OpenRaiser/Intern-Atlas huggingface dataset:
Vis-DSS: An Open-Source toolkit for Visual Data Selection and Summarization
Venue: arXiv cs.CV 2018
Year: 2018

With increasing amounts of visual data being created in the form of videos and images, visual data selection and summarization are becoming ever increasing problems. We present Vis-DSS, an open-source toolkit for Visual Data Selection and Summarization. Vis-DSS implements a framework of models for summarization and data subset selection using submodular functions, which are becoming increasingly popular today for these problems. We present several classes of models, capturing notions of diversity, coverage, representation and importance, along with optimization/inference and learning algorithms. Vis-DSS is the first open source toolkit for several Data selection and summarization tasks including Image Collection Summarization, Video Summarization, Training Data selection for Classification and Diversified Active Learning. We demonstrate state-of-the art performance on all these tasks, and also show how we can scale to large problems. Vis-DSS allows easy integration for applications to be built on it, also can serve as a general skeleton that can be extended to several use cases, including video and image sharing platforms for creating GIFs, image montage creation, or as a component to surveillance systems and we demonstrate this by providing a graphical user-interface (GUI) desktop app built over Qt framework. Vis-DSS is available at https://github.com/rishabhk108/vis-dss
Target Transfer Q-Learning and Its Convergence Analysis
Venue: arXiv cs.LG 2018
Year: 2018

Q-learning is one of the most popular methods in Reinforcement Learning (RL). Transfer Learning aims to utilize the learned knowledge from source tasks to help new tasks to improve the sample complexity of the new tasks. Considering that data collection in RL is both more time and cost consuming and Q-learning converges slowly comparing to supervised learning, different kinds of transfer RL algorithms are designed. However, most of them are heuristic with no theoretical guarantee of the convergence rate. Therefore, it is important for us to clearly understand when and how will transfer learning help RL method and provide the theoretical guarantee for the improvement of the sample complexity. In this paper, we propose to transfer the Q-function learned in the source task to the target of the Q-learning in the new task when certain safe conditions are satisfied. We call this new transfer Q-learning method target transfer Q-Learning. The safe conditions are necessary to avoid the harm to the new tasks and thus ensure the convergence of the algorithm. We study the convergence rate of the target transfer Q-learning. We prove that if the two tasks are similar with respect to the MDPs, the optimal Q-functions in the source and new RL tasks are similar which means the error of the transferred target Q-function in new MDP is small. Also, the convergence rate analysis shows that the target transfer Q-Learning will converge faster than Q-learning if the error of the transferred target Q-function is smaller than the current Q-function in the new task. Based on our theoretical results, we design the safe condition as the Bellman error of the transferred target Q-function is less than the current Q-function. Our experiments are consistent with our theoretical founding and verified the effectiveness of our proposed target transfer Q-learning method.
Finite Sample Analysis of the GTD Policy Evaluation Algorithms in Markov Setting
Venue: arXiv cs.LG 2018
Year: 2018

In reinforcement learning (RL) , one of the key components is policy evaluation, which aims to estimate the value function (i.e., expected long-term accumulated reward) of a policy. With a good policy evaluation method, the RL algorithms will estimate the value function more accurately and find a better policy. When the state space is large or continuous \emph{Gradient-based Temporal Difference(GTD)} policy evaluation algorithms with linear function approximation are widely used. Considering that the collection of the evaluation data is both time and reward consuming, a clear understanding of the finite sample performance of the policy evaluation algorithms is very important to reinforcement learning. Under the assumption that data are i.i.d. generated, previous work provided the finite sample analysis of the GTD algorithms with constant step size by converting them into convex-concave saddle point problems. However, it is well-known that, the data are generated from Markov processes rather than i.i.d. in RL problems.. In this paper, in the realistic Markov setting, we derive the finite sample bounds for the general convex-concave saddle point problems, and hence for the GTD algorithms. We have the following discussions based on our bounds. (1) With variants of step size, GTD algorithms converge. (2) The convergence rate is determined by the step size, with the mixing time of the Markov process as the coefficient. The faster the Markov processes mix, the faster the convergence. (3) We explain that the experience replay trick is effective by improving the mixing property of the Markov process. To the best of our knowledge, our analysis is the first to provide finite sample bounds for the GTD algorithms in Markov setting.
On The Utility of Conditional Generation Based Mutual Information for Characterizing Adversarial Subspaces
Venue: arXiv cs.CV 2018
Year: 2018

Recent studies have found that deep learning systems are vulnerable to adversarial examples; e.g., visually unrecognizable adversarial images can easily be crafted to result in misclassification. The robustness of neural networks has been studied extensively in the context of adversary detection, which compares a metric that exhibits strong discriminate power between natural and adversarial examples. In this paper, we propose to characterize the adversarial subspaces through the lens of mutual information (MI) approximated by conditional generation methods. We use MI as an information-theoretic metric to strengthen existing defenses and improve the performance of adversary detection. Experimental results on MagNet defense demonstrate that our proposed MI detector can strengthen its robustness against powerful adversarial attacks.
Fast Signal Recovery from Saturated Measurements by Linear Loss and Nonconvex Penalties
Venue: arXiv cs.IT 2018
Year: 2018

Sign information is the key to overcoming the inevitable saturation error in compressive sensing systems, which causes information loss and results in bias. For sparse signal recovery from saturation, we propose to use a linear loss to improve the effectiveness from existing methods that utilize hard constraints/hinge loss for sign consistency. Due to the use of linear loss, an analytical solution in the update progress is obtained, and some nonconvex penalties are applicable, e.g., the minimax concave penalty, the $\ell_0$ norm, and the sorted $\ell_1$ norm. Theoretical analysis reveals that the estimation error can still be bounded. Generally, with linear loss and nonconvex penalties, the recovery performance is significantly improved, and the computational time is largely saved, which is verified by the numerical experiments.
Next Generation of Star Patterns
Venue: arXiv cs.GR 2018
Year: 2018

In this paper we present two new ideas for generating star patterns and filling the gaps during the tile operation. Firstly, we introduce a novel parametric method based on concentric circles for generating stars and rosettes. Using proposed method, completely different stars and rosettes and a set of new and complex star patterns convert to each other only by changing nine parameters. Secondly, we demonstrate how three equal tangent circles can be used as a base for generating tile elements. For this reason a surrounded circle is created among tangent circles, which represents the gaps in hexagonal packing. Afterwards, we use our first idea for filling the tangent circles and surrounded circle. This parametric approach can be used for generating infinite new star patterns, which some of them will be presented in result section.Two Android apps of proposed method called Starking and Tilerking are available in Google app store.
Covfefe: A Computer Vision Approach For Estimating Force Exertion
Venue: arXiv cs.HC 2018
Year: 2018

Cumulative exposure to repetitive and forceful activities may lead to musculoskeletal injuries which not only reduce workers' efficiency and productivity, but also affect their quality of life. Thus, widely accessible techniques for reliable detection of unsafe muscle force exertion levels for human activity is necessary for their well-being. However, measurement of force exertion levels is challenging and the existing techniques pose a great challenge as they are either intrusive, interfere with human-machine interface, and/or subjective in the nature, thus are not scalable for all workers. In this work, we use face videos and the photoplethysmography (PPG) signals to classify force exertion levels of 0\%, 50\%, and 100\% (representing rest, moderate effort, and high effort), thus providing a non-intrusive and scalable approach. Efficient feature extraction approaches have been investigated, including standard deviation of the movement of different landmarks of the face, distances between peaks and troughs in the PPG signals. We note that the PPG signals can be obtained from the face videos, thus giving an efficient classification algorithm for the force exertion levels using face videos. Based on the data collected from 20 subjects, features extracted from the face videos give 90\% accuracy in classification among the 100\% and the combination of 0\% and 50\% datasets. Further combining the PPG signals provide 81.7\% accuracy. The approach is also shown to be robust to the correctly identify force level when the person is talking, even though such datasets are not included in the training.
Optimal Equi-difference Conflict-avoiding Codes
Venue: arXiv cs.IT 2018
Year: 2018

An equi-differece conflict-avoiding code $(CAC^{e})\ \mathcal{C}$ of length $n$ and weight $ω$ is a collection of $ω$-subsets (called codewords) which has the form $\{0,i,2i,\cdots,(ω-1)i\}$ of $\mathbb{Z}_{n}$ such that $Δ(c_{1})\capΔ(c_{2})=\emptyset$ holds for any $c_{1},\ c_{2}\in\mathcal{C}$, $c_{1}\neq c_{2}$ where $Δ(c)=\{j-i \ (\mbox{mod}\ n) \; | \; i,j\in c,i\neq j\}.$ A code $\mathcal{C}\in CAC^{e}s$ with maximum code size for given $n$ and $ω$ is called optimal and is said to be perfect if $\cup_{c\in \mathcal{C}}Δ(c)=\mathbb{Z}_{n}\backslash \{0\}.$ In this paper, we show how to combine a $\mathcal{C}_{1}\in CAC^{e}(q_{1},ω)$ and a $\mathcal{C}_{2}\in CAC^{e}(q_{2},ω)$ into a $\mathcal{C}\in CAC^{e}(q_{1}q_{2},ω)$ under certain conditions. One necessary condition for a $CAC^{e}$ of length $q_{1}q_{2}$ and weight $ω$ being optimal is given. We also consider explicit construction of perfect $\mathcal{C}\in CAC^{e}(p,ω)$ of odd prime $p$ and weight $ω\geq3$. Finally, for positive integer $k$ and prime $p\equiv1 \ (\mbox{mod}\ 4k)$, we consider explicit construction of quasi-perfect $\mathcal{C}\in CAC^{e}(2p,4k+1)$.
A Budget Feasible Peer Graded Mechanism For IoT-Based Crowdsourcing
Venue: arXiv cs.GT 2018
Year: 2018

We develop and extend a line of recent works on the design of mechanisms for heterogeneous tasks assignment problem in 'crowdsourcing'. The budgeted market we consider consists of multiple task requesters and multiple IoT devices as task executers; where each task requester is endowed with a single distinct task along with the publicly known budget. Also, each IoT device has valuations as the cost for executing the tasks and quality, which are private. Given such scenario, the objective is to select a subset of IoT devices for each task, such that the total payment made is within the allotted quota of the budget while attaining a threshold quality. For the purpose of determining the unknown quality of the IoT devices, we have utilized the concept of peer grading. In this paper, we have carefully crafted a truthful budget feasible mechanism; namely TUBE-TAP for the problem under investigation that also allows us to have the true information about the quality of the IoT devices. The simulations are performed in order to measure the efficacy of our proposed mechanism.
On finitely ambiguous Büchi automata
Venue: arXiv cs.FL 2018
Year: 2018

Unambiguous Büchi automata, i.e. Büchi automata allowing only one accepting run per word, are a useful restriction of Büchi automata that is well-suited for probabilistic model-checking. In this paper we propose a more permissive variant, namely finitely ambiguous Büchi automata, a generalisation where each word has at most $k$ accepting runs, for some fixed $k$. We adapt existing notions and results concerning finite and bounded ambiguity of finite automata to the setting of $ω$-languages and present a translation from arbitrary nondeterministic Büchi automata with $n$ states to finitely ambiguous automata with at most $3^n$ states and at most $n$ accepting runs per word.
Nested cross-validation when selecting classifiers is overzealous for most practical applications
Venue: arXiv cs.LG 2018
Year: 2018

When selecting a classification algorithm to be applied to a particular problem, one has to simultaneously select the best algorithm for that dataset \emph{and} the best set of hyperparameters for the chosen model. The usual approach is to apply a nested cross-validation procedure; hyperparameter selection is performed in the inner cross-validation, while the outer cross-validation computes an unbiased estimate of the expected accuracy of the algorithm \emph{with cross-validation based hyperparameter tuning}. The alternative approach, which we shall call `flat cross-validation', uses a single cross-validation step both to select the optimal hyperparameter values and to provide an estimate of the expected accuracy of the algorithm, that while biased may nevertheless still be used to select the best learning algorithm. We tested both procedures using 12 different algorithms on 115 real life binary datasets and conclude that using the less computationally expensive flat cross-validation procedure will generally result in the selection of an algorithm that is, for all practical purposes, of similar quality to that selected via nested cross-validation, provided the learning algorithms have relatively few hyperparameters to be optimised.
Machines that test Software like Humans
Venue: arXiv cs.SE 2018
Year: 2018

Automated software testing involves the execution of test scripts by a machine instead of being manually run. This significantly reduces the amount of manual time & effort needed and thus is of great interest to the software testing industry. There have been various tools developed to automate the testing of web applications (e.g. Selenium WebDriver); however, the practical adoption of test automation is still miniscule. This is due to the complexity of creating and maintaining automation scripts. The key problem with the existing methods is that the automation test scripts require certain implementation specifics of the Application Under Test (AUT) (e.g. the html code of a web element, or an image of a web element). On the other hand, if we look at the way manual testing is done, the tester interprets the textual test scripts and interacts with the AUT purely based on what he perceives visually through the GUI. In this paper, we present an approach to build a machine that can mimic human behavior for software testing using recent advances in Computer Vision. We also present four use-cases of how this approach can significantly advance the test automation space making test automation simple enough to be adopted practically.
A Revised and Verified Proof of the Scalable Commutativity Rule
Venue: arXiv cs.DC 2018
Year: 2018

This paper explains a flaw in the published proof of the Scalable Commutativity Rule (SCR), presents a revised and formally verified proof of the SCR in the Coq proof assistant, and discusses the insights and open questions raised from our experience proving the SCR.
Slogatron: Advanced Wealthiness Generator
Venue: arXiv cs.NE 2018
Year: 2018

Creating catchy slogans is a demanding and clearly creative job for ad agencies. The process of slogan creation by humans involves finding key concepts of the company and its products, and developing a memorable short phrase to describe the key concept. We attempt to follow the same sequence, but with an evolutionary algorithm. A user inputs a paragraph describing describing the company or product to be promoted. The system randomly samples initial slogans from a corpus of existing slogans. The initial slogans are then iteratively mutated and improved using an evolutionary algorithm. Mutation randomly replaces words in an individual with words from the input paragraphs. Internal evaluation measures a combination of grammatical correctness, and semantic similarity to the input paragraphs. Subjective analysis of output slogans leads to the conclusion that the algorithm certainly outputs valuable slogans. External evaluation found that the slogans were somewhat successful in conveying a message, because humans were generally able to select the correct promoted item given a slogan.
Deep Neural Networks for Pattern Recognition
Venue: arXiv cs.CV 2018
Year: 2018

In the field of pattern recognition research, the method of using deep neural networks based on improved computing hardware recently attracted attention because of their superior accuracy compared to conventional methods. Deep neural networks simulate the human visual system and achieve human equivalent accuracy in image classification, object detection, and segmentation. This chapter introduces the basic structure of deep neural networks that simulate human neural networks. Then we identify the operational processes and applications of conditional generative adversarial networks, which are being actively researched based on the bottom-up and top-down mechanisms, the most important functions of the human visual perception process. Finally, recent developments in training strategies for effective learning of complex deep neural networks are addressed.
LOBO -- Evaluation of Generalization Deficiencies in Twitter Bot Classifiers
Venue: arXiv cs.CR 2018
Year: 2018

Botnets in online social networks are increasingly often affecting the regular flow of discussion, attacking regular users and their posts, spamming them with irrelevant or offensive content, and even manipulating the popularity of messages and accounts. Researchers and cybercriminals are involved in an arms race, and new and updated botnets designed to defeat current detection systems are constantly developed, rendering such detection systems obsolete. In this paper, we motivate the need for a generalized evaluation in Twitter bot detection and propose a methodology to evaluate bot classifiers by testing them on unseen bot classes. We show that this methodology is empirically robust, using bot classes of varying sizes and characteristics and reaching similar results, and argue that methods trained and tested on single bot classes or datasets might not able to generalize to new bot classes. We train one such classifier on over 200,000 data points and show that it achieves over 97% accuracy. The data used to train and test this classifier includes some of the largest and most varied collections of bots used in literature. We then test this theoretically sound classifier using our methodology, highlighting that it does not generalize well to unseen bot classes. Finally, we discuss the implications of our results, and reasons why some bot classes are easier and faster to detect than others.
On Monotone Sequences of Directed Flips, Triangulations of Polyhedra, and Structural Properties of a Directed Flip Graph
Venue: arXiv cs.DM 2018
Year: 2018

This paper studied the geometric and combinatorial aspects of the classical Lawson's flip algorithm in 1972. Let A be a finite set of points in R2, omega be a height function which lifts the vertices of A into R3. Every flip in triangulations of A can be associated with a direction. We first established a relatively obvious relation between monotone sequences of directed flips between triangulations of A and triangulations of the lifted point set of A in R3. We then studied the structural properties of a directed flip graph (a poset) on the set of all triangulations of A. We proved several general properties of this poset which clearly explain when Lawson's algorithm works and why it may fail in general. We further characterised the triangulations which cause failure of Lawson's algorithm, and showed that they must contain redundant interior vertices which are not removable by directed flips. A special case if this result in 3d has been shown by B.Joe in 1989. As an application, we described a simple algorithm to triangulate a special class of 3d non-convex polyhedra. We proved sufficient conditions for the termination of this algorithm and show that it runs in O(n3) time.
Analyzing CDR/IPDR data to find People Network from Encrypted Messaging Services
Venue: arXiv cs.SI 2018
Year: 2018

Criminals are increasingly using mobile based communication applications, like WhatsApp, that have end-to-end encryption to connect to each other. This makes traditional analysis of call graphs, or traffic analysis, virtually impossible and so is a hindrance for law enforcement personnel. Old methods of traffic analysis have been rendered useless and criminals, including arms dealers and terrorists, are able to engage in criminal activity undetected by police. At present, law enforcement must use extensive manual effort to parse data provided by cell companies to extract information. We have built a system that analyses cellular GPRS metadata and builds a profile and finds potential call connections explicitly which are implicit in the dataset. This paper describes our approach and system in detail and includes results of our evaluation using an anonymized dataset from Delhi Police. Our system permits call graph analysis to be done, and significantly reduces the time needed from the data analysis process.
An Exploration of Mimic Architectures for Residual Network Based Spectral Mapping
Venue: arXiv cs.SD 2018
Year: 2018

Spectral mapping uses a deep neural network (DNN) to map directly from noisy speech to clean speech. Our previous study found that the performance of spectral mapping improves greatly when using helpful cues from an acoustic model trained on clean speech. The mapper network learns to mimic the input favored by the spectral classifier and cleans the features accordingly. In this study, we explore two new innovations: we replace a DNN-based spectral mapper with a residual network that is more attuned to the goal of predicting clean speech. We also examine how integrating long term context in the mimic criterion (via wide-residual biLSTM networks) affects the performance of spectral mapping compared to DNNs. Our goal is to derive a model that can be used as a preprocessor for any recognition system; the features derived from our model are passed through the standard Kaldi ASR pipeline and achieve a WER of 9.3%, which is the lowest recorded word error rate for CHiME-2 dataset using only feature adaptation.
Towards Game-based Metrics for Computational Co-creativity
Venue: arXiv cs.AI 2018
Year: 2018

We propose the following question: what game-like interactive system would provide a good environment for measuring the impact and success of a co-creative, cooperative agent? Creativity is often formulated in terms of novelty, value, surprise and interestingness. We review how these concepts are measured in current computational intelligence research and provide a mapping from modern electronic and tabletop games to open research problems in mixed-initiative systems and computational co-creativity. We propose application scenarios for future research, and a number of metrics under which the performance of cooperative agents in these environments will be evaluated.