OpenRaiser/Intern-Atlas huggingface dataset:
ManyDSL: A Host for Many Languages
Venue: arXiv cs.PL 2016
Year: 2016

Domain-specific languages are becoming increasingly important. Almost every application touches multiple domains. But how to define, use, and combine multiple DSLs within the same application? The most common approach is to split the project along the domain boundaries into multiple pieces and files. Each file is then compiled separately. Alternatively, multiple languages can be embedded in a flexible host language: within the same syntax a new domain semantic is provided. In this paper we follow a less explored route of metamorphic languages. These languages are able to modify their own syntax and semantics on the fly, thus becoming a more flexible host for DSLs. Our language allows for dynamic creation of grammars and switching languages where needed. We achieve this through a novel concept of Syntax-Directed Execution. A language grammar includes semantic actions that are pieces of functional code executed immediately during parsing. By avoiding additional intermediate representation, connecting actions from different languages and domains is greatly simplified. Still, actions can generate highly specialized code though lambda encapsulation and Dynamic Staging.
Convolutional Neural Networks for Passive Monitoring of a Shallow Water Environment using a Single Sensor
Venue: arXiv cs.SD 2016
Year: 2016

A cost effective approach to remote monitoring of protected areas such as marine reserves and restricted naval waters is to use passive sonar to detect, classify, localize, and track marine vessel activity (including small boats and autonomous underwater vehicles). Cepstral analysis of underwater acoustic data enables the time delay between the direct path arrival and the first multipath arrival to be measured, which in turn enables estimation of the instantaneous range of the source (a small boat). However, this conventional method is limited to ranges where the Lloyd's mirror effect (interference pattern formed between the direct and first multipath arrivals) is discernible. This paper proposes the use of convolutional neural networks (CNNs) for the joint detection and ranging of broadband acoustic noise sources such as marine vessels in conjunction with a data augmentation approach for improving network performance in varied signal-to-noise ratio (SNR) situations. Performance is compared with a conventional passive sonar ranging method for monitoring marine vessel activity using real data from a single hydrophone mounted above the sea floor. It is shown that CNNs operating on cepstrum data are able to detect the presence and estimate the range of transiting vessels at greater distances than the conventional method.
Text-guided Attention Model for Image Captioning
Venue: arXiv cs.CV 2016
Year: 2016

Visual attention plays an important role to understand images and demonstrates its effectiveness in generating natural language descriptions of images. On the other hand, recent studies show that language associated with an image can steer visual attention in the scene during our cognitive process. Inspired by this, we introduce a text-guided attention model for image captioning, which learns to drive visual attention using associated captions. For this model, we propose an exemplar-based learning approach that retrieves from training data associated captions with each image, and use them to learn attention on visual features. Our attention model enables to describe a detailed state of scenes by distinguishing small or confusable objects effectively. We validate our model on MS-COCO Captioning benchmark and achieve the state-of-the-art performance in standard metrics.
Generating Code Summaries Using the Power of the Crowd
Venue: arXiv cs.SE 2016
Year: 2016

One of the first steps to perform most of the software maintenance activities, such as updating features or fixing bugs, is to have a relatively good understanding of the program's source code which is often written by other developers. A code summary is a description about a program's entities (e.g., its methods) which helps developers have a better comprehension of the code in a shorter period of time. However, generating code summaries can be a challenging task. To mitigate this problem, in this article, we introduce CrowdSummarizer, a code summarization platform that benefits from the concepts of crowdsourcing, gamification, and natural language processing to automatically generate a high level summary for the methods of a Java program. We have implemented CrowdSummarizer as an Eclipse plugin together with a web-based code summarization game that can be played by the crowd. The results of two empirical studies that evaluate the applicability of the approach and the quality of generated summaries indicate that CrowdSummarizer is effective in generating quality results.
Intersection Graphs of Rays and Grounded Segments
Venue: arXiv cs.DM 2016
Year: 2016

We consider several classes of intersection graphs of line segments in the plane and prove new equality and separation results between those classes. In particular, we show that: (1) intersection graphs of grounded segments and intersection graphs of downward rays form the same graph class, (2) not every intersection graph of rays is an intersection graph of downward rays, and (3) not every intersection graph of rays is an outer segment graph. The first result answers an open problem posed by Cabello and Jejčič. The third result confirms a conjecture by Cabello. We thereby completely elucidate the remaining open questions on the containment relations between these classes of segment graphs. We further characterize the complexity of the recognition problems for the classes of outer segment, grounded segment, and ray intersection graphs. We prove that these recognition problems are complete for the existential theory of the reals. This holds even if a 1-string realization is given as additional input.
FastText.zip: Compressing text classification models
Venue: arXiv cs.CL 2016
Year: 2016

We consider the problem of producing compact architectures for text classification, such that the full model fits in a limited amount of memory. After considering different solutions inspired by the hashing literature, we propose a method built upon product quantization to store word embeddings. While the original technique leads to a loss in accuracy, we adapt this method to circumvent quantization artefacts. Our experiments carried out on several benchmarks show that our approach typically requires two orders of magnitude less memory than fastText while being only slightly inferior with respect to accuracy. As a result, it outperforms the state of the art by a good margin in terms of the compromise between memory usage and accuracy.
A Note on Hamming distance of constacyclic codes of length $p^s$ over $\mathbb F_{p^m} + u\mathbb F_{p^m}$
Venue: arXiv cs.IT 2016
Year: 2016

For any prime $p$, $λ$-constacyclic codes of length $p^s$ over ${\cal R}=\mathbb{F}_{p^m} + u\mathbb{F}_{p^m}$ are precisely the ideals of the local ring ${\cal R}_λ=\frac{{\cal R}[x]}{\left\langle x^{p^s}-λ\right\rangle}$, where $u^2=0$. In this paper, we first investigate the Hamming distances of cyclic codes of length $p^s$ over ${\cal R}$. The minimum Hamming distances of all cyclic codes of length $p^s$ over ${\cal R}$ are determined. Moreover, an isometry between cyclic and $α$-constacyclic codes of length $p^s$ over ${\cal R}$ is established, where $α$ is a nonzero element of $\mathbb{F}_{p^m}$, which carries over the results regarding cyclic codes corresponding to $α$-constacyclic codes of length $p^s$ over ${\cal R}$.
An Optimization Framework For Online Ride-sharing Markets
Venue: arXiv cs.SI 2016
Year: 2016

Taxi services and product delivery services are instrumental for our modern society. Thanks to the emergence of sharing economy, ride-sharing services such as Uber, Didi, Lyft and Google's Waze Rider are becoming more ubiquitous and grow into an integral part of our everyday lives. However, the efficiency of these services are severely limited by the sub-optimal and imbalanced matching between the supply and demand. We need a generalized framework and corresponding efficient algorithms to address the efficient matching, and hence optimize the performance of these markets. Existing studies for taxi and delivery services are only applicable in scenarios of the one-sided market. In contrast, this work investigates a highly generalized model for the taxi and delivery services in the market economy (abbreviated as"taxi and delivery market") that can be widely used in two-sided markets. Further, we present efficient online and offline algorithms for different applications. We verify our algorithm with theoretical analysis and trace-driven simulations under realistic settings.
A Software Reliability Model Based on a Geometric Sequence of Failure Rates
Venue: arXiv cs.SE 2016
Year: 2016

Software reliability models are an important tool in quality management and release planning. There is a large number of different models that often exhibit strengths in different areas. This paper proposes a model that is based on a geometric sequence (or progression) of the failure rates of faults. This property of the failure process was observed in practice at Siemens among others and led to the development of the proposed model. It is described in detail and evaluated using standard criteria. Most importantly, the model performs constantly well over several projects in terms of its predictive validity.
Deep Supervised Hashing with Triplet Labels
Venue: arXiv cs.CV 2016
Year: 2016

Hashing is one of the most popular and powerful approximate nearest neighbor search techniques for large-scale image retrieval. Most traditional hashing methods first represent images as off-the-shelf visual features and then produce hashing codes in a separate stage. However, off-the-shelf visual features may not be optimally compatible with the hash code learning procedure, which may result in sub-optimal hash codes. Recently, deep hashing methods have been proposed to simultaneously learn image features and hash codes using deep neural networks and have shown superior performance over traditional hashing methods. Most deep hashing methods are given supervised information in the form of pairwise labels or triplet labels. The current state-of-the-art deep hashing method DPSH~\cite{li2015feature}, which is based on pairwise labels, performs image feature learning and hash code learning simultaneously by maximizing the likelihood of pairwise similarities. Inspired by DPSH~\cite{li2015feature}, we propose a triplet label based deep hashing method which aims to maximize the likelihood of the given triplet labels. Experimental results show that our method outperforms all the baselines on CIFAR-10 and NUS-WIDE datasets, including the state-of-the-art method DPSH~\cite{li2015feature} and all the previous triplet label based deep hashing methods.
An In Situ Measurement System for Characterizing Orbital Debris
Venue: arXiv cs.SY 2016
Year: 2016

This paper presents the development of an in situ measurement system known as the Debris Resistive Acoustic Grid Orbital Navy/NASA Sensor (DRAGONS). The DRAGONS system is designed to detect impacts caused by particles ranging from 50 micrometers to 1 mm at both low-earth and geostationary orbits. DRAGONS utilizes a combination of low-cost sensor technologies to facilitate accurate measurements and approximations of the size, velocity, and angle of impacting micrometeoroids and orbital debris (MMOD). Two thin layers of kapton sheets with resistive traces are used to detect the changes in resistance that are directly proportional to the impacting force caused by the fast traveling particles. Four polyvinylidene fluoride-based sensors are positioned in the back of each kapton sheet to measure acoustic strain caused by an impact. The electronic hardware module that controls all operations employs a low-power, modular, and compact design that enables it to be installed as a low-resource load on a host satellite. Laboratory results demonstrate that in addition to having the ability to detect an impact event, the DRAGONS system can determine impact location, speed, and angle of impact with a mean error of 1.4 cm, 0.2 km/s, and 5°. The DRAGONS system could be deployed as an add-on subsystem of a payload to enable a real-time, in-depth study of the properties of MMOD.
A Fast Keypoint Based Hybrid Method for Copy Move Forgery Detection
Venue: arXiv cs.CV 2016
Year: 2016

Copy move forgery detection in digital images has become a very popular research topic in the area of image forensics. Due to the availability of sophisticated image editing tools and ever increasing hardware capabilities, it has become an easy task to manipulate the digital images. Passive forgery detection techniques are more relevant as they can be applied without the prior information about the image in question. Block based techniques are used to detect copy move forgery, but have limitations of large time complexity and sensitivity against affine operations like rotation and scaling. Keypoint based approaches are used to detect forgery in large images where the possibility of significant post processing operations like rotation and scaling is more. A hybrid approach is proposed using different methods for keypoint detection and description. Speeded Up Robust Features (SURF) are used to detect the keypoints in the image and Binary Robust Invariant Scalable Keypoints (BRISK) features are used to describe features at these keypoints. The proposed method has performed better than the existing forgery detection method using SURF significantly in terms of detection speed and is invariant to post processing operations like rotation and scaling. The proposed method is also invariant to other commonly applied post processing operations like adding Gaussian noise and JPEG compression
Performance Improvements of Probabilistic Transcript-adapted ASR with Recurrent Neural Network and Language-specific Constraints
Venue: arXiv cs.CL 2016
Year: 2016

Mismatched transcriptions have been proposed as a mean to acquire probabilistic transcriptions from non-native speakers of a language.Prior work has demonstrated the value of these transcriptions by successfully adapting cross-lingual ASR systems for different tar-get languages. In this work, we describe two techniques to refine these probabilistic transcriptions: a noisy-channel model of non-native phone misperception is trained using a recurrent neural net-work, and decoded using minimally-resourced language-dependent pronunciation constraints. Both innovations improve quality of the transcript, and both innovations reduce phone error rate of a trainedASR, by 7% and 9% respectively
Generative Adversarial Parallelization
Venue: arXiv cs.LG 2016
Year: 2016

Generative Adversarial Networks have become one of the most studied frameworks for unsupervised learning due to their intuitive formulation. They have also been shown to be capable of generating convincing examples in limited domains, such as low-resolution images. However, they still prove difficult to train in practice and tend to ignore modes of the data generating distribution. Quantitatively capturing effects such as mode coverage and more generally the quality of the generative model still remain elusive. We propose Generative Adversarial Parallelization, a framework in which many GANs or their variants are trained simultaneously, exchanging their discriminators. This eliminates the tight coupling between a generator and discriminator, leading to improved convergence and improved coverage of modes. We also propose an improved variant of the recently proposed Generative Adversarial Metric and show how it can score individual GANs or their collections under the GAP model.
Construction of Full-Diversity LDPC Lattices for Block-Fading Channels
Venue: arXiv cs.IT 2016
Year: 2016

LDPC lattices were the first family of lattices which have an efficient decoding algorithm in high dimensions over an AWGN channel. Considering Construction D' of lattices with one binary LDPC code as underlying code gives the well known Construction A LDPC lattices or 1-level LDPC lattices. Block-fading channel (BF) is a useful model for various wireless communication channels in both indoor and outdoor environments. Frequency-hopping schemes and orthogonal frequency division multiplexing (OFDM) can conveniently be modelled as block-fading channels. Applying lattices in this type of channel entails dividing a lattice point into multiple blocks such that fading is constant within a block but changes, independently, across blocks. The design of lattices for BF channels offers a challenging problem, which differs greatly from its counterparts like AWGN channels. Recently, the original binary Construction A for lattices, due to Forney, have been generalized to a lattice construction from totally real and complex multiplication fields. This generalized Construction A of lattices provides signal space diversity intrinsically, which is the main requirement for the signal sets designed for fading channels. In this paper we construct full diversity LDPC lattices for block-fading channels using Construction A over totally real number fields. We propose a new iterative decoding method for these family of lattices which has complexity that grows linearly in the dimension of the lattice. In order to implement our decoding algorithm, we propose the definition of a parity check matrix and Tanner graph for full diversity Construction A lattices. We also prove that the constructed LDPC lattices together with the proposed decoding method admit diversity order n-1 over an n-block-fading channel.
Jointly Broadcasting Data and Power with Quality of Service Guarantees
Venue: arXiv cs.IT 2016
Year: 2016

In this work, we consider a scenario wherein an energy harvesting wireless radio equipment sends information to multiple receivers alongside powering them. In addition to harvesting the incoming radio frequency (RF) energy, the receivers also harvest energy from {its environment (e.g., solar energy)}. This communication framework is captured by a fading Gaussian Broadcast Channel (GBC) with energy harvesting transmitter and receivers. In order to ensure {some quality of service (QoS)} in data reception among the receivers, we impose a \textit{minimum-rate} requirement on data transmission. For the setting in place, we characterize the fundamental limits in jointly transmitting information and power subject to a QoS guarantee, for three cardinal receiver structures namely, \textit{ideal}, \textit{time-switching} and \textit{power-splitting}. We show that a time-switching receiver can {switch} between {information reception mode} and {energy harvesting mode}, \textit{without} the transmitter's knowledge of the same and \textit{without} any extra \textit{rate loss}. We also prove that, for the same amount of power transferred, on average, a power-splitting receiver supports higher data rates compared to a time-switching receiver.
DEMoS Manifesto
Venue: arXiv cs.OH 2016
Year: 2016

This is a manifesto for DEMoS, which is a Distributed Embedded Modular System, but also a manifesto addressing the need for more inter-/cross-disciplinary mastery of working knowledge related to installing this class of systems in the real world. There is somehow room for yet another class of systems - complementary to existing embedded systems - complementing distributed operating systems - which takes on an interdisciplinary cyber-physical-materiality approach, a dedicated holistic perspective that recognizes the true value of interdisciplinary mastery vs. the implicit and overlooked expense of narrow intra-disciplinary focus dominating much of systems development (e.g. EE, CE, CS, SE, and IS). Interdisciplinary mastery yields its accumulated value across the development, deployment, use, re-use, and decommission phases for this class of systems: DEMoS is a system architected to be locally distributed, embedded, and modular as outlined herein and with the additional goals of human interdisciplinary mastery in this context: A potential set of goals for developing and applying DEMoS can be found in UN Resolution 70/1.
TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning
Venue: arXiv cs.DC 2016
Year: 2016

TF.Learn is a high-level Python module for distributed machine learning inside TensorFlow. It provides an easy-to-use Scikit-learn style interface to simplify the process of creating, configuring, training, evaluating, and experimenting a machine learning model. TF.Learn integrates a wide range of state-of-art machine learning algorithms built on top of TensorFlow's low level APIs for small to large-scale supervised and unsupervised problems. This module focuses on bringing machine learning to non-specialists using a general-purpose high-level language as well as researchers who want to implement, benchmark, and compare their new methods in a structured environment. Emphasis is put on ease of use, performance, documentation, and API consistency.
Application of Advanced Record Linkage Techniques for Complex Population Reconstruction
Venue: arXiv cs.DB 2016
Year: 2016

Record linkage is the process of identifying records that refer to the same entities from several databases. This process is challenging because commonly no unique entity identifiers are available. Linkage therefore has to rely on partially identifying attributes, such as names and addresses of people. Recent years have seen the development of novel techniques for linking data from diverse application areas, where a major focus has been on linking complex data that contain records about different types of entities. Advanced approaches that exploit both the similarities between record attributes as well as the relationships between entities to identify clusters of matching records have been developed. In this application paper we study the novel problem where rather than different types of entities we have databases where the same entity can have different roles, and where these roles change over time. We specifically develop novel techniques for linking historical birth, death, marriage and census records with the aim to reconstruct the population covered by these records over a period of several decades. Our experimental evaluation on real Scottish data shows that even with advanced linkage techniques that consider group, relationship, and temporal aspects it is challenging to achieve high quality linkage from such complex data.
Incorporating Human Domain Knowledge into Large Scale Cost Function Learning
Venue: arXiv cs.RO 2016
Year: 2016

Recent advances have shown the capability of Fully Convolutional Neural Networks (FCN) to model cost functions for motion planning in the context of learning driving preferences purely based on demonstration data from human drivers. While pure learning from demonstrations in the framework of Inverse Reinforcement Learning (IRL) is a promising approach, we can benefit from well informed human priors and incorporate them into the learning process. Our work achieves this by pretraining a model to regress to a manual cost function and refining it based on Maximum Entropy Deep Inverse Reinforcement Learning. When injecting prior knowledge as pretraining for the network, we achieve higher robustness, more visually distinct obstacle boundaries, and the ability to capture instances of obstacles that elude models that purely learn from demonstration data. Furthermore, by exploiting these human priors, the resulting model can more accurately handle corner cases that are scarcely seen in the demonstration data, such as stairs, slopes, and underpasses.