OpenRaiser/Intern-Atlas huggingface dataset:
Quantity vs. Quality: On Hyperparameter Optimization for Deep Reinforcement Learning
Venue: arXiv cs.LG 2020
Year: 2020

Reinforcement learning algorithms can show strong variation in performance between training runs with different random seeds. In this paper we explore how this affects hyperparameter optimization when the goal is to find hyperparameter settings that perform well across random seeds. In particular, we benchmark whether it is better to explore a large quantity of hyperparameter settings via pruning of bad performers, or if it is better to aim for quality of collected results by using repetitions. For this we consider the Successive Halving, Random Search, and Bayesian Optimization algorithms, the latter two with and without repetitions. We apply these to tuning the PPO2 algorithm on the Cartpole balancing task and the Inverted Pendulum Swing-up task. We demonstrate that pruning may negatively affect the optimization and that repeated sampling does not help in finding hyperparameter settings that perform better across random seeds. From our experiments we conclude that Bayesian optimization with a noise robust acquisition function is the best choice for hyperparameter optimization in reinforcement learning tasks.
Biomedical and Clinical English Model Packages in the Stanza Python NLP Library
Venue: arXiv cs.CL 2020
Year: 2020

We introduce biomedical and clinical English model packages for the Stanza Python NLP library. These packages offer accurate syntactic analysis and named entity recognition capabilities for biomedical and clinical text, by combining Stanza's fully neural architecture with a wide variety of open datasets as well as large-scale unsupervised biomedical and clinical text data. We show via extensive experiments that our packages achieve syntactic analysis and named entity recognition performance that is on par with or surpasses state-of-the-art results. We further show that these models do not compromise speed compared to existing toolkits when GPU acceleration is available, and are made easy to download and use with Stanza's Python interface. A demonstration of our packages is available at: http://stanza.run/bio.
DiLeNA: Distributed Ledger Network Analyzer
Venue: arXiv cs.DC 2020
Year: 2020

This paper describes the Distributed Ledger Network Analyzer (DiLeNA), a new software tool for the analysis of the transactions network recorded in Distributed Ledger Technologies (DLTs). The set of transactions in a DLT forms a complex network. Studying its characteristics and peculiarities is of paramount importance, in order to understand how users interact in the distributed ledger system. The tool design and implementation is introduced and some results are provided. In particular, the Bitcoin and Ethereum blockchains, i.e. the most famous and used DLTs at the time of writing, have been analyzed and compared.
Sample Efficient Interactive End-to-End Deep Learning for Self-Driving Cars with Selective Multi-Class Safe Dataset Aggregation
Venue: arXiv cs.RO 2020
Year: 2020

The objective of this paper is to develop a sample efficient end-to-end deep learning method for self-driving cars, where we attempt to increase the value of the information extracted from samples, through careful analysis obtained from each call to expert driverś policy. End-to-end imitation learning is a popular method for computing self-driving car policies. The standard approach relies on collecting pairs of inputs (camera images) and outputs (steering angle, etc.) from an expert policy and fitting a deep neural network to this data to learn the driving policy. Although this approach had some successful demonstrations in the past, learning a good policy might require a lot of samples from the expert driver, which might be resource-consuming. In this work, we develop a novel framework based on the Safe Dateset Aggregation (safe DAgger) approach, where the current learned policy is automatically segmented into different trajectory classes, and the algorithm identifies trajectory segments or classes with the weak performance at each step. Once the trajectory segments with weak performance identified, the sampling algorithm focuses on calling the expert policy only on these segments, which improves the convergence rate. The presented simulation results show that the proposed approach can yield significantly better performance compared to the standard Safe DAgger algorithm while using the same amount of samples from the expert.
Enriching Video Captions With Contextual Text
Venue: arXiv cs.CV 2020
Year: 2020

Understanding video content and generating caption with context is an important and challenging task. Unlike prior methods that typically attempt to generate generic video captions without context, our architecture contextualizes captioning by infusing extracted information from relevant text data. We propose an end-to-end sequence-to-sequence model which generates video captions based on visual input, and mines relevant knowledge such as names and locations from contextual text. In contrast to previous approaches, we do not preprocess the text further, and let the model directly learn to attend over it. Guided by the visual input, the model is able to copy words from the contextual text via a pointer-generator network, allowing to produce more specific video captions. We show competitive performance on the News Video Dataset and, through ablation studies, validate the efficacy of contextual video captioning as well as individual design choices in our model architecture.
Simulator as a Tool for the Future Maritime Education and Research: A Discussion
Venue: arXiv cs.CY 2020
Year: 2020

A few studies in the maritime domain utilize co-design in ship design workshops, however, none of them addresses a full picture of how co-design can make changes in simulation-based maritime education. In this paper, we reflect how co-design can help to foresight future skills in the maritime domain, especially on how to use simulators to support increasing competence of seafarers and in turn to redesign simulators to support maritime education. Thus, we address collaborative and innovative research activities, to enable all participants (seafarers, trainers, technicians, authorities etc.) to share their experiences so a joint recognition of needed future skills can be reached. Along with the ex-change of experiences, we assert that the supported simulations and simulator techniques could be designed to achieve sustainable growth for all participants.
Maritime Design: A CSCW Territory?
Venue: arXiv cs.HC 2020
Year: 2020

This paper focuses on remote-control and autonomous vessels from a sociological perspective. We report that if CSCW research aims to shed light on other disciplines, researchers should be reflexive insider that first position themselves in such disciplines. Through reflexive practice, CSCW researchers could connect communities of practice, thus narrowing the distance between design and engineering.
Learning Representations for Axis-Aligned Decision Forests through Input Perturbation
Venue: arXiv cs.LG 2020
Year: 2020

Axis-aligned decision forests have long been the leading class of machine learning algorithms for modeling tabular data. In many applications of machine learning such as learning-to-rank, decision forests deliver remarkable performance. They also possess other coveted characteristics such as interpretability. Despite their widespread use and rich history, decision forests to date fail to consume raw structured data such as text, or learn effective representations for them, a factor behind the success of deep neural networks in recent years. While there exist methods that construct smoothed decision forests to achieve representation learning, the resulting models are decision forests in name only: They are no longer axis-aligned, use stochastic decisions, or are not interpretable. Furthermore, none of the existing methods are appropriate for problems that require a Transfer Learning treatment. In this work, we present a novel but intuitive proposal to achieve representation learning for decision forests without imposing new restrictions or necessitating structural changes. Our model is simply a decision forest, possibly trained using any forest learning algorithm, atop a deep neural network. By approximating the gradients of the decision forest through input perturbation, a purely analytical procedure, the decision forest directs the neural network to learn or fine-tune representations. Our framework has the advantage that it is applicable to any arbitrary decision forest and that it allows the use of arbitrary deep neural networks for representation learning. We demonstrate the feasibility and effectiveness of our proposal through experiments on synthetic and benchmark classification datasets.
Bayesian preference elicitation for multiobjective combinatorial optimization
Venue: arXiv cs.AI 2020
Year: 2020

We introduce a new incremental preference elicitation procedure able to deal with noisy responses of a Decision Maker (DM). The originality of the contribution is to propose a Bayesian approach for determining a preferred solution in a multiobjective decision problem involving a combinatorial set of alternatives. We assume that the preferences of the DM are represented by an aggregation function whose parameters are unknown and that the uncertainty about them is represented by a density function on the parameter space. Pairwise comparison queries are used to reduce this uncertainty (by Bayesian revision). The query selection strategy is based on the solution of a mixed integer linear program with a combinatorial set of variables and constraints, which requires to use columns and constraints generation methods. Numerical tests are provided to show the practicability of the approach.
Enhanced well-being assessment as basis for the practical implementation of ethical and rights-based normative principles for AI
Venue: arXiv cs.CY 2020
Year: 2020

Artificial Intelligence (AI) has an increasing impact on all areas of people's livelihoods. A detailed look at existing interdisciplinary and transdisciplinary metrics frameworks could bring new insights and enable practitioners to navigate the challenge of understanding and assessing the impact of Autonomous and Intelligent Systems (A/IS). There has been emerging consensus on fundamental ethical and rights-based AI principles proposed by scholars, governments, civil rights organizations, and technology companies. In order to move from principles to real-world implementation, we adopt a lens motivated by regulatory impact assessments and the well-being movement in public policy. Similar to public policy interventions, outcomes of AI systems implementation may have far-reaching complex impacts. In public policy, indicators are only part of a broader toolbox, as metrics inherently lead to gaming and dissolution of incentives and objectives. Similarly, in the case of A/IS, there's a need for a larger toolbox that allows for the iterative assessment of identified impacts, inclusion of new impacts in the analysis, and identification of emerging trade-offs. In this paper, we propose the practical application of an enhanced well-being impact assessment framework for A/IS that could be employed to address ethical and rights-based normative principles in AI. This process could enable a human-centered algorithmically-supported approach to the understanding of the impacts of AI systems. Finally, we propose a new testing infrastructure which would allow for governments, civil rights organizations, and others, to engage in cooperating with A/IS developers towards implementation of enhanced well-being impact assessments.
Between Subjectivity and Imposition: Power Dynamics in Data Annotation for Computer Vision
Venue: arXiv cs.HC 2020
Year: 2020

The interpretation of data is fundamental to machine learning. This paper investigates practices of image data annotation as performed in industrial contexts. We define data annotation as a sense-making practice, where annotators assign meaning to data through the use of labels. Previous human-centered investigations have largely focused on annotators subjectivity as a major cause for biased labels. We propose a wider view on this issue: guided by constructivist grounded theory, we conducted several weeks of fieldwork at two annotation companies. We analyzed which structures, power relations, and naturalized impositions shape the interpretation of data. Our results show that the work of annotators is profoundly informed by the interests, values, and priorities of other actors above their station. Arbitrary classifications are vertically imposed on annotators, and through them, on data. This imposition is largely naturalized. Assigning meaning to data is often presented as a technical matter. This paper shows it is, in fact, an exercise of power with multiple implications for individuals and society.
Linear Attention Mechanism: An Efficient Attention for Semantic Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, to remedy this deficiency, we propose a Linear Attention Mechanism which is approximate to dot-product attention with much less memory and computational costs. The efficient design makes the incorporation between attention mechanisms and neural networks more flexible and versatile. Experiments conducted on semantic segmentation demonstrated the effectiveness of linear attention mechanism. Code is available at https://github.com/lironui/Linear-Attention-Mechanism.
Dynamic Character Graph via Online Face Clustering for Movie Analysis
Venue: arXiv cs.CV 2020
Year: 2020

An effective approach to automated movie content analysis involves building a network (graph) of its characters. Existing work usually builds a static character graph to summarize the content using metadata, scripts or manual annotations. We propose an unsupervised approach to building a dynamic character graph that captures the temporal evolution of character interaction. We refer to this as the character interaction graph(CIG). Our approach has two components:(i) an online face clustering algorithm that discovers the characters in the video stream as they appear, and (ii) simultaneous creation of a CIG using the temporal dynamics of the resulting clusters. We demonstrate the usefulness of the CIG for two movie analysis tasks: narrative structure (acts) segmentation, and major character retrieval. Our evaluation on full-length movies containing more than 5000 face tracks shows that the proposed approach achieves superior performance for both the tasks.
Cache-enabled HetNets with Limited Backhaul: A Stochastic Geometry Model
Venue: arXiv cs.IT 2020
Year: 2020

With the rapid explosion of data volume from mobile networks, edge caching has received significant attentions as an efficient approach to boost content delivery efficiency by bringing contents near users. In this article, cache-enabled heterogeneous networks (HetNets) considering the limited backhaul is analyzed with the aid of the stochastic geometry approach. A hybrid caching policy, in which the most popular contents are cached in the macro BSs tier with the deterministic caching strategy and the less popular contents are cached in the helpers tier with the probabilistic caching strategy, is proposed. Correspondingly, the content-centric association strategy is designed based on the comprehensive state of the access link, the cache and the backhaul link. Under the hybrid caching policy, new analytical results for successful content delivery probability, average successful delivery rate and energy efficiency are derived in the general scenario, the interference-limited scenario and the mean load scenario. The simulation results show that the proposed caching policy outperforms the most popular caching policy in HetNets with the limited backhaul. The performance gain is dramatically improved when the content popularity is less skewed, the cache capacity is sufficient and the helper density is relatively large. Furthermore, it is confirmed that there exists an optimal helper density to maximize the energy efficiency of the cache-enabled HetNets.
Foundational Oracle Patterns: Connecting Blockchain to the Off-chain World
Venue: arXiv cs.SE 2020
Year: 2020

Blockchain has evolved into a platform for decentralized applications, with beneficial properties like high integrity, transparency, and resilience against censorship and tampering. However, blockchains are closed-world systems which do not have access to external state. To overcome this limitation, oracles have been introduced in various forms and for different purposes. However so far common oracle best practices have not been dissected, classified, and studied in their fundamental aspects. In this paper, we address this gap by studying foundational blockchain oracle patterns in two foundational dimensions characterising the oracles: (i) the data flow direction, i.e., inbound and outbound data flow, from the viewpoint of the blockchain; and (ii) the initiator of the data flow, i.e., whether it is push or pull-based communication. We provide a structured description of the four patterns in detail, and discuss an implementation of these patterns based on use cases. On this basis we conduct a quantitative analysis, which results in the insight that the four different patterns are characterized by distinct performance and costs profiles.
Opacity of Discrete Event Systems with Active Intruder
Venue: arXiv cs.LO 2020
Year: 2020

Opacity is a security property formalizing the information leakage of a system to an external observer, namely intruder. The conventional opacity that has been studied in the Discrete Event System (DES) literature usually assumes passive intruders, who only observe the behavior of the system. However, in many cybersecurity concerns, such as web service, active intruders, who are capable of influencing the system's behavior beyond passive observations, need to be considered and defended against. We are therefore motivated to extend the opacity notions to handle active intruders. For this, we model the system as a non-deterministic finite-state transducer. It is assumed that the intruder has a full knowledge of the system structure and is capable of interacting with the system by injecting different inputs and observing its responses. In this setup, we first introduce reactive current-state opacity (RCSO) notion characterizing a property that the system does not leak its secret state regardless of how the intruder manipulates the system behavior. We furthermore extend this notion to language-based and initial-state reactive opacity notions, and study the relationship among them. It turns out that all the proposed reactive opacity notions are equivalent to RCSO. We therefore focus on RCSO and study its verification problem. It is shown that the RCSO can be verified by constructing an observer automaton.
An Experimental Study of The Effects of Position Bias on Emotion CauseExtraction
Venue: arXiv cs.CL 2020
Year: 2020

Emotion Cause Extraction (ECE) aims to identify emotion causes from a document after annotating the emotion keywords. Some baselines have been proposed to address this problem, such as rule-based, commonsense based and machine learning methods. We show, however, that a simple random selection approach toward ECE that does not require observing the text achieves similar performance compared to the baselines. We utilized only position information relative to the emotion cause to accomplish this goal. Since position information alone without observing the text resulted in higher F-measure, we therefore uncovered a bias in the ECE single genre Sina-news benchmark. Further analysis showed that an imbalance of emotional cause location exists in the benchmark, with a majority of cause clauses immediately preceding the central emotion clause. We examine the bias from a linguistic perspective, and show that high accuracy rate of current state-of-art deep learning models that utilize location information is only evident in datasets that contain such position biases. The accuracy drastically reduced when a dataset with balanced location distribution is introduced. We therefore conclude that it is the innate bias in this benchmark that caused high accuracy rate of these deep learning models in ECE. We hope that the case study in this paper presents both a cautionary lesson, as well as a template for further studies, in interpreting the superior fit of deep learning models without checking for bias.
Improving Performance of Relation Extraction Algorithm via Leveled Adversarial PCNN and Database Expansion
Venue: arXiv cs.IR 2020
Year: 2020

This study introduces database expansion using the Minimum Description Length (MDL) algorithm to expand the database for better relation extraction. Different from other previous relation extraction researches, our method improves system performance by expanding data. The goal of database expansion, together with a robust deep learning classifier, is to diminish wrong labels due to the incomplete or not found nature of relation instances in the relation database (e.g., Freebase). The study uses a deep learning method (Piecewise Convolutional Neural Network or PCNN) as the base classifier of our proposed approach: the leveled adversarial attention neural networks (LATTADV-ATT). In the database expansion process, the semantic entity identification is used to enlarge new instances using the most similar itemsets of the most common patterns of the data to get its pairs of entities. About the deep learning method, the use of attention of selective sentences in PCNN can reduce noisy sentences. Also, the use of adversarial perturbation training is useful to improve the robustness of system performance. The performance even further is improved using a combination of leveled strategy and database expansion. There are two issues: 1) database expansion method: rule generation by allowing step sizes on selected strong semantic of most similar itemsets with aims to find entity pair for generating instances, 2) a better classifier model for relation extraction. Experimental result has shown that the use of the database expansion is beneficial. The MDL database expansion helps improvements in all methods compared to the unexpanded method. The LATTADV-ATT performs as a good classifier with high precision P@100=0.842 (at no expansion). It is even better while implemented on the expansion data with P@100=0.891 (at expansion factor k=7).