OpenRaiser/Intern-Atlas huggingface dataset:
Enabling Microsoft OneDrive Integration with HTCondor
Venue: arXiv cs.DC 2019
Year: 2019

Accessing data from distributed computing is essential in many workflows, but can be complicated for users of cyberinfrastructure. They must perform multiple steps to make data available to distributed computing using unfamiliar tools. Further, most research on data distribution has focused on the efficiency of providing data to computing resources rather than considering the ease of use for distributing data. Creating an easy to use data distribution method can reduce the time researchers spend learning cyberinfrastructure and increase its usefulness. Microsoft OneDrive is a online storage solution providing both file storage and sharing. OneDrive provides many different clients to access data stored in the service. It provides many features that users of cyberinfrastructure could find useful such as automatic synchronization with desktop clients. A barrier to using services such as OneDrive is the credential management necessary to access the service. Recent innovations in HTCondor have allowed the management of OAuth credentials to be handled by the scheduler on the user's behalf. The user no longer has to copy credentials along with the job, HTCondor will handle the acquisition, renewal, and secure transfer of credentials on the user's behalf. In this paper, I will focus on providing an easy to use data distribution method utilizing Microsoft OneDrive. Measuring ease of use is difficult, therefore I will will describe the features and advantages of using OneDrive. Additionally, I will compare it to measurements of data distribution methods currently used on a national cyberinfastructure, the Open Science Grid.
CobWeb: A Research Prototype for Exploring User Bias in Political Fact-Checking
Venue: arXiv cs.IR 2019
Year: 2019

The effect of user bias in fact-checking has not been explored extensively from a user-experience perspective. We estimate the user bias as a function of the user's perceived reputation of the news sources (e.g., a user with liberal beliefs may tend to trust liberal sources). We build an interface to communicate the role of estimated user bias in the context of a fact-checking task. We also explore the utility of helping users visualize their detected level of bias. 80% of the users of our system find that the presence of an indicator for user bias is useful in judging the veracity of a political claim.
APML: An Architecture Proof Modeling Language
Venue: arXiv cs.SE 2019
Year: 2019

To address the increasing size and complexity of modern software systems, compositional verification separates the verification of single components from the verification of their composition. In architecture-based verification, the former is done using Model Checking, while this does not seem to be the case in general the latter is done using interactive theorem proving (ITP). As of today, however, architects are usually not trained in using a full-fledged interactive theorem prover. Thus, to bridge the gap between ITP and the architecture domain, we developed APML: an architecture proof modeling language. APML allows one to sketch proofs about component composition at the level of architecture using notations similar to Message Sequence Charts. With this paper, we introduce APML: We describe the language, show its soundness and completeness for the verification of architecture contracts, and provide an algorithm to map an APML proof to a corresponding proof for the interactive theorem prover Isabelle. Moreover, we describe its implementation in terms of an Eclipse/EMF modeling application, demonstrate it by means of a running example, and evaluate it in terms of a larger case study. Although our results are promising, the case study also reveals some limitations, which lead to new directions for future work.
Distributed Approximation of Functions over Fast Fading Channels with Applications to Distributed Learning and the Max-Consensus Problem
Venue: arXiv cs.IT 2019
Year: 2019

In this work, we consider the problem of distributed approximation of functions over multiple-access channels with additive noise. In contrast to previous works, we take fast fading into account and give explicit probability bounds for the approximation error allowing us to derive bounds on the number of channel uses that are needed to approximate a function up to a given approximation accuracy. Neither the fading nor the noise process is limited to Gaussian distributions. Instead, we consider sub-gaussian random variables which include Gaussian as well as many other distributions of practical relevance. The results are motivated by and have immediate applications to a) computing predictors in models for distributed machine learning and b) the max-consensus problem in ultra-dense networks.
Personalised aesthetics with residual adapters
Venue: arXiv cs.CV 2019
Year: 2019

The use of computational methods to evaluate aesthetics in photography has gained interest in recent years due to the popularization of convolutional neural networks and the availability of new annotated datasets. Most studies in this area have focused on designing models that do not take into account individual preferences for the prediction of the aesthetic value of pictures. We propose a model based on residual learning that is capable of learning subjective, user specific preferences over aesthetics in photography, while surpassing the state-of-the-art methods and keeping a limited number of user-specific parameters in the model. Our model can also be used for picture enhancement, and it is suitable for content-based or hybrid recommender systems in which the amount of computational resources is limited.
Control of Status Updates for Energy Harvesting Devices that Monitor Processes with Alarms
Venue: arXiv cs.IT 2019
Year: 2019

In this work, we derive optimal transmission policies in an energy harvesting status update system. The system monitors a stochastic process which can be either in a normal or in an alarm state of operation. We capture the freshness of status updates for each state of the stochastic process by introducing two Age of Information (AoI) variables and extend the definition of AoI to account for the state changes of the stochastic process. We formulate the problem at hand as a Markov Decision Process which, under the assumption that the demand for status updates is higher when the stochastic process is in the alarm state, utilizes a transition cost function that applies linear and non-linear penalties based on AoI and the state of the stochastic process. Finally, we evaluate numerically the derived policies and illustrate their effectiveness for reserving energy in anticipation of future alarm states.
Profiling Players with Engagement Predictions
Venue: arXiv cs.LG 2019
Year: 2019

The possibility of using player engagement predictions to profile high spending video game users is explored. In particular, individual-player survival curves in terms of days after first login, game level reached and accumulated playtime are used to classify players into different groups. Lifetime value predictions for each player---generated using a deep learning method based on long short-term memory---are also included in the analysis, and the relations between all these variables are thoroughly investigated. Our results suggest this constitutes a promising approach to user profiling.
An Intrinsic Nearest Neighbor Analysis of Neural Machine Translation Architectures
Venue: arXiv cs.CL 2019
Year: 2019

Earlier approaches indirectly studied the information captured by the hidden states of recurrent and non-recurrent neural machine translation models by feeding them into different classifiers. In this paper, we look at the encoder hidden states of both transformer and recurrent machine translation models from the nearest neighbors perspective. We investigate to what extent the nearest neighbors share information with the underlying word embeddings as well as related WordNet entries. Additionally, we study the underlying syntactic structure of the nearest neighbors to shed light on the role of syntactic similarities in bringing the neighbors together. We compare transformer and recurrent models in a more intrinsic way in terms of capturing lexical semantics and syntactic structures, in contrast to extrinsic approaches used by previous works. In agreement with the extrinsic evaluations in the earlier works, our experimental results show that transformers are superior in capturing lexical semantics, but not necessarily better in capturing the underlying syntax. Additionally, we show that the backward recurrent layer in a recurrent model learns more about the semantics of words, whereas the forward recurrent layer encodes more context.
Accurate Nuclear Segmentation with Center Vector Encoding
Venue: arXiv cs.CV 2019
Year: 2019

Nuclear segmentation is important and frequently demanded for pathology image analysis, yet is also challenging due to nuclear crowdedness and possible occlusion. In this paper, we present a novel bottom-up method for nuclear segmentation. The concepts of Center Mask and Center Vector are introduced to better depict the relationship between pixels and nuclear instances. The instance differentiation process are thus largely simplified and easier to understand. Experiments demonstrate the effectiveness of Center Vector Encoding, where our method outperforms state-of-the-arts by a clear margin.
Efficient Cloth Simulation using Miniature Cloth and Upscaling Deep Neural Networks
Venue: arXiv cs.GR 2019
Year: 2019

Cloth simulation requires a fast and stable method for interactively and realistically visualizing fabric materials using computer graphics. We propose an efficient cloth simulation method using miniature cloth simulation and upscaling Deep Neural Networks (DNN). The upscaling DNNs generate the target cloth simulation from the results of physically-based simulations of a miniature cloth that has similar physical properties to those of the target cloth. We have verified the utility of the proposed method through experiments, and the results demonstrate that it is possible to generate fast and stable cloth simulations under various conditions.
Understanding Counterexamples for Relational Properties with DIbugger
Venue: arXiv cs.SE 2019
Year: 2019

Software verification is a tedious process that involves the analysis of multiple failed verification attempts, and adjustments of the program or specification. This is especially the case for complex requirements, e.g., regarding security or fairness, when one needs to compare multiple related runs of the same software. Verification tools often provide counterexamples consisting of program inputs when a proof attempt fails, however it is often not clear why the reported counterexample leads to a violation of the checked property. In this paper, we enhance this aspect of the software verification process by providing DIbugger, a tool for analyzing counterexamples of relational properties, allowing the user to debug multiple related programs simultaneously.
Incremental Semantic Mapping with Unsupervised On-line Learning
Venue: arXiv cs.RO 2019
Year: 2019

This paper introduces an incremental semantic mapping approach, with on-line unsupervised learning, based on Self-Organizing Maps (SOM) for robotic agents. The method includes a mapping module, which incrementally creates a topological map of the environment, enriched with objects recognized around each topological node, and a module of places categorization, endowed with an incremental unsupervised learning SOM with on-line training. The proposed approach was tested in experiments with real-world data, in which it demonstrates promising capabilities of incremental acquisition of topological maps enriched with semantic information, and for clustering together similar places based on this information. The approach was also able to continue learning from newly visited environments without degrading the information previously learned.
Mean Spectral Normalization of Deep Neural Networks for Embedded Automation
Venue: arXiv cs.LG 2019
Year: 2019

Deep Neural Networks (DNNs) have begun to thrive in the field of automation systems, owing to the recent advancements in standardising various aspects such as architecture, optimization techniques, and regularization. In this paper, we take a step towards a better understanding of Spectral Normalization (SN) and its potential for standardizing regularization of a wider range of Deep Learning models, following an empirical approach. We conduct several experiments to study their training dynamics, in comparison with the ubiquitous Batch Normalization (BN) and show that SN increases the gradient sparsity and controls the gradient variance. Furthermore, we show that SN suffers from a phenomenon, we call the mean-drift effect, which mitigates its performance. We, then, propose a weight reparameterization called as the Mean Spectral Normalization (MSN) to resolve the mean drift, thereby significantly improving the network's performance. Our model performs ~16% faster as compared to BN in practice, and has fewer trainable parameters. We also show the performance of our MSN for small, medium, and large CNNs - 3-layer CNN, VGG7 and DenseNet-BC, respectively - and unsupervised image generation tasks using Generative Adversarial Networks (GANs) to evaluate its applicability for a broad range of embedded automation tasks.
SVGD: A Virtual Gradients Descent Method for Stochastic Optimization
Venue: arXiv cs.LG 2019
Year: 2019

Inspired by dynamic programming, we propose Stochastic Virtual Gradient Descent (SVGD) algorithm where the Virtual Gradient is defined by computational graph and automatic differentiation. The method is computationally efficient and has little memory requirements. We also analyze the theoretical convergence properties and implementation of the algorithm. Experimental results on multiple datasets and network models show that SVGD has advantages over other stochastic optimization methods.
Analysing Censorship Circumvention with VPNs via DNS Cache Snooping
Venue: arXiv cs.CR 2019
Year: 2019

Anecdotal evidence suggests an increasing number of people are turning to VPN services for the properties of privacy, anonymity and free communication over the internet. Despite this, there is little research into what these services are actually being used for. We use DNS cache snooping to determine what domains people are accessing through VPNs. This technique is used to discover whether certain queries have been made against a particular DNS server. Some VPNs operate their own DNS servers, ensuring that any cached queries were made by users of the VPN. We explore 3 methods of DNS cache snooping and briefly discuss their strengths and limitations. Using the most reliable of the methods, we perform a DNS cache snooping scan against the DNS servers of several major VPN providers. With this we discover which domains are actually accessed through VPNs. We run this technique against popular domains, as well as those known to be censored in certain countries; China, Indonesia, Iran, and Turkey. Our work gives a glimpse into what users use VPNs for, and provides a technique for discovering the frequency with which domain records are accessed on a DNS server.
Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention
Venue: arXiv cs.CV 2019
Year: 2019

Lesion detection from computed tomography (CT) scans is challenging compared to natural object detection because of two major reasons: small lesion size and small inter-class variation. Firstly, the lesions usually only occupy a small region in the CT image. The feature of such small region may not be able to provide sufficient information due to its limited spatial feature resolution. Secondly, in CT scans, the lesions are often indistinguishable from the background since the lesion and non-lesion areas may have very similar appearances. To tackle both problems, we need to enrich the feature representation and improve the feature discriminativeness. Therefore, we introduce a dual-attention mechanism to the 3D contextual lesion detection framework, including the cross-slice contextual attention to selectively aggregate the information from different slices through a soft re-sampling process. Moreover, we propose intra-slice spatial attention to focus the feature learning in the most prominent regions. Our method can be easily trained end-to-end without adding heavy overhead on the base detection network. We use DeepLesion dataset and train a universal lesion detector to detect all kinds of lesions such as liver tumors, lung nodules, and so on. The results show that our model can significantly boost the results of the baseline lesion detector (with 3D contextual information) but using much fewer slices.
Procedural Content Generation through Quality Diversity
Venue: arXiv cs.NE 2019
Year: 2019

Quality-diversity (QD) algorithms search for a set of good solutions which cover a space as defined by behavior metrics. This simultaneous focus on quality and diversity with explicit metrics sets QD algorithms apart from standard single- and multi-objective evolutionary algorithms, as well as from diversity preservation approaches such as niching. These properties open up new avenues for artificial intelligence in games, in particular for procedural content generation. Creating multiple systematically varying solutions allows new approaches to creative human-AI interaction as well as adaptivity. In the last few years, a handful of applications of QD to procedural content generation and game playing have been proposed; we discuss these and propose challenges for future work.
Event-based attention and tracking on neuromorphic hardware
Venue: arXiv cs.NE 2019
Year: 2019

We present a fully event-driven vision and processing system for selective attention and tracking, realized on a neuromorphic processor Loihi interfaced to an event-based Dynamic Vision Sensor DAVIS. The attention mechanism is realized as a recurrent spiking neural network that implements attractor-dynamics of dynamic neural fields. We demonstrate capability of the system to create sustained activation that supports object tracking when distractors are present or when the object slows down or stops, reducing the number of generated events.
Bi-objective Optimisation of Data-parallel Applications on Heterogeneous Platforms for Performance and Energy via Workload Distribution
Venue: arXiv cs.DC 2019
Year: 2019

Performance and energy are the two most important objectives for optimisation on modern parallel platforms. Latest research demonstrated the importance of workload distribution as a decision variable in the bi-objective optimisation for performance and energy on homogeneous multicore clusters. We show in this work that bi-objective optimisation for performance and energy on heterogeneous processors results in a large number of Pareto-optimal optimal solutions (workload distributions) even in the simple case of linear performance and energy profiles. We then study performance and energy profiles of real-life data-parallel applications and find that their shapes are non-linear, complex and non-smooth. We, therefore, propose an efficient and exact global optimisation algorithm, which takes as an input most general discrete performance and dynamic energy profiles of the heterogeneous processors and solves the bi-objective optimisation problem. The algorithm is also used as a building block to solve the bi-objective optimisation problem for performance and total energy. We also propose a novel methodology to build discrete dynamic energy profiles of individual computing devices, which are input to the algorithm. The methodology is based purely on system-level measurements and addresses the fundamental challenge of accurate component-level energy modelling of a hybrid data-parallel application running on a heterogeneous platform integrating CPUs and accelerators. We experimentally validate the proposed method using two data-parallel applications, matrix multiplication and 2D fast Fourier transform (2D-FFT).
Efficient Pose Selection for Interactive Camera Calibration
Venue: arXiv cs.CV 2019
Year: 2019

The choice of poses for camera calibration with planar patterns is only rarely considered - yet the calibration precision heavily depends on it. This work presents a pose selection method that finds a compact and robust set of calibration poses and is suitable for interactive calibration. Consequently, singular poses that would lead to an unreliable solution are avoided explicitly, while poses reducing the uncertainty of the calibration are favoured. For this, we use uncertainty propagation. Our method takes advantage of a self-identifying calibration pattern to track the camera pose in real-time. This allows to iteratively guide the user to the target poses, until the desired quality level is reached. Therefore, only a sparse set of key-frames is needed for calibration. The method is evaluated on separate training and testing sets, as well as on synthetic data. Our approach performs better than comparable solutions while requiring 30% less calibration frames.
The What-If Tool: Interactive Probing of Machine Learning Models
Venue: arXiv cs.LG 2019
Year: 2019

A key challenge in developing and deploying Machine Learning (ML) systems is understanding their performance across a wide range of inputs. To address this challenge, we created the What-If Tool, an open-source application that allows practitioners to probe, visualize, and analyze ML systems, with minimal coding. The What-If Tool lets practitioners test performance in hypothetical situations, analyze the importance of different data features, and visualize model behavior across multiple models and subsets of input data. It also lets practitioners measure systems according to multiple ML fairness metrics. We describe the design of the tool, and report on real-life usage at different organizations.
Emergency Management Systems and Algorithms: a Comprehensive Survey
Venue: arXiv cs.OH 2019
Year: 2019

Owing to the increasing frequency and destruction of natural and manmade disasters to modern highly-populated societies, emergency management, which provides solutions to prevent or address disasters, have drawn considerable research over the last few decades and become a multidisciplinary area. Because of its open and inclusive nature, new technologies always tend to influence, change or even revolutionise this research area. Hence, it is imperative to consolidate the state-of-the-art studies and knowledge to meet the research needs and identify the future research directions. The paper presents a comprehensive and systemic review of the existing research in the field of emergency management from both the system design aspect and algorithm engineering aspect. We begin with the history and evolution of the emergency management research. Then the two main research topics of this area, "emergency navigation" and "emergency search and rescue planning", are introduced and discussed. Finally, we suggest the emerging challenges and opportunities from system optimisation, evacuee behaviour modelling and optimisation, computing patterns, data analysis, energy and cyber security aspects.
To save the Environmental degradation: Please pay for ecosystem services!
Venue: arXiv cs.CY 2019
Year: 2019

Project planners should consider the monetary value of ecosystem services in the regional service category and add it to the cost calculation of the project as environmental cost. Managers should monitor and regularly maintain the ecological environment within the project area to maintain the benefits of environmental costs.With regard to the problem of predicting how the model changes over time, we use LSTM to realize dynamic evaluation. The analysis that has been made shows that the monetary value of urban ecological services has increased. The environment is still being damaged. Through neural network memory and learning, the problem can be found, and the model can be modified in time.