OpenRaiser/Intern-Atlas huggingface dataset:
Mapping the backbone of the Humanities through the eyes of Wikipedia
Venue: arXiv cs.DL 2019
Year: 2019

The present study aims to establish a valid method by which to apply the theory of co-citations to Wikipedia article references and, subsequently, to map these relationships between scientific papers. This theory, originally applied to scientific literature, will be transferred to the digital environment of collective knowledge generation. To this end, a dataset containing Wikipedia references collected from Altmetric and Scopus' Journal Metrics journals has been used. The articles have been categorized according to the disciplines and specialties established in the All Science Journal Classification (ASJC). They have also been grouped by journal of publication. A set of articles in the Humanities, comprising 25 555 Wikipedia articles with 41 655 references to 32 245 resources, has been selected. Finally, a descriptive statistical study has been conducted and co-citations have been mapped using networks and indicators of degree and betweenness centrality.
Linear codes of 2-designs associated with subcodes of the ternary generalized Reed-Muller codes
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, the 3-rank of the incidence matrices of 2-designs supported by the minimum weight codewords in a family of ternary linear codes considered in [C. Ding, C. Li, Infinite families of 2-designs and 3-designs from linear codes, Discrete Mathematics 340(10) (2017) 2415--2431] are computed. A lower bound on the minimum distance of the ternary codes spanned by the incidence matrices of these designs is derived, and it is proved that the codes are subcodes of the 4th order generalized Reed-Muller codes.
High dimensional affine codes whose square has a designed minimum distance
Venue: arXiv cs.IT 2019
Year: 2019

Given a linear code $\mathcal{C}$, its square code $\mathcal{C}^{(2)}$ is the span of all component-wise products of two elements of $\mathcal{C}$. Motivated by applications in multi-party computation, our purpose with this work is to answer the following question: which families of affine variety codes have simultaneously high dimension $k(\mathcal{C})$ and high minimum distance of $\mathcal{C}^{(2)}$, $d(\mathcal{C}^{(2)})$? More precisely, given a designed minimum distance $d$ we compute an affine variety code $\mathcal{C}$ such that $d(\mathcal{C}^{(2)})\geq d$ and that the dimension of $\mathcal{C}$ is high. The best construction that we propose comes from hyperbolic codes when $d\ge q$ and from weighted Reed-Muller codes otherwise.
How to Make the Preconditioned Conjugate Gradient Method Resilient Against Multiple Node Failures
Venue: arXiv cs.DC 2019
Year: 2019

We study algorithmic approaches for recovering from the failure of several compute nodes in the parallel preconditioned conjugate gradient (PCG) solver on large-scale parallel computers. In particular, we analyze and extend an exact state reconstruction (ESR) approach, which is based on a method proposed by Chen (2011). In the ESR approach, the solver keeps redundant information from previous search directions, so that the solver state can be fully reconstructed if a node fails unexpectedly. ESR does not require checkpointing or external storage for saving dynamic solver data and has low overhead compared to the failure-free situation. In this paper, we improve the fault tolerance of the PCG algorithm based on the ESR approach. In particular, we support recovery from simultaneous or overlapping failures of several nodes for general sparsity patterns of the system matrix, which cannot be handled by Chen's method. For this purpose, we refine the strategy for how to store redundant information across nodes. We analyze and implement our new method and perform numerical experiments with large sparse matrices from real-world applications on 128 nodes of the Vienna Scientific Cluster (VSC). For recovering from three simultaneous node failures we observe average runtime overheads between only 2.8% and 55.0%. The overhead of the improved resilience depends on the sparsity pattern of the system matrix.
CloudDet: Interactive Visual Analysis of Anomalous Performances in Cloud Computing Systems
Venue: arXiv cs.HC 2019
Year: 2019

Detecting and analyzing potential anomalous performances in cloud computing systems is essential for avoiding losses to customers and ensuring the efficient operation of the systems. To this end, a variety of automated techniques have been developed to identify anomalies in cloud computing performance. These techniques are usually adopted to track the performance metrics of the system (e.g., CPU, memory, and disk I/O), represented by a multivariate time series. However, given the complex characteristics of cloud computing data, the effectiveness of these automated methods is affected. Thus, substantial human judgment on the automated analysis results is required for anomaly interpretation. In this paper, we present a unified visual analytics system named CloudDet to interactively detect, inspect, and diagnose anomalies in cloud computing systems. A novel unsupervised anomaly detection algorithm is developed to identify anomalies based on the specific temporal patterns of the given metrics data (e.g., the periodic pattern), the results of which are visualized in our system to indicate the occurrences of anomalies. Rich visualization and interaction designs are used to help understand the anomalies in the spatial and temporal context. We demonstrate the effectiveness of CloudDet through a quantitative evaluation, two case studies with real-world data, and interviews with domain experts.
What should I document? A preliminary systematic mapping study into API documentation knowledge
Venue: arXiv cs.SE 2019
Year: 2019

Background: Good API documentation facilities the development process, improving productivity and quality. While the topic of API documentation quality has been of interest for the last two decades, there have been few studies to map the specific constructs needed to create a good document. In effect, we still need a structured taxonomy against which to capture knowledge. Aims: This study reports emerging results of a systematic mapping study. We capture key conclusions from previous studies that assess API documentation quality, and synthesise the results into a single framework. Method: By conducting a systematic review of 21 key works, we have developed a five dimensional taxonomy based on 34 categorised weighted recommendations. Results: All studies utilise field study techniques to arrive at their recommendations, with seven studies employing some form of interview and questionnaire, and four conducting documentation analysis. The taxonomy we synthesise reinforces that usage description details (code snippets, tutorials, and reference documents) are generally highly weighted as helpful in API documentation, in addition to design rationale and presentation. Conclusions: We propose extensions to this study aligned to developer's utility for each of the taxonomy's categories.
Are Outlier Detection Methods Resilient to Sampling?
Venue: arXiv cs.LG 2019
Year: 2019

Outlier detection is a fundamental task in data mining and has many applications including detecting errors in databases. While there has been extensive prior work on methods for outlier detection, modern datasets often have sizes that are beyond the ability of commonly used methods to process the data within a reasonable time. To overcome this issue, outlier detection methods can be trained over samples of the full-sized dataset. However, it is not clear how a model trained on a sample compares with one trained on the entire dataset. In this paper, we introduce the notion of resilience to sampling for outlier detection methods. Orthogonal to traditional performance metrics such as precision/recall, resilience represents the extent to which the outliers detected by a method applied to samples from a sampling scheme matches those when applied to the whole dataset. We propose a novel approach for estimating the resilience to sampling of both individual outlier methods and their ensembles. We performed an extensive experimental study on synthetic and real-world datasets where we study seven diverse and representative outlier detection methods, compare results obtained from samples versus those obtained from the whole datasets and evaluate the accuracy of our resilience estimates. We observed that the methods are not equally resilient to a given sampling scheme and it is often the case that careful joint selection of both the sampling scheme and the outlier detection method is necessary. It is our hope that the paper initiates research on designing outlier detection algorithms that are resilient to sampling.
An Implementation of a Non-monotonic Logic in an Embedded Computer for a Motor-glider
Venue: arXiv cs.AI 2019
Year: 2019

In this article we present an implementation of non-monotonic reasoning in an embedded system. As a part of an autonomous motor-glider, it simulates piloting decisions of an airplane. A real pilot must take care not only about the information arising from the cockpit (airspeed, altitude, variometer, compass...) but also from outside the cabin. Throughout a flight, a pilot is constantly in communication with the control tower to follow orders, because there is an airspace regulation to respect. In addition, if the control tower sends orders while the pilot has an emergency, he may have to violate these orders and airspace regulations to solve his problem (e.g. emergency landing). On the other hand, climate changes constantly (wind, snow, hail..) and can affect the sensors. All these cases easily lead to contradictions. Switching to reasoning under uncertainty, a pilot must make decisions to carry out a flight. The objective of this implementation is to validate a non-monotonic model which allows to solve the question of incomplete and contradictory information. We formalize the problem using default logic, a non-monotonic logic which allows to find fixed-points in the face of contradictions. For the implementation, the Prolog language is used in an embedded computer running at 1 GHz single core with 512 Mb of RAM and 0.8 watts of energy consumption.
Cost-Driven Offloading for DNN-based Applications over Cloud, Edge and End Devices
Venue: arXiv cs.DC 2019
Year: 2019

Currently, deep neural networks (DNNs) have achieved a great success in various applications. Traditional deployment for DNNs in the cloud may incur a prohibitively serious delay in transferring input data from the end devices to the cloud. To address this problem, the hybrid computing environments, consisting of the cloud, edge and end devices, are adopted to offload DNN layers by combining the larger layers (more amount of data) in the cloud and the smaller layers (less amount of data) at the edge and end devices. A key issue in hybrid computing environments is how to minimize the system cost while accomplishing the offloaded layers with their deadline constraints. In this paper, a self-adaptive discrete particle swarm optimization (PSO) algorithm using the genetic algorithm (GA) operators was proposed to reduce the system cost caused by data transmission and layer execution. This approach considers the characteristics of DNNs partitioning and layers offloading over the cloud, edge and end devices. The mutation operator and crossover operator of GA were adopted to avert the premature convergence of PSO, which distinctly reduces the system cost through enhanced population diversity of PSO. The proposed offloading strategy is compared with benchmark solutions, and the results show that our strategy can effectively reduce the cost of offloading for DNN-based applications over the cloud, edge and end devices relative to the benchmarks.
VASSL: A Visual Analytics Toolkit for Social Spambot Labeling
Venue: arXiv cs.HC 2019
Year: 2019

Social media platforms such as Twitter are filled with social spambots. Detecting these malicious accounts is essential, yet challenging, as they continually evolve and evade traditional detection techniques. In this work, we propose VASSL, a visual analytics system that assists in the process of detecting and labeling spambots. Our tool enhances the performance and scalability of manual labeling by providing multiple connected views and utilizing dimensionality reduction, sentiment analysis and topic modeling techniques, which offer new insights that enable the identification of spambots. The system allows users to select and analyze groups of accounts in an interactive manner, which enables the detection of spambots that may not be identified when examined individually. We conducted a user study to objectively evaluate the performance of VASSL users, as well as capturing subjective opinions about the usefulness and the ease of use of the tool.
What-Why Analysis of Expert Interviews: Analysing Geographically-Embedded Flow Data
Venue: arXiv cs.HC 2019
Year: 2019

In this paper, we present our analysis of five expert interviews, each from a different application domain. Such analysis is crucial to understanding the real-world scenarios of analysing geographically-embedded flow data. The results of our analysis show that similar high-level tasks were conducted in different domains. To better describe the targets of these tasks, we proposed three flow-targets for analysing geographically-embedded flow data: single flow, total flow and regional flow.
Overcoming Catastrophic Forgetting by Neuron-level Plasticity Control
Venue: arXiv cs.CV 2019
Year: 2019

To address the issue of catastrophic forgetting in neural networks, we propose a novel, simple, and effective solution called neuron-level plasticity control (NPC). While learning a new task, the proposed method preserves the knowledge for the previous tasks by controlling the plasticity of the network at the neuron level. NPC estimates the importance value of each neuron and consolidates important \textit{neurons} by applying lower learning rates, rather than restricting individual connection weights to stay close to certain values. The experimental results on the incremental MNIST (iMNIST) and incremental CIFAR100 (iCIFAR100) datasets show that neuron-level consolidation is substantially more effective compared to the connection-level consolidation approaches.
Rate Splitting for Massive MIMO Multi-carrier system using Full Duplex Decode and Forward Relay with Hardware Impairments
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we address the power allocation problem for a decode and forward (DF) relay system, where a massive multiple-input-multiple-output (mMIMO) multi-carrier (MC) base station (BS) node communicates with a MC single antenna node directly and also through the single antenna full duplex (FD) MC relay, using rate splitting (RS) approach. Successive interference cancellation approach is adopted at the destination. We consider orthogonal frequency division multiplexing (OFDM) as our MC strategy. We take into account the impact of hardware distortions resulting in residual self-interference and inter-carrier leakage (ICL), and also imperfect channel state information (CSI). We formulate a joint sub-carrier and power allocation problem to maximize the total sum rate. An iterative optimization method is proposed, which follows successive inner approximation (SIA) framework to reach the convergence point that satisfies the Karush-Kuhn-Tucker (KKT) conditions. Numerical results show the significance of distortion-aware design for such systems, and also the significant gain in terms of sum rate compared to its half duplex (HD) and also non-rate splitting scheme.
Testing performance with and without Block Low Rank Compression in MUMPS and the new PaStiX 6.0 for JOREK nonlinear MHD simulations
Venue: arXiv cs.PF 2019
Year: 2019

The interface to the MUMPS solver was updated in the JOREK MHD code to support Block Low Rank (BLR) compression and an interface to the new PaStiX solver version 6 has been implemented supporting BLR as well. First tests were carried out with JOREK, which solves a large sparse matrix system iteratively in each time step. For the preconditioning, a direct solver is applied in the code to sub-matrices, and at this point BLR was applied with the results being summarized in this report. For a simple case with a linearly growing mode, results with both solvers look promising with a considerable reduction of the memory consumption by several ten percent was obtained. A direct increase in performance was seen in particular configurations already. The choice of the BLR accuracy parameter $ε$ proves to be critical in this simple test and also in more realistic simulations, which were carried out only with MUMPS due to the limited time available. The more realistic test showed an increase in run time when using BLR, which was mitigated when using larger values of $ε$. However, the GMRes iterative solver does not reach convergence anymore when $ε$ is too large, since the preconditioner becomes too inaccurate in that case. It is thus critical to use an $ε$ as large as possible, while still reaching convergence. More tests regarding this optimum will be necessary in the future. BLR can also lead to an indirect speed-up in particular cases, when the simulation can be run on a smaller number of compute nodes due to the reduced memory consumption.
Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space
Venue: arXiv cs.LG 2019
Year: 2019

As machine learning becomes an important part of many real world applications affecting human lives, new requirements, besides high predictive accuracy, become important. One important requirement is transparency, which has been associated with model interpretability. Many machine learning algorithms induce models difficult to interpret, named black box. Moreover, people have difficulty to trust models that cannot be explained. In particular for machine learning, many groups are investigating new methods able to explain black box models. These methods usually look inside the black models to explain their inner work. By doing so, they allow the interpretation of the decision making process used by black box models. Among the recently proposed model interpretation methods, there is a group, named local estimators, which are designed to explain how the label of particular instance is predicted. For such, they induce interpretable models on the neighborhood of the instance to be explained. Local estimators have been successfully used to explain specific predictions. Although they provide some degree of model interpretability, it is still not clear what is the best way to implement and apply them. Open questions include: how to best define the neighborhood of an instance? How to control the trade-off between the accuracy of the interpretation method and its interpretability? How to make the obtained solution robust to small variations on the instance to be explained? To answer to these questions, we propose and investigate two strategies: (i) using data instance properties to provide improved explanations, and (ii) making sure that the neighborhood of an instance is properly defined by taking the geometry of the domain of the feature space into account. We evaluate these strategies in a regression task and present experimental results that show that they can improve local explanations.
A Comparison of Radial and Linear Charts for Visualizing Daily Pattern
Venue: arXiv cs.GR 2019
Year: 2019

Radial charts are generally considered less effective than linear charts. Perhaps the only exception is in visualizing periodical time-dependent data, which is believed to be naturally supported by the radial layout. It has been demonstrated that the drawbacks of radial charts outweigh the benefits of this natural mapping. Visualization of daily patterns, as a special case, has not been systematically evaluated using radial charts. In contrast to yearly or weekly recurrent trends, the analysis of daily patterns on a radial chart may benefit from our trained skill on reading radial clocks that are ubiquitous in our culture. In a crowd-sourced experiment with 92 non-expert users, we evaluated the accuracy, efficiency, and subjective ratings of radial and linear charts for visualizing daily traffic accident patterns. We systematically compared juxtaposed 12-hours variants and single 24-hours variants for both layouts in four low-level tasks and one high-level interpretation task. Our results show that over all tasks, the most elementary 24-hours linear bar chart is most accurate and efficient and is also preferred by the users. This provides strong evidence for the use of linear layouts - even for visualizing periodical daily patterns.
Attention-Wrapped Hierarchical BLSTMs for DDI Extraction
Venue: arXiv cs.IR 2019
Year: 2019

Drug-Drug Interactions (DDIs) Extraction refers to the efforts to generate hand-made or automatic tools to extract embedded information from text and literature in the biomedical domain. Because of restrictions in hand-made efforts and their lower speed, Machine-Learning, or Deep-Learning approaches have become more popular for extracting DDIs. In this study, we propose a novel and generic Deep-Learning model which wraps Hierarchical Bidirectional LSTMs with two Attention Mechanisms that outperforms state-of-the-art models for DDIs Extraction, based on the DDIExtraction-2013 corpora. This model has obtained the macro F1-score of 0.785, and the precision of 0.80.
Binary Component Decomposition Part I: The Positive-Semidefinite Case
Venue: arXiv cs.DS 2019
Year: 2019

This paper studies the problem of decomposing a low-rank positive-semidefinite matrix into symmetric factors with binary entries, either $\{\pm 1\}$ or $\{0,1\}$. This research answers fundamental questions about the existence and uniqueness of these decompositions. It also leads to tractable factorization algorithms that succeed under a mild deterministic condition. A companion paper addresses the related problem of decomposing a low-rank rectangular matrix into a binary factor and an unconstrained factor.
A Structural Average of Labeled Merge Trees for Uncertainty Visualization
Venue: arXiv cs.CG 2019
Year: 2019

Physical phenomena in science and engineering are frequently modeled using scalar fields. In scalar field topology, graph-based topological descriptors such as merge trees, contour trees, and Reeb graphs are commonly used to characterize topological changes in the (sub)level sets of scalar fields. One of the biggest challenges and opportunities to advance topology-based visualization is to understand and incorporate uncertainty into such topological descriptors to effectively reason about their underlying data. In this paper, we study a structural average of a set of labeled merge trees and use it to encode uncertainty in data. Specifically, we compute a 1-center tree that minimizes its maximum distance to any other tree in the set under a well-defined metric called the interleaving distance. We provide heuristic strategies that compute structural averages of merge trees whose labels do not fully agree. We further provide an interactive visualization system that resembles a numerical calculator that takes as input a set of merge trees and outputs a tree as their structural average. We also highlight structural similarities between the input and the average and incorporate uncertainty information for visual exploration. We develop a novel measure of uncertainty, referred to as consistency, via a metric-space view of the input trees. Finally, we demonstrate an application of our framework through merge trees that arise from ensembles of scalar fields. Our work is the first to employ interleaving distances and consistency to study a global, mathematically rigorous, structural average of merge trees in the context of uncertainty visualization.