OpenRaiser/Intern-Atlas huggingface dataset:
Learning with Rethinking: Recurrently Improving Convolutional Neural Networks through Feedback
Venue: arXiv cs.CV 2017
Year: 2017

Recent years have witnessed the great success of convolutional neural network (CNN) based models in the field of computer vision. CNN is able to learn hierarchically abstracted features from images in an end-to-end training manner. However, most of the existing CNN models only learn features through a feedforward structure and no feedback information from top to bottom layers is exploited to enable the networks to refine themselves. In this paper, we propose a "Learning with Rethinking" algorithm. By adding a feedback layer and producing the emphasis vector, the model is able to recurrently boost the performance based on previous prediction. Particularly, it can be employed to boost any pre-trained models. This algorithm is tested on four object classification benchmark datasets: CIFAR-100, CIFAR-10, MNIST-background-image and ILSVRC-2012 dataset. These results have demonstrated the advantage of training CNN models with the proposed feedback mechanism.
ThermalSim: A Thermal Simulator for Error Analysis
Venue: arXiv cs.CE 2017
Year: 2017

Researchers have extensively explored predictive control strategies for controlling heating, ventilation, and air conditioning (HVAC) units in commercial buildings. Predictive control strategies, however, critically rely on weather and occupancy forecasts. Existing state-of-the-art building simulators are incapable of analysing the influence of prediction errors (in weather and occupancy) on HVAC energy consumption and occupant comfort. In this paper, we introduce ThermalSim, a building simulator that can quantify the effect of prediction errors on the HVAC operations. ThermalSim has been implemented in C/C++ and MATLAB. We describe its design, use, and input format.
Learning from Noisy Label Distributions
Venue: arXiv cs.LG 2017
Year: 2017

In this paper, we consider a novel machine learning problem, that is, learning a classifier from noisy label distributions. In this problem, each instance with a feature vector belongs to at least one group. Then, instead of the true label of each instance, we observe the label distribution of the instances associated with a group, where the label distribution is distorted by an unknown noise. Our goals are to (1) estimate the true label of each instance, and (2) learn a classifier that predicts the true label of a new instance. We propose a probabilistic model that considers true label distributions of groups and parameters that represent the noise as hidden variables. The model can be learned based on a variational Bayesian method. In numerical experiments, we show that the proposed model outperforms existing methods in terms of the estimation of the true labels of instances.
Bayesian Non-Exhaustive Classification for Active Online Name Disambiguation
Venue: arXiv cs.IR 2017
Year: 2017

The name disambiguation task partitions a collection of records pertaining to a given name, such that there is a one-to-one correspondence between the partitions and a group of people, all sharing that given name. Most existing solutions for this task are proposed for static data. However, more realistic scenarios stipulate emergence of records in a streaming fashion where records may belong to known as well as unknown persons all sharing the same name. This requires a flexible name disambiguation algorithm that can not only classify records of known persons represented in the train- ing data by their existing records but can also identify records of new ambiguous persons with no existing records included in the initial training dataset. Toward achieving this objective, in this paper we propose a Bayesian non-exhaustive classification frame- work for solving online name disambiguation. In particular, we present a Dirichlet Process Gaussian Mixture Model (DPGMM) as a core engine for online name disambiguation task. Meanwhile, two online inference algorithms, namely one-pass Gibbs sampler and Sequential Importance Sampling with Resampling (also known as particle filtering), are proposed to simultaneously perform online classification and new class discovery. As a case study we consider bibliographic data in a temporal stream format and disambiguate authors by partitioning their papers into homogeneous groups.Our experimental results demonstrate that the proposed method is significantly better than existing methods for performing online name disambiguation task. We also propose an interactive version of our online name disambiguation method designed to leverage user feedback to improve prediction accuracy.
Polynomial-time algorithms for the Longest Induced Path and Induced Disjoint Paths problems on graphs of bounded mim-width
Venue: arXiv cs.DS 2017
Year: 2017

We give the first polynomial-time algorithms on graphs of bounded maximum induced matching width (mim-width) for problems that are not locally checkable. In particular, we give $n^{\mathcal{O}(w)}$-time algorithms on graphs of mim-width at most $w$, when given a decomposition, for the following problems: Longest Induced Path, Induced Disjoint Paths and $H$-Induced Topological Minor for fixed $H$. Our results imply that the following graph classes have polynomial-time algorithms for these three problems: Interval and Bi-Interval graphs, Circular Arc, Permutation and Circular Permutation graphs, Convex graphs, $k$-Trapezoid, Circular $k$-Trapezoid, $k$-Polygon, Dilworth-$k$ and Co-$k$-Degenerate graphs for fixed $k$.
Improved Regularization of Convolutional Neural Networks with Cutout
Venue: arXiv cs.CV 2017
Year: 2017

Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks. However, due to the model capacity required to capture such representations, they are often susceptible to overfitting and therefore require proper regularization in order to generalize well. In this paper, we show that the simple regularization technique of randomly masking out square regions of input during training, which we call cutout, can be used to improve the robustness and overall performance of convolutional neural networks. Not only is this method extremely easy to implement, but we also demonstrate that it can be used in conjunction with existing forms of data augmentation and other regularizers to further improve model performance. We evaluate this method by applying it to current state-of-the-art architectures on the CIFAR-10, CIFAR-100, and SVHN datasets, yielding new state-of-the-art results of 2.56%, 15.20%, and 1.30% test error respectively. Code is available at https://github.com/uoguelph-mlrg/Cutout
Database of Parliamentary Speeches in Ireland, 1919-2013
Venue: arXiv cs.CL 2017
Year: 2017

We present a database of parliamentary debates that contains the complete record of parliamentary speeches from Dáil Éireann, the lower house and principal chamber of the Irish parliament, from 1919 to 2013. In addition, the database contains background information on all TDs (Teachta Dála, members of parliament), such as their party affiliations, constituencies and office positions. The current version of the database includes close to 4.5 million speeches from 1,178 TDs. The speeches were downloaded from the official parliament website and further processed and parsed with a Python script. Background information on TDs was collected from the member database of the parliament website. Data on cabinet positions (ministers and junior ministers) was collected from the official website of the government. A record linkage algorithm and human coders were used to match TDs and ministers.
Enhanced power grid evaluation through efficient stochastic model-based analysis
Venue: arXiv cs.OH 2017
Year: 2017

Electrical infrastructures provide services at the basis of a number of application sectors, several of which are critical from the perspective of human life, environment or financials. Following the increasing trend in electricity generation from renewable sources, pushed by the need to meet sustainable energy goals in many countries, more sophisticated control strategies are being adopted to regulate the operation of the electric power system, driving electrical infrastructures towards the so called Smart Grid scenario. It is therefore paramount to be assisted by technologies able to analyze the Smart Grid behavior in critical scenarios, e.g. where cyber malfunctions or grid disruptions occur. In this context, stochastic model-based analysis are well suited to assess dependability and quality of service related indicators, and continuous improvements in modeling strategies and system models design are required. Thus, my PhD work addresses this topic by contributing to study new Smart Grid scenarios, concerning the advanced interplay between ICT and electrical infrastructures in presence of cyber faults/attacks, define a new modeling approach, based on modularity and composition, and start to study how to improve the electrical grid dynamics representation. In this article these studies are briefly presented and discussed.
Learning Effective Changes for Software Projects
Venue: arXiv cs.SE 2017
Year: 2017

The primary motivation of much of software analytics is decision making. How to make these decisions? Should one make decisions based on lessons that arise from within a particular project? Or should one generate these decisions from across multiple projects? This work is an attempt to answer these questions. Our work was motivated by a realization that much of the current generation software analytics tools focus primarily on prediction. Indeed prediction is a useful task, but it is usually followed by "planning" about what actions need to be taken. This research seeks to address the planning task by seeking methods that support actionable analytics that offer clear guidance on what to do. Specifically, we propose XTREE and BELLTREE algorithms for generating a set of actionable plans within and across projects. Each of these plans, if followed will improve the quality of the software project.
Usability Evaluation of a Mobile Application in Extraordinary Environment for Extraordinary People
Venue: arXiv cs.HC 2017
Year: 2017

In a contemporary world, people become dependent on electronic devices. Technologies help to clarification and structure life in many ways to meet the need of the children oriented requirements. The children suffering from disabilities (e.g. autism) has desperate needs for elucidation and structures their life. MumIES is a research based system facilitates to support and manage their living. This paper works on MumIES system to evaluate usability of the system in extraordinary environment for extraordinary people. The paper shows from the survey observation users need supporting tools to access the children's potential and challenges and to give the full support to overcome disabilities. Usability evaluation has been considered one of the key challenges to MumIES system. The paper represents analysis, design of usability studies for the extraordinary user in environment.
Sequence-to-Label Script Identification for Multilingual OCR
Venue: arXiv cs.CV 2017
Year: 2017

We describe a novel line-level script identification method. Previous work repurposed an OCR model generating per-character script codes, counted to obtain line-level script identification. This has two shortcomings. First, as a sequence-to-sequence model it is more complex than necessary for the sequence-to-label problem of line script identification. This makes it harder to train and inefficient to run. Second, the counting heuristic may be suboptimal compared to a learned model. Therefore we reframe line script identification as a sequence-to-label problem and solve it using two components, trained end-toend: Encoder and Summarizer. The encoder converts a line image into a feature sequence. The summarizer aggregates the sequence to classify the line. We test various summarizers with identical inception-style convolutional networks as encoders. Experiments on scanned books and photos containing 232 languages in 30 scripts show 16% reduction of script identification error rate compared to the baseline. This improved script identification reduces the character error rate attributable to script misidentification by 33%.
DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image
Venue: arXiv cs.CV 2017
Year: 2017

3D reconstruction from a single image is a key problem in multiple applications ranging from robotic manipulation to augmented reality. Prior methods have tackled this problem through generative models which predict 3D reconstructions as voxels or point clouds. However, these methods can be computationally expensive and miss fine details. We introduce a new differentiable layer for 3D data deformation and use it in DeformNet to learn a model for 3D reconstruction-through-deformation. DeformNet takes an image input, searches the nearest shape template from a database, and deforms the template to match the query image. We evaluate our approach on the ShapeNet dataset and show that - (a) the Free-Form Deformation layer is a powerful new building block for Deep Learning models that manipulate 3D data (b) DeformNet uses this FFD layer combined with shape retrieval for smooth and detail-preserving 3D reconstruction of qualitatively plausible point clouds with respect to a single query image (c) compared to other state-of-the-art 3D reconstruction methods, DeformNet quantitatively matches or outperforms their benchmarks by significant margins. For more information, visit: https://deformnet-site.github.io/DeformNet-website/ .
Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors
Venue: arXiv cs.LG 2017
Year: 2017

Thompson sampling has impressive empirical performance for many multi-armed bandit problems. But current algorithms for Thompson sampling only work for the case of conjugate priors since these algorithms require to infer the posterior, which is often computationally intractable when the prior is not conjugate. In this paper, we propose a novel algorithm for Thompson sampling which only requires to draw samples from a tractable distribution, so our algorithm is efficient even when the prior is non-conjugate. To do this, we reformulate Thompson sampling as an optimization problem via the Gumbel-Max trick. After that we construct a set of random variables and our goal is to identify the one with highest mean. Finally, we solve it with techniques in best arm identification.
Computing control invariant sets is easy
Venue: arXiv cs.SY 2017
Year: 2017

In this paper we consider the problem of computing control invariant sets for linear controlled systems with constraints on the input and on the states. We focus in particular on the complexity of the computation of the N-step operator, given by the Minkowski addition of sets, that is the basis of many of the iterative procedures for obtaining control invariant sets. Set inclusions conditions for control invariance are presented that involve the N-step sets and are posed in form of linear programming problems. Such conditions are employed in algorithms based on LP problems that allow to overcome the complexity limitation inherent to the set addition and can be applied also to high dimensional systems. The efficiency and scalability of the method are illustrated by computing in less than two seconds an approximation of the maximal control invariant set, based on the 15-step operator, for a system whose state and input dimensions are 20 and 10 respectively.
Underdetermined source separation using a sparse STFT framework and weighted laplacian directional modelling
Venue: arXiv cs.SD 2017
Year: 2017

The instantaneous underdetermined audio source separation problem of K-sensors, L-sources mixing scenario (where K < L) has been addressed by many different approaches, provided the sources remain quite distinct in the virtual positioning space spanned by the sensors. This problem can be tackled as a directional clustering problem along the source position angles in the mixture. The use of Generalised Directional Laplacian Densities (DLD) in the MDCT domain for underdetermined source separation has been proposed before. Here, we derive weighted mixtures of DLDs in a sparser representation of the data in the STFT domain to perform separation. The proposed approach yields improved results compared to our previous offering and compares favourably with the state-of-the-art.
A Template for Implementing Fast Lock-free Trees Using HTM
Venue: arXiv cs.DC 2017
Year: 2017

Algorithms that use hardware transactional memory (HTM) must provide a software-only fallback path to guarantee progress. The design of the fallback path can have a profound impact on performance. If the fallback path is allowed to run concurrently with hardware transactions, then hardware transactions must be instrumented, adding significant overhead. Otherwise, hardware transactions must wait for any processes on the fallback path, causing concurrency bottlenecks, or move to the fallback path. We introduce an approach that combines the best of both worlds. The key idea is to use three execution paths: an HTM fast path, an HTM middle path, and a software fallback path, such that the middle path can run concurrently with each of the other two. The fast path and fallback path do not run concurrently, so the fast path incurs no instrumentation overhead. Furthermore, fast path transactions can move to the middle path instead of waiting or moving to the software path. We demonstrate our approach by producing an accelerated version of the tree update template of Brown et al., which can be used to implement fast lock-free data structures based on down-trees. We used the accelerated template to implement two lock-free trees: a binary search tree (BST), and an (a,b)-tree (a generalization of a B-tree). Experiments show that, with 72 concurrent processes, our accelerated (a,b)-tree performs between 4.0x and 4.2x as many operations per second as an implementation obtained using the original tree update template.
The Descriptive Complexity of Modal $μ$ Model-checking Games
Venue: arXiv cs.LO 2017
Year: 2017

This paper revisits the well-established relationship between the modal mu calculus and parity games to show that it is even more robust than previously known. It addresses the question of whether the descriptive complexity of modal mu model-checking games, previously known to depend on the syntactic complexity of a formula, depends in fact on its semantic complexity. It shows that up to formulas of semantic co-Büchi complexity, the descriptive complexity of their model-checking games coincides exactly with their semantic complexity. Beyond co-Büchi, the descriptive complexity of the model-checking parity games of a formula is shown to be an upper bound on its semantic complexity; whether it is also a lower bound remains an open question.
SMAUG: Secure Mobile Authentication Using Gestures
Venue: arXiv cs.CR 2017
Year: 2017

We present SMAUG (Secure Mobile Authentication Using Gestures), a novel biometric assisted authentication algorithm for mobile devices that is solely based on data collected from multiple sensors that are usually installed on modern devices -- touch screen, gyroscope and accelerometer. As opposed to existing approaches, our system supports a fully flexible user input such as free-form gestures, multi-touch, and arbitrary amount of strokes. Our experiments confirm that this approach provides a high level of robustness and security. More precisely, in 77% of all our test cases over all gestures considered, a user has been correctly identified during the first authentication attempt and in 99% after the third attempt, while an attacker has been detected in 97% of all test cases. As an example, gestures that have a good balance between complexity and usability, e.g., drawing a two parallel lines using two fingers at the same time, 100% success rate after three login attempts and 97% impostor detection rate were given. We stress that we consider the strongest possible attacker model: an attacker is not only allowed to monitor the legitimate user during the authentication process, but also receives additional information on the biometric properties, for example pressure, speed, rotation, and acceleration. We see this method as a significant step beyond existing authentication methods that can be deployed directly to devices in use without the need of additional hardware.
Interstitial Content Detection
Venue: arXiv cs.CY 2017
Year: 2017

Interstitial content is online content which grays out, or otherwise obscures the main page content. In this technical report, we discuss exploratory research into detecting the presence of interstitial content in web pages. We discuss the use of computer vision techniques to detect interstitials, and the potential use of these techniques to provide a labelled dataset for machine learning.
TheoSea: Marching Theory to Light
Venue: arXiv cs.AI 2017
Year: 2017

There is sufficient information in the far-field of a radiating dipole antenna to rediscover the Maxwell Equations and the wave equations of light, including the speed of light $c.$ TheoSea is a Julia program that does this in about a second, and the key insight is that the compactness of theories drives the search. The program is a computational embodiment of the scientific method: observation, consideration of candidate theories, and validation.
Stacked Deconvolutional Network for Semantic Segmentation
Venue: arXiv cs.CV 2017
Year: 2017

Recent progress in semantic segmentation has been driven by improving the spatial resolution under Fully Convolutional Networks (FCNs). To address this problem, we propose a Stacked Deconvolutional Network (SDN) for semantic segmentation. In SDN, multiple shallow deconvolutional networks, which are called as SDN units, are stacked one by one to integrate contextual information and guarantee the fine recovery of localization information. Meanwhile, inter-unit and intra-unit connections are designed to assist network training and enhance feature fusion since the connections improve the flow of information and gradient propagation throughout the network. Besides, hierarchical supervision is applied during the upsampling process of each SDN unit, which guarantees the discrimination of feature representations and benefits the network optimization. We carry out comprehensive experiments and achieve the new state-of-the-art results on three datasets, including PASCAL VOC 2012, CamVid, GATECH. In particular, our best model without CRF post-processing achieves an intersection-over-union score of 86.6% in the test set.
Introducing the truly chaotic finite state machines and their applications in security field
Venue: arXiv cs.CR 2017
Year: 2017

The truly chaotic finite machines introduced by authors in previous research papers are presented here. A state of the art in this discipline, encompassing all previous mathematical investigations, is provided, explaining how finite state machines can behave chaotically regarding the slight alteration of their inputs. This behavior is explained using Turing machines and formalized thanks to a special family of discrete dynamical systems called chaotic iterations. An illustrative example is finally given in the field of hash functions.
Salt-n-pepper noise filtering using Cellular Automata
Venue: arXiv cs.CV 2017
Year: 2017

Cellular Automata (CA) have been considered one of the most pronounced parallel computational tools in the recent era of nature and bio-inspired computing. Taking advantage of their local connectivity, the simplicity of their design and their inherent parallelism, CA can be effectively applied to many image processing tasks. In this paper, a CA approach for efficient salt-n-pepper noise filtering in grayscale images is presented. Using a 2D Moore neighborhood, the classified "noisy" cells are corrected by averaging the non-noisy neighboring cells. While keeping the computational burden really low, the proposed approach succeeds in removing high-noise levels from various images and yields promising qualitative and quantitative results, compared to state-of-the-art techniques.