OpenRaiser/Intern-Atlas huggingface dataset:
First-order justification logic with constant domain semantics
Venue: arXiv cs.LO 2018
Year: 2018

Justification logic is a term used to identify a relatively new family of modal-like logics. There is an established literature about propositional justification logic, but incursions on the first-order case are scarce. In this paper we present a constant domain semantics for the first-order logic of proofs with the Barcan Formula (FOLPb); then we prove Soundness and Completeness Theorems. A monotonic semantics for a version of this logic without the Barcan Formula is already in the literature, but constant domains requires substantial new machinery, which may prove useful in other contexts as well. Although we work mainly with one system, we also indicate how to generalize these results for the quantified version of JT45, the justification counterpart of the modal logic S5. We believe our methods are more generally applicable, but initially examining specific cases should make the work easier to follow.
Precoding for Dual Polarization Soliton Transmission
Venue: arXiv cs.IT 2018
Year: 2018

We consider dual-polarized optical multi-soliton pulse transmission. Modulating the nonlinear spectrum, it becomes highly correlated in noisy fiber links. We propose a simple precoding to significantly reduce the correlation, allowing disjoint detection of spectral amplitudes.
Limiting the Spread of Fake News on Social Media Platforms by Evaluating Users' Trustworthiness
Venue: arXiv cs.SI 2018
Year: 2018

Today's social media platforms enable to spread both authentic and fake news very quickly. Some approaches have been proposed to automatically detect such "fake" news based on their content, but it is difficult to agree on universal criteria of authenticity (which can be bypassed by adversaries once known). Besides, it is obviously impossible to have each news item checked by a human. In this paper, we a mechanism to limit the spread of fake news which is not based on content. It can be implemented as a plugin on a social media platform. The principle is as follows: a team of fact-checkers reviews a small number of news items (the most popular ones), which enables to have an estimation of each user's inclination to share fake news items. Then, using a Bayesian approach, we estimate the trustworthiness of future news items, and treat accordingly those of them that pass a certain "untrustworthiness" threshold. We then evaluate the effectiveness and overhead of this technique on a large Twitter graph. We show that having a few thousands users exposed to one given news item enables to reach a very precise estimation of its reliability. We thus identify more than 99% of fake news items with no false positives. The performance impact is very small: the induced overhead on the 90th percentile latency is less than 3%, and less than 8% on the throughput of user operations.
Correcting Length Bias in Neural Machine Translation
Venue: arXiv cs.CL 2018
Year: 2018

We study two problems in neural machine translation (NMT). First, in beam search, whereas a wider beam should in principle help translation, it often hurts NMT. Second, NMT has a tendency to produce translations that are too short. Here, we argue that these problems are closely related and both rooted in label bias. We show that correcting the brevity problem almost eliminates the beam problem; we compare some commonly-used methods for doing this, finding that a simple per-word reward works well; and we introduce a simple and quick way to tune this reward using the perceptron algorithm.
Consistent Sampling with Replacement
Venue: arXiv cs.DS 2018
Year: 2018

We describe a very simple method for `consistent sampling' that allows for sampling with replacement. The method extends previous approaches to consistent sampling, which assign a pseudorandom real number to each element, and sample those with the smallest associated numbers. When sampling with replacement, our extension gives the item sampled a new, larger, associated pseudorandom number, and returns it to the pool of items being sampled.
Contribution of Glottal Waveform in Speech Emotion: A Comparative Pairwise Investigation
Venue: arXiv cs.SD 2018
Year: 2018

In this work, we investigated the contribution of the glottal waveform in human vocal emotion expressing. Seven emotional states including moderate and intense versions of three emotional families as anger, joy, and sadness, plus a neutral state are considered, with speech samples in Mandarin Chinese. The glottal waveform extracted from speech samples of different emotion states are first analyzed in both time domain and frequency domain to discover their differences. Comparative emotion classifications are then taken out based on features extracted from original whole speech signal and only glottal wave signal. In experiments of generation of a performance-driven hierarchical classifier architecture, and pairwise classification on individual emotional states, the low difference between accuracies obtained from speech signal and glottal signal proved that a majority of emotional cues in speech could be conveyed through glottal waveform. The best distinguishable emotional pair by glottal waveform is intense anger against moderate sadness, with the accuracy of 92.45%. It is also concluded in this work that glottal waveform represent better valence cues than arousal cues of emotion.
VirtualIdentity: Privacy-Preserving User Profiling
Venue: arXiv cs.CR 2018
Year: 2018

User profiling from user generated content (UGC) is a common practice that supports the business models of many social media companies. Existing systems require that the UGC is fully exposed to the module that constructs the user profiles. In this paper we show that it is possible to build user profiles without ever accessing the user's original data, and without exposing the trained machine learning models for user profiling -- which are the intellectual property of the company -- to the users of the social media site. We present VirtualIdentity, an application that uses secure multi-party cryptographic protocols to detect the age, gender and personality traits of users by classifying their user-generated text and personal pictures with trained support vector machine models in a privacy-preserving manner.
SonarSnoop: Active Acoustic Side-Channel Attacks
Venue: arXiv cs.CR 2018
Year: 2018

We report the first active acoustic side-channel attack. Speakers are used to emit human inaudible acoustic signals and the echo is recorded via microphones, turning the acoustic system of a smart phone into a sonar system. The echo signal can be used to profile user interaction with the device. For example, a victim's finger movements can be inferred to steal Android phone unlock patterns. In our empirical study, the number of candidate unlock patterns that an attacker must try to authenticate herself to a Samsung S4 Android phone can be reduced by up to 70% using this novel acoustic side-channel. Our approach can be easily applied to other application scenarios and device types. Overall, our work highlights a new family of security threats.
A study of integer sorting on multicores
Venue: arXiv cs.DC 2018
Year: 2018

Integer sorting on multicores and GPUs can be realized by a variety of approaches that include variants of distribution-based methods such as radix-sort, comparison-oriented algorithms such as deterministic regular sampling and random sampling parallel sorting, and network-based algorithms such as Batcher's bitonic sorting algorithm. In this work we present an experimental study of integer sorting on multicore processors. We have implemented serial and parallel radix-sort for various radixes, deterministic regular oversampling and random oversampling parallel sorting, and also some previously little explored or unexplored variants of bitonic-sort and odd-even transposition sort. The study uses multithreading and multiprocessing parallel programming libraries with the C language implementations working under Open MPI, MulticoreBSP, and BSPlib utilizing the same source code. A secondary objective is to attempt to model the performance of these algorithm implementations under the MBSP (Multi-memory BSP) model. We first provide some general high-level observations on the performance of these implementations. If we can conclude anything is that accurate prediction of performance by taking into consideration architecture dependent features such as the structure and characteristics of multiple memory hierarchies is difficult and more often than not untenable. To some degree this is affected by the overhead imposed by the high-level library used in the programming effort. We can still draw however some reliable conclusions and reason about the performance of these implementations using the MBSP model, thus making MBSP useful and usable.
Self-stabilizing Overlays for high-dimensional Monotonic Searchability
Venue: arXiv cs.DC 2018
Year: 2018

We extend the concept of monotonic searchability for self-stabilizing systems from one to multiple dimensions. A system is self-stabilizing if it can recover to a legitimate state from any initial illegal state. These kind of systems are most often used in distributed applications. Monotonic searchability provides guarantees when searching for nodes while the recovery process is going on. More precisely, if a search request started at some node $u$ succeeds in reaching its destination $v$, then all future search requests from $u$ to $v$ succeed as well. Although there already exists a self-stabilizing protocol for a two-dimensional topology and an universal approach for monotonic searchability, it is not clear how both of these concepts fit together effectively. The latter concept even comes with some restrictive assumptions on messages, which is not the case for our protocol. We propose a simple novel protocol for a self-stabilizing two-dimensional quadtree that satisfies monotonic searchability. Our protocol can easily be extended to higher dimensions and offers routing in $\mathcal O(\log n)$ hops for any search request.
Parametric Topology Optimization with Multi-Resolution Finite Element Models
Venue: arXiv cs.NA 2018
Year: 2018

We present a methodical procedure for topology optimization under uncertainty with multi-resolution finite element models. We use our framework in a bi-fidelity setting where a coarse and a fine mesh corresponding to low- and high-resolution models are available. The inexpensive low-resolution model is used to explore the parameter space and approximate the parameterized high-resolution model and its sensitivity where parameters are considered in both structural load and stiffness. We provide error bounds for bi-fidelity finite element (FE) approximations and their sensitivities and conduct numerical studies to verify these theoretical estimates. We demonstrate our approach on benchmark compliance minimization problems where we show significant reduction in computational cost for expensive problems such as topology optimization under manufacturing variability while generating almost identical designs to those obtained with single resolution mesh. We also compute the parametric Von-Mises stress for the generated designs via our bi-fidelity FE approximation and compare them with standard Monte Carlo simulations. The implementation of our algorithm which extends the well-known 88-line topology optimization code in MATLAB is provided.
Robot_gym: accelerated robot training through simulation in the cloud with ROS and Gazebo
Venue: arXiv cs.RO 2018
Year: 2018

Rather than programming, training allows robots to achieve behaviors that generalize better and are capable to respond to real-world needs. However, such training requires a big amount of experimentation which is not always feasible for a physical robot. In this work, we present robot_gym, a framework to accelerate robot training through simulation in the cloud that makes use of roboticists' tools, simplifying the development and deployment processes on real robots. We unveil that, for simple tasks, simple 3DoF robots require more than 140 attempts to learn. For more complex, 6DoF robots, the number of attempts increases to more than 900 for the same task. We demonstrate that our framework, for simple tasks, accelerates the robot training time by more than 33% while maintaining similar levels of accuracy and repeatability.
Asymptotic analysis of the Friedkin-Johnsen model when the matrix of the susceptibility weights approaches the identity matrix
Venue: arXiv cs.SI 2018
Year: 2018

In this paper we analyze the Friedkin-Johnsen model of opinions when the coefficients weighting the agent susceptibilities to interpersonal influence approach 1. We will show that in this case, under suitable assumptions, the model converges to a quasi-consensus condition among the agents. In general the achieved consensus value will be different to the one obtained by the corresponding DeGroot model
Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translation
Venue: arXiv cs.CL 2018
Year: 2018

We reassess a recent study (Hassan et al., 2018) that claimed that machine translation (MT) has reached human parity for the translation of news from Chinese into English, using pairwise ranking and considering three variables that were not taken into account in that previous study: the language in which the source side of the test set was originally written, the translation proficiency of the evaluators, and the provision of inter-sentential context. If we consider only original source text (i.e. not translated from another language, or translationese), then we find evidence showing that human parity has not been achieved. We compare the judgments of professional translators against those of non-experts and discover that those of the experts result in higher inter-annotator agreement and better discrimination between human and machine translations. In addition, we analyse the human translations of the test set and identify important translation issues. Finally, based on these findings, we provide a set of recommendations for future human evaluations of MT.
Modified Self-Organized Task Allocation in a Group of Robots
Venue: arXiv cs.RO 2018
Year: 2018

This paper introduces a modified self-organized task allocation algorithm, where robots are assigned to pick up one of the two types of object. This paper also demonstrates both algorithms by showing the simulation results of the conventional self-organized task allocation algorithm and the simulation results of its modification.
Towards a Service-oriented Platform for Intelligent Apps in Intermediate Cities
Venue: arXiv cs.SE 2018
Year: 2018

Smart cities are a growing trend in many cities in Argentina. In particular, the so-called intermediate cities present a context and requirements different from those of large cities with respect to smart cities. One aspect of relevance is to encourage the development of applications (generally for mobile devices) that enable citizens to take advantage of data and services normally associated with the city, for example, in the urban mobility domain. In this work, a platform is proposed for intermediate cities that provide "high level" services and that allow the construction of software applications that consume those services. Our platform-centric strategy focused aims to integrate systems and heterogeneous data sources, and provide "intelligent" services to different applications. Examples of these services include: construction of user profiles, recommending local events, and collaborative sensing based on data mining techniques, among others. In this work, the design of this platform (currently in progress) is described, and experiences of applications for urban mobility are discussed, which are being migrated in the form of reusable services provided by the platform
Iterative Recursive Attention Model for Interpretable Sequence Classification
Venue: arXiv cs.CL 2018
Year: 2018

Natural language processing has greatly benefited from the introduction of the attention mechanism. However, standard attention models are of limited interpretability for tasks that involve a series of inference steps. We describe an iterative recursive attention model, which constructs incremental representations of input data through reusing results of previously computed queries. We train our model on sentiment classification datasets and demonstrate its capacity to identify and combine different aspects of the input in an easily interpretable manner, while obtaining performance close to the state of the art.
Understanding Security Requirements and Challenges in Internet of Things (IoTs): A Review
Venue: arXiv cs.CR 2018
Year: 2018

Internet of Things (IoT) is realized by the idea of free flow of information amongst various low power embedded devices that use Internet to communicate with one another. It is predicted that the IoT will be widely deployed and it will find applicability in various domains of life. Demands of IoT have lately attracted huge attention and organizations are excited about the business value of the data that will be generated by the IoT paradigm. On the other hand, IoT have various security and privacy concerns for the end users that limit its proliferation. In this paper we have identified, categorized and discussed various security challenges and state of the art efforts to resolve these challenges.
A Self-Attention Network for Hierarchical Data Structures with an Application to Claims Management
Venue: arXiv cs.LG 2018
Year: 2018

Insurance companies must manage millions of claims per year. While most of these claims are non-fraudulent, fraud detection is core for insurance companies. The ultimate goal is a predictive model to single out the fraudulent claims and pay out the non-fraudulent ones immediately. Modern machine learning methods are well suited for this kind of problem. Health care claims often have a data structure that is hierarchical and of variable length. We propose one model based on piecewise feed forward neural networks (deep learning) and another model based on self-attention neural networks for the task of claim management. We show that the proposed methods outperform bag-of-words based models, hand designed features, and models based on convolutional neural networks, on a data set of two million health care claims. The proposed self-attention method performs the best.
LUCSS: Language-based User-customized Colourization of Scene Sketches
Venue: arXiv cs.CV 2018
Year: 2018

We introduce LUCSS, a language-based system for interactive col- orization of scene sketches, based on their semantic understanding. LUCSS is built upon deep neural networks trained via a large-scale repository of scene sketches and cartoon-style color images with text descriptions. It con- sists of three sequential modules. First, given a scene sketch, the segmenta- tion module automatically partitions an input sketch into individual object instances. Next, the captioning module generates the text description with spatial relationships based on the instance-level segmentation results. Fi- nally, the interactive colorization module allows users to edit the caption and produce colored images based on the altered caption. Our experiments show the effectiveness of our approach and the desirability of its compo- nents to alternative choices.
On the Use of Emoticons in Open Source Software Development
Venue: arXiv cs.SE 2018
Year: 2018

Background: Using sentiment analysis to study software developers' behavior comes with challenges such as the presence of a large amount of technical discussion unlikely to express any positive or negative sentiment. However, emoticons provide information about developer sentiments that can easily be extracted from software repositories. Aim: We investigate how software developers use emoticons differently in issue trackers in order to better understand the differences between developers and determine to which extent emoticons can be used as in place of sentiment analysis. Method: We extract emoticons from 1.3M comments from Apache's issue tracker and 4.5M from Mozilla's issue tracker using regular expressions built from a list of emoticons used by SentiStrength and Wikipedia. We check for statistical differences using Mann-Whitney U tests and determine the effect size with Cliff's delta. Results: Overall Mozilla developers rely more on emoticons than Apache developers. While the overall ratio of comments with emoticons is of 2% and 3.6% for Apache and Mozilla, some individual developers can have a ratio above 20%. Looking specifically at Mozilla developers, we find that western developers use significantly more emoticons (with large size effect) than eastern developers. While the majority of emoticons are used to express joy, we find that Mozilla developers use emoticons more frequently to express sadness and surprise than Apache developers. Finally, we find that developers use overall more emoticons during weekends than during weekdays, with the share of sad and surprised emoticons increasing during weekends. Conclusions: While emoticons are primarily used to express joy, the more occasional use of sad and surprised emoticons can potentially be utilized to detect frustration in place of sentiment analysis among developers using emoticons frequently enough.
Whispered-to-voiced Alaryngeal Speech Conversion with Generative Adversarial Networks
Venue: arXiv cs.SD 2018
Year: 2018

Most methods of voice restoration for patients suffering from aphonia either produce whispered or monotone speech. Apart from intelligibility, this type of speech lacks expressiveness and naturalness due to the absence of pitch (whispered speech) or artificial generation of it (monotone speech). Existing techniques to restore prosodic information typically combine a vocoder, which parameterises the speech signal, with machine learning techniques that predict prosodic information. In contrast, this paper describes an end-to-end neural approach for estimating a fully-voiced speech waveform from whispered alaryngeal speech. By adapting our previous work in speech enhancement with generative adversarial networks, we develop a speaker-dependent model to perform whispered-to-voiced speech conversion. Preliminary qualitative results show effectiveness in re-generating voiced speech, with the creation of realistic pitch contours.
MobiBits: Multimodal Mobile Biometric Database
Venue: arXiv cs.CV 2018
Year: 2018

This paper presents a novel database comprising representations of five different biometric characteristics, collected in a mobile, unconstrained or semi-constrained setting with three different mobile devices, including characteristics previously unavailable in existing datasets, namely hand images, thermal hand images, and thermal face images, all acquired with a mobile, off-the-shelf device. In addition to this collection of data we perform an extensive set of experiments providing insight on benchmark recognition performance that can be achieved with these data, carried out with existing commercial and academic biometric solutions. This is the first known to us mobile biometric database introducing samples of biometric traits such as thermal hand images and thermal face images. We hope that this contribution will make a valuable addition to the already existing databases and enable new experiments and studies in the field of mobile authentication. The MobiBits database is made publicly available to the research community at no cost for non-commercial purposes.