OpenRaiser/Intern-Atlas huggingface dataset:
On Verifying Timed Hyperproperties
Venue: arXiv cs.LO 2018
Year: 2018

We study the satisfiability and model-checking problems for timed hyperproperties specified with HyperMTL, a timed extension of HyperLTL. Depending on whether interleaving of events in different traces is allowed, two possible semantics can be defined for timed hyperproperties: asynchronous and synchronous. While the satisfiability problem can be decided similarly to HyperLTL regardless of the choice of semantics, we show that the model-checking problem, unless the specification is alternation-free, is undecidable even when very restricted timing constraints are allowed. On the positive side, we show that model checking HyperMTL with quantifier alternations is possible under certain conditions in the synchronous semantics, or when there is a fixed bound on the length of the time domain.
Building a Neural Semantic Parser from a Domain Ontology
Venue: arXiv cs.CL 2018
Year: 2018

Semantic parsing is the task of converting natural language utterances into machine interpretable meaning representations which can be executed against a real-world environment such as a database. Scaling semantic parsing to arbitrary domains faces two interrelated challenges: obtaining broad coverage training data effectively and cheaply; and developing a model that generalizes to compositional utterances and complex intentions. We address these challenges with a framework which allows to elicit training data from a domain ontology and bootstrap a neural parser which recursively builds derivations of logical forms. In our framework meaning representations are described by sequences of natural language templates, where each template corresponds to a decomposed fragment of the underlying meaning representation. Although artificial, templates can be understood and paraphrased by humans to create natural utterances, resulting in parallel triples of utterances, meaning representations, and their decompositions. These allow us to train a neural semantic parser which learns to compose rules in deriving meaning representations. We crowdsource training data on six domains, covering both single-turn utterances which exhibit rich compositionality, and sequential utterances where a complex task is procedurally performed in steps. We then develop neural semantic parsers which perform such compositional tasks. In general, our approach allows to deploy neural semantic parsers quickly and cheaply from a given domain ontology.
Tensor-Train Long Short-Term Memory for Monaural Speech Enhancement
Venue: arXiv cs.SD 2018
Year: 2018

In recent years, Long Short-Term Memory (LSTM) has become a popular choice for speech separation and speech enhancement task. The capability of LSTM network can be enhanced by widening and adding more layers. However, this would introduce millions of parameters in the network and also increase the requirement of computational resources. These limitations hinders the efficient implementation of RNN models in low-end devices such as mobile phones and embedded systems with limited memory. To overcome these issues, we proposed to use an efficient alternative approach of reducing parameters by representing the weight matrix parameters of LSTM based on Tensor-Train (TT) format. We called this Tensor-Train factorized LSTM as TT-LSTM model. Based on this TT-LSTM units, we proposed a deep TensorNet model for single-channel speech enhancement task. Experimental results in various test conditions and in terms of standard speech quality and intelligibility metrics, demonstrated that the proposed deep TT-LSTM based speech enhancement framework can achieve competitive performances with the state-of-the-art uncompressed RNN model, even though the proposed model architecture is orders of magnitude less complex.
The algorithm of the impulse noise filtration in images based on an algorithm of community detection in graphs
Venue: arXiv cs.CV 2018
Year: 2018

This article suggests an algorithm of impulse noise filtration, based on the community detection in graphs. The image is representing as non-oriented weighted graph. Each pixel of an image is corresponding to a vertex of the graph. Community detection algorithm is running on the given graph. Assumed that communities that contain only one pixel are corresponding to noised pixels of an image. Suggested method was tested with help of computer experiment. This experiment was conducted on grayscale, and on colored images, on artificial images and on photos. It is shown that the suggested method is better than median filter by 20% regardless of noise percent. Higher efficiency is justified by the fact that most of filters are changing all of image pixels, but suggested method is finding and restoring only noised pixels. The dependence of the effectiveness of the proposed method on the percentage of noise in the image is shown.
Learning to Refine Source Representations for Neural Machine Translation
Venue: arXiv cs.CL 2018
Year: 2018

Neural machine translation (NMT) models generally adopt an encoder-decoder architecture for modeling the entire translation process. The encoder summarizes the representation of input sentence from scratch, which is potentially a problem if the sentence is ambiguous. When translating a text, humans often create an initial understanding of the source sentence and then incrementally refine it along the translation on the target side. Starting from this intuition, we propose a novel encoder-refiner-decoder framework, which dynamically refines the source representations based on the generated target-side information at each decoding step. Since the refining operations are time-consuming, we propose a strategy, leveraging the power of reinforcement learning models, to decide when to refine at specific decoding steps. Experimental results on both Chinese-English and English-German translation tasks show that the proposed approach significantly and consistently improves translation performance over the standard encoder-decoder framework. Furthermore, when refining strategy is applied, results still show reasonable improvement over the baseline without much decrease in decoding speed.
Reasoning About Safety-Critical Information Flow Between Pilot and Computer
Venue: arXiv cs.LO 2018
Year: 2018

This paper presents research results that develop a dynamic logic for reasoning about safety-critical information flow among humans and computers. The logic advances previous efforts to develop logics of agent knowledge, which make assumptions that are too strong for realistic human agents. We introduce Dynamic Agent Safety Logic (DASL), based on Dynamic Epistemic Logic (DEL), with extensions to account for safe actions, belief, and the logical relationships among knowledge, belief, and safe action. With this logic we can infer which safety-critical information a pilot is missing when executing an unsafe action. We apply the logic to the Air France 447 incident as a case study and provide a mechanization of the case study in the Coq proof assistant.
Decision Support System for Renal Transplantation
Venue: arXiv cs.CY 2018
Year: 2018

The burgeoning need for kidney transplantation mandates immediate attention. Mismatch of deceased donor-recipient kidney leads to post-transplant death. To ensure ideal kidney donor-recipient match and minimize post-transplant deaths, the paper develops a prediction model that identifies factors that determine the probability of success of renal transplantation, that is, if the kidney procured from the deceased donor can be transplanted or discarded. The paper conducts a study enveloping data for 584 imported kidneys collected from 12 transplant centers associated with an organ procurement organization located in New York City, NY. The predicting model yielding best performance measures can be beneficial to the healthcare industry. Transplant centers and organ procurement organizations can take advantage of the prediction model to efficiently predict the outcome of kidney transplantation. Consequently, it will reduce the mortality rate caused by mismatching of donor-recipient kidney transplantation during the surgery. Keywords
Same but Different: Distant Supervision for Predicting and Understanding Entity Linking Difficulty
Venue: arXiv cs.CL 2018
Year: 2018

Entity Linking (EL) is the task of automatically identifying entity mentions in a piece of text and resolving them to a corresponding entity in a reference knowledge base like Wikipedia. There is a large number of EL tools available for different types of documents and domains, yet EL remains a challenging task where the lack of precision on particularly ambiguous mentions often spoils the usefulness of automated disambiguation results in real applications. A priori approximations of the difficulty to link a particular entity mention can facilitate flagging of critical cases as part of semi-automated EL systems, while detecting latent factors that affect the EL performance, like corpus-specific features, can provide insights on how to improve a system based on the special characteristics of the underlying corpus. In this paper, we first introduce a consensus-based method to generate difficulty labels for entity mentions on arbitrary corpora. The difficulty labels are then exploited as training data for a supervised classification task able to predict the EL difficulty of entity mentions using a variety of features. Experiments over a corpus of news articles show that EL difficulty can be estimated with high accuracy, revealing also latent features that affect EL performance. Finally, evaluation results demonstrate the effectiveness of the proposed method to inform semi-automated EL pipelines.
Machine Learning in Official Statistics
Venue: arXiv cs.CY 2018
Year: 2018

In the first half of 2018, the Federal Statistical Office of Germany (Destatis) carried out a "Proof of Concept Machine Learning" as part of its Digital Agenda. A major component of this was surveys on the use of machine learning methods in official statistics, which were conducted at selected national and international statistical institutions and among the divisions of Destatis. It was of particular interest to find out in which statistical areas and for which tasks machine learning is used and which methods are applied. This paper is intended to make the results of the surveys publicly accessible.
Structure Learning of Sparse GGMs over Multiple Access Networks
Venue: arXiv cs.LG 2018
Year: 2018

A central machine is interested in estimating the underlying structure of a sparse Gaussian Graphical Model (GGM) from datasets distributed across multiple local machines. The local machines can communicate with the central machine through a wireless multiple access channel. In this paper, we are interested in designing effective strategies where reliable learning is feasible under power and bandwidth limitations. Two approaches are proposed: Signs and Uncoded methods. In Signs method, the local machines quantize their data into binary vectors and an optimal channel coding scheme is used to reliably send the vectors to the central machine where the structure is learned from the received data. In Uncoded method, data symbols are scaled and transmitted through the channel. The central machine uses the received noisy symbols to recover the structure. Theoretical results show that both methods can recover the structure with high probability for large enough sample size. Experimental results indicate the superiority of Signs method over Uncoded method under several circumstances.
Age of Information in Multihop Multicast Networks
Venue: arXiv cs.IT 2018
Year: 2018

We consider the age of information in a multihop multicast network where there is a single source node sending time-sensitive updates to $n^L$ end nodes, and $L$ denotes the number of hops. In the first hop, the source node sends updates to $n$ first-hop receiver nodes, and in the second hop each first-hop receiver node relays the update packets that it has received to $n$ further users that are connected to it. This network architecture continues in further hops such that each receiver node in hop $\ell$ is connected to $n$ further receiver nodes in hop $\ell+1$. We study the age of information experienced by the end nodes, and in particular, its scaling as a function of $n$. We show that, using an earliest $k$ transmission scheme in each hop, the age of information at the end nodes can be made a constant independent of $n$. In particular, the source node transmits each update packet to the earliest $k_1$ of the $n$ first-hop nodes, and each first-hop node that receives the update relays it to the earliest $k_2$ out of $n$ second-hop nodes that are connected to it and so on. We determine the optimum $k_\ell$ stopping value for each hop $\ell$ for arbitrary shifted exponential link delays.
Specialization vs diversification in research activities: the extent, intensity and relatedness of field diversification by individual scientists
Venue: arXiv cs.DL 2018
Year: 2018

We investigate whether and in what measure scientists tend to diversify their research activity, and if this tendency varies according to their belonging to different disciplinary areas. We analyze the nature of research diversification along three dimensions: extent of diversification, intensity of diversification, and degree of relatedness of topics in which researchers diversifies. For this purpose we propose three bibliometric indicators, based on the disciplinary placement of scientific output of individual scientists. The empirical investigation shows that the extent of diversification is lowest for scientists in Mathematics and highest in Chemistry; intensity of diversification is lowest in Earth sciences and highest in Industrial and information engineering; and degree of relatedness is lowest in Earth sciences and highest in Chemistry.
Deception Detection by 2D-to-3D Face Reconstruction from Videos
Venue: arXiv cs.CV 2018
Year: 2018

Lies and deception are common phenomena in society, both in our private and professional lives. However, humans are notoriously bad at accurate deception detection. Based on the literature, human accuracy of distinguishing between lies and truthful statements is 54% on average, in other words it is slightly better than a random guess. While people do not much care about this issue, in high-stakes situations such as interrogations for series crimes and for evaluating the testimonies in court cases, accurate deception detection methods are highly desirable. To achieve a reliable, covert, and non-invasive deception detection, we propose a novel method that jointly extracts reliable low- and high-level facial features namely, 3D facial geometry, skin reflectance, expression, head pose, and scene illumination in a video sequence. Then these features are modeled using a Recurrent Neural Network to learn temporal characteristics of deceptive and honest behavior. We evaluate the proposed method on the Real-Life Trial (RLT) dataset that contains high-stake deceptive and honest videos recorded in courtrooms. Our results show that the proposed method (with an accuracy of 72.8%) improves the state of the art as well as outperforming the use of manually coded facial attributes 67.6%) in deception detection.
Sanctorum: A lightweight security monitor for secure enclaves
Venue: arXiv cs.CR 2018
Year: 2018

Enclaves have emerged as a particularly compelling primitive to implement trusted execution environments: strongly isolated sensitive user-mode processes in a largely untrusted software environment. While the threat models employed by various enclave systems differ, the high-level guarantees they offer are essentially the same: attestation of an enclave's initial state, as well as a guarantee of enclave integrity and privacy in the presence of an adversary. This work describes Sanctorum, a small trusted code base (TCB), consisting of a generic enclave-capable system, which is sufficient to implement secure enclaves akin to the primitive offered by Intel's SGX. While enclaves may be implemented via unconditionally trusted hardware and microcode, as it is the case in SGX, we employ a smaller TCB principally consisting of authenticated, privileged software, which may be replaced or patched as needed. Sanctorum implements a formally verified specification for generic enclaves on an in-order multiprocessor system meeting baseline security requirements, e.g., the MIT Sanctum processor and the Keystone enclave framework. Sanctorum requires trustworthy hardware including a random number generator, a private cryptographic key pair derived via a secure bootstrapping protocol, and a robust isolation primitive to safeguard sensitive information. Sanctorum's threat model is informed by the threat model of the isolation primitive, and is suitable for adding enclaves to a variety of processor systems.
Sampling Using Neural Networks for colorizing the grayscale images
Venue: arXiv cs.GR 2018
Year: 2018

The main idea of this paper is to explore the possibilities of generating samples from the neural networks, mostly focusing on the colorization of the grey-scale images. I will compare the existing methods for colorization and explore the possibilities of using new generative modeling to the task of colorization. The contributions of this paper are to compare the existing structures with similar generating structures(Decoders) and to apply the novel structures including Conditional VAE(CVAE), Conditional Wasserstein GAN with Gradient Penalty(CWGAN-GP), CWGAN-GP with L1 reconstruction loss, Adversarial Generative Encoders(AGE) and Introspective VAE(IVAE). I trained these models using CIFAR-10 images. To measure the performance, I use Inception Score(IS) which measures how distinctive each image is and how diverse overall samples are as well as human eyes for CIFAR-10 images. It turns out that CVAE with L1 reconstruction loss and IVAE achieve the highest score in IS. CWGAN-GP with L1 tends to learn faster than CWGAN-GP, but IS does not increase from CWGAN-GP. CWGAN-GP tends to generate more diverse images than other models using reconstruction loss. Also, I figured out that the proper regularization plays a vital role in generative modeling.
Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty
Venue: arXiv cs.LG 2018
Year: 2018

Deep learning provides a powerful tool for machine perception when the observations resemble the training data. However, real-world robotic systems must react intelligently to their observations even in unexpected circumstances. This requires a system to reason about its own uncertainty given unfamiliar, out-of-distribution observations. Approximate Bayesian approaches are commonly used to estimate uncertainty for neural network predictions, but can struggle with out-of-distribution observations. Generative models can in principle detect out-of-distribution observations as those with a low estimated density. However, the mere presence of an out-of-distribution input does not by itself indicate an unsafe situation. In this paper, we present a method for uncertainty-aware robotic perception that combines generative modeling and model uncertainty to cope with uncertainty stemming from out-of-distribution states. Our method estimates an uncertainty measure about the model's prediction, taking into account an explicit (generative) model of the observation distribution to handle out-of-distribution inputs. This is accomplished by probabilistically projecting observations onto the training distribution, such that out-of-distribution inputs map to uncertain in-distribution observations, which in turn produce uncertain task-related predictions, but only if task-relevant parts of the image change. We evaluate our method on an action-conditioned collision prediction task with both simulated and real data, and demonstrate that our method of projecting out-of-distribution observations improves the performance of four standard Bayesian and non-Bayesian neural network approaches, offering more favorable trade-offs between the proportion of time a robot can remain autonomous and the proportion of impending crashes successfully avoided.
Skew Cyclic Codes Over $\mathbb{F}_4 R$
Venue: arXiv cs.IT 2018
Year: 2018

This paper considers a new alphabet set, which is a ring that we call $\mathbb{F}_4R$, to construct linear error-control codes. Skew cyclic codes over the ring are then investigated in details. We define a nondegenerate inner product and provide a criteria to test for self-orthogonality. Results on the algebraic structures lead us to characterize $\mathbb{F}_4R$-skew cyclic codes. Interesting connections between the image of such codes under the Gray map to linear cyclic and skew-cyclic codes over $\mathbb{F}_4$ are shown. These allow us to learn about the relative dimension and distance profile of the resulting codes. Our setup provides a natural connection to DNA codes where additional biomolecular constraints must be incorporated into the design. We present a characterization of $R$-skew cyclic codes which are reversible complement.
QRFA: A Data-Driven Model of Information-Seeking Dialogues
Venue: arXiv cs.IR 2018
Year: 2018

Understanding the structure of interaction processes helps us to improve information-seeking dialogue systems. Analyzing an interaction process boils down to discovering patterns in sequences of alternating utterances exchanged between a user and an agent. Process mining techniques have been successfully applied to analyze structured event logs, discovering the underlying process models or evaluating whether the observed behavior is in conformance with the known process. In this paper, we apply process mining techniques to discover patterns in conversational transcripts and extract a new model of information-seeking dialogues, QRFA, for Query, Request, Feedback, Answer. Our results are grounded in an empirical evaluation across multiple conversational datasets from different domains, which was never attempted before. We show that the QRFA model better reflects conversation flows observed in real information-seeking conversations than models proposed previously. Moreover, QRFA allows us to identify malfunctioning in dialogue system transcripts as deviations from the expected conversation flow described by the model via conformance analysis.
Hyperspectral Unmixing Based on Clustered Multitask Networks
Venue: arXiv cs.CV 2018
Year: 2018

Hyperspectral remote sensing is a prominent research topic in data processing. Most of the spectral unmixing algorithms are developed by adopting the linear mixing models. Nonnegative matrix factorization (NMF) and its developments are used widely for estimation of signatures and fractional abundances in the SU problem. Sparsity constraints was added to NMF, and was regularized by $ L_ {q} $ norm. In this paper, at first hyperspectral images are clustered by fuzzy c- means method, and then a new algorithm based on sparsity constrained distributed optimization is used for spectral unmixing. In the proposed algorithm, a network including clusters is employed. Each pixel in the hyperspectral images considered as a node in this network. The proposed algorithm is optimized with diffusion LMS strategy, and then the update equations for fractional abundance and signature matrices are obtained. Simulation results based on defined performance metrics illustrate advantage of the proposed algorithm in spectral unmixing of hyperspectral data compared with other methods.
Above guarantee parameterization for vertex cover on graphs with maximum degree 4
Venue: arXiv cs.DS 2018
Year: 2018

In the vertex cover problem, the input is a graph $G$ and an integer $k$, and the goal is to decide whether there is a set of vertices $S$ of size at most $k$ such that every edge of $G$ is incident on at least one vertex in $S$. We study the vertex cover problem on graphs with maximum degree 4 and minimum degree at least 2, parameterized by $r = k-n/3$. We give an algorithm for this problem whose running time is $O^*(1.6253^r)$. As a corollary, we obtain an $O^*(1.2403^k)$-time algorithm for vertex cover on graphs with maximum degree 4.