OpenRaiser/Intern-Atlas huggingface dataset:
An Empirical Exploration on the Supervision of PhD Students Closely Collaborating with Industry
Venue: arXiv cs.CY 2019
Year: 2019

With an increase of PhD students working in industry, there is a need to understand what factors are influencing supervision for industrial students. This paper aims at exploring the challenges and good approaches to supervision of industrial PhD students. Data was collected through semi-structured interviews of six PhD students and supervisors with experience in PhD studies at several organizations in the embedded software industry in Sweden. The data was anonymized and it was analyzed by means of thematic analysis. The results indicate that there are many challenges and opportunities to improve the supervision of industrial PhD students.
Deterrence and Prevention-based Model to Mitigate Information Security Insider Threats in Organisations
Venue: arXiv cs.CY 2019
Year: 2019

Previous studies show that information security breaches and privacy violations are important issues for organisations and people. It is acknowledged that decreasing the risk in this domain requires consideration of the technological aspects of information security alongside human aspects. Employees intentionally or unintentionally account for a significant portion of the threats to information assets in organisations. This research presents a novel conceptual framework to mitigate the risk of insiders using deterrence and prevention approaches. Deterrence factors discourage employees from engaging in information security misbehaviour in organisations, and situational crime prevention factors encourage them to prevent information security misconduct. Our findings show that perceived sanctions certainty and severity significantly influence individuals' attitudes and deter them from information security misconduct. In addition, the output revealed that increasing the effort, risk and reducing the reward (benefits of crime) influence the employees' attitudes towards prevent information security misbehaviour. However, removing excuses and reducing provocations do not significantly influence individuals' attitudes towards prevent information security misconduct. Finally, the output of the data analysis also showed that subjective norms, perceived behavioural control and attitude influence individuals' intentions, and, ultimately, their behaviour towards avoiding information security misbehaviour.
More Parallelism in Dijkstra's Single-Source Shortest Path Algorithm
Venue: arXiv cs.DC 2019
Year: 2019

Dijkstra's algorithm for the Single-Source Shortest Path (SSSP) problem is notoriously hard to parallelize in $o(n)$ depth, $n$ being the number of vertices in the input graph, without increasing the required parallel work unreasonably. Crauser et al.\ (1998) presented observations that allow to identify more than a single vertex at a time as correct and correspondingly more edges to be relaxed simultaneously. Their algorithm runs in parallel phases, and for certain random graphs they showed that the number of phases is $O(n^{1/3})$ with high probability. A work-efficient CRCW PRAM with this depth was given, but no implementation on a real, parallel system. In this paper we strengthen the criteria of Crauser et al., and discuss tradeoffs between work and number of phases in their implementation. We present simulation results with a range of common input graphs for the depth that an ideal parallel algorithm that can apply the criteria at no cost and parallelize relaxations without conflicts can achieve. These results show that the number of phases is indeed a small root of $n$, but still off from the shortest path length lower bound that can also be computed. We give a shared-memory parallel implementation of the most work-efficient version of a Dijkstra's algorithm running in parallel phases, which we compare to an own implementation of the well-known $Δ$-stepping algorithm. We can show that the work-efficient SSSP algorithm applying the criteria of Crauser et al. is competitive to and often better than $Δ$-stepping on our chosen input graphs. Despite not providing an $o(n)$ guarantee on the number of required phases, criteria allowing concurrent relaxation of many correct vertices may be a viable approach to practically fast, parallel SSSP implementations.
Quality Assessment of Free-viewpoint Videos by Quantifying the Elastic Changes of Multi-Scale Motion Trajectories
Venue: arXiv cs.MM 2019
Year: 2019

Virtual viewpoints synthesis is an essential process for many immersive applications including Free-viewpoint TV (FTV). A widely used technique for viewpoints synthesis is Depth-Image-Based-Rendering (DIBR) technique. However, such techniques may introduce challenging non-uniform spatial-temporal structure-related distortions. Most of the existing state-of-the-art quality metrics fail to handle these distortions, especially the temporal structure inconsistencies observed during the switch of different viewpoints. To tackle this problem, an elastic metric and multi-scale trajectory based video quality metric (EM-VQM) is proposed in this paper. Dense motion trajectory is first used as a proxy for selecting temporal sensitive regions, where local geometric distortions might significantly diminish the perceived quality. Afterwards, the amount of temporal structure inconsistencies and unsmooth viewpoints transitions are quantified by calculating 1) the amount of motion trajectory deformations with elastic metric and, 2) the spatial-temporal structural dissimilarity. According to the comprehensive experimental results on two FTV video datasets, the proposed metric outperforms the state-of-the-art metrics designed for free-viewpoint videos significantly and achieves a gain of 12.86% and 16.75% in terms of median Pearson linear correlation coefficient values on the two datasets compared to the best one, respectively.
Building Automated Survey Coders via Interactive Machine Learning
Venue: arXiv cs.IR 2019
Year: 2019

Software systems trained via machine learning to automatically classify open-ended answers (a.k.a. verbatims) are by now a reality. Still, their adoption in the survey coding industry has been less widespread than it might have been. Among the factors that have hindered a more massive takeup of this technology are the effort involved in manually coding a sufficient amount of training data, the fact that small studies do not seem to justify this effort, and the fact that the process needs to be repeated anew when brand new coding tasks arise. In this paper we will argue for an approach to building verbatim classifiers that we will call "Interactive Learning", and that addresses all the above problems. We will show that, for the same amount of training effort, interactive learning delivers much better coding accuracy than standard "non-interactive" learning. This is especially true when the amount of data we are willing to manually code is small, which makes this approach attractive also for small-scale studies. Interactive learning also lends itself to reusing previously trained classifiers for dealing with new (albeit related) coding tasks. Interactive learning also integrates better in the daily workflow of the survey specialist, and delivers a better user experience overall.
A Counterexample-guided Approach to Finding Numerical Invariants
Venue: arXiv cs.SE 2019
Year: 2019

Numerical invariants, e.g., relationships among numerical variables in a program, represent a useful class of properties to analyze programs. General polynomial invariants represent more complex numerical relations, but they are often required in many scientific and engineering applications. We present NumInv, a tool that implements a counterexample-guided invariant generation (CEGIR) technique to automatically discover numerical invariants, which are polynomial equality and inequality relations among numerical variables. This CEGIR technique infers candidate invariants from program traces and then checks them against the program source code using the KLEE test-input generation tool. If the invariants are incorrect KLEE returns counterexample traces, which help the dynamic inference obtain better results. Existing CEGIR approaches often require sound invariants, however NumInv sacrifices soundness and produces results that KLEE cannot refute within certain time bounds. This design and the use of KLEE as a verifier allow NumInv to discover useful and important numerical invariants for many challenging programs. Preliminary results show that NumInv generates required invariants for understanding and verifying correctness of programs involving complex arithmetic. We also show that NumInv discovers polynomial invariants that capture precise complexity bounds of programs used to benchmark existing static complexity analysis techniques. Finally, we show that NumInv performs competitively comparing to state of the art numerical invariant analysis tools.
Using Latent Class Analysis to Identify ARDS Sub-phenotypes for Enhanced Machine Learning Predictive Performance
Venue: arXiv cs.LG 2019
Year: 2019

In this work, we utilize Machine Learning for early recognition of patients at high risk of acute respiratory distress syndrome (ARDS), which is critical for successful prevention strategies for this devastating syndrome. The difficulty in early ARDS recognition stems from its complex and heterogenous nature. In this study, we integrate knowledge of the heterogeneity of ARDS patients into predictive model building. Using MIMIC-III data, we first apply latent class analysis (LCA) to identify homogeneous sub-groups in the ARDS population, and then build predictive models on the partitioned data. The results indicate that significantly improved performances of prediction can be obtained for two of the three identified sub-phenotypes of ARDS. Experiments suggests that identifying sub-phenotypes is beneficial for building predictive model for ARDS.
Dynamic Streaming Spectral Sparsification in Nearly Linear Time and Space
Venue: arXiv cs.DS 2019
Year: 2019

In this paper we consider the problem of computing spectral approximations to graphs in the single pass dynamic streaming model. We provide a linear sketching based solution that given a stream of edge insertions and deletions to a $n$-node undirected graph, uses $\tilde O(n)$ space, processes each update in $\tilde O(1)$ time, and with high probability recovers a spectral sparsifier in $\tilde O(n)$ time. Prior to our work, state of the art results either used near optimal $\tilde O(n)$ space complexity, but brute-force $Ω(n^2)$ recovery time [Kapralov et al.'14], or with subquadratic runtime, but polynomially suboptimal space complexity [Ahn et al.'14, Kapralov et al.'19]. Our main technical contribution is a novel method for `bucketing' vertices of the input graph into clusters that allows fast recovery of edges of sufficiently large effective resistance. Our algorithm first buckets vertices of the graph by performing ball-carving using (an approximation to) its effective resistance metric, and then recovers the high effective resistance edges from a sketched version of an electrical flow between vertices in a bucket, taking nearly linear time in the number of vertices overall. This process is performed at different geometric scales to recover a sample of edges with probabilities proportional to effective resistances and obtain an actual sparsifier of the input graph. This work provides both the first efficient $\ell_2$-sparse recovery algorithm for graphs and new primitives for manipulating the effective resistance embedding of a graph, both of which we hope have further applications.
3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles
Venue: arXiv cs.CV 2019
Year: 2019

Detailed whole brain segmentation is an essential quantitative technique, which provides a non-invasive way of measuring brain regions from a structural magnetic resonance imaging (MRI). Recently, deep convolution neural network (CNN) has been applied to whole brain segmentation. However, restricted by current GPU memory, 2D based methods, downsampling based 3D CNN methods, and patch-based high-resolution 3D CNN methods have been the de facto standard solutions. 3D patch-based high resolution methods typically yield superior performance among CNN approaches on detailed whole brain segmentation (>100 labels), however, whose performance are still commonly inferior compared with multi-atlas segmentation methods (MAS) due to the following challenges: (1) a single network is typically used to learn both spatial and contextual information for the patches, (2) limited manually traced whole brain volumes are available (typically less than 50) for training a network. In this work, we propose the spatially localized atlas network tiles (SLANT) method to distribute multiple independent 3D fully convolutional networks (FCN) for high-resolution whole brain segmentation. To address the first challenge, multiple spatially distributed networks were used in the SLANT method, in which each network learned contextual information for a fixed spatial location. To address the second challenge, auxiliary labels on 5111 initially unlabeled scans were created by multi-atlas segmentation for training. Since the method integrated multiple traditional medical image processing methods with deep learning, we developed a containerized pipeline to deploy the end-to-end solution. From the results, the proposed method achieved superior performance compared with multi-atlas segmentation methods, while reducing the computational time from >30 hours to 15 minutes (https://github.com/MASILab/SLANTbrainSeg).
The Algorithmic Automation Problem: Prediction, Triage, and Human Effort
Venue: arXiv cs.CV 2019
Year: 2019

In a wide array of areas, algorithms are matching and surpassing the performance of human experts, leading to consideration of the roles of human judgment and algorithmic prediction in these domains. The discussion around these developments, however, has implicitly equated the specific task of prediction with the general task of automation. We argue here that automation is broader than just a comparison of human versus algorithmic performance on a task; it also involves the decision of which instances of the task to give to the algorithm in the first place. We develop a general framework that poses this latter decision as an optimization problem, and we show how basic heuristics for this optimization problem can lead to performance gains even on heavily-studied applications of AI in medicine. Our framework also serves to highlight how effective automation depends crucially on estimating both algorithmic and human error on an instance-by-instance basis, and our results show how improvements in these error estimation problems can yield significant gains for automation as well.
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Venue: arXiv cs.LG 2019
Year: 2019

In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Then we propose a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. Unlike recent robustness research, this benchmark evaluates performance on common corruptions and perturbations not worst-case adversarial perturbations. We find that there are negligible changes in relative corruption robustness from AlexNet classifiers to ResNet classifiers. Afterward we discover ways to enhance corruption and perturbation robustness. We even find that a bypassed adversarial defense provides substantial common perturbation robustness. Together our benchmarks may aid future work toward networks that robustly generalize.
Towards Standardization of Data Licenses: The Montreal Data License
Venue: arXiv cs.CY 2019
Year: 2019

This paper provides a taxonomy for the licensing of data in the fields of artificial intelligence and machine learning. The paper's goal is to build towards a common framework for data licensing akin to the licensing of open source software. Increased transparency and resolving conceptual ambiguities in existing licensing language are two noted benefits of the approach proposed in the paper. In parallel, such benefits may help foster fairer and more efficient markets for data through bringing about clearer tools and concepts that better define how data can be used in the fields of AI and ML. The paper's approach is summarized in a new family of data license language - \textit{the Montreal Data License (MDL)}. Alongside this new license, the authors and their collaborators have developed a web-based tool to generate license language espousing the taxonomies articulated in this paper.
Mesh-based Tools to Analyze Deep Reinforcement Learning Policies for Underactuated Biped Locomotion
Venue: arXiv cs.RO 2019
Year: 2019

In this paper, we present a mesh-based approach to analyze stability and robustness of the policies obtained via deep reinforcement learning for various biped gaits of a five-link planar model. Intuitively, one would expect that including perturbations and/or other types of noise during training would likely result in more robustness of the resulting control policy. However, one would also like to have a quantitative and computationally-efficient means of evaluating the degree to which this might be so. Rather than relying on Monte Carlo simulations, which can become quite computationally burdensome in quantifying performance metrics, our goal is to provide more sophisticated tools to assess robustness properties of such policies. Our work is motivated by the twin hypotheses that contraction of dynamics, when achievable, can simplify the required complexity of a control policy and that control policies obtained via deep learning may therefore exhibit tendency to contract to lower-dimensional manifolds within the full state space, as a result. The tractability of our mesh-based tools in this work provides some evidence that this may be so.
A User-centered Design Study in Scientific Visualization Targeting Domain Experts
Venue: arXiv cs.HC 2019
Year: 2019

The development and design of visualization solutions that are truly usable is essential for ensuring both their adoption and effectiveness. User-centered design principles, which focus on involving users throughout the entire development process, are well suited for visualization and have been shown to be effective in numerous information visualization endeavors. In this paper, we report a two year long collaboration with combustion scientists that, by applying these design principles, generated multiple results including an in situ visualization technique and a post hoc probability distribution function (PDF) exploration tool. Furthermore, we examine the importance of user-centered design principles and describe lessons learned over the design process in an effort to aid others who also seek to work with scientists for developing effective and usable scientific visualization solutions.
Training neural networks to encode symbols enables combinatorial generalization
Venue: arXiv cs.AI 2019
Year: 2019

Combinatorial generalization - the ability to understand and produce novel combinations of already familiar elements - is considered to be a core capacity of the human mind and a major challenge to neural network models. A significant body of research suggests that conventional neural networks can't solve this problem unless they are endowed with mechanisms specifically engineered for the purpose of representing symbols. In this paper we introduce a novel way of representing symbolic structures in connectionist terms - the vectors approach to representing symbols (VARS), which allows training standard neural architectures to encode symbolic knowledge explicitly at their output layers. In two simulations, we show that neural networks not only can learn to produce VARS representations, but in doing so they achieve combinatorial generalization in their symbolic and non-symbolic output. This adds to other recent work that has shown improved combinatorial generalization under specific training conditions, and raises the question of whether specific mechanisms or training routines are needed to support symbolic processing.
MCTS-based Automated Negotiation Agent (Extended Abstract)
Venue: arXiv cs.AI 2019
Year: 2019

This paper introduces a new Negotiating Agent for automated negotiation on continuous domains and without considering a specified deadline. The agent bidding strategy relies on Monte Carlo Tree Search, which is a trendy method since it has been used with success on games with high branching factor such as Go. It uses two opponent modeling techniques for its bidding strategy and its utility: Gaussian process regression and Bayesian learning. Evaluation is done by confronting the existing agents that are able to negotiate in such context: Random Walker, Tit-for-tat and Nice Tit-for-Tat. None of those agents succeeds in beating our agent; moreover the modular and adaptive nature of our approach is a huge advantage when it comes to optimize it in specific applicative contexts.
Multiplication method for factoring natural numbers
Venue: arXiv cs.DS 2019
Year: 2019

We offer multiplication method for factoring big natural numbers which extends the group of the Fermat's and Lehman's factorization algorithms and has run-time complexity $O(n^{1/3})$. This paper is argued the finiteness of proposed algorithm depending on the value of the factorizable number n. We provide here comparative tests results of related algorithms on a large amount of computational checks. We describe identified advantages of the proposed algorithm over others. The possibilities of algorithm optimization for reducing the complexity of factorization are also shown here.
Distributed Ledger Technology for Smart Mobility: Variable Delay Models
Venue: arXiv cs.SY 2019
Year: 2019

Recently, Directed Acyclic Graph (DAG) based Distributed Ledgers have been proposed for various applications in the smart mobility domain [1]. While many application studies have been described in the literature, an open problem in the DLT community concerns the lack of mathematical models describing their behaviour, and their validation. Building on a previous work in [1], we present, in this paper, a fluid based approximation for the IOTA Foundation DAG based DLT that incorporates varying transaction delays. This extension, namely the inclusion of varying delays, is important for feedback control applications (such as transactive control [2]). Extensive simulations are presented to illustrate the efficacy of our approach.
Deep, spatially coherent Occupancy Maps based on Radar Measurements
Venue: arXiv cs.RO 2019
Year: 2019

One essential step to realize modern driver assistance technology is the accurate knowledge about the location of static objects in the environment. In this work, we use artificial neural networks to predict the occupation state of a whole scene in an end-to-end manner. This stands in contrast to the traditional approach of accumulating each detection's influence on the occupancy state and allows to learn spatial priors which can be used to interpolate the environment's occupancy state. We show that these priors make our method suitable to predict dense occupancy estimations from sparse, highly uncertain inputs, as given by automotive radars, even for complex urban scenarios. Furthermore, we demonstrate that these estimations can be used for large-scale mapping applications.