OpenRaiser/Intern-Atlas huggingface dataset:
Extensions of Generic DOL for Generic Ontology Design Patterns
Venue: arXiv cs.LO 2019
Year: 2019

Generic ontologies were introduced as an extension (Generic DOL) of the Distributed Ontology, Modeling and Specification Language, DOL, with the aim to provide a language for Generic Ontology Design Patterns. In this paper we present a number of new language constructs that increase the expressivity and the generality of Generic DOL, among them sequential and optional parameters, list parameters with recursion, and local sub-patterns. These are illustrated with non-trivial patterns: generic value sets and (nested) qualitatively graded relations, demonstrated as definitional building blocks in an application domain.
A Lattice Based Joint Encryption, Encoding and Modulation Scheme
Venue: arXiv cs.IT 2019
Year: 2019

A new nonlinear Rao-Nam like symmetric key encryption scheme is presented in this paper. QC-LDPC lattices that are practically implementable in high dimensions due to their low complexity encoding and decoding algorithms, are used in our design. Then, a joint scheme is proposed which is capable of encrypting, encoding and data modulation simultaneously. The proposed cryptosystem withstands all variants of chosen plaintext attacks applied on Rao-Nam like cryptosystems due to its nonlinearity. The sparseness of the parity-check matrix of QC-LDPC lattices, quasi-cyclic nature of their generator and parity-check matrices, simple hardware structure for generating intentional error vector, permutation and nonlinear functions, result in a small key size for our scheme. The lattice codes related to the lattices used in this paper have high rate which are suitable for bandlimited AWGN channels. Therefore, the joint scheme based on these lattices facilitates secure, reliable and efficient data transmission in bandlimited AWGN channels.
Learning Correlated Latent Representations with Adaptive Priors
Venue: arXiv cs.LG 2019
Year: 2019

Variational Auto-Encoders (VAEs) have been widely applied for learning compact, low-dimensional latent representations of high-dimensional data. When the correlation structure among data points is available, previous work proposed Correlated Variational Auto-Encoders (CVAEs), which employ a structured mixture model as prior and a structured variational posterior for each mixture component to enforce that the learned latent representations follow the same correlation structure. However, as we demonstrate in this work, such a choice cannot guarantee that CVAEs capture all the correlations. Furthermore, it prevents us from obtaining a tractable joint and marginal variational distribution. To address these issues, we propose Adaptive Correlated Variational Auto-Encoders (ACVAEs), which apply an adaptive prior distribution that can be adjusted during training and can learn a tractable joint variational distribution. Its tractable form also enables further refinement with belief propagation. Experimental results on link prediction and hierarchical clustering show that ACVAEs significantly outperform CVAEs among other benchmarks.
Tagged Back-Translation
Venue: arXiv cs.CL 2019
Year: 2019

Recent work in Neural Machine Translation (NMT) has shown significant quality gains from noised-beam decoding during back-translation, a method to generate synthetic parallel data. We show that the main role of such synthetic noise is not to diversify the source side, as previously suggested, but simply to indicate to the model that the given source is synthetic. We propose a simpler alternative to noising techniques, consisting of tagging back-translated source sentences with an extra token. Our results on WMT outperform noised back-translation in English-Romanian and match performance on English-German, re-defining state-of-the-art in the former.
Constructions and necessities of some permutation polynomials
Venue: arXiv cs.IT 2019
Year: 2019

Permutation polynomials over finite fields have important applications in many areas of science and engineering such as coding theory, cryptography, combinatorial design, etc. In this paper, we construct several new classes of permutation polynomials, and the necessities of some permutation polynomials are studied.
Correlating Twitter Language with Community-Level Health Outcomes
Venue: arXiv cs.CL 2019
Year: 2019

We study how language on social media is linked to diseases such as atherosclerotic heart disease (AHD), diabetes and various types of cancer. Our proposed model leverages state-of-the-art sentence embeddings, followed by a regression model and clustering, without the need of additional labelled data. It allows to predict community-level medical outcomes from language, and thereby potentially translate these to the individual level. The method is applicable to a wide range of target variables and allows us to discover known and potentially novel correlations of medical outcomes with life-style aspects and other socioeconomic risk factors.
Accelerating Concurrent Heap on GPUs
Venue: arXiv cs.DC 2019
Year: 2019

Priority queue, often implemented as a heap, is an abstract data type that has been used in many well-known applications like Dijkstra's shortest path algorithm, Prim's minimum spanning tree, Huffman encoding, and the branch-and-bound algorithm. However, it is challenging to exploit the parallelism of the heap on GPUs since the control divergence and memory irregularity must be taken into account. In this paper, we present a parallel generalized heap model that works effectively on GPUs. We also prove the linearizability of our generalized heap model which enables us to reason about the expected results. We evaluate our concurrent heap thoroughly and show a maximum 19.49X speedup compared to the sequential CPU implementation and 2.11X speedup compared with the existing GPU implementation. We also apply our heap to single source shortest path with up to 1.23X speedup and 0/1 knapsack problem with up to 12.19X speedup.
Optimized Blockchain Model for Internet of Things based Healthcare Applications
Venue: arXiv cs.CR 2019
Year: 2019

There continues to be a recent push to taking the cryptocurrency based ledger system known as Blockchain and applying its techniques to non-financial applications. One of the main areas for application remains Internet of Things (IoT) as we see many areas of improvement as we move into an age of smart cities. In this paper, we examine an initial look at applying the key aspects of Blockchain to a health application network where patients health data can be used to create alerts important to authenticated healthcare providers in a secure and private manner. This paper also presents the benefits and also practical obstacles of the blockchain-based security approaches in IoT.
Volumetric Isosurface Rendering with Deep Learning-Based Super-Resolution
Venue: arXiv cs.GR 2019
Year: 2019

Rendering an accurate image of an isosurface in a volumetric field typically requires large numbers of data samples. Reducing the number of required samples lies at the core of research in volume rendering. With the advent of deep learning networks, a number of architectures have been proposed recently to infer missing samples in multi-dimensional fields, for applications such as image super-resolution and scan completion. In this paper, we investigate the use of such architectures for learning the upscaling of a low-resolution sampling of an isosurface to a higher resolution, with high fidelity reconstruction of spatial detail and shading. We introduce a fully convolutional neural network, to learn a latent representation generating a smooth, edge-aware normal field and ambient occlusions from a low-resolution normal and depth field. By adding a frame-to-frame motion loss into the learning stage, the upscaling can consider temporal variations and achieves improved frame-to-frame coherence. We demonstrate the quality of the network for isosurfaces which were never seen during training, and discuss remote and in-situ visualization as well as focus+context visualization as potential applications
Interactive health communication and the construction of the identity of the person with low vision in social media
Venue: arXiv cs.SI 2019
Year: 2019

With use and appropriation of digital environments by the Green Cane movement in Brazil, the informational character in health is a central discursive resource, and occurs imbued with (self) conceptions about the characteristic ways of thinking, feeling and acting of the person with low vision. This work aims at exploring the discourse of the person with low vision in the social media about their own social identity. Based on the Interactive Health Communication, videos on Youtube and Facebook and Instagram posts of the Virtual Group Stargardt, a virtual community made up of people with low vision and suffering from Stargardt's Disease, were investigated. From the selection of excerpts and their coding, 42 identity traits were identified and grouped into a synthetic model with 08 categories: (T1) we are low vision and we use the green cane; (T2) we have sensitivity to light and we wear sunglasses; (T3) we have difficulties in the day-to-day, but we use assistive strategies and technologies; (T4) we are a PcD (Person with Disability) and we have rights guaranteed by law; (T5) we seek to understand about our disease and we are the spokesperson of it; (T6) we have a visual impairment, but we are not just that; (T7) we live with conflicts and contradictions by the way we see things, and; (T8) we recognize as a community. Such traits are constructed interactively, through a construction based on moments of agreement, disagreement and good humor. The discourses are oriented in the consolidation of a proper identity, distinct from the person with blindness, but similar in the legal framework of the visually impaired person.
A Practical Solution to Yao's Millionaires' Problem and Its Application in Designing Secure Combinatorial Auction
Venue: arXiv cs.CR 2019
Year: 2019

The emergence of e-commerce and e-voting platforms has resulted in the rise in the volume of sensitive information over the Internet. This has resulted in an increased demand for secure and private means of information computation. Towards this, the Yao's Millionaires' problem, i.e., to determine the richer among two millionaires' securely, finds an application. In this work, we present a new solution to the Yao's Millionaires' problem namely, Privacy Preserving Comparison (PPC). We show that PPC achieves this comparison in constant time as well as in one execution. PPC uses semi-honest third parties for the comparison who do not learn any information about the values. Further, we show that PPC is collusion-resistance. To demonstrate the significance of PPC, we present a secure, approximate single-minded combinatorial auction, which we call TPACAS, i.e., Truthful, Privacy-preserving Approximate Combinatorial Auction for Single-minded bidders. We show that TPACAS, unlike previous works, preserves the following privacies relevant to an auction: agent privacy, the identities of the losing bidders must not be revealed to any other agent except the auctioneer (AU), bid privacy, the bid values must be hidden from the other agents as well as the AU and bid-topology privacy, the items for which the agents are bidding must be hidden from the other agents as well as the AU. We demonstrate the practicality of TPACAS through simulations. Lastly, we also look at TPACAS' implementation over a publicly distributed ledger, such as the Ethereum blockchain.
A New Approach to Real Time Impulsive Sound Detection for Surveillance Applications
Venue: arXiv cs.SD 2019
Year: 2019

Most of the surveillance systems for public safety are solely based on one or more video cameras. These camera systems have some drawbacks such that they have poor performance in adverse weather conditions or during night time. Therefore most of the time, some other sensors should accompany to video cameras. Although audio surveillance is in its early stage, there has been considerable amount of work in this area in the last decade. In this paper we make a review of impulsive sound detection algorithms. Sounds from dangerous events such as gunshots, explosions, human screaming can be classified as impulsive sounds, so this paper reviews all impulsive sound detection algorithms along with impulsive noise detection algorithms although they progress in their own path. These dangerous sound events have no other detection means except audio. We try to adapt some algorithms used in impulsive noise detection to the area of impulsive sound detection. Tests show that Warped Linear Prediction (WLP) can be used for impulsive sound detection.
Learning Restricted Boltzmann Machines with Arbitrary External Fields
Venue: arXiv cs.LG 2019
Year: 2019

We study the problem of learning graphical models with latent variables. We give the first algorithm for learning locally consistent (ferromagnetic or antiferromagnetic) Restricted Boltzmann Machines (or RBMs) with {\em arbitrary} external fields. Our algorithm has optimal dependence on dimension in the sample complexity and run time however it suffers from a sub-optimal dependency on the underlying parameters of the RBM. Prior results have been established only for {\em ferromagnetic} RBMs with {\em consistent} external fields (signs must be same)\cite{bresler2018learning}. The proposed algorithm strongly relies on the concavity of magnetization which does not hold in our setting. We show the following key structural property: even in the presence of arbitrary external field, for any two observed nodes that share a common latent neighbor, the covariance is high. This enables us to design a simple greedy algorithm that maximizes covariance to iteratively build the neighborhood of each vertex.
Lattice Coding for Downlink Multiuser Transmission
Venue: arXiv cs.IT 2019
Year: 2019

In this thesis, we mainly investigate the lattice coding problem of the downlink communication between a base station and multiple users. The base station broadcasts a message containing each user's intended message. The capacity limit of such a system setting is already well-known while the design of practical coding and modulation schemes to approach the theoretical limit has not been fully studied and investigated in the literature. This thesis attempts to address this problem by providing a systematic design on lattice coding and modulation schemes for downlink multiuser communication systems. The main idea is to exploit the structure property of lattices to harness interference from downlink users.
Toward the Internet of No Things: The Role of O2O Communications and Extended Reality
Venue: arXiv cs.NI 2019
Year: 2019

Future fully interconnected virtual reality (VR) systems and the Tactile Internet diminish the boundary between virtual (online) and real (offline) worlds, while extending the digital and physical capabilities of humans via edge computing and teleoperated robots, respectively. In this paper, we focus on the Internet of No Things as an extension of immersive VR from virtual to real environments, where human-intended Internet services - either digital or physical - appear when needed and disappear when not needed. We first introduce the concept of integrated online-to-offline (O2O) communications, which treats online and offline channels as complementary to bridge the virtual and physical worlds and provide O2O multichannel experiences. We then elaborate on the emerging extended reality (XR), which brings the different forms of virtual/augmented/mixed reality together to realize the entire reality-virtuality continuum and, more importantly, supports human-machine interaction as envisioned by the Tactile Internet, while posing challenges to conventional handhelds, e.g., smartphones. Building on the so-called invisible-to-visible (I2V) technology concept, we present our extrasensory perception network (ESPN) and investigate how O2O communications and XR can be combined for the nonlocal extension of human "sixth-sense" experiences in space and time. We conclude by putting our ideas in perspective of the 6G vision.
The impact of Use Cases in real-world software development projects: A systematic mapping study
Venue: arXiv cs.SE 2019
Year: 2019

Objective: To identify and classify the positive and negative impacts of using Use Cases in real-world settings. Method: We conducted a systematic mapping study. The search strategies retrieved a set of 4,431 papers out of which 47 were selected as primary studies. We defined four facets to classify these studies: 1) the positive impact (advantages), 2) the negative impact (disadvantages), 3) the industry's domain and 4) the type of research reported. Results: Our study identified eight categories of advantages related to the application of Use Cases. The most mentioned were estimation, analysis and automation. These advantages had a heterogeneous distribution along the years. On the other hand, the granularity of the scenarios described in the Use Cases, the lack of a standardized format for specifying requirements, and the lack of appropriate guidelines for analysing them were the most mentioned disadvantages. We identified a variety of industry domains, grouped into seven categories. As we can expect most of the papers report experiences in the Information Technology domain, followed by financials applications. Almost half the papers applied evaluation research, including an empirical validation. Only one third of the analysed papers reported threats to validity, the most mentioned being generalizability (38%). Conclusions: Use Cases have proven to be a useful tool in software development, particularly during the early stages. The positive effects far outweigh the few negative effects reported, although this may be due to the researchers' tendency of not reporting negative results.
Usability Squared: Principles for doing good systems research in robotics
Venue: arXiv cs.RO 2019
Year: 2019

Despite recent major advances in robotics research, massive injections of capital into robotics startups, and significant market appetite for robotic solutions, large-scale real-world deployments of robotic systems remain relatively scarce outside of heavy industry and (recently) warehouse logistics. In this paper, we posit that this scarcity comes from the difficulty of building even merely functional, first-pass robotic applications without a dizzying breadth and depth of expertise, in contrast to the relative ease with which non-experts in cloud computing can build complex distributed applications that function reasonably well. We trace this difficulty in application building to the paucity of good systems research in robotics, and lay out a path toward enabling application building by centering usability in systems research in two different ways: privileging the usability of the abstractions defined in systems research, and ensuring that the research itself is usable by application developers in the context of evaluating it for its applicability to their target domain by following principles of realism, empiricism, and exhaustive explication. In addition, we make some suggestions for community-level changes, incentives, and initiatives to create a better environment for systems work in robotics.
A gray-box approach for curriculum learning
Venue: arXiv cs.LG 2019
Year: 2019

Curriculum learning is often employed in deep reinforcement learning to let the agent progress more quickly towards better behaviors. Numerical methods for curriculum learning in the literature provides only initial heuristic solutions, with little to no guarantee on their quality. We define a new gray-box function that, including a suitable scheduling problem, can be effectively used to reformulate the curriculum learning problem. We propose different efficient numerical methods to address this gray-box reformulation. Preliminary numerical results on a benchmark task in the curriculum learning literature show the viability of the proposed approach.
A Temporal Sequence Learning for Action Recognition and Prediction
Venue: arXiv cs.CV 2019
Year: 2019

In this work\footnote {This work was supported in part by the National Science Foundation under grant IIS-1212948.}, we present a method to represent a video with a sequence of words, and learn the temporal sequencing of such words as the key information for predicting and recognizing human actions. We leverage core concepts from the Natural Language Processing (NLP) literature used in sentence classification to solve the problems of action prediction and action recognition. Each frame is converted into a word that is represented as a vector using the Bag of Visual Words (BoW) encoding method. The words are then combined into a sentence to represent the video, as a sentence. The sequence of words in different actions are learned with a simple but effective Temporal Convolutional Neural Network (T-CNN) that captures the temporal sequencing of information in a video sentence. We demonstrate that a key characteristic of the proposed method is its low-latency, i.e. its ability to predict an action accurately with a partial sequence (sentence). Experiments on two datasets, \textit{UCF101} and \textit{HMDB51} show that the method on average reaches 95\% of its accuracy within half the video frames. Results, also demonstrate that our method achieves compatible state-of-the-art performance in action recognition (i.e. at the completion of the sentence) in addition to action prediction.
LPaintB: Learning to Paint from Self-Supervision
Venue: arXiv cs.LG 2019
Year: 2019

We present a novel reinforcement learning-based natural media painting algorithm. Our goal is to reproduce a reference image using brush strokes and we encode the objective through observations. Our formulation takes into account that the distribution of the reward in the action space is sparse and training a reinforcement learning algorithm from scratch can be difficult. We present an approach that combines self-supervised learning and reinforcement learning to effectively transfer negative samples into positive ones and change the reward distribution. We demonstrate the benefits of our painting agent to reproduce reference images with brush strokes. The training phase takes about one hour and the runtime algorithm takes about 30 seconds on a GTX1080 GPU reproducing a 1000x800 image with 20,000 strokes.
Near Optimal Coflow Scheduling in Networks
Venue: arXiv cs.DC 2019
Year: 2019

The coflow scheduling problem has emerged as a popular abstraction in the last few years to study data communication problems within a data center. In this basic framework, each coflow has a set of communication demands and the goal is to schedule many coflows in a manner that minimizes the total weighted completion time. A coflow is said to complete when all its communication needs are met. This problem has been extremely well studied for the case of complete bipartite graphs that model a data center with full bisection bandwidth and several approximation algorithms and effective heuristics have been proposed recently. In this work, we study a slightly different model of coflow scheduling in general graphs (to capture traffic between data centers) and develop practical and efficient approximation algorithms for it. Our main result is a randomized 2 approximation algorithm for the single path and free path model, significantly improving prior work. In addition, we demonstrate via extensive experiments that the algorithm is practical, easy to implement and performs well in practice.