OpenRaiser/Intern-Atlas huggingface dataset:
Fast Patch-based Style Transfer of Arbitrary Style
Venue: arXiv cs.CV 2016
Year: 2016

Artistic style transfer is an image synthesis problem where the content of an image is reproduced with the style of another. Recent works show that a visually appealing style transfer can be achieved by using the hidden activations of a pretrained convolutional neural network. However, existing methods either apply (i) an optimization procedure that works for any style image but is very expensive, or (ii) an efficient feedforward network that only allows a limited number of trained styles. In this work we propose a simpler optimization objective based on local matching that combines the content structure and style textures in a single layer of the pretrained network. We show that our objective has desirable properties such as a simpler optimization landscape, intuitive parameter tuning, and consistent frame-by-frame performance on video. Furthermore, we use 80,000 natural images and 80,000 paintings to train an inverse network that approximates the result of the optimization. This results in a procedure for artistic style transfer that is efficient but also allows arbitrary content and style images.
Building Large Machine Reading-Comprehension Datasets using Paragraph Vectors
Venue: arXiv cs.CL 2016
Year: 2016

We present a dual contribution to the task of machine reading-comprehension: a technique for creating large-sized machine-comprehension (MC) datasets using paragraph-vector models; and a novel, hybrid neural-network architecture that combines the representation power of recurrent neural networks with the discriminative power of fully-connected multi-layered networks. We use the MC-dataset generation technique to build a dataset of around 2 million examples, for which we empirically determine the high-ceiling of human performance (around 91% accuracy), as well as the performance of a variety of computer models. Among all the models we have experimented with, our hybrid neural-network architecture achieves the highest performance (83.2% accuracy). The remaining gap to the human-performance ceiling provides enough room for future model improvements.
A Robotic Prosthesis for an Amputee Drummer
Venue: arXiv cs.RO 2016
Year: 2016

The design and evaluation of a robotic prosthesis for a drummer with a transradial amputation is presented. The principal objective of the prosthesis is to simulate the role fingers play in drumming. This primarily includes controlling the manner in which the drum stick rebounds after initial impact. This is achieved using a DC motor driven by a variable impedance control framework in a shared control system. The user's ability to perform with and control the prosthesis is evaluated using a musical synchronization study. A secondary objective of the prosthesis is to explore the implications of musical expression and human-robotic interaction when a second, completely autonomous, stick is added to the prosthesis. This wearable robotic musician interacts with the user by listening to the music and responding with different rhythms and behaviors. We recount some empirical findings based on the user's experience of performing under such a paradigm.
An argumentative agent-based model of scientific inquiry
Venue: arXiv cs.SI 2016
Year: 2016

In this paper we present an agent-based model (ABM) of scientific inquiry aimed at investigating how different social networks impact the efficiency of scientists in acquiring knowledge. As such, the ABM is a computational tool for tackling issues in the domain of scientific methodology and science policy. In contrast to existing ABMs of science, our model aims to represent the argumentative dynamics that underlies scientific practice. To this end we employ abstract argumentation theory as the core design feature of the model. This helps to avoid a number of problematic idealizations which are present in other ABMs of science and which impede their relevance for actual scientific practice.
Analysis of proposed PDE-based underwater image enhancement algorithms
Venue: arXiv cs.CV 2016
Year: 2016

This report describes the experimental analysis of proposed underwater image enhancement algorithms based on partial differential equations (PDEs). The algorithms perform simultaneous smoothing and enhancement due to the combination of both processes within the PDE-formulation. The framework enables the incorporation of suitable colour and contrast enhancement algorithms within one unified functional. Additional modification of the formulation includes the combination of the popular Contrast Limited Adaptive Histogram Equalization (CLAHE) with the proposed approach. This modification enables the hybrid algorithm to provide both local enhancement (due to the CLAHE) and global enhancement (due to the proposed contrast term). Additionally, the CLAHE clip limit parameter is computed dynamically in each iteration and used to gauge the amount of local enhancement performed by the CLAHE within the formulation. This enables the algorithm to reduce or prevent the enhancement of noisy artifacts, which if present, are also smoothed out by the anisotropic diffusion term within the PDE formulation. In other words, the modified algorithm combines the strength of the CLAHE, AD and the contrast term while minimizing their weaknesses. Ultimately, the system is optimized using image data metrics for automated enhancement and compromise between visual and quantitative results. Experiments indicate that the proposed algorithms perform a series of functions such as illumination correction, colour enhancement correction and restoration, contrast enhancement and noise suppression. Moreover, the proposed approaches surpass most other conventional algorithms found in the literature.
Efficient Realization of Householder Transform through Algorithm-Architecture Co-design for Acceleration of QR Factorization
Venue: arXiv cs.PF 2016
Year: 2016

We present efficient realization of Householder Transform (HT) based QR factorization through algorithm-architecture co-design where we achieve performance improvement of 3-90x in-terms of Gflops/watt over state-of-the-art multicore, General Purpose Graphics Processing Units (GPGPUs), Field Programmable Gate Arrays (FPGAs), and ClearSpeed CSX700. Theoretical and experimental analysis of classical HT is performed for opportunities to exhibit higher degree of parallelism where parallelism is quantified as a number of parallel operations per level in the Directed Acyclic Graph (DAG) of the transform. Based on theoretical analysis of classical HT, an opportunity re-arrange computations in the classical HT is identified that results in Modified HT (MHT) where it is shown that MHT exhibits 1.33x times higher parallelism than classical HT. Experiments in off-the-shelf multicore and General Purpose Graphics Processing Units (GPGPUs) for HT and MHT suggest that MHT is capable of achieving slightly better or equal performance compared to classical HT based QR factorization realizations in the optimized software packages for Dense Linear Algebra (DLA). We implement MHT on a customized platform for Dense Linear Algebra (DLA) and show that MHT achieves 1.3x better performance than native implementation of classical HT on the same accelerator. For custom realization of HT and MHT based QR factorization, we also identify macro operations in the DAGs of HT and MHT that are realized on a Reconfigurable Data-path (RDP). We also observe that due to re-arrangement in the computations in MHT, custom realization of MHT is capable of achieving 12% better performance improvement over multicore and GPGPUs than the performance improvement reported by General Matrix Multiplication (GEMM) over highly tuned DLA software packages for multicore and GPGPUs which is counter-intuitive.
The Ethereum Scratch Off Puzzle
Venue: arXiv cs.CR 2016
Year: 2016

Ethereum represents new innovation in the fields of cryptocurrency which has become relatively stagnate, promising many things, including an entire programming language and development enviroment built into the network. However the current trend is to write implementations and proof of concepts before doing the rigor involved with proving security. Miller's recent thesis is an attempt to remedy this, and we apply his provable security techniques to the algorithm description of CASPER, the new "proof-of-stake" consensus protocol scheme to be implemented in ethereum. We conclude by stating it satisfies almost all the definitions, except one, leaving room for improvement.
Permutation-equivariant neural networks applied to dynamics prediction
Venue: arXiv cs.CV 2016
Year: 2016

The introduction of convolutional layers greatly advanced the performance of neural networks on image tasks due to innately capturing a way of encoding and learning translation-invariant operations, matching one of the underlying symmetries of the image domain. In comparison, there are a number of problems in which there are a number of different inputs which are all 'of the same type' --- multiple particles, multiple agents, multiple stock prices, etc. The corresponding symmetry to this is permutation symmetry, in that the algorithm should not depend on the specific ordering of the input data. We discuss a permutation-invariant neural network layer in analogy to convolutional layers, and show the ability of this architecture to learn to predict the motion of a variable number of interacting hard discs in 2D. In the same way that convolutional layers can generalize to different image sizes, the permutation layer we describe generalizes to different numbers of objects.
The Mehler-Fock Transform and some Applications in Texture Analysis and Color Processing
Venue: arXiv cs.CV 2016
Year: 2016

Many stochastic processes are defined on special geometrical objects like spheres and cones. We describe how tools from harmonic analysis, i.e. Fourier analysis on groups, can be used to investigate probability density functions (pdfs) on groups and homogeneous spaces. We consider the special case of the Lorentz group SU(1,1) and the unit disk with its hyperbolic geometry, but the procedure can be generalized to a much wider class of Lie-groups. We mainly concentrate on the Mehler-Fock transform which is the radial part of the Fourier transform on the disk. Some of the characteristic features of this transform are the relation to group-convolutions, the isometry between signal and transform space, the relation to the Laplace-Beltrami operator and the relation to group representation theory. We will give an overview over these properties and their applications in signal processing. We will illustrate the theory with two examples from low-level vision and color image processing.
Reverse Engineering of Irreducible Polynomials in GF(2^m) Arithmetic
Venue: arXiv cs.SC 2016
Year: 2016

Current techniques for formally verifying circuits implemented in Galois field (GF) arithmetic are limited to those with a known irreducible polynomial P(x). This paper presents a computer algebra based technique that extracts the irreducible polynomial P(x) used in the implementation of a multiplier in GF(2^m). The method is based on first extracting a unique polynomial in Galois field of each output bit independently. P(x) is then obtained by analyzing the algebraic expression in GF(2^m) of each output bit. We demonstrate that this method is able to reverse engineer the irreducible polynomial of an n-bit GF multiplier in n threads. Experiments were performed on Mastrovito and Montgomery multipliers with different P (x), including NIST-recommended polynomials and optimal polynomials for different microprocessor architectures.
A literature survey of the quality economics of defect-detection techniques
Venue: arXiv cs.SE 2016
Year: 2016

Over the last decades, a considerable amount of empirical knowledge about the efficiency of defect-detection techniques has been accumulated. Also a few surveys have summarised those studies with different focuses, usually for a specific type of technique. This work reviews the results of empirical studies and associates them with a model of software quality economics. This allows a better comparison of the different techniques and supports the application of the model in practice as several parameters can be approximated with typical average values. The main contributions are the provision of average values of several interesting quantities w.r.t. defect detection and the identification of areas that need further research because of the limited knowledge available.
A Comprehensive Model of Usability
Venue: arXiv cs.HC 2016
Year: 2016

Usability is a key quality attribute of successful software systems. Unfortunately, there is no common understanding of the factors influencing usability and their interrelations. Hence, the lack of a comprehensive basis for designing, analyzing, and improving user interfaces. This paper proposes a 2-dimensional model of usability that associates system properties with the activities carried out by the user. By separating activities and properties, sound quality criteria can be identified, thus facilitating statements concerning their interdependencies. This model is based on a tested quality meta-model that fosters preciseness and completeness. A case study demonstrates the manner by which such a model aids in revealing contradictions and omissions in existing usability standards. Furthermore, the model serves as a central and structured knowledge base for the entire quality assurance process, e.g. the automatic generation of guideline documents.
Efficient Sampling for Better OSN Data Provisioning
Venue: arXiv cs.DS 2016
Year: 2016

Data concerning the users and usage of Online Social Networks (OSNs) has become available externally, from public resources (e.g., user profiles), participation in OSNs (e.g., establishing relationships and recording transactions such as user updates) and APIs of the OSN provider (such as the Twitter API). APIs let OSN providers monetize the release of data while helping control measurement load, e.g. by providing samples with different cost-granularity tradeoffs. To date, this approach has been more suited to releasing transactional data, with graphical data still being obtained by resource intensive methods such a graph crawling. In this paper, we propose a method for OSNs to provide samples of the user graph of tunable size, in non-intersecting increments, with sample selection that can be weighted to enhance accuracy when estimating different features of the graph.
Algorithmic Analysis of Invisible Video Watermarking using LSB Encoding Over a Client-Server Framework
Venue: arXiv cs.MM 2016
Year: 2016

Video watermarking is extensively used in many media-oriented applications for embedding watermarks, i.e. hidden digital data, in a video sequence to protect the video from illegal copying and to identify manipulations made in the video. In case of an invisible watermark, the human eye can not perceive any difference in the video, but a watermark extraction application can read the watermark and obtain the embedded information. Although numerous methodologies exist for embedding watermarks, many of them have shortcomings with respect to performance efficiency, especially over a distributed network. This paper proposes and analyses a 2-bit Least Significant Bit (LSB) parallel algorithmic approach for achieving performance efficiency to watermark and distribute videos over a client-server framework.
Temporal-Needle: A view and appearance invariant video descriptor
Venue: arXiv cs.CV 2016
Year: 2016

The ability to detect similar actions across videos can be very useful for real-world applications in many fields. However, this task is still challenging for existing systems, since videos that present the same action, can be taken from significantly different viewing directions, performed by different actors and backgrounds and under various video qualities. Video descriptors play a significant role in these systems. In this work we propose the "temporal-needle" descriptor which captures the dynamic behavior, while being invariant to viewpoint and appearance. The descriptor is computed using multi temporal scales of the video and by computing self-similarity for every patch through time in every temporal scale. The descriptor is computed for every pixel in the video. However, to find similar actions across videos, we consider only a small subset of the descriptors - the statistical significant descriptors. This allow us to find good correspondences across videos more efficiently. Using the descriptor, we were able to detect the same behavior across videos in a variety of scenarios. We demonstrate the use of the descriptor in tasks such as temporal and spatial alignment, action detection and even show its potential in unsupervised video clustering into categories. In this work we handled only videos taken with stationary cameras, but the descriptor can be extended to handle moving camera as well.
A 700uW 1GS/s 4-bit Folding-Flash ADC in 65nm CMOS for Wideband Wireless Communications
Venue: arXiv cs.AR 2016
Year: 2016

We present the design of a low-power 4-bit 1GS/s folding-flash ADC with a folding factor of two. The design of a new unbalanced double-tail dynamic comparator affords an ultra-low power operation and a high dynamic range. Unlike the conventional approaches, this design uses a fully matched input stage, an unbalanced latch stage, and a two-clock operation scheme. A combination of these features yields significant reduction of the kick-back noise, while allowing the design flexibility for adjusting the trip points of the comparators. As a result, the ADC achieves SNDR of 22.3 dB at 100MHz and 21.8 dB at 500MHz (i.e. the Nyquist frequency). The maximum INL and DNL are about 0.2 LSB. The converter consumes about 700uW from a 1-V supply yielding a figure of merit of 65fJ/conversion step. These attributes make the proposed folding-flash ADC attractive for the next-generation wireless applications.
A fuzzy approach for segmentation of touching characters
Venue: arXiv cs.CV 2016
Year: 2016

The problem of correctly segmenting touching characters is an hard task to solve and it is of major relevance in pattern recognition. In the recent years, many methods and algorithms have been proposed; still, a definitive solution is far from being found. In this paper, we propose a novel method based on fuzzy logic. The proposed method combines in a novel way three features for segmenting touching characters that have been already proposed in other studies but have been exploited only singularly so far. The proposed strategy is based on a 3--input/1--output fuzzy inference system with fuzzy rules specifically optimized for segmenting touching characters in the case of Latin printed and handwritten characters. The system performances are illustrated and supported by numerical examples showing that our approach can achieve a reasonable good overall accuracy in segmenting characters even on tricky conditions of touching characters. Moreover, numerical results suggest that the method can be applied to many different datasets of characters by means of a convenient tuning of the fuzzy sets and rules.
Interpretable Semantic Textual Similarity: Finding and explaining differences between sentences
Venue: arXiv cs.CL 2016
Year: 2016

User acceptance of artificial intelligence agents might depend on their ability to explain their reasoning, which requires adding an interpretability layer that fa- cilitates users to understand their behavior. This paper focuses on adding an in- terpretable layer on top of Semantic Textual Similarity (STS), which measures the degree of semantic equivalence between two sentences. The interpretability layer is formalized as the alignment between pairs of segments across the two sentences, where the relation between the segments is labeled with a relation type and a similarity score. We present a publicly available dataset of sentence pairs annotated following the formalization. We then develop a system trained on this dataset which, given a sentence pair, explains what is similar and different, in the form of graded and typed segment alignments. When evaluated on the dataset, the system performs better than an informed baseline, showing that the dataset and task are well-defined and feasible. Most importantly, two user studies show how the system output can be used to automatically produce explanations in natural language. Users performed better when having access to the explanations, pro- viding preliminary evidence that our dataset and method to automatically produce explanations is useful in real applications.
Collaborative creativity with Monte-Carlo Tree Search and Convolutional Neural Networks
Venue: arXiv cs.AI 2016
Year: 2016

We investigate a human-machine collaborative drawing environment in which an autonomous agent sketches images while optionally allowing a user to directly influence the agent's trajectory. We combine Monte Carlo Tree Search with image classifiers and test both shallow models (e.g. multinomial logistic regression) and deep Convolutional Neural Networks (e.g. LeNet, Inception v3). We found that using the shallow model, the agent produces a limited variety of images, which are noticably recogonisable by humans. However, using the deeper models, the agent produces a more diverse range of images, and while the agent remains very confident (99.99%) in having achieved its objective, to humans they mostly resemble unrecognisable 'random' noise. We relate this to recent research which also discovered that 'deep neural networks are easily fooled' \cite{Nguyen2015} and we discuss possible solutions and future directions for the research.
A Data-Driven Compressive Sensing Framework Tailored For Energy-Efficient Wearable Sensing
Venue: arXiv cs.LG 2016
Year: 2016

Compressive sensing (CS) is a promising technology for realizing energy-efficient wireless sensors for long-term health monitoring. However, conventional model-driven CS frameworks suffer from limited compression ratio and reconstruction quality when dealing with physiological signals due to inaccurate models and the overlook of individual variability. In this paper, we propose a data-driven CS framework that can learn signal characteristics and personalized features from any individual recording of physiologic signals to enhance CS performance with a minimized number of measurements. Such improvements are accomplished by a co-training approach that optimizes the sensing matrix and the dictionary towards improved restricted isometry property and signal sparsity, respectively. Experimental results upon ECG signals show that the proposed method, at a compression ratio of 10x, successfully reduces the isometry constant of the trained sensing matrices by 86% against random matrices and improves the overall reconstructed signal-to-noise ratio by 15dB over conventional model-driven approaches.