OpenRaiser/Intern-Atlas huggingface dataset:
Voliro: An Omnidirectional Hexacopter With Tiltable Rotors
Venue: arXiv cs.RO 2018
Year: 2018

Extending the maneuverability of unmanned areal vehicles promises to yield a considerable increase in the areas in which these systems can be used. Some such applications are the performance of more complicated inspection tasks and the generation of complex uninterrupted movements of an attached camera. In this paper we address this challenge by presenting Voliro, a novel aerial platform that combines the advantages of existing multi-rotor systems with the agility of omnidirectionally controllable platforms. We propose the use of a hexacopter with tiltable rotors allowing the system to decouple the control of position and orientation. The contributions of this work involve the mechanical design as well as a controller with the corresponding allocation scheme. This work also discusses the design challenges involved when turning the concept of a hexacopter with tiltable rotors into an actual prototype. The agility of the system is demonstrated and evaluated in real- world experiments.
Distributed dynamic load balancing for task parallel programming
Venue: arXiv cs.DC 2018
Year: 2018

In this paper, we derive and investigate approaches to dynamically load balance a distributed task parallel application software. The load balancing strategy is based on task migration. Busy processes export parts of their ready task queue to idle processes. Idle--busy pairs of processes find each other through a random search process that succeeds within a few steps with high probability. We evaluate the load balancing approach for a block Cholesky factorization implementation and observe a reduction in execution time on the order of 5\% in the selected test cases.
Deep Reinforcement Fuzzing
Venue: arXiv cs.AI 2018
Year: 2018

Fuzzing is the process of finding security vulnerabilities in input-processing code by repeatedly testing the code with modified inputs. In this paper, we formalize fuzzing as a reinforcement learning problem using the concept of Markov decision processes. This in turn allows us to apply state-of-the-art deep Q-learning algorithms that optimize rewards, which we define from runtime properties of the program under test. By observing the rewards caused by mutating with a specific set of actions performed on an initial program input, the fuzzing agent learns a policy that can next generate new higher-reward inputs. We have implemented this new approach, and preliminary empirical evidence shows that reinforcement fuzzing can outperform baseline random fuzzing.
Deep Net Triage: Analyzing the Importance of Network Layers via Structural Compression
Venue: arXiv cs.CV 2018
Year: 2018

Despite their prevalence, deep networks are poorly understood. This is due, at least in part, to their highly parameterized nature. As such, while certain structures have been found to work better than others, the significance of a model's unique structure, or the importance of a given layer, and how these translate to overall accuracy, remains unclear. In this paper, we analyze these properties of deep neural networks via a process we term deep net triage. Like medical triage---the assessment of the importance of various wounds---we assess the importance of layers in a neural network, or as we call it, their criticality. We do this by applying structural compression, whereby we reduce a block of layers to a single layer. After compressing a set of layers, we apply a combination of initialization and training schemes, and look at network accuracy, convergence, and the layer's learned filters to assess the criticality of the layer. We apply this analysis across four data sets of varying complexity. We find that the accuracy of the model does not depend on which layer was compressed; that accuracy can be recovered or exceeded after compression by fine-tuning across the entire model; and, lastly, that Knowledge Distillation can be used to hasten convergence of a compressed network, but constrains the accuracy attainable to that of the base model.
The decoding failure probability of MDPC codes
Venue: arXiv cs.IT 2018
Year: 2018

Moderate Density Parity Check (MDPC) codes are defined here as codes which have a parity-check matrix whose row weight is $O(\sqrt{n})$ where $n$ is the length $n$ of the code. They can be decoded like LDPC codes but they decode much less errors than LDPC codes: the number of errors they can decode in this case is of order $Θ(\sqrt{n})$. Despite this fact they have been proved very useful in cryptography for devising key exchange mechanisms. They have also been proposed in McEliece type cryptosystems. However in this case, the parameters that have been proposed in \cite{MTSB13} were broken in \cite{GJS16}. This attack exploits the fact that the decoding failure probability is non-negligible. We show here that this attack can be thwarted by choosing the parameters in a more conservative way. We first show that such codes can decode with a simple bit-flipping decoder any pattern of $O\left(\frac{\sqrt{n} \log \log n}{\log n}\right)$ errors. This avoids the previous attack at the cost of significantly increasing the key size of the scheme. We then show that under a very reasonable assumption the decoding failure probability decays almost exponentially with the codelength with just two iterations of bit-flipping. With an additional assumption it has even been proved that it decays exponentially with an unbounded number of iterations and we show that in this case the increase of the key size which is required for resisting to the attack of \cite{GJS16} is only moderate.
SPIN: A Fast and Scalable Matrix Inversion Method in Apache Spark
Venue: arXiv cs.DC 2018
Year: 2018

The growth of big data in domains such as Earth Sciences, Social Networks, Physical Sciences, etc. has lead to an immense need for efficient and scalable linear algebra operations, e.g. Matrix inversion. Existing methods for efficient and distributed matrix inversion using big data platforms rely on LU decomposition based block-recursive algorithms. However, these algorithms are complex and require a lot of side calculations, e.g. matrix multiplication, at various levels of recursion. In this paper, we propose a different scheme based on Strassen's matrix inversion algorithm (mentioned in Strassen's original paper in 1969), which uses far fewer operations at each level of recursion. We implement the proposed algorithm, and through extensive experimentation, show that it is more efficient than the state of the art methods. Furthermore, we provide a detailed theoretical analysis of the proposed algorithm, and derive theoretical running times which match closely with the empirically observed wall clock running times, thus explaining the U-shaped behaviour w.r.t. block-sizes.
SAR Image Despeckling Using Quadratic-Linear Approximated L1-Norm
Venue: arXiv cs.CV 2018
Year: 2018

Speckle noise, inherent in synthetic aperture radar (SAR) images, degrades the performance of the various SAR image analysis tasks. Thus, speckle noise reduction is a critical preprocessing step for smoothing homogeneous regions while preserving details. This letter proposes a variational despeckling approach where L1-norm total variation regularization term is approximated in a quadratic and linear manner to increase accuracy while decreasing the computation time. Despeckling performance and computational efficiency of the proposed method are shown using synthetic and real-world SAR images.
A Quantitative Approach in Heuristic Evaluation of E-commerce Websites
Venue: arXiv cs.HC 2018
Year: 2018

This paper presents a pilot study on developing an instrument to predict the quality of e-commerce websites. The 8C model was adopted as the reference model of the heuristic evaluation. Each dimension of the 8C was mapped into a set of quantitative website elements, selected websites were scraped to get the quantitative website elements, and the score of each dimension was calculated. A software was developed in PHP for the experiments. In the training process, 10 experiments were conducted and quantitative analyses were regressively conducted between the experiments. The conversion rate was used to verify the heuristic evaluation of an e-commerce website after each experiment. The results showed that the mapping revisions between the experiments improved the performance of the evaluation instrument, therefore the experiment process and the quantitative mapping revision guideline proposed was on the right track. The software resulted from the experiment 10 can serve as the aimed e-commerce website evaluation instrument. The experiment results and the future work have been discussed.
Building a Conversational Agent Overnight with Dialogue Self-Play
Venue: arXiv cs.AI 2018
Year: 2018

We propose Machines Talking To Machines (M2M), a framework combining automation and crowdsourcing to rapidly bootstrap end-to-end dialogue agents for goal-oriented dialogues in arbitrary domains. M2M scales to new tasks with just a task schema and an API client from the dialogue system developer, but it is also customizable to cater to task-specific interactions. Compared to the Wizard-of-Oz approach for data collection, M2M achieves greater diversity and coverage of salient dialogue flows while maintaining the naturalness of individual utterances. In the first phase, a simulated user bot and a domain-agnostic system bot converse to exhaustively generate dialogue "outlines", i.e. sequences of template utterances and their semantic parses. In the second phase, crowd workers provide contextual rewrites of the dialogues to make the utterances more natural while preserving their meaning. The entire process can finish within a few hours. We propose a new corpus of 3,000 dialogues spanning 2 domains collected with M2M, and present comparisons with popular dialogue datasets on the quality and diversity of the surface forms and dialogue flows.
Classification of histopathological breast cancer images using iterative VMD aided Zernike moments & textural signatures
Venue: arXiv cs.CV 2018
Year: 2018

In this paper we present a novel method for an automated diagnosis of breast carcinoma through multilevel iterative variational mode decomposition (VMD) and textural features encompassing Zernaike moments, fractal dimension and entropy features namely, Kapoor entropy, Renyi entropy, Yager entropy features are extracted from VMD components. The proposed method considers the histopathological image as a set of multidimensional spatially-evolving signals. ReliefF algorithm is used to select the discriminatory features and statistically most significant features are fed to squares support vector machine (SVM) for classification. We evaluate the efficiency of the proposed methodology on publicly available Breakhis dataset containing 7,909 breast cancer histological images, collected from 82 patients, of both benign and malignant cases. Experimental results shows the efficacy of the proposed method in outperforming the state of the art while achieving an average classification rates of 89.61% and 88:23% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the pathologist in accurate and reliable diagnosis of biopsy samples. BreaKHis, a publicly dataset available at http://web.inf.ufpr.br/vri/breast-cancer-database.
An MDS-PIR Capacity-Achieving Protocol for Distributed Storage Using Non-MDS Linear Codes
Venue: arXiv cs.IT 2018
Year: 2018

We propose a private information retrieval (PIR) protocol for distributed storage systems with noncolluding nodes where data is stored using an arbitrary linear code. An expression for the PIR rate, i.e., the ratio of the amount of retrieved data per unit of downloaded data, is derived, and a necessary and a sufficient condition for codes to achieve the maximum distance separable (MDS) PIR capacity are given. The necessary condition is based on the generalized Hamming weights of the storage code, while the sufficient condition is based on code automorphisms. We show that cyclic codes and Reed-Muller codes satisfy the sufficient condition and are thus MDS-PIR capacity-achieving.
Encrypt Flip-Flop: A Novel Logic Encryption Technique For Sequential Circuits
Venue: arXiv cs.CR 2018
Year: 2018

Logic Encryption is one of the most popular hardware security techniques which can prevent IP piracy and illegal IC overproduction. It introduces obfuscation by inserting some extra hardware into a design to hide its functionality from unauthorized users. Correct functionality of an encrypted design depends upon the application of correct keys, shared only with the authorized users. In the recent past, extensive efforts have been devoted in extracting the secret key of an encrypted design. At the same time, several countermeasures have also been proposed by the research community to thwart different state-of-the-art attacks on logic encryption. However, most of the proposed countermeasures fail to prevent the powerful SAT attack. Although a few researchers have proposed different solutions to withstand SAT attack, those solutions suffer from several drawbacks such as high design overheads, low output corruptibility, and vulnerability against removal attack. Almost all the known logic encryption strategies are vulnerable to scan based attack. In this paper, we propose a novel encryption technique called Encrypt Flip-Flop, which encrypts the outputs of selected flip-flops by inserting multiplexers (MUX). The proposed strategy can thwart all the known attacks including SAT and scan based attacks. The scheme has low design overhead and implementation complexity. Experimental results on several ISCAS'89 and ITC'99 benchmarks show that our proposed method can produce reasonable output corruption for wrong keys.
Analytic Provenance Datasets: A Data Repository of Human Analysis Activity and Interaction Logs
Venue: arXiv cs.HC 2018
Year: 2018

We present an analytic provenance data repository that can be used to study human analysis activity, thought processes, and software interaction with visual analysis tools during exploratory data analysis. We conducted a series of user studies involving exploratory data analysis scenario with textual and cyber security data. Interactions logs, think-alouds, videos and all coded data in this study are available online for research purposes. Analysis sessions are segmented in multiple sub-task steps based on user think-alouds, video and audios captured during the studies. These analytic provenance datasets can be used for research involving tools and techniques for analyzing interaction logs and analysis history. By providing high-quality coded data along with interaction logs, it is possible to compare algorithmic data processing techniques to the ground-truth records of analysis history.
Don't Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition
Venue: arXiv cs.RO 2018
Year: 2018

When a human drives a car along a road for the first time, they later recognize where they are on the return journey typically without needing to look in their rear-view mirror or turn around to look back, despite significant viewpoint and appearance change. Such navigation capabilities are typically attributed to our semantic visual understanding of the environment [1] beyond geometry to recognizing the types of places we are passing through such as "passing a shop on the left" or "moving through a forested area". Humans are in effect using place categorization [2] to perform specific place recognition even when the viewpoint is 180 degrees reversed. Recent advances in deep neural networks have enabled high-performance semantic understanding of visual places and scenes, opening up the possibility of emulating what humans do. In this work, we develop a novel methodology for using the semantics-aware higher-order layers of deep neural networks for recognizing specific places from within a reference database. To further improve the robustness to appearance change, we develop a descriptor normalization scheme that builds on the success of normalization schemes for pure appearance-based techniques such as SeqSLAM [3]. Using two different datasets - one road-based, one pedestrian-based, we evaluate the performance of the system in performing place recognition on reverse traversals of a route with a limited field of view camera and no turn-back-and-look behaviours, and compare to existing state-of-the-art techniques and vanilla off-the-shelf features. The results demonstrate significant improvements over the existing state of the art, especially for extreme perceptual challenges that involve both great viewpoint change and environmental appearance change. We also provide experimental analyses of the contributions of the various system components.
One Way Function Candidate based on the Collatz Problem
Venue: arXiv cs.CC 2018
Year: 2018

The one way function based on the Collatz problem is proposed. It is based on the problem's conditional branching structure which is not considered as important even the 3x+1 question is quite famous. The analysis shows why the problem is mathematically so inaccessible and how the algorithm conditional branching structure can be used to construct one way functions. It also shows exponential dependence between algorithm's conditional branching and running cost of algorithm branch-less reductions.
Localization-Aware Active Learning for Object Detection
Venue: arXiv cs.CV 2018
Year: 2018

Active learning - a class of algorithms that iteratively searches for the most informative samples to include in a training dataset - has been shown to be effective at annotating data for image classification. However, the use of active learning for object detection is still largely unexplored as determining informativeness of an object-location hypothesis is more difficult. In this paper, we address this issue and present two metrics for measuring the informativeness of an object hypothesis, which allow us to leverage active learning to reduce the amount of annotated data needed to achieve a target object detection performance. Our first metric measures 'localization tightness' of an object hypothesis, which is based on the overlapping ratio between the region proposal and the final prediction. Our second metric measures 'localization stability' of an object hypothesis, which is based on the variation of predicted object locations when input images are corrupted by noise. Our experimental results show that by augmenting a conventional active-learning algorithm designed for classification with the proposed metrics, the amount of labeled training data required can be reduced up to 25%. Moreover, on PASCAL 2007 and 2012 datasets our localization-stability method has an average relative improvement of 96.5% and 81.9% over the baseline method using classification only.
Round- and Message-Optimal Distributed Graph Algorithms
Venue: arXiv cs.DC 2018
Year: 2018

Distributed graph algorithms that separately optimize for either the number of rounds used or the total number of messages sent have been studied extensively. However, algorithms simultaneously efficient with respect to both measures have been elusive. For example, only very recently was it shown that for Minimum Spanning Tree (MST), an optimal message and round complexity is achievable (up to polylog terms) by a single algorithm in the CONGEST model of communication. In this paper we provide algorithms that are simultaneously round- and message-optimal for a number of well-studied distributed optimization problems. Our main result is such a distributed algorithm for the fundamental primitive of computing simple functions over each part of a graph partition. From this algorithm we derive round- and message-optimal algorithms for multiple problems, including MST, Approximate Min-Cut and Approximate Single Source Shortest Paths, among others. On general graphs all of our algorithms achieve worst-case optimal $\tilde{O}(D+\sqrt n)$ round complexity and $\tilde{O}(m)$ message complexity. Furthermore, our algorithms require an optimal $\tilde{O}(D)$ rounds and $\tilde{O}(n)$ messages on planar, genus-bounded, treewidth-bounded and pathwidth-bounded graphs.
Constraint-free Natural Image Reconstruction from fMRI Signals Based on Convolutional Neural Network
Venue: arXiv cs.CV 2018
Year: 2018

In recent years, research on decoding brain activity based on functional magnetic resonance imaging (fMRI) has made remarkable achievements. However, constraint-free natural image reconstruction from brain activity is still a challenge. The existing methods simplified the problem by using semantic prior information or just reconstructing simple images such as letters and digitals. Without semantic prior information, we present a novel method to reconstruct nature images from fMRI signals of human visual cortex based on the computation model of convolutional neural network (CNN). Firstly, we extracted the units output of viewed natural images in each layer of a pre-trained CNN as CNN features. Secondly, we transformed image reconstruction from fMRI signals into the problem of CNN feature visualizations by training a sparse linear regression to map from the fMRI patterns to CNN features. By iteratively optimization to find the matched image, whose CNN unit features become most similar to those predicted from the brain activity, we finally achieved the promising results for the challenging constraint-free natural image reconstruction. As there was no use of semantic prior information of the stimuli when training decoding model, any category of images (not constraint by the training set) could be reconstructed theoretically. We found that the reconstructed images resembled the natural stimuli, especially in position and shape. The experimental results suggest that hierarchical visual features can effectively express the visual perception process of human brain.
Refining Properties of Filter Models: Sensibility, Approximability and Reducibility
Venue: arXiv cs.LO 2018
Year: 2018

In this paper, we study the tedious link between the properties of sensibility and approximability of models of untyped λ-calculus. Approximability is known to be a slightly, but strictly stronger property that sensibility. However, we will see that so far, each and every (filter) model that have been proven sensible are in fact approximable. We explain this result as a weakness of the sole known approach of sensibility: the Tait reducibility candidates and its realizability variants. In fact, we will reduce the approximability of a filter model D for the λ-calculus to the sensibility of D but for an extension of the λ-calculus that we call λ-calculus with D-tests. Then we show that traditional proofs of sensibility of D for the λ-calculus are smoothly extendable for this λ-calculus with D-tests.
Embedding a $θ$-invariant code into a complete one
Venue: arXiv cs.DM 2018
Year: 2018

Let A be a finite or countable alphabet and let $θ$ be a literal (anti-)automorphism onto A * (by definition, such a correspondence is determinated by a permutation of the alphabet). This paper deals with sets which are invariant under $θ$ ($θ$-invariant for short) that is, languages L such that $θ$ (L) is a subset of L.We establish an extension of the famous defect theorem. With regards to the so-called notion of completeness, we provide a series of examples of finite complete $θ$-invariant codes. Moreover, we establish a formula which allows to embed any non-complete $θ$-invariant code into a complete one. As a consequence, in the family of the so-called thin $θ$--invariant codes, maximality and completeness are two equivalent notions.