OpenRaiser/Intern-Atlas huggingface dataset:
Probabilistic Regressor Chains with Monte Carlo Methods
Venue: arXiv cs.LG 2019
Year: 2019

A large number and diversity of techniques have been offered in the literature in recent years for solving multi-label classification tasks, including classifier chains where predictions are cascaded to other models as additional features. The idea of extending this chaining methodology to multi-output regression has already been suggested and trialed: regressor chains. However, this has so-far been limited to greedy inference and has provided relatively poor results compared to individual models, and of limited applicability. In this paper we identify and discuss the main limitations, including an analysis of different base models, loss functions, explainability, and other desiderata of real-world applications. To overcome the identified limitations we study and develop methods for regressor chains. In particular we present a sequential Monte Carlo scheme in the framework of a probabilistic regressor chain, and we show it can be effective, flexible and useful in several types of data. We place regressor chains in context in general terms of multi-output learning with continuous outputs, and in doing this shed additional light on classifier chains.
A Survey of Data Quality Measurement and Monitoring Tools
Venue: arXiv cs.DB 2019
Year: 2019

High-quality data is key to interpretable and trustworthy data analytics and the basis for meaningful data-driven decisions. In practical scenarios, data quality is typically associated with data preprocessing, profiling, and cleansing for subsequent tasks like data integration or data analytics. However, from a scientific perspective, a lot of research has been published about the measurement (i.e., the detection) of data quality issues and different generally applicable data quality dimensions and metrics have been discussed. In this work, we close the gap between research into data quality measurement and practical implementations by investigating the functional scope of current data quality tools. With a systematic search, we identified 667 software tools dedicated to "data quality", from which we evaluated 13 tools with respect to three functionality areas: (1) data profiling, (2) data quality measurement in terms of metrics, and (3) continuous data quality monitoring. We selected the evaluated tools with regard to pre-defined exclusion criteria to ensure that they are domain-independent, provide the investigated functions, and are evaluable freely or as trial. This survey aims at a comprehensive overview on state-of-the-art data quality tools and reveals potential for their functional enhancement. Additionally, the results allow a critical discussion on concepts, which are widely accepted in research, but hardly implemented in any tool observed, for example, generally applicable data quality metrics.
On the Evaluation of Conditional GANs
Venue: arXiv cs.CV 2019
Year: 2019

Conditional Generative Adversarial Networks (cGANs) are finding increasingly widespread use in many application domains. Despite outstanding progress, quantitative evaluation of such models often involves multiple distinct metrics to assess different desirable properties, such as image quality, conditional consistency, and intra-conditioning diversity. In this setting, model benchmarking becomes a challenge, as each metric may indicate a different "best" model. In this paper, we propose the Frechet Joint Distance (FJD), which is defined as the Frechet distance between joint distributions of images and conditioning, allowing it to implicitly capture the aforementioned properties in a single metric. We conduct proof-of-concept experiments on a controllable synthetic dataset, which consistently highlight the benefits of FJD when compared to currently established metrics. Moreover, we use the newly introduced metric to compare existing cGAN-based models for a variety of conditioning modalities (e.g. class labels, object masks, bounding boxes, images, and text captions). We show that FJD can be used as a promising single metric for cGAN benchmarking and model selection. Code can be found at https://github.com/facebookresearch/fjd.
Neural Probabilistic Logic Programming in DeepProbLog
Venue: arXiv cs.AI 2019
Year: 2019

We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques of the underlying probabilistic logic programming language ProbLog can be adapted for the new language. We theoretically and experimentally demonstrate that DeepProbLog supports (i) both symbolic and subsymbolic representations and inference, (ii) program induction, (iii) probabilistic (logic) programming, and (iv) (deep) learning from examples. To the best of our knowledge, this work is the first to propose a framework where general-purpose neural networks and expressive probabilistic-logical modeling and reasoning are integrated in a way that exploits the full expressiveness and strengths of both worlds and can be trained end-to-end based on examples.
On the Monniaux Problem in Abstract Interpretation
Venue: arXiv cs.LO 2019
Year: 2019

The Monniaux Problem in abstract interpretation asks, roughly speaking, whether the following question is decidable: given a program $P$, a safety (\emph{e.g.}, non-reachability) specification $φ$, and an abstract domain of invariants $\mathcal{D}$, does there exist an inductive invariant $I$ in $\mathcal{D}$ guaranteeing that program $P$ meets its specification $φ$. The Monniaux Problem is of course parameterised by the classes of programs and invariant domains that one considers. In this paper, we show that the Monniaux Problem is undecidable for unguarded affine programs and semilinear invariants (unions of polyhedra). Moreover, we show that decidability is recovered in the important special case of simple linear loops.
An AI-based, Multi-stage detection system of banking botnets
Venue: arXiv cs.CR 2019
Year: 2019

Banking Trojans, botnets are primary drivers of financially-motivated cybercrime. In this paper, we first analyzed how an APT-based banking botnet works step by step through the whole lifecycle. Specifically, we present a multi-stage system that detects malicious banking botnet activities which potentially target the organizations. The system leverages Cyber Data Lake as well as multiple artificial intelligence techniques at different stages. The evaluation results using public datasets showed that Deep Learning based detections were highly successful compared with baseline models.
Compositional Deep Learning
Venue: arXiv cs.LG 2019
Year: 2019

Neural networks have become an increasingly popular tool for solving many real-world problems. They are a general framework for differentiable optimization which includes many other machine learning approaches as special cases. In this thesis we build a category-theoretic formalism around a class of neural networks exemplified by CycleGAN. CycleGAN is a collection of neural networks, closed under composition, whose inductive bias is increased by enforcing composition invariants, i.e. cycle-consistencies. Inspired by Functorial Data Migration, we specify the interconnection of these networks using a categorical schema, and network instances as set-valued functors on this schema. We also frame neural network architectures, datasets, models, and a number of other concepts in a categorical setting and thus show a special class of functors, rather than functions, can be learned using gradient descent. We use the category-theoretic framework to conceive a novel neural network architecture whose goal is to learn the task of object insertion and object deletion in images with unpaired data. We test the architecture on three different datasets and obtain promising results.
A Tale of Two Set Theories
Venue: arXiv cs.LO 2019
Year: 2019

We describe the relationship between two versions of Tarski-Grothendieck set theory: the first-order set theory of Mizar and the higher-order set theory of Egal. We show how certain higher-order terms and propositions in Egal have equivalent first-order presentations. We then prove Tarski's Axiom A (an axiom in Mizar) in Egal and construct a Grothendieck Universe operator (a primitive with axioms in Egal) in Mizar.
Enhancing magic sets with an application to ontological reasoning
Venue: arXiv cs.AI 2019
Year: 2019

Magic sets are a Datalog to Datalog rewriting technique to optimize query answering. The rewritten program focuses on a portion of the stable model(s) of the input program which is sufficient to answer the given query. However, the rewriting may introduce new recursive definitions, which can involve even negation and aggregations, and may slow down program evaluation. This paper enhances the magic set technique by preventing the creation of (new) recursive definitions in the rewritten program. It turns out that the new version of magic sets is closed for Datalog programs with stratified negation and aggregations, which is very convenient to obtain efficient computation of the stable model of the rewritten program. Moreover, the rewritten program is further optimized by the elimination of subsumed rules and by the efficient handling of the cases where binding propagation is lost. The research was stimulated by a challenge on the exploitation of Datalog/\textsc{dlv} for efficient reasoning on large ontologies. All proposed techniques have been hence implemented in the \textsc{dlv} system, and tested for ontological reasoning, confirming their effectiveness. Under consideration for publication in Theory and Practice of Logic Programming.
Exploring sentence informativeness
Venue: arXiv cs.CL 2019
Year: 2019

This study is a preliminary exploration of the concept of informativeness -how much information a sentence gives about a word it contains- and its potential benefits to building quality word representations from scarce data. We propose several sentence-level classifiers to predict informativeness, and we perform a manual annotation on a set of sentences. We conclude that these two measures correspond to different notions of informativeness. However, our experiments show that using the classifiers' predictions to train word embeddings has an impact on embedding quality.
A Practical Fixed-Parameter Algorithm for Constructing Tree-Child Networks from Multiple Binary Trees
Venue: arXiv cs.DM 2019
Year: 2019

We present the first fixed-parameter algorithm for constructing a tree-child phylogenetic network that displays an arbitrary number of binary input trees and has the minimum number of reticulations among all such networks. The algorithm uses the recently introduced framework of cherry picking sequences and runs in $O((8k)^k \mathrm{poly}(n, t))$ time, where $n$ is the number of leaves of every tree, $t$ is the number of trees, and $k$ is the reticulation number of the constructed network. Moreover, we provide an efficient parallel implementation of the algorithm and show that it can deal with up to $100$ input trees on a standard desktop computer, thereby providing a major improvement over previous phylogenetic network construction methods.
A Graph Neural Network Approach for Scalable Wireless Power Control
Venue: arXiv cs.IT 2019
Year: 2019

Deep neural networks have recently emerged as a disruptive technology to solve NP-hard wireless resource allocation problems in a real-time manner. However, the adopted neural network structures, e.g., multi-layer perceptron (MLP) and convolutional neural network (CNN), are inherited from deep learning for image processing tasks, and thus are not tailored to problems in wireless networks. In particular, the performance of these methods deteriorates dramatically when the wireless network size becomes large. In this paper, we propose to utilize graph neural networks (GNNs) to develop scalable methods for solving the power control problem in $K$-user interference channels. Specifically, a $K$-user interference channel is first modeled as a complete graph, where the quantitative information of wireless channels is incorporated as the features of the graph. We then propose an interference graph convolutional neural network (IGCNet) to learn the optimal power control in an unsupervised manner. It is shown that one-layer IGCNet is a universal approximator to continuous set functions, which well matches the permutation invariance property of interference channels and it is robust to imperfect channel state information (CSI). Extensive simulations will show that the proposed IGCNet outperforms existing methods and achieves significant speedup over the classic algorithm for power control, namely, WMMSE. The code is available on https://github.com/yshenaw/Globecom2019.
Performance evaluation of THz wireless systems under the joint impact of misalignment fading and phase noise
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we investigate the joint impact of misalignment fading and local oscillator (LO) phase noise (PHN) in multi-carrier terahertz (THz) wireless systems. In more detail, after establishing a suitable system model that takes into account the particularities of the THz channel, as well as the transceivers characteristics, we present simulation results that quantify the joint impact of misalignment fading and PHN in terms of average signal-to-interference-plus-noise-ratio (SINR) and outage probability (OP).
Criteria for Rigor in Visualization Design Study
Venue: arXiv cs.HC 2019
Year: 2019

We develop a new perspective on research conducted through visualization design study that emphasizes design as a method of inquiry and the broad range of knowledge-contributions achieved through it as multiple, subjective, and socially constructed. From this interpretivist position we explore the nature of visualization design study and develop six criteria for rigor. We propose that rigor is established and judged according to the extent to which visualization design study research and its reporting are INFORMED, REFLEXIVE, ABUNDANT, PLAUSIBLE, RESONANT, and TRANSPARENT. This perspective and the criteria were constructed through a four-year engagement with the discourse around rigor and the nature of knowledge in social science, information systems, and design. We suggest methods from cognate disciplines that can support visualization researchers in meeting these criteria during the planning, execution, and reporting of design study. Through a series of deliberately provocative questions, we explore implications of this new perspective for design study research in visualization, concluding that as a discipline, visualization is not yet well positioned to embrace, nurture, and fully benefit from a rigorous, interpretivist approach to design study. The perspective and criteria we present are intended to stimulate dialogue and debate around the nature of visualization design study and the broader underpinnings of the discipline.
A survey on Crosstalk and Routing, Modulation Selection, Core and Spectrum Allocation in Elastic Optical Networks
Venue: arXiv cs.NI 2019
Year: 2019

Elastic Optical Networks (EON) emerge as a viable solution to supply the current growing demand for bandwidth. With the application of multi-core fibers (MCF) in EON links, it is possible to increase the availability of spectral resources. An EON network with MCF enables Space-Division Multiplexing (SDM), allowing the use of more resources in the fibers and increasing the capacity of attending circuit requests. However, the use of SDM brings some problems of interference between the circuits of a fiber, with greater emphasis on crosstalk interference. In this paper, some important concepts around EON are presented, along with the characterization of SDM technology. The Routing, Modulation, Spectrum and Core Allocation (RMSCA) problem is also characterized, and some solutions currently found in the literature are cited. After, the impact of crosstalk interference is discussed, and which elements are responsible for its occurrence. The paper is concludes with an evaluation of the state of the art, and the discrimination of the main points found from the study of papers related to the SDM-EON scenario.
LightGuider: Guiding Interactive Lighting Design using Suggestions, Provenance, and Quality Visualization
Venue: arXiv cs.GR 2019
Year: 2019

LightGuider is a novel guidance-based approach to interactive lighting design, which typically consists of interleaved 3D modeling operations and light transport simulations. Rather than having designers use a trial-and-error approach to match their illumination constraints and aesthetic goals, LightGuider supports the process by simulating potential next modeling steps that can deliver the most significant improvements. LightGuider takes predefined quality criteria and the current focus of the designer into account to visualize suggestions for lighting-design improvements via a specialized provenance tree. This provenance tree integrates snapshot visualizations of how well a design meets the given quality criteria weighted by the designer's preferences. This integration facilitates the analysis of quality improvements over the course of a modeling workflow as well as the comparison of alternative design solutions. We evaluate our approach with three lighting designers to illustrate its usefulness.
Testing with Fewer Resources: An Adaptive Approach to Performance-Aware Test Case Generation
Venue: arXiv cs.SE 2019
Year: 2019

Automated test case generation is an effective technique to yield high-coverage test suites. While the majority of research effort has been devoted to satisfying coverage criteria, a recent trend emerged towards optimizing other non-coverage aspects. In this regard, runtime and memory usage are two essential dimensions: less expensive tests reduce the resource demands for the generation process and later regression testing phases. This study shows that performance-aware test case generation requires solving two main challenges: providing a good approximation of resource usage with minimal overhead and avoiding detrimental effects on both final coverage and fault detection effectiveness. To tackle these challenges, we conceived a set of performance proxies -- inspired by previous work on performance testing -- that provide a reasonable estimation of the test execution costs (i.e., runtime and memory usage). Thus, we propose an adaptive strategy, called aDynaMOSA, which leverages these proxies by extending DynaMOSA, a state-of-the-art evolutionary algorithm in unit testing. Our empirical study -- involving 110 non-trivial Java classes -- reveals that our adaptive approach generates test suite with statistically significant improvements in runtime (-25%) and heap memory consumption (-15%) compared to DynaMOSA. Additionally, aDynaMOSA has comparable results to DynaMOSA over seven different coverage criteria and similar fault detection effectiveness. Our empirical investigation also highlights that the usage of performance proxies (i.e., without the adaptiveness) is not sufficient to generate more performant test cases without compromising the overall coverage.
On Approximate Range Mode and Range Selection
Venue: arXiv cs.DS 2019
Year: 2019

For any $ε\in (0,1)$, a $(1+ε)$-approximate range mode query asks for the position of an element whose frequency in the query range is at most a factor $(1+ε)$ smaller than the true mode. For this problem, we design an $O(n/ε)$ bit data structure supporting queries in $O(\lg(1/ε))$ time. This is an encoding data structure which does not require access to the input sequence; we prove the space cost is asymptotically optimal for constant $ε$. Our solution improves the previous best result of Greve et al. (Cell Probe Lower Bounds and Approximations for Range Mode, ICALP'10) by reducing the space cost by a factor of $\lg n$ while achieving the same query time. We also design an $O(n)$-word dynamic data structure that answers queries in $O(\lg n /\lg\lg n)$ time and supports insertions and deletions in $O(\lg n)$ time, for any constant $ε\in (0,1)$. This is the first result on dynamic approximate range mode; it can also be used to obtain the first static data structure for approximate 3-sided range mode queries in two dimensions. We also consider approximate range selection. For any $α\in (0,1/2)$, an $α$-approximate range selection query asks for the position of an element whose rank in the query range is in $[k - αs, k + αs]$, where $k$ is a rank given by the query and $s$ is the size of the query range. When $α$ is a constant, we design an $O(n)$-bit encoding data structure that can answer queries in constant time and prove this space cost is asymptotically optimal. The previous best result by Krizanc et al. (Range Mode and Range Median Queries on Lists and Trees, Nordic Journal of Computing, 2005) uses $O(n\lg n)$ bits, or $O(n)$ words, to achieve constant approximation for range median only. Thus we not only improve the space cost, but also provide support for any arbitrary $k$ given at query time.
A Wearable Medical Sensor for Provisional Healthcare
Venue: arXiv cs.HC 2019
Year: 2019

Thispaper presents the design and realization of a context-aware wireless health monitoring system for recording the heartbeat (HR) and respiration (RR) rate based on an indirect measurement approach. The system consists of a contact-less medical sensor as well as a communication infrastructure for handling the transmission and reception of the measured results. The contact-less sensor includes a highly sensitive tri-axial accelerometer, an accurate temperature and air pressure sensor that enable one to inspect patients' health condition by continuously monitoring of two critical signs related to the cardiorespiratory system. The developed system can also be utilized in performing a number of long-term inspection on the heart and lungs while measuring the HR and RR values in addition to calculating the HR and RR ratio, which is denoted by HRR. The obtained results show the potential of the developed system for versatile monitoring applications applied to telemedicine
Snomed2Vec: Random Walk and Poincaré Embeddings of a Clinical Knowledge Base for Healthcare Analytics
Venue: arXiv cs.LG 2019
Year: 2019

Representation learning methods that transform encoded data (e.g., diagnosis and drug codes) into continuous vector spaces (i.e., vector embeddings) are critical for the application of deep learning in healthcare. Initial work in this area explored the use of variants of the word2vec algorithm to learn embeddings for medical concepts from electronic health records or medical claims datasets. We propose learning embeddings for medical concepts by using graph-based representation learning methods on SNOMED-CT, a widely popular knowledge graph in the healthcare domain with numerous operational and research applications. Current work presents an empirical analysis of various embedding methods, including the evaluation of their performance on multiple tasks of biomedical relevance (node classification, link prediction, and patient state prediction). Our results show that concept embeddings derived from the SNOMED-CT knowledge graph significantly outperform state-of-the-art embeddings, showing 5-6x improvement in ``concept similarity" and 6-20\% improvement in patient diagnosis.