OpenRaiser/Intern-Atlas huggingface dataset:
A Least Squares Approach for Stable Phase Retrieval from Short-Time Fourier Transform Magnitude
Venue: arXiv cs.IT 2015
Year: 2015

We address the problem of recovering a signal (up to global phase) from its short-time Fourier transform (STFT) magnitude measurements. This problem arises in several applications, including optical imaging and speech processing. In this paper we suggest three interrelated algorithms. The first algorithm estimates the signal efficiently from noisy measurements by solving a simple least-squares (LS) problem. In contrast to previously proposed algorithms, the LS approach has stability guarantees and does not require any prior knowledge on the sought signal. However, the recovery is guaranteed under relatively strong restrictions on the STFT window. The second approach is guaranteed to recover a non-vanishing signal efficiently from noise-free measurements, under very moderate conditions on the STFT window. Finally, the third method estimates the signal robustly from noisy measurements by solving a semi-definite program (SDP). The proposed SDP algorithm contains an inherent trade-off between its robustness and the restrictions on the STFT windows that can be used.
Capacity Bounds for Diamond Networks with an Orthogonal Broadcast Channel
Venue: arXiv cs.IT 2015
Year: 2015

A class of diamond networks is studied where the broadcast component is orthogonal and modeled by two independent bit-pipes. New upper and lower bounds on the capacity are derived. The proof technique for the upper bound generalizes bounding techniques of Ozarow for the Gaussian multiple description problem (1981) and Kang and Liu for the Gaussian diamond network (2011). The lower bound is based on Marton's coding technique and superposition coding. The bounds are evaluated for Gaussian and binary adder multiple access channels (MACs). For Gaussian MACs, both the lower and upper bounds strengthen the Kang-Liu bounds and establish capacity for interesting ranges of bit-pipe capacities. For binary adder MACs, the capacity is established for all ranges of bit-pipe capacities.
Alternative forms of representation of Boolean functions in Cryptographic Information Security Facilities
Venue: arXiv cs.CR 2015
Year: 2015

The work offers a new approach to the formation of functions which are used in cryptography and cryptanalysis. It will use alternative forms of representation of Boolean functions, that is, those which are different from the classical form, which is formed in a Boolean basis AND-OR-NOT. An example of this is, in particular, the formation of cryptographic functions with the use of alternative forms of representation, namely Cognate-representation of Boolean functions. This form is, by definition, multivariant and allows you to choose the best variant from a plurality of possible and permissible forms. Moreover, criteria of admissibility can be also selected depending on the particular situation, since it is known that an improvement of one criterion usually leads to deterioration of others. The methods suggested in the project, exemplified by Cognate-form representations of Boolean functions, show that the use of alternative forms of representation of Boolean functions in forming of cryptographic functions, algorithms and devices can significantly improve their parameters and properties. And their use in cryptographic means of protection allow to optimize the process of logical design of cryptographic devices and improve the safety performance of information and communication systems.
Centrality metrics and localization in core-periphery networks
Venue: arXiv cs.SI 2015
Year: 2015

Two concepts of centrality have been defined in complex networks. The first considers the centrality of a node and many different metrics for it has been defined (e.g. eigenvector centrality, PageRank, non-backtracking centrality, etc). The second is related to a large scale organization of the network, the core-periphery structure, composed by a dense core plus an outlying and loosely-connected periphery. In this paper we investigate the relation between these two concepts. We consider networks generated via the Stochastic Block Model, or its degree corrected version, with a strong core-periphery structure and we investigate the centrality properties of the core nodes and the ability of several centrality metrics to identify them. We find that the three measures with the best performance are marginals obtained with belief propagation, PageRank, and degree centrality, while non-backtracking and eigenvector centrality (or MINRES}, showed to be equivalent to the latter in the large network limit) perform worse in the investigated networks.
Feedback and Time are Essential for the Optimal Control of Computing Systems
Venue: arXiv cs.SY 2015
Year: 2015

The performance, reliability, cost, size and energy usage of computing systems can be improved by one or more orders of magnitude by the systematic use of modern control and optimization methods. Computing systems rely on the use of feedback algorithms to schedule tasks, data and resources, but the models that are used to design these algorithms are validated using open-loop metrics. By using closed-loop metrics instead, such as the gap metric developed in the control community, it should be possible to develop improved scheduling algorithms and computing systems that have not been over-engineered. Furthermore, scheduling problems are most naturally formulated as constraint satisfaction or mathematical optimization problems, but these are seldom implemented using state of the art numerical methods, nor do they explicitly take into account the fact that the scheduling problem itself takes time to solve. This paper makes the case that recent results in real-time model predictive control, where optimization problems are solved in order to control a process that evolves in time, are likely to form the basis of scheduling algorithms of the future. We therefore outline some of the research problems and opportunities that could arise by explicitly considering feedback and time when designing optimal scheduling algorithms for computing systems.
Reverberation time estimation on the ACE corpus using the SDD method
Venue: arXiv cs.SD 2015
Year: 2015

Reverberation Time (T60) is an important measure for characterizing the properties of a room. The author's T60 estimation algorithm was previously tested on simulated data where the noise is artificially added to the speech after convolution with a impulse responses simulated using the image method. We test the algorithm on speech convolved with real recorded impulse responses and noise from the same rooms from the Acoustic Characterization of Environments (ACE) corpus and achieve results comparable results to those using simulated data.
Implications of Burn-In Stress on NBTI Degradation
Venue: arXiv cs.OH 2015
Year: 2015

Burn-in is accepted as a way to evaluate ageing effects in an accelerated manner. It has been suggested that burn-in stress may have a significant effect on the Negative Bias Temperature Instability (NBTI) of subthreshold CMOS circuits. This paper analyses the effect of burn-in on NBTI in the context of a Digital to Analogue Converter (DAC) circuit. Analogue circuits require matched device pairs; NBTI may cause mismatches and hence circuit failure. The NBTI degradation observed in the simulation analysis indicates that under severe stress conditions, a significant voltage threshold mismatch in the DAC beyond the design specification of 2 mV limit can result. Experimental results confirm the sensitivity of the DAC circuit design to NBTI resulting from burn-in.
Thousands of Positive Reviews: Distributed Mentoring in Online Fan Communities
Venue: arXiv cs.HC 2015
Year: 2015

Young people worldwide are participating in ever-increasing numbers in online fan communities. Far from mere shallow repositories of pop culture, these sites are accumulating significant evidence that sophisticated informal learning is taking place online in novel and unexpected ways. In order to understand and analyze in more detail how learning might be occurring, we conducted an in-depth nine-month ethnographic investigation of online fanfiction communities, including participant observation and fanfiction author interviews. Our observations led to the development of a theory we term distributed mentoring, which we present in detail in this paper. Distributed mentoring exemplifies one instance of how networked technology affords new extensions of behaviors that were previously bounded by time and space. Distributed mentoring holds potential for application beyond the spontaneous mentoring observed in this investigation and may help students receive diverse, thoughtful feedback in formal learning environments as well.
SentiCap: Generating Image Descriptions with Sentiments
Venue: arXiv cs.CV 2015
Year: 2015

The recent progress on image recognition and language modeling is making automatic description of image content a reality. However, stylized, non-factual aspects of the written description are missing from the current systems. One such style is descriptions with emotions, which is commonplace in everyday communication, and influences decision-making and interpersonal relationships. We design a system to describe an image with emotions, and present a model that automatically generates captions with positive or negative sentiments. We propose a novel switching recurrent neural network with word-level regularization, which is able to produce emotional image captions using only 2000+ training sentences containing sentiments. We evaluate the captions with different automatic and crowd-sourcing metrics. Our model compares favourably in common quality metrics for image captioning. In 84.6% of cases the generated positive captions were judged as being at least as descriptive as the factual captions. Of these positive captions 88% were confirmed by the crowd-sourced workers as having the appropriate sentiment.
High Precision Fault Injections on the Instruction Cache of ARMv7-M Architectures
Venue: arXiv cs.CR 2015
Year: 2015

Hardware and software of secured embedded systems are prone to physical attacks. In particular, fault injection attacks revealed vulnerabilities on the data and the control flow allowing an attacker to break cryptographic or secured algorithms implementations. While many research studies concentrated on successful attacks on the data flow, only a few targets the instruction flow. In this paper, we focus on electromagnetic fault injection (EMFI) on the control flow, especially on the instruction cache. We target the very widespread (smartphones, tablets, settop-boxes, health-industry monitors and sensors, etc.) ARMv7-M architecture. We describe a practical EMFI platform and present a methodology providing high control level and high reproducibility over fault injections. Indeed, we observe that a precise fault model occurs in up to 96% of the cases. We then characterize and exhibit this practical fault model on the cache that is not yet considered in the literature. We comprehensively describe its effects and show how it can be used to reproduce well known fault attacks. Finally, we describe how it can benefits attackers to mount new powerful attacks or simplify existing ones.
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection
Venue: arXiv cs.CV 2015
Year: 2015

We present a novel unsupervised deep learning framework for anomalous event detection in complex video scenes. While most existing works merely use hand-crafted appearance and motion features, we propose Appearance and Motion DeepNet (AMDN) which utilizes deep neural networks to automatically learn feature representations. To exploit the complementary information of both appearance and motion patterns, we introduce a novel double fusion framework, combining both the benefits of traditional early fusion and late fusion strategies. Specifically, stacked denoising autoencoders are proposed to separately learn both appearance and motion features as well as a joint representation (early fusion). Based on the learned representations, multiple one-class SVM models are used to predict the anomaly scores of each input, which are then integrated with a late fusion strategy for final anomaly detection. We evaluate the proposed method on two publicly available video surveillance datasets, showing competitive performance with respect to state of the art approaches.
Where to look first? Behaviour control for fetch-and-carry missions of service robots
Venue: arXiv cs.RO 2015
Year: 2015

This paper presents the behaviour control of a service robot for intelligent object search in a domestic environment. A major challenge in service robotics is to enable fetch-and-carry missions that are satisfying for the user in terms of efficiency and human-oriented perception. The proposed behaviour controller provides an informed intelligent search based on a semantic segmentation framework for indoor scenes and integrates it with object recognition and grasping. Instead of manually annotating search positions in the environment, the framework automatically suggests likely locations to search for an object based on contextual information, e.g. next to tables and shelves. In a preliminary set of experiments we demonstrate that this behaviour control is as efficient as using manually annotated locations. Moreover, we argue that our approach will reduce the intensity of labour associated with programming fetch-and-carry tasks for service robots and that it will be perceived as more human-oriented.
Predicting Daily Activities From Egocentric Images Using Deep Learning
Venue: arXiv cs.CV 2015
Year: 2015

We present a method to analyze images taken from a passive egocentric wearable camera along with the contextual information, such as time and day of week, to learn and predict everyday activities of an individual. We collected a dataset of 40,103 egocentric images over a 6 month period with 19 activity classes and demonstrate the benefit of state-of-the-art deep learning techniques for learning and predicting daily activities. Classification is conducted using a Convolutional Neural Network (CNN) with a classification method we introduce called a late fusion ensemble. This late fusion ensemble incorporates relevant contextual information and increases our classification accuracy. Our technique achieves an overall accuracy of 83.07% in predicting a person's activity across the 19 activity classes. We also demonstrate some promising results from two additional users by fine-tuning the classifier with one day of training data.
Disjunctive Answer Set Solvers via Templates
Venue: arXiv cs.AI 2015
Year: 2015

Answer set programming is a declarative programming paradigm oriented towards difficult combinatorial search problems. A fundamental task in answer set programming is to compute stable models, i.e., solutions of logic programs. Answer set solvers are the programs that perform this task. The problem of deciding whether a disjunctive program has a stable model is $Σ^P_2$-complete. The high complexity of reasoning within disjunctive logic programming is responsible for few solvers capable of dealing with such programs, namely DLV, GnT, Cmodels, CLASP and WASP. In this paper we show that transition systems introduced by Nieuwenhuis, Oliveras, and Tinelli to model and analyze satisfiability solvers can be adapted for disjunctive answer set solvers. Transition systems give a unifying perspective and bring clarity in the description and comparison of solvers. They can be effectively used for analyzing, comparing and proving correctness of search algorithms as well as inspiring new ideas in the design of disjunctive answer set solvers. In this light, we introduce a general template, which accounts for major techniques implemented in disjunctive solvers. We then illustrate how this general template captures solvers DLV, GnT and Cmodels. We also show how this framework provides a convenient tool for designing new solving algorithms by means of combinations of techniques employed in different solvers.
On the Problem of Computing the Probability of Regular Sets of Trees
Venue: arXiv cs.FL 2015
Year: 2015

We consider the problem of computing the probability of regular languages of infinite trees with respect to the natural coin-flipping measure. We propose an algorithm which computes the probability of languages recognizable by \emph{game automata}. In particular this algorithm is applicable to all deterministic automata. We then use the algorithm to prove through examples three properties of measure: (1) there exist regular sets having irrational probability, (2) there exist comeager regular sets having probability $0$ and (3) the probability of \emph{game languages} $W_{i,k}$, from automata theory, is $0$ if $k$ is odd and is $1$ otherwise.
Helping Domain Experts Build Speech Translation Systems
Venue: arXiv cs.HC 2015
Year: 2015

We present a new platform, "Regulus Lite", which supports rapid development and web deployment of several types of phrasal speech translation systems using a minimal formalism. A distinguishing feature is that most development work can be performed directly by domain experts. We motivate the need for platforms of this type and discuss three specific cases: medical speech translation, speech-to-sign-language translation and voice questionnaires. We briefly describe initial experiences in developing practical systems.
A Box Decomposition Algorithm to Compute the Hypervolume Indicator
Venue: arXiv cs.DM 2015
Year: 2015

We propose a new approach to the computation of the hypervolume indicator, based on partitioning the dominated region into a set of axis-parallel hyperrectangles or boxes. We present a nonincremental algorithm and an incremental algorithm, which allows insertions of points, whose time complexities are $O(n^{\lfloor \frac{p-1}{2} \rfloor+1})$ and $O(n^{\lfloor \frac{p}{2} \rfloor+1})$, respectively. While the theoretical complexity of such a method is lower bounded by the complexity of the partition, which is, in the worst-case, larger than the best upper bound on the complexity of the hypervolume computation, we show that it is practically efficient. In particular, the nonincremental algorithm competes with the currently most practically efficient algorithms. Finally, we prove an enhanced upper bound of $O(n^{p-1})$ and a lower bound of $Ω(n^{\lfloor \frac{p}{2}\rfloor} \log n )$ for $p \geq 4$ on the worst-case complexity of the WFG algorithm.
Endorsement Deduction and Ranking in Social Networks
Venue: arXiv cs.SI 2015
Year: 2015

Some social networks, such as LinkedIn and ResearchGate, allow user endorsements for specific skills. In this way, for each skill we get a directed graph where the nodes correspond to users' profiles and the arcs represent endorsement relations. From the number and quality of the endorsements received, an authority score can be assigned to each profile. In this paper we propose an authority score computation method that takes into account the relations existing among different skills. Our method is based on enriching the information contained in the digraph of endorsements corresponding to a specific skill, and then applying a ranking method admitting weighted digraphs, such as PageRank. We describe the method, and test it on a synthetic network of 1493 nodes, fitted with endorsements.
Stochastic Optimization for Deep CCA via Nonlinear Orthogonal Iterations
Venue: arXiv cs.LG 2015
Year: 2015

Deep CCA is a recently proposed deep neural network extension to the traditional canonical correlation analysis (CCA), and has been successful for multi-view representation learning in several domains. However, stochastic optimization of the deep CCA objective is not straightforward, because it does not decouple over training examples. Previous optimizers for deep CCA are either batch-based algorithms or stochastic optimization using large minibatches, which can have high memory consumption. In this paper, we tackle the problem of stochastic optimization for deep CCA with small minibatches, based on an iterative solution to the CCA objective, and show that we can achieve as good performance as previous optimizers and thus alleviate the memory requirement.
Non-regular unary language and parallel communicating Watson-Crick automata systems
Venue: arXiv cs.FL 2015
Year: 2015

In 2006, Czeizler et.al. introduced parallel communicating Watson-Crick automata system. They showed that parallel communicating Watson-Crick automata system can accept the non-regular unary language L={a^(n^2 ),where n>1} using non-injective complementarity relation and three components. In this paper, we improve on Czeizler et.al. work by showing that parallel communicating Watson-Crick automata system can accept the same language L using just two components.
Leveraging Context to Support Automated Food Recognition in Restaurants
Venue: arXiv cs.CV 2015
Year: 2015

The pervasiveness of mobile cameras has resulted in a dramatic increase in food photos, which are pictures reflecting what people eat. In this paper, we study how taking pictures of what we eat in restaurants can be used for the purpose of automating food journaling. We propose to leverage the context of where the picture was taken, with additional information about the restaurant, available online, coupled with state-of-the-art computer vision techniques to recognize the food being consumed. To this end, we demonstrate image-based recognition of foods eaten in restaurants by training a classifier with images from restaurant's online menu databases. We evaluate the performance of our system in unconstrained, real-world settings with food images taken in 10 restaurants across 5 different types of food (American, Indian, Italian, Mexican and Thai).
Building Resource Adaptive Software Systems (BRASS): Objectives and System Evaluation
Venue: arXiv cs.SE 2015
Year: 2015

As modern software systems continue inexorably to increase in complexity and capability, users have become accustomed to periodic cycles of updating and upgrading to avoid obsolescence -- if at some cost in terms of frustration. In the case of the U.S. military, having access to well-functioning software systems and underlying content is critical to national security, but updates are no less problematic than among civilian users and often demand considerable time and expense. To address these challenges, DARPA has announced a new four-year research project to investigate the fundamental computational and algorithmic requirements necessary for software systems and data to remain robust and functional in excess of 100 years. The Building Resource Adaptive Software Systems, or BRASS, program seeks to realize foundational advances in the design and implementation of long-lived software systems that can dynamically adapt to changes in the resources they depend upon and environments in which they operate. MIT Lincoln Laboratory will provide the test framework and evaluation of proposed software tools in support of this revolutionary vision.
A Remote Procedure Call Approach for Extreme-scale Services
Venue: arXiv cs.DC 2015
Year: 2015

When working at exascale, the various constraints imposed by the extreme scale of the system bring new challenges for application users and software/middleware developers. In that context, and to provide best performance, resiliency and energy efficiency, software may be provided as a service oriented approach, adjusting resource utilization to best meet facility and user requirements. Remote procedure call (RPC) is a technique that originally followed a client/server model and allowed local calls to be transparently executed on remote resources. RPC consists of serializing the local function parameters into a memory buffer and sending that buffer to a remote target that in turn deserializes the parameters and executes the corresponding function call, returning the result back to the caller. Building reusable services requires the definition of a communication model to remotely access these services and for this purpose, RPC can serve as a foundation for accessing them. We introduce the necessary building blocks to enable this ecosystem to software and middleware developers with an RPC framework called Mercury.