OpenRaiser/Intern-Atlas huggingface dataset:
Benford's Law and First Letter of Word
Venue: arXiv cs.CL 2017
Year: 2017

A universal First-Letter Law (FLL) is derived and described. It predicts the percentages of first letters for words in novels. The FLL is akin to Benford's law (BL) of first digits, which predicts the percentages of first digits in a data collection of numbers. Both are universal in the sense that FLL only depends on the numbers of letters in the alphabet, whereas BL only depends on the number of digits in the base of the number system. The existence of these types of universal laws appears counter-intuitive. Nonetheless both describe data very well. Relations to some earlier works are given. FLL predicts that an English author on the average starts about 16 out of 100 words with the English letter `t'. This is corroborated by data, yet an author can freely write anything. Fuller implications and the applicability of FLL remain for the future.
Deep Learning for Distant Speech Recognition
Venue: arXiv cs.CL 2017
Year: 2017

Deep learning is an emerging technology that is considered one of the most promising directions for reaching higher levels of artificial intelligence. Among the other achievements, building computers that understand speech represents a crucial leap towards intelligent machines. Despite the great efforts of the past decades, however, a natural and robust human-machine speech interaction still appears to be out of reach, especially when users interact with a distant microphone in noisy and reverberant environments. The latter disturbances severely hamper the intelligibility of a speech signal, making Distant Speech Recognition (DSR) one of the major open challenges in the field. This thesis addresses the latter scenario and proposes some novel techniques, architectures, and algorithms to improve the robustness of distant-talking acoustic models. We first elaborate on methodologies for realistic data contamination, with a particular emphasis on DNN training with simulated data. We then investigate on approaches for better exploiting speech contexts, proposing some original methodologies for both feed-forward and recurrent neural networks. Lastly, inspired by the idea that cooperation across different DNNs could be the key for counteracting the harmful effects of noise and reverberation, we propose a novel deep learning paradigm called network of deep neural networks. The analysis of the original concepts were based on extensive experimental validations conducted on both real and simulated data, considering different corpora, microphone configurations, environments, noisy conditions, and ASR tasks.
Railway Track Specific Traffic Signal Selection Using Deep Learning
Venue: arXiv cs.CV 2017
Year: 2017

With the railway transportation Industry moving actively towards automation, accurate location and inventory of wayside track assets like traffic signals, crossings, switches, mileposts, etc. is of extreme importance. With the new Positive Train Control (PTC) regulation coming into effect, many railway safety rules will be tied directly to location of assets like mileposts and signals. Newer speed regulations will be enforced based on location of the Train with respect to a wayside asset. Hence it is essential for the railroads to have an accurate database of the types and locations of these assets. This paper talks about a real-world use-case of detecting railway signals from a camera mounted on a moving locomotive and tracking their locations. The camera is engineered to withstand the environment factors on a moving train and provide a consistent steady image at around 30 frames per second. Using advanced image analysis and deep learning techniques, signals are detected in these camera images and a database of their locations is created. Railway signals differ a lot from road signals in terms of shapes and rules for placement with respect to track. Due to space constraint and traffic densities in urban areas signals are not placed on the same side of the track and multiple lines can run in parallel. Hence there is need to associate signal detected with the track on which the train runs. We present a method to associate the signals to the specific track they belong to using a video feed from the front facing camera mounted on the lead locomotive. A pipeline of track detection, region of interest selection, signal detection has been implemented which gives an overall accuracy of 94.7% on a route covering 150km with 247 signals.
Deep Neural Networks as 0-1 Mixed Integer Linear Programs: A Feasibility Study
Venue: arXiv cs.LG 2017
Year: 2017

Deep Neural Networks (DNNs) are very popular these days, and are the subject of a very intense investigation. A DNN is made by layers of internal units (or neurons), each of which computes an affine combination of the output of the units in the previous layer, applies a nonlinear operator, and outputs the corresponding value (also known as activation). A commonly-used nonlinear operator is the so-called rectified linear unit (ReLU), whose output is just the maximum between its input value and zero. In this (and other similar cases like max pooling, where the max operation involves more than one input value), one can model the DNN as a 0-1 Mixed Integer Linear Program (0-1 MILP) where the continuous variables correspond to the output values of each unit, and a binary variable is associated with each ReLU to model its yes/no nature. In this paper we discuss the peculiarity of this kind of 0-1 MILP models, and describe an effective bound-tightening technique intended to ease its solution. We also present possible applications of the 0-1 MILP model arising in feature visualization and in the construction of adversarial examples. Preliminary computational results are reported, aimed at investigating (on small DNNs) the computational performance of a state-of-the-art MILP solver when applied to a known test case, namely, hand-written digit recognition.
Quantitative Control Approach for Wind Turbine Generators to Provide Fast Frequency Response with Guarantee of Rotor Security
Venue: arXiv cs.SY 2017
Year: 2017

Wind generation is expected to reach substantially higher levels of penetration in the near future. With the converter interface, the rotor inertia of doubly-fed induction generator (DFIG) based wind turbine generator is effectively decoupled from the system, causing a reduction in inertial response. This can be compensated by enabling the DFIG to provide fast frequency response. This paper proposes a quantitative control approach for DFIG to deliver fast frequency response in the inertial response time scale. A supplementary power surge function is added to the active power reference of DFIG. The exact amount of power surge that is available from DFIG-based wind turbine is quantified based on estimation of maximum extractable energy. Moreover, the operational constraints such as rotor limits and converter over-load limit are considered at the same time. Thus, the proposed approach not only provides adequate inertial response but also ensures the rotor speed is kept within a specified operating range. Rotor safety is guaranteed without the need for an additional rotor speed protection scheme.
Minimizing Embedding Distortion with Weighted Bigraph Matching in Reversible Data Hiding
Venue: arXiv cs.MM 2017
Year: 2017

For a required payload, the existing reversible data hiding (RDH) methods always expect to reduce the embedding distortion as much as possible, such as by utilizing a well-designed predictor, taking into account the carrier-content characteristics, and/or improving modification efficiency etc. However, due to the diversity of natural images, it is actually very hard to accurately model the statistical characteristics of natural images, which has limited the practical use of traditional RDH methods that rely heavily on the content characteristics. Based on this perspective, instead of directly exploiting the content characteristics, in this paper, we model the embedding operation on a weighted bipartite graph to reduce the introduced distortion due to data embedding, which is proved to be equivalent to a graph problem called as \emph{minimum weight maximum matching (MWMM)}. By solving the MWMM problem, we can find the optimal histogram shifting strategy under the given condition. Since the proposed method is essentially a general embedding model for the RDH, it can be utilized for designing an RDH scheme. In our experiments, we incorporate the proposed method into some related works, and, our experimental results have shown that the proposed method can significantly improve the payload-distortion performance, indicating that the proposed method could be desirable and promising for practical use and the design of RDH schemes.
Meeting Energy-Efficient and QoS Requirements of 5G Using D2D Communications
Venue: arXiv cs.IT 2017
Year: 2017

Device-to-device (D2D) communication is a promising technology for the future wireless systems. It allows direct communication between devices, which provides improvements in terms of delay, throughput and energy consumption. Therefore, it can contribute to achieving the ambitious requirements of future 5G wireless system. In this sense, energy efficiency has become a key requirement in the design of 5G technology. In this paper we analyze the energy-efficiency improvement provided by D2D communications in an overlaying scenario, in the context of a realistic wireless network system. This analysis takes into account the two D2D phases, discovery and communication. A centralized architecture is considered to manage discovery, which is a key phase on D2D communications. Numerical evaluation shows improvement in terms of energy-efficiency, reachable throughput and outage probability.
Graphic Narrative with Interactive Stylization Design
Venue: arXiv cs.GR 2017
Year: 2017

We present a system to convert any set of images (e.g., a video clip or a photo album) into a storyboard. We aim to create multiple pleasing graphic representations of the content at interactive rates, so the user can explore and find the storyboard (images, layout, and stylization) that best suits their needs and taste. The main challenges of this work are: selecting the content images, placing them into panels, and applying a stylization. For the latter, we propose an interactive design tool to create new stylizations using a wide range of filter blocks. This approach unleashes the creativity by allowing the user to tune, modify, and intuitively design new sequences of filters. In parallel to this manual design, we propose a novel procedural approach that automatically assembles sequences of filters for innovative results. We aim to keep the algorithm complexity as low as possible such that it can run interactively on a mobile device. Our results include examples of styles designed using both our interactive and procedural tools, as well as their final composition into interesting and appealing storyboards.
word representation or word embedding in Persian text
Venue: arXiv cs.CL 2017
Year: 2017

Text processing is one of the sub-branches of natural language processing. Recently, the use of machine learning and neural networks methods has been given greater consideration. For this reason, the representation of words has become very important. This article is about word representation or converting words into vectors in Persian text. In this research GloVe, CBOW and skip-gram methods are updated to produce embedded vectors for Persian words. In order to train a neural networks, Bijankhan corpus, Hamshahri corpus and UPEC corpus have been compound and used. Finally, we have 342,362 words that obtained vectors in all three models for this words. These vectors have many usage for Persian natural language processing.
Synthesizing Novel Pairs of Image and Text
Venue: arXiv cs.CV 2017
Year: 2017

Generating novel pairs of image and text is a problem that combines computer vision and natural language processing. In this paper, we present strategies for generating novel image and caption pairs based on existing captioning datasets. The model takes advantage of recent advances in generative adversarial networks and sequence-to-sequence modeling. We make generalizations to generate paired samples from multiple domains. Furthermore, we study cycles -- generating from image to text then back to image and vise versa, as well as its connection with autoencoders.
User modeling for point-of-interest recommendations in location-based social networks: the state-of-the-art
Venue: arXiv cs.SI 2017
Year: 2017

The rapid growth of location-based services(LBSs)has greatly enriched people's urban lives and attracted millions of users in recent years. Location-based social networks(LBSNs)allow users to check-in at a physical location and share daily tips on points-of-interest (POIs) with their friends anytime and anywhere. Such check-in behavior can make daily real-life experiences spread quickly through the Internet. Moreover, such check-in data in LBSNs can be fully exploited to understand the basic laws of human daily movement and mobility. This paper focuses on reviewing the taxonomy of user modeling for POI recommendations through the data analysis of LBSNs. First, we briefly introduce the structure and data characteristics of LBSNs,then we present a formalization of user modeling for POI recommendations in LBSNs. Depending on which type of LBSNs data was fully utilized in user modeling approaches for POI recommendations, we divide user modeling algorithms into four categories: pure check-in data-based user modeling, geographical information-based user modeling, spatio-temporal information-based user modeling, and geo-social information-based user modeling. Finally,summarizing the existing works, we point out the future challenges and new directions in five possible aspects
An Exploratory Survey of Hybrid Testing Techniques Involving Symbolic Execution and Fuzzing
Venue: arXiv cs.SE 2017
Year: 2017

Recent efforts in practical symbolic execution have successfully mitigated the path-explosion problem to some extent with search-based heuristics and compositional approaches. Similarly, due to an increase in the performance of cheap multi-core commodity computers, fuzzing as a viable method of random mutation-based testing has also seen promise. However, the possibility of combining symbolic execution and fuzzing, thereby providing an opportunity to mitigate drawbacks in each other, has not been sufficiently explored. Fuzzing could, for example, expedite path-exploration in symbolic execution, and symbolic execution could make seed input generation in fuzzing more efficient. There have only been, in our view, very few hybrid solution proposals with symbolic execution and fuzzing at their centre. By analyzing 77 relevant and systematically selected papers, we (1) present an overview of hybrid solution proposals of symbolic execution and fuzzing, (2) perform a gap analysis in research of hybrid techniques to improve both, plain symbolic execution and fuzzing, (3) propose new ideas for hybrid test-case generation techniques.
Comparison of fingerprint authentication algorithms for small imaging sensors
Venue: arXiv cs.CV 2017
Year: 2017

The demand for biometric systems has been increasing with the growth of the smartphone market. Biometric devices allow the user to authenticate easily while securing its private data without the need to remember any access code. Amongst them, fingerprint sensors are the most widespread because they seem to provide a good balance between reliability, cost and ease of use. According to smartphone manufacturers, the security level is guaranteed to be high. However, the size of those sensors, which is only a few millimeters squared, prevents the use of minutiae algorithms. To the best of our knowledge, very few studies shed light onto this problem, though many pattern recognition algorithms already exist as well as commercial solutions which are supposedly robust. In this article we try to provide insights on how to tackle this problem by analyzing the performance of three algorithms dedicated to pattern recognition.
On the Evaluation of Video Keyframe Summaries using User Ground Truth
Venue: arXiv cs.CV 2017
Year: 2017

Given the great interest in creating keyframe summaries from video, it is surprising how little has been done to formalise their evaluation and comparison. User studies are often carried out to demonstrate that a proposed method generates a more appealing summary than one or two rival methods. But larger comparison studies cannot feasibly use such user surveys. Here we propose a discrimination capacity measure as a formal way to quantify the improvement over the uniform baseline, assuming that one or more ground truth summaries are available. Using the VSUMM video collection, we examine 10 video feature types, including CNN and SURF, and 6 methods for matching frames from two summaries. Our results indicate that a simple frame representation through hue histograms suffices for the purposes of comparing keyframe summaries. We subsequently propose a formal protocol for comparing summaries when ground truth is available.
Bipartite Graph Matching for Keyframe Summary Evaluation
Venue: arXiv cs.CV 2017
Year: 2017

A keyframe summary, or "static storyboard", is a collection of frames from a video designed to summarise its semantic content. Many algorithms have been proposed to extract such summaries automatically. How best to evaluate these outputs is an important but little-discussed question. We review the current methods for matching frames between two summaries in the formalism of graph theory. Our analysis revealed different behaviours of these methods, which we illustrate with a number of case studies. Based on the results, we recommend a greedy matching algorithm due to Kannappan et al.
A Production Oriented Approach for Vandalism Detection in Wikidata - The Buffaloberry Vandalism Detector at WSDM Cup 2017
Venue: arXiv cs.IR 2017
Year: 2017

Wikidata is a free and open knowledge base from the Wikimedia Foundation, that not only acts as a central storage of structured data for other projects of the organization, but also for a growing array of information systems, including search engines. Like Wikipedia, Wikidata's content can be created and edited by anyone; which is the main source of its strength, but also allows for malicious users to vandalize it, risking the spreading of misinformation through all the systems that rely on it as a source of structured facts. Our task at the WSDM Cup 2017 was to come up with a fast and reliable prediction system that narrows down suspicious edits for human revision. Elaborating on previous works by Heindorf et al. we were able to outperform all other contestants, while incorporating new interesting features, unifying the programming language used to only Python and refactoring the feature extractor into a simpler and more compact code base.
Linear Pentapods with a Simple Singularity Variety
Venue: arXiv cs.RO 2017
Year: 2017

There exists a bijection between the configuration space of a linear pentapod and all points $(u,v,w,p_x,p_y,p_z)\in\mathbb{R}^{6}$ located on the singular quadric $Γ: u^2+v^2+w^2=1$, where $(u,v,w)$ determines the orientation of the linear platform and $(p_x,p_y,p_z)$ its position. Then the set of all singular robot configurations is obtained by intersecting $Γ$ with a cubic hypersurface $Σ$ in $\mathbb{R}^{6}$, which is only quadratic in the orientation variables and position variables, respectively. This article investigates the restrictions to be imposed on the design of this mechanism in order to obtain a reduction in degree. In detail we study the cases where $Σ$ is (1) linear in position variables, (2) linear in orientation variables and (3) quadratic in total. The resulting designs of linear pentapods have the advantage of considerably simplified computation of singularity-free spheres in the configuration space. Finally we propose three kinematically redundant designs of linear pentapods with a simple singularity surface.
Bilinear residual Neural Network for the identification and forecasting of dynamical systems
Venue: arXiv cs.LG 2017
Year: 2017

Due to the increasing availability of large-scale observation and simulation datasets, data-driven representations arise as efficient and relevant computation representations of dynamical systems for a wide range of applications, where model-driven models based on ordinary differential equation remain the state-of-the-art approaches. In this work, we investigate neural networks (NN) as physically-sound data-driven representations of such systems. Reinterpreting Runge-Kutta methods as graphical models, we consider a residual NN architecture and introduce bilinear layers to embed non-linearities which are intrinsic features of dynamical systems. From numerical experiments for classic dynamical systems, we demonstrate the relevance of the proposed NN-based architecture both in terms of forecasting performance and model identification.
Sonification of Network Traffic Flow for Monitoring and Situational Awareness
Venue: arXiv cs.HC 2017
Year: 2017

Maintaining situational awareness of what is happening within a network is challenging, not least because the behaviour happens within computers and communications networks, but also because data traffic speeds and volumes are beyond human ability to process. Visualisation is widely used to present information about the dynamics of network traffic dynamics. Although it provides operators with an overall view and specific information about particular traffic or attacks on the network, it often fails to represent the events in an understandable way. Visualisations require visual attention and so are not well suited to continuous monitoring scenarios in which network administrators must carry out other tasks. Situational awareness is critical and essential for decision-making in the domain of computer network monitoring where it is vital to be able to identify and recognize network environment behaviours.Here we present SoNSTAR (Sonification of Networks for SiTuational AwaReness), a real-time sonification system to be used in the monitoring of computer networks to support the situational awareness of network administrators. SoNSTAR provides an auditory representation of all the TCP/IP protocol traffic within a network based on the different traffic flows between between network hosts. SoNSTAR raises situational awareness levels for computer network defence by allowing operators to achieve better understanding and performance while imposing less workload compared to visual techniques. SoNSTAR identifies the features of network traffic flows by inspecting the status flags of TCP/IP packet headers and mapping traffic events to recorded sounds to generate a soundscape representing the real-time status of the network traffic environment. Listening to the soundscape allows the administrator to recognise anomalous behaviour quickly and without having to continuously watch a computer screen.