OpenRaiser/Intern-Atlas huggingface dataset:
Improving Image Clustering With Multiple Pretrained CNN Feature Extractors
Venue: arXiv cs.CV 2018
Year: 2018

For many image clustering problems, replacing raw image data with features extracted by a pretrained convolutional neural network (CNN), leads to better clustering performance. However, the specific features extracted, and, by extension, the selected CNN architecture, can have a major impact on the clustering results. In practice, this crucial design choice is often decided arbitrarily due to the impossibility of using cross-validation with unsupervised learning problems. However, information contained in the different pretrained CNN architectures may be complementary, even when pretrained on the same data. To improve clustering performance, we rephrase the image clustering problem as a multi-view clustering (MVC) problem that considers multiple different pretrained feature extractors as different "views" of the same data. We then propose a multi-input neural network architecture that is trained end-to-end to solve the MVC problem effectively. Our experimental results, conducted on three different natural image datasets, show that: 1. using multiple pretrained CNNs jointly as feature extractors improves image clustering; 2. using an end-to-end approach improves MVC; and 3. combining both produces state-of-the-art results for the problem of image clustering.
Specification Mining for Smart Contracts with Automatic Abstraction Tuning
Venue: arXiv cs.SE 2018
Year: 2018

Smart contracts are programs that manage digital assets according to a certain protocol, expressing for instance the rules of an auction. Understanding the possible behaviors of a smart contract is difficult, which complicates development, auditing, and the post-mortem analysis of attacks. This paper presents the first specification mining technique for smart contracts. Our technique extracts the possible behaviors of smart contracts from contract executions recorded on a blockchain and expresses them as finite automata. A novel dependency analysis allows us to separate independent interactions with a contract. Our technique tunes the abstractions for the automata construction automatically based on configurable metrics, for instance, to maximize readability or precision. We implemented our technique for the Ethereum blockchain and evaluated its usability on several real-world contracts.
Biclustering Using Modified Matrix Bandwidth Minimization and Biogeography-based Optimization
Venue: arXiv cs.DS 2018
Year: 2018

Data matrix having different sets of entities in its rows and columns are known as two mode data or affiliation data. Many practical problems require to find relationships between the two modes by simultaneously clustering the rows and columns, a problem commonly known as biclustering. We propose a novel biclustering algorithm by using matrix reordering approach introduced by Cuthill-McKee's bandwidth minimization algorithm, and adapting it to operate on non-square and non-binary matrices, without the need to know apriori the number of naturally occurring biclusters. This transforms a two-mode matrix into almost block diagonals, where the blocks indicate the clusters between the two modes of the matrix. To optimize the bandwidth minimization problem, we adapted the Biogeography-based Optimization algorithm using logistic equation to model its migration rates. Preliminary studies indicate that this technique can reveal the underlying biclusters in the data and has potential of further research for two-mode data analysis.
Distance-based Kernels for Surrogate Model-based Neuroevolution
Venue: arXiv cs.NE 2018
Year: 2018

The topology optimization of artificial neural networks can be particularly difficult if the fitness evaluations require expensive experiments or simulations. For that reason, the optimization methods may need to be supported by surrogate models. We propose different distances for a suitable surrogate model, and compare them in a simple numerical test scenario.
Boosting algorithms for uplift modeling
Venue: arXiv cs.LG 2018
Year: 2018

Uplift modeling is an area of machine learning which aims at predicting the causal effect of some action on a given individual. The action may be a medical procedure, marketing campaign, or any other circumstance controlled by the experimenter. Building an uplift model requires two training sets: the treatment group, where individuals have been subject to the action, and the control group, where no action has been performed. An uplift model allows then to assess the gain resulting from taking the action on a given individual, such as the increase in probability of patient recovery or of a product being purchased. This paper describes an adaptation of the well-known boosting techniques to the uplift modeling case. We formulate three desirable properties which an uplift boosting algorithm should have. Since all three properties cannot be satisfied simultaneously, we propose three uplift boosting algorithms, each satisfying two of them. Experiments demonstrate the usefulness of the proposed methods, which often dramatically improve performance of the base models and are thus new and powerful tools for uplift modeling.
Optimal Bounds on the VC-dimension
Venue: arXiv cs.LG 2018
Year: 2018

The VC-dimension of a set system is a way to capture its complexity and has been a key parameter studied extensively in machine learning and geometry communities. In this paper, we resolve two longstanding open problems on bounding the VC-dimension of two fundamental set systems: $k$-fold unions/intersections of half-spaces, and the simplices set system. Among other implications, it settles an open question in machine learning that was first studied in the 1989 foundational paper of Blumer, Ehrenfeucht, Haussler and Warmuth as well as by Eisenstat and Angluin and Johnson.
A Fully Convolutional Neural Network Approach to End-to-End Speech Enhancement
Venue: arXiv cs.SD 2018
Year: 2018

This paper will describe a novel approach to the cocktail party problem that relies on a fully convolutional neural network (FCN) architecture. The FCN takes noisy audio data as input and performs nonlinear, filtering operations to produce clean audio data of the target speech at the output. Our method learns a model for one specific speaker, and is then able to extract that speakers voice from babble background noise. Results from experimentation indicate the ability to generalize to new speakers and robustness to new noise environments of varying signal-to-noise ratios. A potential application of this method would be for use in hearing aids. A pre-trained model could be quickly fine tuned for an individuals family members and close friends, and deployed onto a hearing aid to assist listeners in noisy environments.
Baidu Apollo EM Motion Planner
Venue: arXiv cs.RO 2018
Year: 2018

In this manuscript, we introduce a real-time motion planning system based on the Baidu Apollo (open source) autonomous driving platform. The developed system aims to address the industrial level-4 motion planning problem while considering safety, comfort and scalability. The system covers multilane and single-lane autonomous driving in a hierarchical manner: (1) The top layer of the system is a multilane strategy that handles lane-change scenarios by comparing lane-level trajectories computed in parallel. (2) Inside the lane-level trajectory generator, it iteratively solves path and speed optimization based on a Frenet frame. (3) For path and speed optimization, a combination of dynamic programming and spline-based quadratic programming is proposed to construct a scalable and easy-to-tune framework to handle traffic rules, obstacle decisions and smoothness simultaneously. The planner is scalable to both highway and lower-speed city driving scenarios. We also demonstrate the algorithm through scenario illustrations and on-road test results. The system described in this manuscript has been deployed to dozens of Baidu Apollo autonomous driving vehicles since Apollo v1.5 was announced in September 2017. As of May 16th, 2018, the system has been tested under 3,380 hours and approximately 68,000 kilometers (42,253 miles) of closed-loop autonomous driving under various urban scenarios. The algorithm described in this manuscript is available at https://github.com/ApolloAuto/apollo/tree/master/modules/planning.
A Pipeline for Creative Visual Storytelling
Venue: arXiv cs.CL 2018
Year: 2018

Computational visual storytelling produces a textual description of events and interpretations depicted in a sequence of images. These texts are made possible by advances and cross-disciplinary approaches in natural language processing, generation, and computer vision. We define a computational creative visual storytelling as one with the ability to alter the telling of a story along three aspects: to speak about different environments, to produce variations based on narrative goals, and to adapt the narrative to the audience. These aspects of creative storytelling and their effect on the narrative have yet to be explored in visual storytelling. This paper presents a pipeline of task-modules, Object Identification, Single-Image Inferencing, and Multi-Image Narration, that serve as a preliminary design for building a creative visual storyteller. We have piloted this design for a sequence of images in an annotation task. We present and analyze the collected corpus and describe plans towards automation.
Design and Implementation of Global Path Planning System for Unmanned Surface Vehicle among Multiple Task Points
Venue: arXiv cs.RO 2018
Year: 2018

Global path planning is the key technology in the design of unmanned surface vehicles. This paper establishes global environment modelling based on electronic charts and hexagonal grids which are proved to be better than square grids in validity, safety and rapidity. Besides, we introduce Cube coordinate system to simplify hexagonal algorithms. Furthermore, we propose an improved A* algorithm to realize the path planning between two points. Based on that, we build the global path planning modelling for multiple task points and present an improved ant colony optimization to realize it accurately. The simulation results show that the global path planning system can plan an optimal path to tour multiple task points safely and quickly, which is superior to traditional methods in safety, rapidity and path length. Besides, the planned path can directly apply to actual applications of USVs.
On Computational Infraestruture Requirements to Smart and Autonomic Cities Framework
Venue: arXiv cs.NI 2018
Year: 2018

Smart cities are an actual trend being pursued by research that, fundamentally, tries to improve city's management on behalf of a better human quality of live. This paper proposes a new autonomic complementary approach for smart cities management. It is argued that smart city management systems with autonomic characteristics will improve and facilitate management functionalities in general. A framework is also presented as use case considering specific application scenarios like smart-health, smart-grid, smart-environment and smart-streets.
Towards Distributed Coevolutionary GANs
Venue: arXiv cs.NE 2018
Year: 2018

Generative Adversarial Networks (GANs) have become one of the dominant methods for deep generative modeling. Despite their demonstrated success on multiple vision tasks, GANs are difficult to train and much research has been dedicated towards understanding and improving their gradient-based learning dynamics. Here, we investigate the use of coevolution, a class of black-box (gradient-free) co-optimization techniques and a powerful tool in evolutionary computing, as a supplement to gradient-based GAN training techniques. Experiments on a simple model that exhibits several of the GAN gradient-based dynamics (e.g., mode collapse, oscillatory behavior, and vanishing gradients) show that coevolution is a promising framework for escaping degenerate GAN training behaviors.
Asynchronous Advantage Actor-Critic Agent for Starcraft II
Venue: arXiv cs.AI 2018
Year: 2018

Deep reinforcement learning, and especially the Asynchronous Advantage Actor-Critic algorithm, has been successfully used to achieve super-human performance in a variety of video games. Starcraft II is a new challenge for the reinforcement learning community with the release of pysc2 learning environment proposed by Google Deepmind and Blizzard Entertainment. Despite being a target for several AI developers, few have achieved human level performance. In this project we explain the complexities of this environment and discuss the results from our experiments on the environment. We have compared various architectures and have proved that transfer learning can be an effective paradigm in reinforcement learning research for complex scenarios requiring skill transfer.
German Dialect Identification Using Classifier Ensembles
Venue: arXiv cs.CL 2018
Year: 2018

In this paper we present the GDI_classification entry to the second German Dialect Identification (GDI) shared task organized within the scope of the VarDial Evaluation Campaign 2018. We present a system based on SVM classifier ensembles trained on characters and words. The system was trained on a collection of speech transcripts of five Swiss-German dialects provided by the organizers. The transcripts included in the dataset contained speakers from Basel, Bern, Lucerne, and Zurich. Our entry in the challenge reached 62.03% F1-score and was ranked third out of eight teams.
Rapid Autonomous Car Control based on Spatial and Temporal Visual Cues
Venue: arXiv cs.RO 2018
Year: 2018

We present a novel approach to modern car control utilizing a combination of Deep Convolutional Neural Networks and Long Short-Term Memory Systems: Both of which are a subsection of Hierarchical Representations Learning, more commonly known as Deep Learning. Using Deep Convolutional Neural Networks and Long Short-Term Memory Systems (DCNN/LSTM), we propose an end-to-end approach to accurately predict steering angles and throttle values. We use this algorithm on our latest robot, El Toro Grande 1 (ETG) which is equipped with a variety of sensors in order to localize itself in its environment. Using previous training data and the data that it collects during circuit and drag races, it predicts throttle and steering angles in order to stay on path and avoid colliding into other robots. This allows ETG to theoretically race on any track with sufficient training data.
Competitive Equilibria in Combinatorial Exchanges with Financially Constrained Buyers:Computational Hardness and Algorithmic Solutions
Venue: arXiv cs.GT 2018
Year: 2018

Advances in computational optimization allow for the organization of large combinatorial markets. We aim for allocations and competitive equilibrium prices, i.e. outcomes that are in the core. The research is motivated by the design of environmental markets, but similar problems appear in energy and logistics markets or in the allocation of airport time slots. Budget constraints are an important concern in many of these markets. While the allocation problem in combinatorial exchanges is already NP-hard with payoff- maximizing bidders, we find that the allocation and pricing problem becomes even $Σ_2^p$-hard if buyers are financially constrained. We introduce mixed integer bilevel linear programs (MIBLP) to compute core prices, and propose pricing functions based on the least core if the core is empty. We also discuss restricted but simpler cases and effective computational techniques for the problem. In numerical experiments we show that in spite of the computational hardness of these problems, we can hope to solve practical problem sizes, in particular if we restrict the size of the coalitions considered in the core computations.
What Can (and Can't) we Do with Sparse Polynomials?
Venue: arXiv cs.SC 2018
Year: 2018

Simply put, a sparse polynomial is one whose zero coefficients are not explicitly stored. Such objects are ubiquitous in exact computing, and so naturally we would like to have efficient algorithms to handle them. However, with this compact storage comes new algorithmic challenges, as fast algorithms for dense polynomials may no longer be efficient. In this tutorial we examine the state of the art for sparse polynomial algorithms in three areas: arithmetic, interpolation, and factorization. The aim is to highlight recent progress both in theory and in practice, as well as opportunities for future work.
A Statistical Method for Object Counting
Venue: arXiv cs.CV 2018
Year: 2018

In this paper we present a new object counting method that is intended for counting similarly sized and mostly round objects. Unlike many other algorithms of the same purpose, the proposed method does not rely on identifying every object, it uses statistical data obtained from the image instead. The method is evaluated on images with human bone cells, oranges and pills achieving good accuracy. Its strengths are ability to deal with touching and partly overlapping objects, ability to work with different kinds of objects without prior configuration and good performance.
Tracking Mobile Intruders in an Art Gallery: Guard Deployment Strategies, Fundamental Limitations, and Performance Guarantees
Venue: arXiv cs.CG 2018
Year: 2018

This paper addresses the problem of tracking mobile intruders in a polygonal environment. We assume that a team of diagonal guards is deployed inside the polygon to provide mobile coverage. First, we formulate the problem of tracking a mobile intruder inside a polygonal environment as a multi-robot task allocation (MRTA) problem. Leveraging on guard deployment strategies in art gallery problems for mobile coverage, we show that the problem of finding the minimum speed of guards to persistently track a single mobile intruder is NP-hard. Next, for a given maximum speed of the intruder and the guards, we propose a technique to partition a polygon, and compute a feasible allocation of guards to the partitions. We prove the correctness of the proposed algorithm, and show its completeness for a specific class of inputs. We classify the guards based on the structural properties of the partitions allocated to them. Based on the classification, we propose motion strategy for the guards to track the mobile intruder when it is located in the partition allocated to the guard. Finally, we extend the proposed technique to address guard deployment and allocation strategies for non-simple polygons and multiple intruders.
SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination
Venue: arXiv cs.CV 2018
Year: 2018

Despite generative adversarial networks (GANs) can hallucinate photo-realistic high-resolution (HR) faces from low-resolution (LR) faces, they cannot guarantee preserving the identities of hallucinated HR faces, making the HR faces poorly recognizable. To address this problem, we propose a Siamese GAN (SiGAN) to reconstruct HR faces that visually resemble their corresponding identities. On top of a Siamese network, the proposed SiGAN consists of a pair of two identical generators and one discriminator. We incorporate reconstruction error and identity label information in the loss function of SiGAN in a pairwise manner. By iteratively optimizing the loss functions of the generator pair and discriminator of SiGAN, we cannot only achieve photo-realistic face reconstruction, but also ensures the reconstructed information is useful for identity recognition. Experimental results demonstrate that SiGAN significantly outperforms existing face hallucination GANs in objective face verification performance, while achieving photo-realistic reconstruction. Moreover, for input LR faces from unknown identities who are not included in training, SiGAN can still do a good job.
Question Relevance in Visual Question Answering
Venue: arXiv cs.CV 2018
Year: 2018

Free-form and open-ended Visual Question Answering systems solve the problem of providing an accurate natural language answer to a question pertaining to an image. Current VQA systems do not evaluate if the posed question is relevant to the input image and hence provide nonsensical answers when posed with irrelevant questions to an image. In this paper, we solve the problem of identifying the relevance of the posed question to an image. We address the problem as two sub-problems. We first identify if the question is visual or not. If the question is visual, we then determine if it's relevant to the image or not. For the second problem, we generate a large dataset from existing visual question answering datasets in order to enable the training of complex architectures and model the relevance of a visual question to an image. We also compare the results of our Long Short-Term Memory Recurrent Neural Network based models to Logistic Regression, XGBoost and multi-layer perceptron based approaches to the problem.
2P-DNN : Privacy-Preserving Deep Neural Networks Based on Homomorphic Cryptosystem
Venue: arXiv cs.CR 2018
Year: 2018

Machine Learning as a Service (MLaaS), such as Microsoft Azure, Amazon AWS, offers an effective DNN model to complete the machine learning task for small businesses and individuals who are restricted to the lacking data and computing power. However, here comes an issue that user privacy is ex-posed to the MLaaS server, since users need to upload their sensitive data to the MLaaS server. In order to preserve their privacy, users can encrypt their data before uploading it. This makes it difficult to run the DNN model because it is not designed for running in ciphertext domain. In this paper, using the Paillier homomorphic cryptosystem we present a new Privacy-Preserving Deep Neural Network model that we called 2P-DNN. This model can fulfill the machine leaning task in ciphertext domain. By using 2P-DNN, MLaaS is able to provide a Privacy-Preserving machine learning ser-vice for users. We build our 2P-DNN model based on LeNet-5, and test it with the encrypted MNIST dataset. The classification accuracy is more than 97%, which is close to the accuracy of LeNet-5 running with the MNIST dataset and higher than that of other existing Privacy-Preserving machine learning models
Models of using cloud technologies at the IT professionals training
Venue: arXiv cs.CY 2018
Year: 2018

The article is devoted to the rationale of the use of cloud technologies in teaching mathematical informatics students of technical universities. Purpose of the article - the analysis of domestic and foreign experience in the use of cloud-oriented ICT in the training of future professionals in the field of information technology. Based on a review of experiences and comparisons tools of distance learning technologies and cloud technologies identified the advantages of using cloud technologies for different categories of the learning process participants and models of cloud services, which should be used in training the regulatory academic disciplines cycles of mathematical, scientific and vocational & practical training of the future IT professionals.