OpenRaiser/Intern-Atlas huggingface dataset:
Gemini: A Grammar and Recommender System for AnimatedTransitions in Statistical Graphics
Venue: arXiv cs.HC 2020
Year: 2020

Animated transitions help viewers follow changes between related visualizations. Specifying effective animations demands significant effort: authors must select the elements and properties to animate, provide transition parameters, and coordinate the timing of stages. To facilitate this process, we present Gemini, a declarative grammar and recommendation system for animated transitions between single-view statistical graphics. Gemini specifications define transition "steps" in terms of high-level visual components (marks, axes, legends) and composition rules to synchronize and concatenate steps. With this grammar, Gemini can recommend animation designs to augment and accelerate designers' work. Gemini enumerates staged animation designs for given start and end states, and ranks those designs using a cost function informed by prior perceptual studies. To evaluate Gemini, we conduct both a formative study on Mechanical Turk to assess and tune our ranking function, and a summative study in which 8 experienced visualization developers implement animations in D3 that we then compare to Gemini's suggestions. We find that most designs (9/11) are exactly replicable in Gemini, with many (8/11) achievable via edits to suggestions, and that Gemini suggestions avoid multiple participant errors.
FairXGBoost: Fairness-aware Classification in XGBoost
Venue: arXiv cs.AI 2020
Year: 2020

Highly regulated domains such as finance have long favoured the use of machine learning algorithms that are scalable, transparent, robust and yield better performance. One of the most prominent examples of such an algorithm is XGBoost. Meanwhile, there is also a growing interest in building fair and unbiased models in these regulated domains and numerous bias-mitigation algorithms have been proposed to this end. However, most of these bias-mitigation methods are restricted to specific model families such as logistic regression or support vector machine models, thus leaving modelers with a difficult decision of choosing between fairness from the bias-mitigation algorithms and scalability, transparency, performance from algorithms such as XGBoost. We aim to leverage the best of both worlds by proposing a fair variant of XGBoost that enjoys all the advantages of XGBoost, while also matching the levels of fairness from the state-of-the-art bias-mitigation algorithms. Furthermore, the proposed solution requires very little in terms of changes to the original XGBoost library, thus making it easy for adoption. We provide an empirical analysis of our proposed method on standard benchmark datasets used in the fairness community.
Quasi-synchronization of bounded confidence opinion dynamics with stochastic asynchronous rule
Venue: arXiv cs.MA 2020
Year: 2020

Recently the theory of noise-induced synchronization of Hegselmann-Krause (HK) dynamics has been well developed. As a typical opinion dynamics of bounded confidence, the HK model obeys a synchronous updating rule, i.e., \emph{all} agents check and update their opinions at each time point. However, whether asynchronous bounded confidence models, including the famous Deffuant-Weisbuch (DW) model, can be synchronized by noise have not been theoretically proved. In this paper, we propose a generalized bounded confidence model which possesses a stochastic asynchronous rule. The model takes the DW model and the HK model as special cases and can significantly generalize the bounded confidence models to practical application. We discover that the asynchronous model possesses a different noise-based synchronization behavior compared to the synchronous HK model. Generally, the HK dynamics can achieve quasi-synchronization \emph{almost surely} under the drive of noise. For the asynchronous dynamics, we prove that the model can achieve quasi-synchronization \emph{in mean}, which is a new type of quasi-synchronization weaker than the "almost surely" sense. The results unify the theory of noise-induced synchronization of bounded confidence opinion dynamics and hence proves the noise-induced synchronization of DW model theoretically for the first time. Moreover, the results provide a theoretical foundation for developing noise-based control strategy of more complex social opinion systems with stochastic asynchronous rules.
Optimality-based Analysis of XCSF Compaction in Discrete Reinforcement Learning
Venue: arXiv cs.LG 2020
Year: 2020

Learning classifier systems (LCSs) are population-based predictive systems that were originally envisioned as agents to act in reinforcement learning (RL) environments. These systems can suffer from population bloat and so are amenable to compaction techniques that try to strike a balance between population size and performance. A well-studied LCS architecture is XCSF, which in the RL setting acts as a Q-function approximator. We apply XCSF to a deterministic and stochastic variant of the FrozenLake8x8 environment from OpenAI Gym, with its performance compared in terms of function approximation error and policy accuracy to the optimal Q-functions and policies produced by solving the environments via dynamic programming. We then introduce a novel compaction algorithm (Greedy Niche Mass Compaction - GNMC) and study its operation on XCSF's trained populations. Results show that given a suitable parametrisation, GNMC preserves or even slightly improves function approximation error while yielding a significant reduction in population size. Reasonable preservation of policy accuracy also occurs, and we link this metric to the commonly used steps-to-goal metric in maze-like environments, illustrating how the metrics are complementary rather than competitive.
Approximating $(k,\ell)$-Median Clustering for Polygonal Curves
Venue: arXiv cs.CG 2020
Year: 2020

In 2015, Driemel, Krivošija and Sohler introduced the $(k,\ell)$-median problem for clustering polygonal curves under the Fréchet distance. Given a set of input curves, the problem asks to find $k$ median curves of at most $\ell$ vertices each that minimize the sum of Fréchet distances over all input curves to their closest median curve. A major shortcoming of their algorithm is that the input curves are restricted to lie on the real line. In this paper, we present a randomized bicriteria-approximation algorithm that works for polygonal curves in $\mathbb{R}^d$ and achieves approximation factor $(1+ε)$ with respect to the clustering costs. The algorithm has worst-case running-time linear in the number of curves, polynomial in the maximum number of vertices per curve, i.e. their complexity, and exponential in $d$, $\ell$, $ε$ and $δ$, i.e., the failure probability. We achieve this result through a shortcutting lemma, which guarantees the existence of a polygonal curve with similar cost as an optimal median curve of complexity $\ell$, but of complexity at most $2\ell-2$, and whose vertices can be computed efficiently. We combine this lemma with the superset-sampling technique by Kumar et al. to derive our clustering result. In doing so, we describe and analyze a generalization of the algorithm by Ackermann et al., which may be of independent interest.
Best practices for software maturity improvement: a GÉANT case study
Venue: arXiv cs.SE 2020
Year: 2020

Maturity models for software indicate the key areas that contribute to quality improvements. They usually combine technical, organisational and human aspects relevant for effective software development, to focus the efforts and draw the direction for optimisations. In this paper, we present the process of defining best practices that support the GÉANT Software Maturity Model (GSMM), aligned to the needs of a distributed, innovation-driven, pan-European organisation. Based on the identification of specific goals relevant for GÉANT and a preliminary maturity assessment, we created a catalogue of best practices that help the software teams to attain the goals defined in the GSMM.
A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and Reports
Venue: arXiv cs.CV 2020
Year: 2020

Joint image-text embedding extracted from medical images and associated contextual reports is the bedrock for most biomedical vision-and-language (V+L) tasks, including medical visual question answering, clinical image-text retrieval, clinical report auto-generation. In this study, we adopt four pre-trained V+L models: LXMERT, VisualBERT, UNIER and PixelBERT to learn multimodal representation from MIMIC-CXR radiographs and associated reports. The extrinsic evaluation on OpenI dataset shows that in comparison to the pioneering CNN-RNN model, the joint embedding learned by pre-trained V+L models demonstrate performance improvement in the thoracic findings classification task. We conduct an ablation study to analyze the contribution of certain model components and validate the advantage of joint embedding over text-only embedding. We also visualize attention maps to illustrate the attention mechanism of V+L models.
Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs
Venue: arXiv cs.LG 2020
Year: 2020

This paper proposes an approach to analyze an event log of a business process in order to generate case-level recommendations of treatments that maximize the probability of a given outcome. Users classify the attributes in the event log into controllable and non-controllable, where the former correspond to attributes that can be altered during an execution of the process (the possible treatments). We use an action rule mining technique to identify treatments that co-occur with the outcome under some conditions. Since action rules are generated based on correlation rather than causation, we then use a causal machine learning technique, specifically uplift trees, to discover subgroups of cases for which a treatment has a high causal effect on the outcome after adjusting for confounding variables. We test the relevance of this approach using an event log of a loan application process and compare our findings with recommendations manually produced by process mining experts.
On Population-Based Algorithms for Distributed Constraint Optimization Problems
Venue: arXiv cs.AI 2020
Year: 2020

Distributed Constraint Optimization Problems (DCOPs) are a widely studied class of optimization problems in which interaction between a set of cooperative agents are modeled as a set of constraints. DCOPs are NP-hard and significant effort has been devoted to developing methods for finding incomplete solutions. In this paper, we study an emerging class of such incomplete algorithms that are broadly termed as population-based algorithms. The main characteristic of these algorithms is that they maintain a population of candidate solutions of a given problem and use this population to cover a large area of the search space and to avoid local-optima. In recent years, this class of algorithms has gained significant attention due to their ability to produce high-quality incomplete solutions. With the primary goal of further improving the quality of solutions compared to the state-of-the-art incomplete DCOP algorithms, we present two new population-based algorithms in this paper. Our first approach, Anytime Evolutionary DCOP or AED, exploits evolutionary optimization meta-heuristics to solve DCOPs. We also present a novel anytime update mechanism that gives AED its anytime property. While in our second contribution, we show that population-based approaches can be combined with local search approaches. Specifically, we develop an algorithm called DPSA based on the Simulated Annealing meta-heuristic. We empirically evaluate these two algorithms to illustrate their respective effectiveness in different settings against the state-of-the-art incomplete DCOP algorithms including all existing population-based algorithms in a wide variety of benchmarks. Our evaluation shows AED and DPSA markedly outperform the state-of-the-art and produce up to 75% improved solutions.
Software Effort Estimation using parameter tuned Models
Venue: arXiv cs.SE 2020
Year: 2020

Software estimation is one of the most important activities in the software project. The software effort estimation is required in the early stages of software life cycle. Project Failure is the major problem undergoing nowadays as seen by software project managers. The imprecision of the estimation is the reason for this problem. Assize of software size grows, it also makes a system complex, thus difficult to accurately predict the cost of software development process. The greatest pitfall of the software industry was the fast-changing nature of software development which has made it difficult to develop parametric models that yield high accuracy for software development in all domains. We need the development of useful models that accurately predict the cost of developing a software product. This study presents the novel analysis of various regression models with hyperparameter tuning to get the effective model. Nine different regression techniques are considered for model development
The Sound of Silence: Mining Security Vulnerabilities from Secret Integration Channels in Open-Source Projects
Venue: arXiv cs.SE 2020
Year: 2020

Public development processes are a key characteristic of open source projects. However, fixes for vulnerabilities are usually discussed privately among a small group of trusted maintainers, and integrated without prior public involvement. This is supposed to prevent early disclosure, and cope with embargo and non-disclosure agreement (NDA) rules. While regular development activities leave publicly available traces, fixes for vulnerabilities that bypass the standard process do not. We present a data-mining based approach to detect code fragments that arise from such infringements of the standard process. By systematically mapping public development artefacts to source code repositories, we can exclude regular process activities, and infer irregularities that stem from non-public integration channels. For the Linux kernel, the most crucial component of many systems, we apply our method to a period of seven months before the release of Linux 5.4. We find 29 commits that address 12 vulnerabilities. For these vulnerabilities, our approach provides a temporal advantage of 2 to 179 days to design exploits before public disclosure takes place, and fixes are rolled out. Established responsible disclosure approaches in open development processes are supposed to limit premature visibility of security vulnerabilities. However, our approach shows that, instead, they open additional possibilities to uncover such changes that thwart the very premise. We conclude by discussing implications and partial countermeasures.
Linking Stakeholders' Viewpoint Concerns and Microservices-based Architecture
Venue: arXiv cs.SE 2020
Year: 2020

Widespread adoption of agile project management, independent delivery with microservices, and automated deployment with DevOps has tremendously speedup the systems development. The real game-changer is continuous integration (CI), continuous delivery, and continuous deployment (CD). Organizations can do multiple releases a day, shortening the test, release, and deployment cycles from weeks to minutes. Maturity of container technologies like Docker and container orchestration platforms like Kubernetes has promoted microservices architecture, especially in the cloud-native developments. Various tools are available for setting up CI/CD pipelines. Organizations are moving away from monolith applications and moving towards microservices-based architectures. Organizations can quickly accumulate hundreds of such microservices accessible via application programming interfaces (APIs). The primary purpose of these modern methodologies is agility, speed, and reusability. While DevOps offers speed and time to market, agility and reusability may not be guaranteed unless microservices and API's are linked to enterprise-wide stakeholders' needs. The link between business needs and microservices/APIs is not well captured nor adequately defined. In this publication, we describe a structured method to create a logical link among APIs and microservices-based agile developments with enterprise stakeholders' needs and viewpoint concerns. This method enables capturing and documenting enterprise-wide stakeholders' needs, whether these are business owners, planners (product owners, architects), designers (developers, DevOps engineers), or the partners and subscribers of an enterprise.
Exploring Artist Gender Bias in Music Recommendation
Venue: arXiv cs.IR 2020
Year: 2020

Music Recommender Systems (mRS) are designed to give personalised and meaningful recommendations of items (i.e. songs, playlists or artists) to a user base, thereby reflecting and further complementing individual users' specific music preferences. Whilst accuracy metrics have been widely applied to evaluate recommendations in mRS literature, evaluating a user's item utility from other impact-oriented perspectives, including their potential for discrimination, is still a novel evaluation practice in the music domain. In this work, we center our attention on a specific phenomenon for which we want to estimate if mRS may exacerbate its impact: gender bias. Our work presents an exploratory study, analyzing the extent to which commonly deployed state of the art Collaborative Filtering(CF) algorithms may act to further increase or decrease artist gender bias. To assess group biases introduced by CF, we deploy a recently proposed metric of bias disparity on two listening event datasets: the LFM-1b dataset, and the earlier constructed Celma's dataset. Our work traces the causes of disparity to variations in input gender distributions and user-item preferences, highlighting the effect such configurations can have on user's gender bias after recommendation generation.
What Makes Agile Test Artifacts Useful? An Activity-Based Quality Model from a Practitioners' Perspective
Venue: arXiv cs.SE 2020
Year: 2020

Background: The artifacts used in Agile software testing and the reasons why these artifacts are used are fairly well-understood. However, empirical research on how Agile test artifacts are eventually designed in practice and which quality factors make them useful for software testing remains sparse. Aims: Our objective is two-fold. First, we identify current challenges in using test artifacts to understand why certain quality factors are considered good or bad. Second, we build an Activity-Based Artifact Quality Model that describes what Agile test artifacts should look like. Method: We conduct an industrial survey with 18 practitioners from 12 companies operating in seven different domains. Results: Our analysis reveals nine challenges and 16 factors describing the quality of six test artifacts from the perspective of Agile testers. Interestingly, we observed mostly challenges regarding language and traceability, which are well-known to occur in non-Agile projects. Conclusions: Although Agile software testing is becoming the norm, we still have little confidence about general do's and don'ts going beyond conventional wisdom. This study is the first to distill a list of quality factors deemed important to what can be considered as useful test artifacts.
Optimal Streaming of 360 VR Videos with Perfect, Imperfect and Unknown FoV Viewing Probabilities
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we investigate wireless streaming of multi-quality tiled 360 virtual reality (VR) videos from a multi-antenna server to multiple single-antenna users in a multi-carrier system. To capture the impact of field-of-view (FoV) prediction, we consider three cases of FoV viewing probability distributions, i.e., perfect, imperfect and unknown FoV viewing probability distributions, and use the average total utility, worst average total utility and worst total utility as the respective performance metrics. We adopt rate splitting with successive decoding for efficient transmission of multiple sets of tiles of different 360 VR videos to their requesting users. In each case, we optimize the encoding rates of the tiles, minimum encoding rates of the FoVs, rates of the common and private messages and transmission beamforming vectors to maximize the total utility. The problems in the three cases are all challenging nonconvex optimization problems. We successfully transform the problem in each case into a difference of convex (DC) programming problem with a differentiable objective function, and obtain a suboptimal solution using concave-convex procedure (CCCP). Finally, numerical results demonstrate the proposed solutions achieve notable gains over existing schemes in all three cases. To the best of our knowledge, this is the first work revealing the impact of FoV prediction and its accuracy on the performance of streaming of multi-quality tiled 360 VR videos.
Reading In-Between the Lines: An Analysis of Dissenter
Venue: arXiv cs.CY 2020
Year: 2020

Efforts by content creators and social networks to enforce legal and policy-based norms, e.g. blocking hate speech and users, has driven the rise of unrestricted communication platforms. One such recent effort is Dissenter, a browser and web application that provides a conversational overlay for any web page. These conversations hide in plain sight - users of Dissenter can see and participate in this conversation, whereas visitors using other browsers are oblivious to their existence. Further, the website and content owners have no power over the conversation as it resides in an overlay outside their control. In this work, we obtain a history of Dissenter comments, users, and the websites being discussed, from the initial release of Dissenter in Feb. 2019 through Apr. 2020 (14 months). Our corpus consists of approximately 1.68M comments made by 101k users commenting on 588k distinct URLs. We first analyze macro characteristics of the network, including the user-base, comment distribution, and growth. We then use toxicity dictionaries, Perspective API, and a Natural Language Processing model to understand the nature of the comments and measure the propensity of particular websites and content to elicit hateful and offensive Dissenter comments. Using curated rankings of media bias, we examine the conditional probability of hateful comments given left and right-leaning content. Finally, we study Dissenter as a social network, and identify a core group of users with high comment toxicity.