Toward a Recommendation System for focusing Testing

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OOAD - Ch.09 - Software Project Estimation.pptx

Applications in many areas analyze an ever-changing environment. On billion vertices graphs, providing snapshots imposes a large performance cost. We propose the first formal model for graph analysis running concurrently with streaming data updates. We consider an algorithm valid if its output is correct for the initial graph plus some implicit subset of concurrent changes. We show theoretical properties of the model, demonstrate the model on various algorithms, and extend it to updating results incrementally.

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In the format of hands-on session, this workshop will introduce participants to the Language Variation Suite (LVS), a user-friendly interactive web application built in R. LVS provides access to advanced statistical methods and visualization techniques, such as mixed-effects modeling, conditional and random tree analyses, cluster analysis. These advanced methods enable researchers to handle imbalanced data, measure individual and group variation, estimate significance, and rank variables according to their significance.

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IRJET- Supervised Learning Classification Algorithms Comparison

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Social and information networks have been extensively studied over years. This paper studies a new query on sub graph search on heterogeneous networks. Given an uncertain network of N objects, where each object is associated with a network to an underlying critical problem of discovering, top-k sub graphs of entities with rare and surprising associations returns k objects such that the expected matching sub graph queries efficiently involves, Compute all matching sub graphs which satisfy "Nebula computing requests" and this query is useful in ranking such results based on the rarity and the interestingness of the associations among nebula requests in the sub graphs. "In evaluating Top k-selection queries, "we compute information nebula using a global structural context similarity, and our similarity measure is independent of connection sub graphs". We need to compute the previous work on the matching problem can be harnessed for expected best for a naive ranking after matching for large graphs. Top k-selection sets and search for the optimal selection set with the large graphs; sub graphs may have enormous number of matches. In this paper, we identify several important properties of top-k selection queries, We propose novel top–K mechanisms to exploit these indexes for answering interesting sub graph queries efficiently.

Sub-Graph Finding Information over Nebula Networks

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IRJET- Review on Scheduling using Dependency Structure Matrix

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안녕하세요 딥러닝 논문 읽기 모임입니다. 오늘 업로드된 논문 리뷰 영상은 'Transformer Interpretability Beyond Attention Visualization'라는 제목의 논문입니다. 트랜스포머는 지금 까지 논문 리뷰 영상을 업로드 하면서 가장 많이 언급한 모델중 하나입니다. NLP를 넘어, 이미지 처리 매우 많은 영역에서 소타 네트워크로 쓰였습니다. 해당 논문은 이미지 처리 영역에서의 Transformer가 의사결정을 내리는 과정에 대해 특히 self Attention 모듈에 관해 다양한 방법으로 심층적으로 연구한 논문 입니다! 오늘 논문 리뷰를 위해 펀디멘탈팀 김채현님이 자세한리뷰 도와주셨습니다! 많은 관심 미리 감사드립니다! https://youtu.be/XCED5bd2WT0

220206 transformer interpretability beyond attention visualization

taeseon ryu

In this paper Compare the performance of two classification algorithm. I t is useful to differentiate algorithms based on computational performance rather than classification accuracy alone. As although classification accuracy between the algorithms is similar, computational performance can differ significantly and it can affect to the final results. So the objective of this paper is to perform a comparative analysis of two machine learning algorithms namely, K Nearest neighbor, classification and Logistic Regression. In this paper it was considered a large dataset of 7981 data points and 112 features. Then the performance of the above mentioned machine learning algorithms are examined. In this paper the processing time and accuracy of the different machine learning techniques are being estimated by considering the collected data set, over a 60% for train and remaining 40% for testing. The paper is organized as follows. In Section I, introduction and background analysis of the research is included and in section II, problem statement. In Section III, our application and data analyze Process, the testing environment, and the Methodology of our analysis are being described briefly. Section IV comprises the results of two algorithms. Finally, the paper concludes with a discussion of future directions for research by eliminating the problems existing with the current research methodology.

Performance Comparision of Machine Learning Algorithms

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An overview research talk given during at JPL on July 22, 2015. Abstract: Today’s complex cyber-physical systems require the use of a variety of models to capture different aspects of these systems: physical models, software models, control models, and so on. A critical challenge is to ensure consistency and completeness of these models. In this talk we describe an approach that uses architectural models as the basis for reconciliation. Specifically, a base architecture of the system is used as a unifying representation to compare the structure and semantics of the associated models through a set of architectural projections, or views. Each model is related to the base architecture through the abstraction of a corresponding architectural view, which captures structural and semantic correspondences between model elements and system entities. The use of the architectural view framework to relate system models from different domains is illustrated in the context of a quad-rotor air vehicle and an on-going collaboration with Toyota on future braking system design.

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Data Science as a Career and Intro to R

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QuASD/PROFES 2017, Innsbruck (Austria): Workshop with Marcus Ciolkowski (Principal IT Consultant at QAware), Liliana Guzmán, Adam Trendowicz, Felix Salfner Abstract: Due to cost and time constraints, software quality is often neglected in the evolution and adaptation of software. Thus, maintainability suffers, maintenance costs rise, and the development takes longer. These effects are referred to as “technical debt”. The challenge for project managers is to find a balance when using the given budget and schedule, either by reducing technical debt or by adding technical features. This balance is needed to keep time to market for current product releases short and future maintenance costs at an acceptable level. Method: The project ProDebt aimed at developing an innovative methodology and a software tool to support the strategic planning of technical debt in the context of agile software development. In this project, we created quality models and collected corresponding measurement data for two case studies in two different companies. Alltogether, the two case studies contributed 5-6 years of data, from the end of 2011, resp. mid-2012, until today. Using measurement and effort data, we trained a machine-learning model to predict productivity based on measurement data - representing the technical debt of a file at a given point in time. Result: We developed a prototype and a prediction model for forecasting potential savings based on proposed refactorings of key drivers of technical debt identified by the model. In this paper, we present the approach and the experiences made during model development.

ProDebt's Lessons Learned from Planning Technical Debt Strategically

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Similar to Toward a Recommendation System for focusing Testing (20)

OOAD - Ch.09 - Software Project Estimation.pptx

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Accounting for uncertainty in species delineation during the analysis of envi...

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