OpenRaiser/Intern-Atlas huggingface dataset:
How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

Explainable AI (XAI) aims to provide interpretations for predictions made by learning machines, such as deep neural networks, in order to make the machines more transparent for the user and furthermore trustworthy also for applications in e.g. safety-critical areas. So far, however, no methods for quantifying uncertainties of explanations have been conceived, which is problematic in domains where a high confidence in explanations is a prerequisite. We therefore contribute by proposing a new framework that allows to convert any arbitrary explanation method for neural networks into an explanation method for Bayesian neural networks, with an in-built modeling of uncertainties. Within the Bayesian framework a network's weights follow a distribution that extends standard single explanation scores and heatmaps to distributions thereof, in this manner translating the intrinsic network model uncertainties into a quantification of explanation uncertainties. This allows us for the first time to carve out uncertainties associated with a model explanation and subsequently gauge the appropriate level of explanation confidence for a user (using percentiles). We demonstrate the effectiveness and usefulness of our approach extensively in various experiments, both qualitatively and quantitatively.
Learning to Solve Vehicle Routing Problems with Time Windows through Joint Attention
Venue: arXiv cs.LG 2020
Year: 2020

Many real-world vehicle routing problems involve rich sets of constraints with respect to the capacities of the vehicles, time windows for customers etc. While in recent years first machine learning models have been developed to solve basic vehicle routing problems faster than optimization heuristics, complex constraints rarely are taken into consideration. Due to their general procedure to construct solutions sequentially route by route, these methods generalize unfavorably to such problems. In this paper, we develop a policy model that is able to start and extend multiple routes concurrently by using attention on the joint action space of several tours. In that way the model is able to select routes and customers and thus learns to make difficult trade-offs between routes. In comprehensive experiments on three variants of the vehicle routing problem with time windows we show that our model called JAMPR works well for different problem sizes and outperforms the existing state-of-the-art constructive model. For two of the three variants it also creates significantly better solutions than a comparable meta-heuristic solver.
How to Probe Sentence Embeddings in Low-Resource Languages: On Structural Design Choices for Probing Task Evaluation
Venue: arXiv cs.CL 2020
Year: 2020

Sentence encoders map sentences to real valued vectors for use in downstream applications. To peek into these representations - e.g., to increase interpretability of their results - probing tasks have been designed which query them for linguistic knowledge. However, designing probing tasks for lesser-resourced languages is tricky, because these often lack large-scale annotated data or (high-quality) dependency parsers as a prerequisite of probing task design in English. To investigate how to probe sentence embeddings in such cases, we investigate sensitivity of probing task results to structural design choices, conducting the first such large scale study. We show that design choices like size of the annotated probing dataset and type of classifier used for evaluation do (sometimes substantially) influence probing outcomes. We then probe embeddings in a multilingual setup with design choices that lie in a 'stable region', as we identify for English, and find that results on English do not transfer to other languages. Fairer and more comprehensive sentence-level probing evaluation should thus be carried out on multiple languages in the future.
Improving accuracy and speeding up Document Image Classification through parallel systems
Venue: arXiv cs.CV 2020
Year: 2020

This paper presents a study showing the benefits of the EfficientNet models compared with heavier Convolutional Neural Networks (CNNs) in the Document Classification task, essential problem in the digitalization process of institutions. We show in the RVL-CDIP dataset that we can improve previous results with a much lighter model and present its transfer learning capabilities on a smaller in-domain dataset such as Tobacco3482. Moreover, we present an ensemble pipeline which is able to boost solely image input by combining image model predictions with the ones generated by BERT model on extracted text by OCR. We also show that the batch size can be effectively increased without hindering its accuracy so that the training process can be sped up by parallelizing throughout multiple GPUs, decreasing the computational time needed. Lastly, we expose the training performance differences between PyTorch and Tensorflow Deep Learning frameworks.
Building a Collaborative Phone Blacklisting System with Local Differential Privacy
Venue: arXiv cs.CR 2020
Year: 2020

Spam phone calls have been rapidly growing from nuisance to an increasingly effective scam delivery tool. To counter this increasingly successful attack vector, a number of commercial smartphone apps that promise to block spam phone calls have appeared on app stores, and are now used by hundreds of thousands or even millions of users. However, following a business model similar to some online social network services, these apps often collect call records or other potentially sensitive information from users' phones with little or no formal privacy guarantees. In this paper, we study whether it is possible to build a practical collaborative phone blacklisting system that makes use of local differential privacy (LDP) mechanisms to provide clear privacy guarantees. We analyze the challenges and trade-offs related to using LDP, evaluate our LDP-based system on real-world user-reported call records collected by the FTC, and show that it is possible to learn a phone blacklist using a reasonable overall privacy budget and at the same time preserve users' privacy while maintaining utility for the learned blacklist.
Digital Contact Tracing Using IP Colocation
Venue: arXiv cs.SI 2020
Year: 2020

The spread of an infectious disease through a population can be modeled using a network or a graph. In digital advertising, internet device graphs are graph data sets that organize identifiers produced by mobile phones, PCs, TVs, and tablets as they access media on the internet. Characterized by immense scale, they have become ubiquitous as they enable targeted advertising, content customization and tracking. This paper posits that internet device graphs, in particular those based on IP colocation, can provide significant utility in predicting and modeling the spread of infectious disease. Starting the week of March 16th, 2020, in the United States, many individuals began to `shelter-in-place' as schools and workplaces across the nation closed because of the COVID-19 pandemic. This paper quantifies the effect of the shelter-in-place orders on a large scale internet device graph with more than a billion nodes by studying the graph before and after orders went into effect. The effects are clearly visible. The structure of the graph suggests behavior least conducive to transmission of infection occurred in the US between April 12th and 19th, 2020. This paper also discusses the utility of device graphs for i) contact tracing, ii) prediction of `hot spots', iii) simulation of infectious disease spread, and iv) delivery of advertisement-based warnings to potentially exposed individuals. The paper also posits an overarching question: can systems and datasets amassed by entities in the digital ad ecosystem aid in the fight against COVID-19?
Play with One's Feelings: A Study on Emotion Awareness for Player Experience
Venue: arXiv cs.HC 2020
Year: 2020

Affective interaction between players of video games can elicit rich and varying patterns of emotions. In multiplayer activities that take place in a common space (such as sports and board games), players are generally aware of the emotions of their teammates or opponents as they can directly observe their behavioral patterns, facial expressions, head pose, body stance and so on. Players of online video games, however, are not generally aware of the other players' emotions given the limited channels of direct interaction among them (e.g. via emojis or chat boxes). It also turns out that the impact of real-time emotionawareness on play is still unexplored in the space of online digital games. Motivated by this lack of empirical knowledge on the role of the affect of others to one's gameplay performance in this paper we investigate the degrees to which the expression of manifested emotions of an opponent can affect the emotions of the player and consequently his gameplay behavior. In this initial study, we test our hypothesis on a two-player adversarial car racing game. We perform a comprehensive user study to evaluate the emotions, behaviors, and attitudes of players in emotion aware versus emotion agnostic game versions. Our findings suggest that expressing the emotional state of the opponent through an emoji in real-time affects the emotional state and behavior of players that can consequently affect their playing experience.
On sparse connectivity, adversarial robustness, and a novel model of the artificial neuron
Venue: arXiv cs.NE 2020
Year: 2020

Deep neural networks have achieved human-level accuracy on almost all perceptual benchmarks. It is interesting that these advances were made using two ideas that are decades old: (a) an artificial neuron based on a linear summator and (b) SGD training. However, there are important metrics beyond accuracy: computational efficiency and stability against adversarial perturbations. In this paper, we propose two closely connected methods to improve these metrics on contour recognition tasks: (a) a novel model of an artificial neuron, a "strong neuron," with low hardware requirements and inherent robustness against adversarial perturbations and (b) a novel constructive training algorithm that generates sparse networks with $O(1)$ connections per neuron. We demonstrate the feasibility of our approach through experiments on SVHN and GTSRB benchmarks. We achieved an impressive 10x-100x reduction in operations count (10x when compared with other sparsification approaches, 100x when compared with dense networks) and a substantial reduction in hardware requirements (8-bit fixed-point math was used) with no reduction in model accuracy. Superior stability against adversarial perturbations (exceeding that of adversarial training) was achieved without any counteradversarial measures, relying on the robustness of strong neurons alone. We also proved that constituent blocks of our strong neuron are the only activation functions with perfect stability against adversarial attacks.
From Ancient Contemplative Practice to the App Store: Designing a Digital Container for Mindfulness
Venue: arXiv cs.HC 2020
Year: 2020

Hundreds of popular mobile apps today market their ties to mindfulness. What activities do these apps support and what benefits do they claim? How do mindfulness teachers, as domain experts, view these apps? We first conduct an exploratory review of 370 mindfulness-related apps on Google Play, finding that mindfulness is presented primarily as a tool for relaxation and stress reduction. We then interviewed 15 U.S. mindfulness teachers from the therapeutic, Buddhist, and Yogic traditions about their perspectives on these apps. Teachers expressed concern that apps that introduce mindfulness only as a tool for relaxation neglect its full potential. We draw upon the experiences of these teachers to suggest design implications for linking mindfulness with further contemplative practices like the cultivation of compassion. Our findings speak to the importance of coherence in design: that the metaphors and mechanisms of a technology align with the underlying principles it follows.
Bayesian Evaluation of User App Choices in the Presence of Risk Communication on Android Devices
Venue: arXiv cs.CR 2020
Year: 2020

In the age of ubiquitous technologies, security- and privacy-focused choices have turned out to be a significant concern for individuals and organizations. Risks of such pervasive technologies are extensive and often misaligned with user risk perception, thus failing to help users in taking privacy-aware decisions. Researchers usually try to find solutions for coherently extending trust into our often inscrutable electronic networked environment. To enable security- and privacy-focused decision-making, we mainly focused on the realm of the mobile marketplace, examining how risk indicators can help people choose more secure and privacy-preserving apps. We performed a naturalistic experiment with N=60 participants, where we asked them to select applications on Android tablets with accurate real-time marketplace data. We found that, in aggregate, app selections changed to be more risk-averse in the presence of user risk-perception-aligned visual indicators. Our study design and research propose practical and usable interactions that enable more informed, risk-aware comparisons for individuals during app selections. We include an explicit argument for the role of human decision-making during app selection, beyond the current trend of using machine learning to automate privacy preferences after selection during run-time.
COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learning
Venue: arXiv cs.RO 2020
Year: 2020

Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics. For many decades, they have been subject to academic study, leading to a vast number of proposed approaches. However, they have mostly been treated as separate problems, and have typically relied on non-linear first-principles models with parameters that can only be determined experimentally. The rise of Deep Reinforcement Learning (DRL) in recent years suggests an alternative approach: end-to-end learning of the optimal guidance policy from scratch by means of a trial-and-error based approach. In this article, we explore the potential of Proximal Policy Optimization (PPO), a DRL algorithm with demonstrated state-of-the-art performance on Continuous Control tasks, when applied to the dual-objective problem of controlling an underactuated Autonomous Surface Vehicle in a COLREGs compliant manner such that it follows an a priori known desired path while avoiding collisions with other vessels along the way. Based on high-fidelity elevation and AIS tracking data from the Trondheim Fjord, an inlet of the Norwegian sea, we evaluate the trained agent's performance in challenging, dynamic real-world scenarios where the ultimate success of the agent rests upon its ability to navigate non-uniform marine terrain while handling challenging, but realistic vessel encounters.
Modeling Subjective Assessments of Guilt in Newspaper Crime Narratives
Venue: arXiv cs.CL 2020
Year: 2020

Crime reporting is a prevalent form of journalism with the power to shape public perceptions and social policies. How does the language of these reports act on readers? We seek to address this question with the SuspectGuilt Corpus of annotated crime stories from English-language newspapers in the U.S. For SuspectGuilt, annotators read short crime articles and provided text-level ratings concerning the guilt of the main suspect as well as span-level annotations indicating which parts of the story they felt most influenced their ratings. SuspectGuilt thus provides a rich picture of how linguistic choices affect subjective guilt judgments. In addition, we use SuspectGuilt to train and assess predictive models, and show that these models benefit from genre pretraining and joint supervision from the text-level ratings and span-level annotations. Such models might be used as tools for understanding the societal effects of crime reporting.
ExSampling: a system for the real-time ensemble performance of field-recorded environmental sounds
Venue: arXiv cs.HC 2020
Year: 2020

We propose ExSampling: an integrated system of recording application and Deep Learning environment for a real-time music performance of environmental sounds sampled by field recording. Automated sound mapping to Ableton Live tracks by Deep Learning enables field recording to be applied to real-time performance, and create interactions among sound recorders, composers and performers.
Self-Supervised Representation Learning for Visual Anomaly Detection
Venue: arXiv cs.CV 2020
Year: 2020

Self-supervised learning allows for better utilization of unlabelled data. The feature representation obtained by self-supervision can be used in downstream tasks such as classification, object detection, segmentation, and anomaly detection. While classification, object detection, and segmentation have been investigated with self-supervised learning, anomaly detection needs more attention. We consider the problem of anomaly detection in images and videos, and present a new visual anomaly detection technique for videos. Numerous seminal and state-of-the-art self-supervised methods are evaluated for anomaly detection on a variety of image datasets. The best performing image-based self-supervised representation learning method is then used for video anomaly detection to see the importance of spatial features in visual anomaly detection in videos. We also propose a simple self-supervision approach for learning temporal coherence across video frames without the use of any optical flow information. At its core, our method identifies the frame indices of a jumbled video sequence allowing it to learn the spatiotemporal features of the video. This intuitive approach shows superior performance of visual anomaly detection compared to numerous methods for images and videos on UCF101 and ILSVRC2015 video datasets.
Optimizing Information Freshness in Two-Way Relay Networks
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we investigate an amplify-and-forward (AF) based two-way cooperative status update system, where two sources aim to exchange status updates with each other as timely as possible with the help of a relay. Specifically, the relay receives the sum signal from the two sources in one time slot, and then amplifies and forwards the received signal to both the sources in the next time slot. We adopt a recently proposed concept, the age of information (AoI), to characterize the timeliness of the status updates. Assuming that the two sources are able to generate status updates at the beginning of each time slot (i.e., generate-at-will model), we derive a closed-form expression of the expected weighted sum AoI of the considered system. We further minimize the expected weighted sum AoI by optimizing the transmission power at each node under the peak power constraints. Simulation results corroborate the correctness of our theoretical analysis.
Evaluation of 3D CNN Semantic Mapping for Rover Navigation
Venue: arXiv cs.CV 2020
Year: 2020

Terrain assessment is a key aspect for autonomous exploration rovers, surrounding environment recognition is required for multiple purposes, such as optimal trajectory planning and autonomous target identification. In this work we present a technique to generate accurate three-dimensional semantic maps for Martian environment. The algorithm uses as input a stereo image acquired by a camera mounted on a rover. Firstly, images are labeled with DeepLabv3+, which is an encoder-decoder Convolutional Neural Networl (CNN). Then, the labels obtained by the semantic segmentation are combined to stereo depth-maps in a Voxel representation. We evaluate our approach on the ESA Katwijk Beach Planetary Rover Dataset.
Improving unsupervised neural aspect extraction for online discussions using out-of-domain classification
Venue: arXiv cs.CL 2020
Year: 2020

Deep learning architectures based on self-attention have recently achieved and surpassed state of the art results in the task of unsupervised aspect extraction and topic modeling. While models such as neural attention-based aspect extraction (ABAE) have been successfully applied to user-generated texts, they are less coherent when applied to traditional data sources such as news articles and newsgroup documents. In this work, we introduce a simple approach based on sentence filtering in order to improve topical aspects learned from newsgroups-based content without modifying the basic mechanism of ABAE. We train a probabilistic classifier to distinguish between out-of-domain texts (outer dataset) and in-domain texts (target dataset). Then, during data preparation we filter out sentences that have a low probability of being in-domain and train the neural model on the remaining sentences. The positive effect of sentence filtering on topic coherence is demonstrated in comparison to aspect extraction models trained on unfiltered texts.
An Evolutional Algorithm for Automatic 2D Layer Segmentation in Laser-aided Additive Manufacturing
Venue: arXiv cs.GR 2020
Year: 2020

Toolpath planning is an important task in laser aided additive manufacturing (LAAM) and other direct energy deposition (DED) processes. The deposition toolpaths for complex geometries with slender structures can be further optimized by partitioning the sliced 2D layers into sub-regions, and enable the design of appropriate infill toolpaths for different sub-regions. However, reported approaches for 2D layer segmentation generally require manual operations that are tedious and time-consuming. To increase segmentation efficiency, this paper proposes an autonomous approach based on evolutional computation for 2D layer segmentation. The algorithm works in an identify-and-segment manner. Specifically, the largest quasi-quadrilateral is identified and segmented from the target layer iteratively. Results from case studies have validated the effectiveness and efficacy of the developed algorithm. To further improve its performance, a roughing-finishing strategy is proposed. Via multi-processing, the strategy can remarkably increase the solution variety without affecting solution quality and search time, thus providing great application potential in LAAM toolpath planning. To the best of the authors knowledge, this work is the first to address automatic 2D layer segmentation problem in LAAM process. Therefore, it may be a valuable supplement to the state of the art in this area.
Probabilistic Models for the Execution Time in Stochastic Scheduling
Venue: arXiv cs.DC 2020
Year: 2020

The execution time of programs is a key element in many areas of computer science, mainly those where achieving good performance (e.g., scheduling in cloud computing) or a predictable one (e.g., meeting deadlines in embedded systems) is the objective. Despite being random variables, execution times are most often treated as deterministic in the literature, with few works taking advantage of their randomness; even in those, the underlying distributions are assumed as being normal or uniform for no particular reason. In this work we investigate these distributions in various machines and algorithms. A mathematical problem arises when dealing with samples whose populational minimum is unknown, so a significant portion of this monograph is dedicated to such problem. We propose several different effective or computationally cheap ways to overcome the problem, which also apply to execution times. These methods are tested experimentally, and results point to the superiority of our proposed inference methods. We demonstrate the existence of execution time distributions with long tails, and also conclude that two particular probability distributions were the most suitable for modelling all execution times. While we do not discuss direct applications to stochastic scheduling, we hope to promote the usage of probabilistic execution times to yield better results in, for example, task scheduling.