A database application differs form regular applications in that some of its inputs may be database queries. The program will execute the queries on a database and may use any result values in its subsequent program logic. This means that a user-supplied query may determine the values that the application will use in subsequent branching conditions. At the same time, a new database application is often required to work well on a body of existing data stored in some large database. For systematic testing of database applications, recent techniques replace the existing database with carefully crafted mock databases. Mock databases return values that will trigger as many execution paths in the application as possible and thereby maximize overall code coverage of the database application.
In this paper we offer an alternative approach to database application testing. Our goal is to support software engineers in focusing testing on the existing body of data the application is required to work well on. For that, we propose to side-step mock database generation and instead generate queries for the existing database. Our key insight is that we can use the information collected during previous program executions to systematically generate new queries that will maximize the coverage of the application under test, while guaranteeing that the generated test cases focus on the existing data.
A database application differs form regular applications in that some of its inputs may be database queries. The program will execute the queries on a database and may use any result values in its subsequent program logic. This means that a user-supplied query may determine the values that the application will use in subsequent branching conditions. At the same time, a new database application is often required to work well on a body of existing data stored in some large database. For systematic testing of database applications, recent techniques replace the existing database with carefully crafted mock databases. Mock databases return values that will trigger as many execution paths in the application as possible and thereby maximize overall code coverage of the database application.
In this paper we offer an alternative approach to database application testing. Our goal is to support software engineers in focusing testing on the existing body of data the application is required to work well on. For that, we propose to side-step mock database generation and instead generate queries for the existing database. Our key insight is that we can use the information collected during previous program executions to systematically generate new queries that will maximize the coverage of the application under test, while guaranteeing that the generated test cases focus on the existing data.
https://telecombcn-dl.github.io/2018-dlcv/
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These slides aim to be a complementary material for the short paper: HOP-Rec @ RecSys18. It explains the intuition and some abstract idea behind the descriptions and mathematical symbols by illustrating some plots and figures.
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http://imatge-upc.github.io/telecombcn-2016-dlcv/
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Abstract: The processing power of computing devices has increased with number of available cores. This paper presents an approach
towards clustering of categorical data on multi-core platform. K-modes algorithm is used for clustering of categorical data which
uses simple dissimilarity measure for distance computation. The multi-core approach aims to achieve speedup in processing. Open
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https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
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The problem of scene classification in surveillance footage is of great importance for ensuring security in public areas. With challenges such as low quality feeds, occlusion, viewpoint variations, background clutter etc. The task is both challenging and error-prone. Therefore it is important to keep the false positives low to maintain a high accuracy of detection. In this paper, we adapt high performing CNN architectures to identify abandoned luggage in a surveillance feed. We explore several CNN based approaches, from Transfer Learning on the Imagenet dataset to object classification using Faster R-CNNs on the COCO dataset. Using network visualization techniques, we gain insight into what the neural network sees and the basis of classification decision. The experiments have been conducted on real world datasets, and highlights the complexity in such classifications. Obtained results indicate that a combination of proposed techniques outperforms the individual approaches.
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The model selection process is a search for the best combination of features, algorithm, and hyperparameters that maximize F1, R2, or silhouette scores after cross-validation. This view of machine learning often leads us toward automated processes such as grid searches and random walks. Although this approach allows us to try many combinations, we are often left wondering if we have actually succeeded.
By enhancing model selection with visual diagnostics, data scientists can inject human guidance to steer the search process. Visualizing feature transformations, algorithmic behavior, cross-validation methods, and model performance allows us a peek into the high dimensional realm that our models operate. As we continue to tune our models, trying to minimize both bias and variance, these glimpses allow us to be more strategic in our choices. The result is more effective modeling, speedier results, and greater understanding of underlying processes.
Visualization is an integral part of the data science workflow, but visual diagnostics are directly tied to machine learning transformers and models. The Yellowbrick library extends the scikit-learn API providing a Visualizer object, an estimator that learns from data and produces a visualization as a result. In this talk, we will explore feature visualizers, visualizers for classification, clustering, and regression, as well as model analysis visualizers. We'll work through several examples and show how visual diagnostics steer model selection, making machine learning more effective.
Machine learning is the hacker art of describing the features of instances that we want to make predictions about, then fitting the data that describes those instances to a model form. Applied machine learning has come a long way from it's beginnings in academia, and with tools like Scikit-Learn, it's easier than ever to generate operational models for a wide variety of applications. Thanks to the ease and variety of the tools in Scikit-Learn, the primary job of the data scientist is model selection. Model selection involves performing feature engineering, hyperparameter tuning, and algorithm selection. These dimensions of machine learning often lead computer scientists towards automatic model selection via optimization (maximization) of a model's evaluation metric. However, the search space is large, and grid search approaches to machine learning can easily lead to failure and frustration. Human intuition is still essential to machine learning, and visual analysis in concert with automatic methods can allow data scientists to steer model selection towards better fitted models, faster. In this talk, we will discuss interactive visual methods for better understanding, steering, and tuning machine learning models.
Keynote of HOP-Rec @ RecSys 2018
Presenter: Jheng-Hong Yang
These slides aim to be a complementary material for the short paper: HOP-Rec @ RecSys18. It explains the intuition and some abstract idea behind the descriptions and mathematical symbols by illustrating some plots and figures.
Invited lecture on Machine Learning in Medicine at the joint "Integrated Omics" course of Hanze University and University Hospital UMCG, Groningen, The Netherlands
Using AI Planning to Automate the Performance Analysis of SimulatorsRoland Ewald
Analyzing simulation algorithm performance is cumbersome: execute some runs, observe a performance metric, and analyze the results. Often, the results motivate follow-up experiments, which in turn may lead to additional experiments, and so on. This time-consuming and error-prone process can be automated with planning approaches from artificial intelligence, making simulator performance analysis more convenient and rigorous. This paper introduces ALeSiA, a prototypical system for automatic simulator performance analysis. It is independent of any specific simulation system and realizes a hypothesis-driven approach to evaluate performance.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
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Multi Processing (OpenMP) is used to achieve parallelism in k-modes algorithm. OpenMP is a shared memory API that uses
thread approach using the fork-join model. The dataset used for experiment is Congressional Voting Dataset collected from UCI
repository. The dataset contains votes of members in categorical format provided in CSV format. The experiment is performed for
increased number of clusters and increasing size of dataset.
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PR157: Best of both worlds: human-machine collaboration for object annotation
1. Best of both worlds:
Human-machine
collaboration
for object annotation (CVPR2015)
visionNoob
(이재원)
PR-157:
Olga Russakovksy, Li-Jia Li, Li Fei-Fei
Stanford University, Snapchat
[paper] http://ai.stanford.edu/~olga/papers/RussakovskyCVPR15.pdf
[CVPR’15 poster] http://ai.stanford.edu/~olga/posters/cvpr15-poster.pdf
[supplements] http://ai.stanford.edu/~olga/papers/RussakovskyCVPR15_supp.pdf
[slides made by first author] http://ai.stanford.edu/~olga/slides/best_of_both_worlds_slides.pdf
2. Goal
efficiently and accurately detect all objects in an image
Green boxes
RCNN results (with NMS)
Yellow boxes
ILSVRC dataset classes
(but RCNN fail)
Pink boxes
outside of range of capabilities
of object detectors
6. Related Works
1. Recognition with human in the loop
2. Better object detection
3. Cheaper manual annotation
- Weakly supervised learning [42, 23, 52, 8, 24, 15]
- Active learning [32, 56] (see also [PR-119])
- Mine the web for object detection [8, 11, 15]
-> minimize human annotation
http://mpawankumar.info/tutorials/cvpr2013/index.html
7. Related Works
1. Recognition with human in the loop
2. Better object detection
3. Cheaper manual annotation
Crowdsourcing techniques
- Annotation games [57, 12, 30]
- Tricks to reduce the annotation search space [13, 4]
- Effective user interface design [50, 58]
- Making use of existing annotations [5]
Making use of weak human supervision [26, 7]
Accurately computing the number of required workers [46]
12. Method
Model : Markov Decision Process (MDP)
State
Action
Transition probability
Reward
Optimization
13. Method
Model : Markov Decision Process (MDP)
State : set of object detections, with probabilities
cls(C | I, U)
det(B, C | I, U)
moreinst(B, X | I, U)
obj(B | I , U)
morecls(C | I, U)
14. Method
Model : Markov Decision Process (MDP)
State : set of object detections, with probabilities
Action : a question to ask humans
15. Method
Model : Markov Decision Process (MDP)
State : set of object detections, with probabilities
Action : a questions to ask humans
Transition probability : probability distribution over user responses
Reward : increase in estimated quality of labeling divided by the cost of actions
Optimization : 2-step lookahead search
16. Method
Model : Markov Decision Process (MDP)
State : set of object detections, with probabilities
Action : a questions to ask humans
Transition probability : probability distribution over user responses
Reward : increase in estimated quality of labeling divided by the cost of actions
Optimization : 2-step lookahead search
Note that
17. Method
Model : Markov Decision Process (MDP)
State : set of object detections, with probabilities
Action : a questions to ask humans
Transition probability : probability distribution over user responses
Reward : increase in estimated quality of labeling divided by the cost of actions
Optimization : 2-step lookahead search
Note that
22. Experimental Setup
dataset
ImageNet Large Scale Visual Recognition Challenge(ILSVRC) detection dataset
train set : 400K
validation : 200K (split the val set into two sets(val1, val2) for test)
computer vision models
1. Image classifier : 200 class CNN classifiers [Hoffman NIPS14]
2. Object detector : 200 class RCNN [Girshick CVPR14]
3. Probability of object region : Objectness measure [Alexe PAMI2012]
4. Probability of another instance of same class : statistics from ILSVRC2014 val-DET data
5. Probability of another class in image : statistics from ILSVRC2014 val-DET data
23. Experimental Results The ILSVRC detection system :
Step1 : determining what object classes are present in the images
Step2 : Asking users to draw bounding boxes.
24. Conclusions
We presented a principled approach to unifying multiple inputs
from both computer vision and humans to label objects in images.