver relevant information to users. This dissertation is devoted to exploring semantic techniques and Semantic Web technologies to address the above mentioned challenges in building a personalized information filtering system for social media. Particularly, the necessary semantics (knowledge) is derived from crowd sourced knowledge bases such as Wikipedia to improve context for understanding short-text and dynamic topics on social media.
Ashutosh Jadhav PhD Defense: Knowledge Driven Search Intent Mining
Ashutosh Jadhav PhD Defense: Knowledge Driven Search Intent MiningArtificial Intelligence Institute at UofSC
Understanding users’ latent intents behind search queries is essential for satisfying a user’s search needs. Search intent mining can help search engines to enhance its ranking of search results, enabling new search features like instant answers, personalization, search result diversification, and the recommendation of more relevant ads. Consequently, there has been increasing attention on studying how to effectively mine search intents by analyzing search engine query logs. While state-of-the-art techniques can identify the domain of the queries (e.g. sports, movies, health), identifying domain-specific intent is still an open problem. Among all the topics available on the Internet, health is one of the most important in terms of impact on the user and it is one of the most frequently searched areas. This dissertation presents a knowledge-driven approach for domain-specific search intent mining with a focus on health-related search queries.
First, we identified 14 consumer-oriented health search intent classes based on inputs from focus group studies and based on analyses of popular health websites, literature surveys, and an empirical study of search queries. We defined the problem of classifying millions of health search queries into zero or more intent classes as a multi-label classification problem. Popular machine learning approaches for multi-label classification tasks (namely, problem transformation and algorithm adaptation methods) were not feasible due to the limitation of label data creations and health domain constraints. Another challenge in solving the search intent identification problem was mapping terms used by laymen to medical terms. To address these challenges, we developed a semantics-driven, rule-based search intent mining approach leveraging rich background knowledge encoded in Unified Medical Language System (UMLS) and a crowd sourced encyclopedia (Wikipedia). The approach can identify search intent in a disease-agnostic manner and has been evaluated on three major diseases.
While users often turn to search engines to learn about health conditions, a surprising amount of health information is also shared and consumed via social media, such as public social platforms like Twitter. Although Twitter is an excellent information source, the identification of informative tweets from the deluge of tweets is the major challenge. We used a hybrid approach consisting of supervised machine learning, rule-based classifiers, and biomedical domain knowledge to facilitate the retrieval of relevant and reliable health information shared on Twitter in real time. Furthermore, we extended our search intent mining algorithm to classify health-related tweets into health categories. Finally, we performed a large-scale study to compare health search intents and features that contribute in the expression of search intent from 100+ million search queries from smarts devices (smartphones/tablets) and personal computers (desktops/laptops)Delroy Cameron's Dissertation Defense: A Contenxt-Driven Subgraph Model for L...
Delroy Cameron's Dissertation Defense: A Contenxt-Driven Subgraph Model for L...Artificial Intelligence Institute at UofSC
Literature-Based Discovery (LBD) refers to the process of uncovering hidden connections that are implicit in scientific literature. Numerous hypotheses have been generated from scientific literature, which influenced innovations in diagnosis, treatment, preventions and overall public health. However, much of the existing research on discovering hidden connections among concepts have used distributional statistics and graph-theoretic measures to capture implicit associations. Such metrics do not explicitly capture the semantics of hidden connections. ...
While effective in some situations, the practice of relying on domain expertise, structured background knowledge and heuristics to complement distributional and graph-theoretic approaches, has serious limitations. ..
This dissertation proposes an innovative context-driven, automatic subgraph creation method for finding hidden and complex associations among concepts, along multiple thematic dimensions. It outlines definitions for context and shared context, based on implicit and explicit (or formal) semantics, which compensate for deficiencies in statistical and graph-based metrics. It also eliminates the need for heuristics a priori. An evidence-based evaluation of the proposed framework showed that 8 out of 9 existing scientific discoveries could be recovered using this approach. Additionally, insights into the meaning of associations could be obtained using provenance provided by the system. In a statistical evaluation to determine the interestingness of the generated subgraphs, it was observed that an arbitrary association is mentioned in only approximately 4 articles in MEDLINE, on average. These results suggest that leveraging implicit and explicit context, as defined in this dissertation, is an advancement of the state-of-the-art in LBD research.
Ph.D. Committee: Drs. Amit Sheth (Advisor), TK Prasad, Michael Raymer,
Ramakanth Kavuluru (UKY), Thomas C. Rindflesch (NLM) and Varun Bhagwan (Yahoo! Labs)
Relevant Publications (more at: http://knoesis.wright.edu/students/delroy/)
D. Cameron, R. Kavuluru, T. C. Rindflesch, O. Bodenreider, A. P. Sheth, K. Thirunarayan. Leveraging Distributional Semantics for Domain Agnostic Literature-Based Discovery (under preparation)
D. Cameron, O. Bodenreider, H. Yalamanchili, T. Danh, S. Vallabhaneni, K. Thirunarayan, A. P. Sheth, T. C. Rindflesch. A Graph-based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications. Journal of Biomedical Informatics (JBI13), 46(2): 238–251, 2013
D. Cameron, R. Kavuluru, O. Bodenreider, P. N. Mendes, A. P. Sheth, K. Thirunarayan. Semantic Predications for Complex Information Needs in Biomedical Literature International Bioinformatics and Biomedical Conference (BIBM11), pp. 512–519, 2011 (acceptance rate=19.4%)
D. Cameron, P. N. Mendes, A. P. Sheth, V. Chan. Semantics-empowered Text Exploration for Knowledge Discovery. ACM Southeast Conference (ACMSE10), 14, 2010Knowledge-empowered Probabilistic Graphical Models for Physical-Cyber-Social ...
Knowledge-empowered Probabilistic Graphical Models for Physical-Cyber-Social ...Artificial Intelligence Institute at UofSC
There is a rapid intertwining of sensors and mobile devices into the fabric of our lives. This has resulted in unprecedented growth in the number of observations from the physical and social worlds reported in the cyber world. Sensing and computational components embedded in the physical world is termed as Cyber-Physical System (CPS). Current science of CPS is yet to effectively integrate citizen observations in CPS analysis. We demonstrate the role of citizen observations in CPS and propose a novel approach to perform a holistic analysis of machine and citizen sensor observations. Specifically, we demonstrate the complementary, corroborative, and timely aspects of citizen sensor observations compared to machine sensor observations in Physical-Cyber-Social (PCS) Systems.
Physical processes are inherently complex and embody uncertainties. They manifest as machine and citizen sensor observations in PCS Systems. We propose a generic framework to move from observations to decision-making and actions in PCS systems consisting of: (a) PCS event extraction, (b) PCS event understanding, and (c) PCS action recommendation. We demonstrate the role of Probabilistic Graphical Models (PGMs) as a unified framework to deal with uncertainty, complexity, and dynamism that help translate observations into actions. Data driven approaches alone are not guaranteed to be able to synthesize PGMs reflecting real-world dependencies accurately. To overcome this limitation, we propose to empower PGMs using the declarative domain knowledge. Specifically, we propose four techniques: (a) automatic creation of massive training data for Conditional Random Fields (CRFs) using domain knowledge of entities used in PCS event extraction, (b) Bayesian Network structure refinement using causal knowledge from Concept Net used in PCS event understanding, (c) knowledge-driven piecewise linear approximation of nonlinear time series dynamics using Linear Dynamical Systems (LDS) used in PCS event understanding, and the (d) transforming knowledge of goals and actions into a Markov Decision Process (MDP) model used in PCS action recommendation.
We evaluate the benefits of the proposed techniques on real-world applications involving traffic analytics and Internet of Things (IoT).Pablo Mendes' Defense: Adaptive Semantic Annotation of Entity and Concept Men...
Pablo Mendes' Defense: Adaptive Semantic Annotation of Entity and Concept Men...Artificial Intelligence Institute at UofSC
PhD defense held at Kno.e.sis Center, Wright State University, December 03, 2013.Hemant Purohit PhD Defense: Mining Citizen Sensor Communities for Cooperation...
Hemant Purohit PhD Defense: Mining Citizen Sensor Communities for Cooperation...Artificial Intelligence Institute at UofSC
Social media provides a natural platform for dynamic emergence of citizen (as) sensor communities, where the citizens share information, express opinions, and engage in discussions. Often such a Online Citizen Sensor Community (CSC) has stated or implied goals related to workflows of organizational actors with defined roles and responsibilities. For example, a community of crisis response volunteers, for informing the prioritization of responses for resource needs (e.g., medical) to assist the managers of crisis response organizations. However, in CSC, there are challenges related to information overload for organizational actors, including finding reliable information providers and finding the actionable information from citizens. This threatens awareness and articulation of workflows to enable cooperation between citizens and organizational actors. CSCs supported by Web 2.0 social media platforms offer new opportunities and pose new challenges. This work addresses issues of ambiguity in interpreting unconstrained natural language (e.g., ‘wanna help’ appearing in both types of messages for asking and offering help during crises), sparsity of user and group behaviors (e.g., expression of specific intent), and diversity of user demographics (e.g., medical or technical professional) for interpreting user-generated data of citizen sensors. Interdisciplinary research involving social and computer sciences is essential to address these socio-technical issues in CSC, and allow better accessibility to user-generated data at higher level of information abstraction for organizational actors. This study presents a novel web information processing framework focused on actors and actions in cooperation, called Identify-Match-Engage (IME), which fuses top-down and bottom-up computing approaches to design a cooperative web information system between citizens and organizational actors. It includes a.) identification of action related seeking-offering intent behaviors from short, unstructured text documents using both declarative and statistical knowledge based classification model, b.) matching of intentions about seeking and offering, and c.) engagement models of users and groups in CSC to prioritize whom to engage, by modeling context with social theories using features of users, their generated content, and their dynamic network connections in the user interaction networks. The results show an improvement in modeling efficiency from the fusion of top-down knowledge-driven and bottom-up data-driven approaches than from conventional bottom-up approaches alone for modeling intent and engagement. Several applications of this work include use of the engagement interface tool during recent crises to enable efficient citizen engagement for spreading critical information of prioritized needs to ensure donation of only required supplies by the citizens. The engagement interface application also won the United Nations ICT agency ITU's Young Innovator 2014 award.2015 Kno.e.sis Center Annual Review
Annual review with the President, Provost, and VP of Research of Wright State University
Trust Management: A Tutorial
Krishnaprasad Thirunarayan, Trust Management: Multimodal Data Perspective,
Invited Tutorial, The 2015 International Conference on Collaboration
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Web and Complex Systems Lab @ Kno.e.sis
Prof. Derek Doran leads work in Web and Complex Systems at Kno.e.sis-- here is exciting overview of his group's work.
Kno.e.sis Approach to Impactful Research & Training for Exceptional Careers
Abstract
Kno.e.sis (http://knoesis.org) is a world-class research center that uses semantic, cognitive, and perceptual computing for gathering insights from physical/IoT, cyber/Web, and social and enterprise (e.g., clinical) big data. We innovate and employ semantic web, machine learning, NLP/IR, data mining, network science and highly scalable computing techniques. Our highly interdisciplinary research impacts health and clinical applications, biomedical and translational research, epidemiology, cognitive science, social good, policy, development, etc. A majority of our $12+ million in active funds come from the NSF and NIH. In this talk, I will provide an overview of some of our major research projects.
Kno.e.sis is highly successful in its primary mission of exceptional student outcomes: our students have exceptional publication and real-world impact and our PhDs compete with their counterparts from top 10 schools for initial jobs in research universities, top industry research labs, and highly competitive companies. A key reason for Kno.e.sis' success is its unique work culture involving teamwork to solve complex problems. Practically all our work involves real-world challenges, real-world data, interdisciplinary collaborators, path-breaking research to solve challenges, real-world deployments, real-world use, and measurable real-world impact.
In this talk, I will also seek to discuss our choice of research topics and our unique ecosystem that prepares our students for exceptional careers.
Data Processing and Semantics for Advanced Internet of Things (IoT) Applicati...
Data Processing and Semantics for Advanced Internet of Things (IoT) Applicati...Artificial Intelligence Institute at UofSC
This tutorial presents tools and techniques for effectively utilizing the Internet of Things (IoT) for building advanced applications, including the Physical-Cyber-Social (PCS) systems. The issues and challenges related to IoT, semantic data modelling, annotation, knowledge representation (e.g. modelling for constrained environments, complexity issues and time/location dependency of data), integration, analy- sis, and reasoning will be discussed. The tutorial will de- scribe recent developments on creating annotation models and semantic description frameworks for IoT data (e.g. such as W3C Semantic Sensor Network ontology). A review of enabling technologies and common scenarios for IoT applications from the data and knowledge engineering point of view will be discussed. Information processing, reasoning, and knowledge extraction, along with existing solutions re- lated to these topics will be presented. The tutorial summarizes state-of-the-art research and developments on PCS systems, IoT related ontology development, linked data, do- main knowledge integration and management, querying large- scale IoT data, and AI applications for automated knowledge extraction from real world data.
Related: Semantic Sensor Web: http://knoesis.org/projects/ssw
Physical-Cyber-Social Computing: http://wiki.knoesis.org/index.php/PCSSmart Data - How you and I will exploit Big Data for personalized digital hea...
Amit Sheth's keynote at IEEE BigData 2014, Oct 29, 2014.
Abstract from:
http://cci.drexel.edu/bigdata/bigdata2014/keynotespeech.htm
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is personalized digital health that related to taking better decisions about our health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (e.g., information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will describe Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If my child is an asthma patient, for all the data relevant to my child with the four V-challenges, what I care about is simply, “How is her current health, and what are the risk of having an asthma attack in her current situation (now and today), especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP. I will motivate the need for a synergistic combination of techniques similar to the close interworking of the top brain and the bottom brain in the cognitive models.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city.
Kno.e.sis Review: late 2012 to mid 2013
A quick, high level review of the Kno.e.sis Center, with a focus on Computer Science & Engineering component
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Transcript: Selling digital books in 2024: Insights from industry leaders - T...
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
By Design, not by Accident - Agile Venture Bolzano 2024
As presented at the Agile Venture Bolzano, 4.06.2024
Securing your Kubernetes cluster_ a step-by-step guide to success !
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
UiPath Community Day Dubai: AI at Work..
Welcome to the first live UiPath Community Day Dubai! Join us for this unique occasion to meet our local and global UiPath Community and leaders. You will get a full view of the MEA region's automation landscape and the AI Powered automation technology capabilities of UiPath. Also, hosted by our local partners Marc Ellis, you will enjoy a half-day packed with industry insights and automation peers networking.
📕 Curious on our agenda? Wait no more!
10:00 Welcome note - UiPath Community in Dubai
Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
10:35: Customer Success Journey
Deepthi Deepak, Head of Intelligent Automation CoE, First Abu Dhabi Bank
11:15 The UiPath approach to GenAI with our three principles: improve accuracy, supercharge productivity, and automate more
Boris Krumrey, Global VP, Automation Innovation, UiPath
12:15 To discover how Marc Ellis leverages tech-driven solutions in recruitment and managed services.
Brendan Lingam, Director of Sales and Business Development, Marc Ellis
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
A tale of scale & speed: How the US Navy is enabling software delivery from l...
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
DevOps and Testing slides at DASA Connect
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
UiPath Test Automation using UiPath Test Suite series, part 4
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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Leading Change strategies and insights for effective change management pdf 1.pdf
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Transcript: Selling digital books in 2024: Insights from industry leaders - T...
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Cartic Ramakrishnan's dissertation defense
- 1. Extracting, representing and mining Semantic Metadata from text: Facilitating Knowledge Discovery in Biomedicine Cartic Ramakrishnan Advisor: Dr. Amit Sheth Committee Members: Dr. Michael Raymer Dr. Guozhu Dong Dr. Thaddeus Tarpey Dr. Vasant Honavar Dr. Shaojun Wang
- 5. Element of surprise – Swanson’s discoveries Magnesium Migraine PubMed ? Stress Spreading Cortical Depression Calcium Channel Blockers Swanson’s Discoveries Associations Discovered based on keyword searches followed by manually analysis of text to establish possible relevant relationships 11 possible associations found
- 6. Knowledge Discovery in AI- The robot scientist Planned search over an well-defined (axiomatic) space leading to knowledge discovery. Knowledge discovery by humans is done in non-axiomatic ill-defined spaces over multi-modal data . Scientific literature is ill-defined and loosely structured source of data used in scientific investigations. Assigning structure and interpretation to text (Semantics) Syntax Structure Semantics
- 7. Knowledge Discovery = Extraction + Heuristic Aggregation Undiscovered Public Knowledge
- 8. Information Extraction & Text Mining This MEK dependency was observed in BRAF mutant cells regardless of tissue lineage, and correlated with both downregulation of cyclin D1 protein expression and the induction of G1 arrest. *MEK dependency ISA Dependency_on_an_Organic_chemical *BRAF mutant cells ISA Cell_type *downregulation of cyclin D1 protein expression ISA Biological_process *tissue lineage ISA Biological_concept *induction of G1 arrest ISA Biological_process Information Extraction = segmentation+classification+association+mining Text mining = entity identification+named relationship extraction+discovering association chains…. Segmentation Classification Named Relationship Extraction MEK dependency observed in BRAF mutant cells downregulation of cyclin D1 protein expression correlated with induction of G1 arrest correlated with
- 10. Knowledge Discovery over text Extraction of Semantics from text Semantic Metadata Guided Knowledge Explorations Assigning interpretation to text Semantic Metadata Guided Knowledge Discovery Triple-based Semantic Search Semantic browser Subgraph discovery Semantic metadata in the form of semi-structured data Text
- 11. Ontology-enabled Information Extraction Cartic Ramakrishnan , Krys Kochut, Amit P. Sheth: A Framework for Schema-Driven Relationship Discovery from Unstructured Text. International Semantic Web Conference 2006 : 583-596
- 13. Information Extraction via Ontology assisted text mining – Relationship extraction Biologically active substance Lipid Disease or Syndrome affects causes affects causes complicates Fish Oils Raynaud’s Disease ??????? instance_of instance_of UMLS Semantic Network MeSH PubMed 9284 documents 4733 documents 5 documents
- 16. Method – Identify entities and relationships in Parse Tree TOP NP VP S NP VBZ induces NP PP NP IN of DT the NN endometrium JJ adenomatous NN hyperplasia NP PP IN by NN estrogen DT the JJ excessive ADJP NN stimulation JJ endogenous JJ exogenous CC or MeSHID D004967 MeSHID D006965 MeSHID D004717 UMLS ID T147 Modifiers Modified entities Composite Entities
- 17. Representation – Resulting RDF Modifiers Modified entities Composite Entities
- 19. Paths between Migraine and Magnesium Paths are considered interesting if they have one or more named relationship Other than hasPart or hasModifiers in them
- 21. Interesting Observations from this preliminary work
- 25. Unsupervised Joint Extraction of Compound Entities and Relationship Cartic Ramakrishnan , Pablo N. Mendes, Shaojun Wang and Amit P. Sheth "Unsupervised Discovery of Compound Entities for Relationship Extraction" EKAW 2008 - 16th International Conference on Knowledge Engineering and Knowledge Management Knowledge Patterns
- 29. Algorithm Relationship head Subject head Object head Object head
- 42. Results of Manual Evaluation
- 44. Semantic Metadata Guided Knowledge Explorations and Discovery
- 47. Schema-driven edge weight assignment company Entertainment company Manufacturing company Oil company Automotive company Electronics company Sporting goods company Ford Motors Cartic’s Company 0.67 0.33 <0.5 Schema Instances 1.0 1.0 1.0 1.0 1.0 1.0 0.33
- 51. Results
- 56. Hypothesis Driven retrieval of Scientific Literature PubMed Keyword query: Migraine[MH] + Magnesium[MH] Complex Query Supporting Document sets retrieved Migraine Stress Patient affects isa Magnesium Calcium Channel Blockers inhibit
- 64. On a lighter note