OpenRaiser/Intern-Atlas huggingface dataset:
An Experimental Comparison of Map-like Visualisations and Treemaps
Venue: arXiv cs.HC 2019
Year: 2019

Treemaps have been used in information visualisation for over two decades. They make use of nested filled areas to represent information hierarchies such as file systems, library catalogues, etc. Recent years have witnessed the emergence of visualisations that resemble geographic maps. In this paper we present a study that compares the performance of one such map-like visualisation with the original two forms of the treemap, namely nested and non-nested treemaps. Our study employed a mixed-method evaluation of accuracy, speed and usability (such as the ease-of-use and helpfulness of understanding the information). We found that accuracy was highest for the map-like visualisations, followed by nested treemaps and lastly non-nested treemaps. Task performance was fastest for nested treemaps, followed by non-nested treemaps, and then map-like visualisations. For usability, nested treemaps was considered slightly more helpful than map-like visualisations while non-nested performed poorly. We conclude that the results regarding accuracy are promising for the use of map-like visualisations in tasks involving the visualisation of hierarchical information, while non-nested treemap are favoured in tasks requiring speed.
Locality, Statefulness, and Causality in Distributed Information Systems (Concerning the Scale Dependence Of System Promises)
Venue: arXiv cs.DC 2019
Year: 2019

Several popular best-practice manifestos for IT design and architecture use terms like `stateful', `stateless', `shared nothing', etc, and describe `fact based' or `functional' descriptions of causal evolution to describe computer processes, especially in cloud computing. The concepts are used ambiguously and sometimes in contradictory ways, which has led to many imprecise beliefs about their implications. This paper outlines the simple view of state and causation in Promise Theory, which accounts for the scaling of processes and the relativity of different observers in a natural way. It's shown that the concepts of statefulness or statelessness are artifacts of observational scale and causal bias towards functional evaluation. If we include feedback loops, recursion, and process convergence, which appear acausal to external observers, the arguments about (im)mutable state need to be modified in a scale-dependent way. In most cases the intended focus of such remarks is not terms like `statelessness' but process predictability. A simple principle may be substituted in most cases as a guide to system design: the principle the separation of dynamic scales. Understanding data reliance and the ability to keep stable promises is of crucial importance to the consistency of data pipelines, and distributed client-server interactions, albeit in different ways. With increasingly data intensive processes over widely separated distributed deployments, e.g. in the Internet of Things and AI applications, the effects of instability need a more careful treatment. These notes are part of an initiative to engage with thinkers and practitioners towards a more rational and disciplined language for systems engineering for era of ubiquitous extended-cloud computing.
Generating Philosophical Statements using Interpolated Markov Models and Dynamic Templates
Venue: arXiv cs.CL 2019
Year: 2019

Automatically imitating input text is a common task in natural language generation, often used to create humorous results. Classic algorithms for learning to imitate text, e.g. simple Markov chains, usually have a trade-off between originality and syntactic correctness. We present two ways of automatically parodying philosophical statements from examples overcoming this issue, and show how these can work in interactive systems as well. The first algorithm uses interpolated Markov models with extensions to improve the quality of the generated texts. For the second algorithm, we propose dynamically extracting templates and filling these with new content. To illustrate these algorithms, we implemented TorfsBot, a Twitterbot imitating the witty, semi-philosophical tweets of professor Rik Torfs, the previous KU Leuven rector. We found that users preferred generative models that focused on local coherent sentences, rather than those mimicking the global structure of a philosophical statement. The proposed algorithms are thus valuable new tools for automatic parody as well as template learning systems.
Deep Contextualized Pairwise Semantic Similarity for Arabic Language Questions
Venue: arXiv cs.CL 2019
Year: 2019

Question semantic similarity is a challenging and active research problem that is very useful in many NLP applications, such as detecting duplicate questions in community question answering platforms such as Quora. Arabic is considered to be an under-resourced language, has many dialects, and rich in morphology. Combined together, these challenges make identifying semantically similar questions in Arabic even more difficult. In this paper, we introduce a novel approach to tackle this problem, and test it on two benchmarks; one for Modern Standard Arabic (MSA), and another for the 24 major Arabic dialects. We are able to show that our new system outperforms state-of-the-art approaches by achieving 93% F1-score on the MSA benchmark and 82% on the dialectical one. This is achieved by utilizing contextualized word representations (ELMo embeddings) trained on a text corpus containing MSA and dialectic sentences. This in combination with a pairwise fine-grained similarity layer, helps our question-to-question similarity model to generalize predictions on different dialects while being trained only on question-to-question MSA data.
On Recovering Latent Factors From Sampling And Firing Graph
Venue: arXiv cs.LG 2019
Year: 2019

Consider a set of latent factors whose observable effect of activation is caught on a measure space that appears as a grid of bits tacking value in $\{0, 1 \}$. This paper intend to deliver a theoretical and practical answer to the question: Given that we have access to a perfect indicator of the activation of latent factors that label a finite dataset of grid's activity, can we imagine a procedure to build a generic identificator of factor's activations ?
An Empirical Exploration of Deep Recurrent Connections and Memory Cells Using Neuro-Evolution
Venue: arXiv cs.NE 2019
Year: 2019

Neuro-evolution and neural architecture search algorithms have gained increasing interest due to the challenges involved in designing optimal artificial neural networks (ANNs). While these algorithms have been shown to possess the potential to outperform the best human crafted architectures, a less common use of them is as a tool for analysis of ANN structural components and connectivity structures. In this work, we focus on this particular use-case to develop a rigorous examination and comparison framework for analyzing recurrent neural networks (RNNs) applied to time series prediction using the novel neuro-evolutionary process known as Evolutionary eXploration of Augmenting Memory Models (EXAMM). Specifically, we use our EXAMM-based analysis to investigate the capabilities of recurrent memory cells and the generalization ability afforded by various complex recurrent connectivity patterns that span one or more steps in time, i.e., deep recurrent connections. EXAMM, in this study, was used to train over 10.56 million RNNs in 5,280 repeated experiments with varying components. While many modern, often hand-crafted RNNs rely on complex memory cells (which have internal recurrent connections that only span a single time step) operating under the assumption that these sufficiently latch information and handle long term dependencies, our results show that networks evolved with deep recurrent connections perform significantly better than those without. More importantly, in some cases, the best performing RNNs consisted of only simple neurons and deep time skip connections, without any memory cells. These results strongly suggest that utilizing deep time skip connections in RNNs for time series data prediction not only deserves further, dedicated study, but also demonstrate the potential of neuro-evolution as a means to better study, understand, and train effective RNNs.
Creative GANs for generating poems, lyrics, and metaphors
Venue: arXiv cs.CL 2019
Year: 2019

Generative models for text have substantially contributed to tasks like machine translation and language modeling, using maximum likelihood optimization (MLE). However, for creative text generation, where multiple outputs are possible and originality and uniqueness are encouraged, MLE falls short. Methods optimized for MLE lead to outputs that can be generic, repetitive and incoherent. In this work, we use a Generative Adversarial Network framework to alleviate this problem. We evaluate our framework on poetry, lyrics and metaphor datasets, each with widely different characteristics, and report better performance of our objective function over other generative models.
What are Neural Networks made of?
Venue: arXiv cs.NE 2019
Year: 2019

The success of Deep Learning methods is not well understood, though various attempts at explaining it have been made, typically centered on properties of stochastic gradient descent. Even less clear is why certain neural network architectures perform better than others. We provide a potential opening with the hypothesis that neural network training is a form of Genetic Programming.
Quantifying the Impact of Cognitive Biases in Question-Answering Systems
Venue: arXiv cs.HC 2019
Year: 2019

Crowdsourcing can identify high-quality solutions to problems; however, individual decisions are constrained by cognitive biases. We investigate some of these biases in an experimental model of a question-answering system. In both natural and controlled experiments, we observe a strong position bias in favor of answers appearing earlier in a list of choices. This effect is enhanced by three cognitive factors: the attention an answer receives, its perceived popularity, and cognitive load, measured by the number of choices a user has to process. While separately weak, these effects synergistically amplify position bias and decouple user choices of best answers from their intrinsic quality. We end our paper by discussing the novel ways we can apply these findings to substantially improve how high-quality answers are found in question-answering systems.
Measuring Domain Portability and ErrorPropagation in Biomedical QA
Venue: arXiv cs.CL 2019
Year: 2019

In this work we present Google's submission to the BioASQ 7 biomedical question answering (QA) task (specifically Task 7b, Phase B). The core of our systems are based on BERT QA models, specifically the model of \cite{alberti2019bert}. In this report, and via our submissions, we aimed to investigate two research questions. We start by studying how domain portable are QA systems that have been pre-trained and fine-tuned on general texts, e.g., Wikipedia. We measure this via two submissions. The first is a non-adapted model that uses a public pre-trained BERT model and is fine-tuned on the Natural Questions data set \cite{kwiatkowski2019natural}. The second system takes this non-adapted model and fine-tunes it with the BioASQ training data. Next, we study the impact of error propagation in end-to-end retrieval and QA systems. Again we test this via two submissions. The first uses human annotated relevant documents and snippets as input to the model and the second predicted documents and snippets. Our main findings are that domain specific fine-tuning can benefit Biomedical QA. However, the biggest quality bottleneck is at the retrieval stage, where we see large drops in metrics -- over 10pts absolute -- when using non gold inputs to the QA model.
Human Position Detection & Tracking with On-robot Time-of-Flight Laser Ranging Sensors
Venue: arXiv cs.RO 2019
Year: 2019

In this paper, we propose a simple methodology to detect the partial pose of a human occupying the manipulator work-space using only on-robot time--of--flight laser ranging sensors. The sensors are affixed on each link of the robot in a circular array fashion where each array possesses sixteen single unit laser ranging lidar(s). The detection is performed by leveraging an artificial neural network which takes a highly sparse 3-D point cloud input to produce an estimate of the partial pose which is the ground projection frame of the human footprint. We also present a particle filter based approach to the tracking problem when the input data is unreliable. Ultimately, the simulation results are presented and analyzed.
Advances in Computer-Aided Diagnosis of Diabetic Retinopathy
Venue: arXiv cs.CV 2019
Year: 2019

Diabetic Retinopathy is a critical health problem influences 100 million individuals worldwide, and these figures are expected to rise, particularly in Asia. Diabetic Retinopathy is a chronic eye disease which can lead to irreversible vision loss. Considering the visual complexity of retinal images, the early-stage diagnosis of Diabetic Retinopathy can be challenging for human experts. However, Early detection of Diabetic Retinopathy can significantly help to avoid permanent vision loss. The capability of computer-aided detection systems to accurately and efficiently detect the diabetic retinopathy had popularized them among researchers. In this review paper, the literature search was conducted on PubMed, Google Scholar, IEEE Explorer with a focus on the computer-aided detection of Diabetic Retinopathy using either of Machine Learning or Deep Learning algorithms. Moreover, this study also explores the typical methodology utilized for the computer-aided diagnosis of Diabetic Retinopathy. This review paper is aimed to direct the researchers about the limitations of current methods and identify the specific areas in the field to boost future research.
Uncertainty Quantification with Statistical Guarantees in End-to-End Autonomous Driving Control
Venue: arXiv cs.LG 2019
Year: 2019

Deep neural network controllers for autonomous driving have recently benefited from significant performance improvements, and have begun deployment in the real world. Prior to their widespread adoption, safety guarantees are needed on the controller behaviour that properly take account of the uncertainty within the model as well as sensor noise. Bayesian neural networks, which assume a prior over the weights, have been shown capable of producing such uncertainty measures, but properties surrounding their safety have not yet been quantified for use in autonomous driving scenarios. In this paper, we develop a framework based on a state-of-the-art simulator for evaluating end-to-end Bayesian controllers. In addition to computing pointwise uncertainty measures that can be computed in real time and with statistical guarantees, we also provide a method for estimating the probability that, given a scenario, the controller keeps the car safe within a finite horizon. We experimentally evaluate the quality of uncertainty computation by several Bayesian inference methods in different scenarios and show how the uncertainty measures can be combined and calibrated for use in collision avoidance. Our results suggest that uncertainty estimates can greatly aid decision making in autonomous driving.
Techniques and Applications for Crawling, Ingesting and Analyzing Blockchain Data
Venue: arXiv cs.CR 2019
Year: 2019

As the public Ethereum network surpasses half a billion transactions and enterprise Blockchain systems becoming highly capable of meeting the demands of global deployments, production Blockchain applications are fast becoming commonplace across a diverse range of business and scientific verticals. In this paper, we reflect on work we have been conducting recently surrounding the ingestion, retrieval and analysis of Blockchain data. We describe the scaling and semantic challenges when extracting Blockchain data in a way that preserves the original metadata of each transaction by cross referencing the Smart Contract interface with the on-chain data. We then discuss a scientific use case in the area of Scientific workflows by describing how we can harvest data from tasks and dependencies in a generic way. We then discuss how crawled public blockchain data can be analyzed using two unsupervised machine learning algorithms, which are designed to identify outlier accounts or smart contracts in the system. We compare and contrast the two machine learning methods and cross correlate with public Websites to illustrate the effectiveness such approaches.
From Data Disclosure to Privacy Nudges: A Privacy-aware and User-centric Personal Data Management Framework
Venue: arXiv cs.CR 2019
Year: 2019

Although there are privacy-enhancing tools designed to protect users' online privacy, it is surprising to see a lack of user-centric solutions allowing privacy control based on the joint assessment of privacy risks and benefits, due to data disclosure to multiple platforms. In this paper, we propose a conceptual framework to fill the gap: aiming at the user-centric privacy protection, we show the framework can not only assess privacy risks in using online services but also the added values earned from data disclosure. Through following a human-in-the-loop approach, it is expected the framework provides a personalized solution via preference learning, continuous privacy assessment, behavior monitoring and nudging. Finally, we describe a case study towards "leisure travelers" and several future areas to be studied in the ongoing project.
A Fractional Subgrid-scale Model for Turbulent Flows: Theoretical Formulation and a Priori Study
Venue: arXiv cs.CE 2019
Year: 2019

Coherent structures/motions in turbulence inherently give rise to intermittent signals with sharp peaks, heavy-skirt, and skewed distributions of velocity increments, highlighting the non-Gaussian nature of turbulence. That suggests that the spatial nonlocal interactions cannot be ruled out of the turbulence physics. Furthermore, filtering the Navier-Stokes equations in the large eddy simulation of turbulent flows would further enhance the existing nonlocality, emerging in the corresponding subgrid scale fluid motions. That urges the development of new nonlocal closure models, which respect the corresponding non-Gaussian statistics of the subgrid stochastic motions. To this end and starting from the filtered Boltzmann equation, we model the corresponding equilibrium distribution function with a \textit{Lévy}-stable distribution, leading to the proposed fractional-order modeling of subgrid-scale stresses. We approximate the filtered equilibrium distribution function with a power-law term, and derive the corresponding filtered Navier-Stokes equations. Subsequently in our functional modeling, the divergence of subgrid-scale stresses emerges as a single-parameter fractional Laplacian, $(-Δ)^α(\cdot)$, $α\in (0,1]$, of the filtered velocity field. The only model parameter, i.e., the fractional exponent, appears to be strictly depending on the filter-width and the flow Reynolds number. We furthermore explore the main physical and mathematical properties of the proposed model under a set of mild conditions. Finally, the introduced model undergoes \textit{a priori} evaluations based on the direct numerical simulation database of forced and decaying homogeneous isotropic turbulent flows at relatively high and moderate Reynolds numbers, respectively. Such analysis provides a comparative study of predictability and performance of the proposed fractional model.
Entanglement-Assisted Capacity of Quantum Channels with Side Information
Venue: arXiv cs.IT 2019
Year: 2019

Entanglement-assisted communication over a random-parameter quantum channel with either causal or non-causal channel side information (CSI) at the encoder is considered. This describes a scenario where the quantum channel depends on the quantum state of the input environment. While Bob, the decoder, has no access to this state, Alice, the transmitter, performs a sequence of projective measurements on her environment and encodes her message accordingly. Dupuis established the entanglement-assisted capacity with non-causal CSI. Here, we establish characterization in the causal setting, and also give an alternative proof technique and further observations for the non-causal setting.
Incremental Fairness in Two-Sided Market Platforms: On Smoothly Updating Recommendations
Venue: arXiv cs.SI 2019
Year: 2019

Major online platforms today can be thought of as two-sided markets with producers and customers of goods and services. There have been concerns that over-emphasis on customer satisfaction by the platforms may affect the well-being of the producers. To counter such issues, few recent works have attempted to incorporate fairness for the producers. However, these studies have overlooked an important issue in such platforms -- to supposedly improve customer utility, the underlying algorithms are frequently updated, causing abrupt changes in the exposure of producers. In this work, we focus on the fairness issues arising out of such frequent updates, and argue for incremental updates of the platform algorithms so that the producers have enough time to adjust (both logistically and mentally) to the change. However, naive incremental updates may become unfair to the customers. Thus focusing on recommendations deployed on two-sided platforms, we formulate an ILP based online optimization to deploy changes incrementally in n steps, where we can ensure smooth transition of the exposure of items while guaranteeing a minimum utility for every customer. Evaluations over multiple real world datasets show that our proposed mechanism for platform updates can be efficient and fair to both the producers and the customers in two-sided platforms.
Is change the only constant? Profile change perspective on #LokSabhaElections2019
Venue: arXiv cs.CL 2019
Year: 2019

Users on Twitter are identified with the help of their profile attributes that consists of username, display name, profile image, to name a few. The profile attributes that users adopt can reflect their interests, belief, or thematic inclinations. Literature has proposed the implications and significance of profile attribute change for a random population of users. However, the use of profile attribute for endorsements and to start a movement have been under-explored. In this work, we consider #LokSabhaElections2019 as a movement and perform a large-scale study of the profile of users who actively made changes to profile attributes centered around #LokSabhaElections2019. We collect the profile metadata for 49.4M users for a period of 2 months from April 5, 2019 to June 5, 2019 amid #LokSabhaElections2019. We investigate how the profile changes vary for the influential leaders and their followers over the social movement. We further differentiate the organic and inorganic ways to show the political inclination from the prism of profile changes. We report how the addition of election campaign related keywords lead to spread of behavior contagion and further investigate it with respect to "Chowkidar Movement" in detail.