OpenRaiser/Intern-Atlas huggingface dataset:
Coalition-Safe Equilibria with Virtual Payoffs
Venue: arXiv cs.GT 2019
Year: 2019

Consider a set of parties invited to execute a protocol $Π$. The protocol will incur some cost to run while in the end (or at regular intervals), it will populate and update local tables that assign (virtual) rewards to participants. Each participant aspires to offset the costs of participation by these virtual payoffs that are provided in the course of the protocol. In this setting, we introduce and study a notion of coalition-safe equilibrium. In particular, we consider a strategic coalition of participants that is centrally coordinated and potentially deviates from $Π$ with the objective to increase its utility with respect to the view of {\em at least one} of the other participants. The protocol $Π$ is called a coalition-safe equilibrium with virtual payoffs (EVP) if no such protocol deviation exists. We apply our notion to study incentives in blockchain protocols. We proceed to use our framework to provide a unified picture of incentives in the Bitcoin blockchain, for absolute and relative rewards based utility functions, as well as prove novel results regarding incentives of the Fruitchain blockchain protocol [PODC 2017] showing that the equilibrium condition holds for collusions up to $n-1$ players for absolute rewards based utility functions and less than $n/2$ for relative rewards based utility functions, with the latter result holding for any "weakly fair" blockchain protocol, a new property that we introduce and may be of independent interest.
Deep Attentive Ranking Networks for Learning to Order Sentences
Venue: arXiv cs.CL 2019
Year: 2019

We present an attention-based ranking framework for learning to order sentences given a paragraph. Our framework is built on a bidirectional sentence encoder and a self-attention based transformer network to obtain an input order invariant representation of paragraphs. Moreover, it allows seamless training using a variety of ranking based loss functions, such as pointwise, pairwise, and listwise ranking. We apply our framework on two tasks: Sentence Ordering and Order Discrimination. Our framework outperforms various state-of-the-art methods on these tasks on a variety of evaluation metrics. We also show that it achieves better results when using pairwise and listwise ranking losses, rather than the pointwise ranking loss, which suggests that incorporating relative positions of two or more sentences in the loss function contributes to better learning.
Visual Evaluation of Generative Adversarial Networks for Time Series Data
Venue: arXiv cs.LG 2019
Year: 2019

A crucial factor to trust Machine Learning (ML) algorithm decisions is a good representation of its application field by the training dataset. This is particularly true when parts of the training data have been artificially generated to overcome common training problems such as lack of data or imbalanced dataset. Over the last few years, Generative Adversarial Networks (GANs) have shown remarkable results in generating realistic data. However, this ML approach lacks an objective function to evaluate the quality of the generated data. Numerous GAN applications focus on generating image data mostly because they can be easily evaluated by a human eye. Less efforts have been made to generate time series data. Assessing their quality is more complicated, particularly for technical data. In this paper, we propose a human-centered approach supporting a ML or domain expert to accomplish this task using Visual Analytics (VA) techniques. The presented approach consists of two views, namely a GAN Iteration View showing similarity metrics between real and generated data over the iterations of the generation process and a Detailed Comparative View equipped with different time series visualizations such as TimeHistograms, to compare the generated data at different iteration steps. Starting from the GAN Iteration View, the user can choose suitable iteration steps for detailed inspection. We evaluate our approach with a usage scenario that enabled an efficient comparison of two different GAN models.
Resilient Cyberphysical Systems and their Application Drivers: A Technology Roadmap
Venue: arXiv cs.CY 2019
Year: 2019

Cyberphysical systems (CPS) are ubiquitous in our personal and professional lives, and they promise to dramatically improve micro-communities (e.g., urban farms, hospitals), macro-communities (e.g., cities and metropolises), urban structures (e.g., smart homes and cars), and living structures (e.g., human bodies, synthetic genomes). The question that we address in this article pertains to designing these CPS systems to be resilient-from-the-ground-up, and through progressive learning, resilient-by-reaction. An optimally designed system is resilient to both unique attacks and recurrent attacks, the latter with a lower overhead. Overall, the notion of resilience can be thought of in the light of three main sources of lack of resilience, as follows: exogenous factors, such as natural variations and attack scenarios; mismatch between engineered designs and exogenous factors ranging from DDoS (distributed denial-of-service) attacks or other cybersecurity nightmares, so called "black swan" events, disabling critical services of the municipal electrical grids and other connected infrastructures, data breaches, and network failures; and the fragility of engineered designs themselves encompassing bugs, human-computer interactions (HCI), and the overall complexity of real-world systems. In the paper, our focus is on design and deployment innovations that are broadly applicable across a range of CPS application areas.
Building chatbots from large scale domain-specific knowledge bases: challenges and opportunities
Venue: arXiv cs.CL 2019
Year: 2019

Popular conversational agents frameworks such as Alexa Skills Kit (ASK) and Google Actions (gActions) offer unprecedented opportunities for facilitating the development and deployment of voice-enabled AI solutions in various verticals. Nevertheless, understanding user utterances with high accuracy remains a challenging task with these frameworks. Particularly, when building chatbots with large volume of domain-specific entities. In this paper, we describe the challenges and lessons learned from building a large scale virtual assistant for understanding and responding to equipment-related complaints. In the process, we describe an alternative scalable framework for: 1) extracting the knowledge about equipment components and their associated problem entities from short texts, and 2) learning to identify such entities in user utterances. We show through evaluation on a real dataset that the proposed framework, compared to off-the-shelf popular ones, scales better with large volume of entities being up to 30% more accurate, and is more effective in understanding user utterances with domain-specific entities.
With Great Power Comes Great Responsibility: Utilizing Privacy Technology for the Greater Bad
Venue: arXiv cs.CR 2019
Year: 2019

As people across the world become increasingly aware of how their privacy is compromised in this digital era, the field of Privacy Enhancing Technologies, or PETs, has boomed. The first workshop on Privacy Enhancing Technology was in 2000, then called the "Workshop on Design Issues in Anonymity and Unobservability." By 2007, the workshop had ballooned into a full symposium. In 2015, the first issue of the Proceedings on Privacy Enhancing Technologies journal was published. This year in 2019, there were 4 volumes of Proceedings on Privacy Enhancing Technologies published, containing a total of 66 papers. Of these papers, we identified 14 which specifically describe PETs that have been newly developed or utilized in a new way. We focused on 3 papers that seemed to have widespread use cases. Some of these use cases, however, include criminal activity. While heavily focused on adversarial actions and capability, much of security research does not focus on or consider the social science and motivations behind bad actors. We believe that this is a critical factor to consider when working on the problem of cybercrime. In this paper, we will analyze some of the cutting edge PETs and the potential risks associated with them from the lenses of computer science and criminal justice. We argue that the continued development of privacy enhancing technology contributes to the global rise of cybercrime.
Engineers Code: reusable open learning modules for engineering computations
Venue: arXiv cs.CY 2019
Year: 2019

Undergraduate programs in science and engineering include at least one course in basic programming, but seldom presented in a contextualized format, where computing is a tool for thinking and learning in the discipline. We have created a series of learning modules to embed computing in engineering education, and share this content under permissive public licenses. The modules are created as a set of lessons using Jupyter notebooks, and complemented by online courses in the Open edX platform, using new integrations we developed. Learning sequences in the online course pull content dynamically from public Jupyter notebooks and assessments are auto-graded on-the-fly, using our Jupyter Viewer and Jupyter Grader third-party extensions for Open edX (XBlocks). The learning content is modularized and designed for reuse in various formats. In one of these formats---short but intense workshops---our university library is leveraging the curriculum to offer extra-curricular training for all, at high demands.
An Approach Towards Intelligent Accident Detection, Location Tracking and Notification System
Venue: arXiv cs.OH 2019
Year: 2019

Advancement in transportation system has boosted speed of our lives. Meantime, road traffic accident is a major global health issue resulting huge loss of lives, properties and valuable time. It is considered as one of the reasons of highest rate of death nowadays. Accident creates catastrophic situation for victims, especially accident occurs in highways imposes great adverse impact on large numbers of victims. In this paper, we develop an intelligent accident detection, location tracking and notification system that detects an accident immediately when it takes place. Global Positioning System (GPS) device finds the exact location of accident. Global System for Mobile (GSM) module sends a notification message including the link of location in the google map to the nearest police control room and hospital so that they can visit the link, find out the shortest route of the accident spot and take initiatives to speed up the rescue process.
Joint Robust Voicing Detection and Pitch Estimation Based on Residual Harmonics
Venue: arXiv cs.SD 2019
Year: 2019

This paper focuses on the problem of pitch tracking in noisy conditions. A method using harmonic information in the residual signal is presented. The proposed criterion is used both for pitch estimation, as well as for determining the voicing segments of speech. In the experiments, the method is compared to six state-of-the-art pitch trackers on the Keele and CSTR databases. The proposed technique is shown to be particularly robust to additive noise, leading to a significant improvement in adverse conditions.
Detection of Glottal Closure Instants from Speech Signals: a Quantitative Review
Venue: arXiv cs.SD 2019
Year: 2019

The pseudo-periodicity of voiced speech can be exploited in several speech processing applications. This requires however that the precise locations of the Glottal Closure Instants (GCIs) are available. The focus of this paper is the evaluation of automatic methods for the detection of GCIs directly from the speech waveform. Five state-of-the-art GCI detection algorithms are compared using six different databases with contemporaneous electroglottographic recordings as ground truth, and containing many hours of speech by multiple speakers. The five techniques compared are the Hilbert Envelope-based detection (HE), the Zero Frequency Resonator-based method (ZFR), the Dynamic Programming Phase Slope Algorithm (DYPSA), the Speech Event Detection using the Residual Excitation And a Mean-based Signal (SEDREAMS) and the Yet Another GCI Algorithm (YAGA). The efficacy of these methods is first evaluated on clean speech, both in terms of reliabililty and accuracy. Their robustness to additive noise and to reverberation is also assessed. A further contribution of the paper is the evaluation of their performance on a concrete application of speech processing: the causal-anticausal decomposition of speech. It is shown that for clean speech, SEDREAMS and YAGA are the best performing techniques, both in terms of identification rate and accuracy. ZFR and SEDREAMS also show a superior robustness to additive noise and reverberation.
Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

Robustness to out-of-distribution (OOD) data is an important goal in building reliable machine learning systems. Especially in autonomous systems, wrong predictions for OOD inputs can cause safety critical situations. As a first step towards a solution, we consider the problem of detecting such data in a value-based deep reinforcement learning (RL) setting. Modelling this problem as a one-class classification problem, we propose a framework for uncertainty-based OOD classification: UBOOD. It is based on the effect that an agent's epistemic uncertainty is reduced for situations encountered during training (in-distribution), and thus lower than for unencountered (OOD) situations. Being agnostic towards the approach used for estimating epistemic uncertainty, combinations with different uncertainty estimation methods, e.g. approximate Bayesian inference methods or ensembling techniques are possible. We further present a first viable solution for calculating a dynamic classification threshold, based on the uncertainty distribution of the training data. Evaluation shows that the framework produces reliable classification results when combined with ensemble-based estimators, while the combination with concrete dropout-based estimators fails to reliably detect OOD situations. In summary, UBOOD presents a viable approach for OOD classification in deep RL settings by leveraging the epistemic uncertainty of the agent's value function.
Emergent Behaviors from Folksonomy Driven Interactions
Venue: arXiv cs.AI 2019
Year: 2019

To reflect the evolving knowledge on the Web this paper considers ontologies based on folksonomies according to a new concept structure called "Folksodriven" to represent folksonomies. This paper describes a research program for studying Folksodriven tags interactions leading to Folksodriven cluster behavior. The goal of the research is to understand the type of simple local interactions which produce complex and purposive group behaviors on Folksodriven tags. We describe a synthetic, bottom-up approach to studying group behavior, consisting of designing and testing a variety of social interactions and cultural scenarios with Folksodriven tags. We propose a set of basic interactions which can be used to structure and simplify the process of both designing and analyzing emergent group behaviors. The presented behavior repertories was developed and tested on a folksonomy environment.
Unsupervised Online Feature Selection for Cost-Sensitive Medical Diagnosis
Venue: arXiv cs.LG 2019
Year: 2019

In medical diagnosis, physicians predict the state of a patient by checking measurements (features) obtained from a sequence of tests, e.g., blood test, urine test, followed by invasive tests. As tests are often costly, one would like to obtain only those features (tests) that can establish the presence or absence of the state conclusively. Another aspect of medical diagnosis is that we are often faced with unsupervised prediction tasks as the true state of the patients may not be known. Motivated by such medical diagnosis problems, we consider a {\it Cost-Sensitive Medical Diagnosis} (CSMD) problem, where the true state of patients is unknown. We formulate the CSMD problem as a feature selection problem where each test gives a feature that can be used in a prediction model. Our objective is to learn strategies for selecting the features that give the best trade-off between accuracy and costs. We exploit the `Weak Dominance' property of problem to develop online algorithms that identify a set of features which provides an `optimal' trade-off between cost and accuracy of prediction without requiring to know the true state of the medical condition. Our empirical results validate the performance of our algorithms on problem instances generated from real-world datasets.
Academic Performance Estimation with Attention-based Graph Convolutional Networks
Venue: arXiv cs.LG 2019
Year: 2019

Student's academic performance prediction empowers educational technologies including academic trajectory and degree planning, course recommender systems, early warning and advising systems. Given a student's past data (such as grades in prior courses), the task of student's performance prediction is to predict a student's grades in future courses. Academic programs are structured in a way that prior courses lay the foundation for future courses. The knowledge required by courses is obtained by taking multiple prior courses, which exhibits complex relationships modeled by graph structures. Traditional methods for student's performance prediction usually neglect the underlying relationships between multiple courses; and how students acquire knowledge across them. In addition, traditional methods do not provide interpretation for predictions needed for decision making. In this work, we propose a novel attention-based graph convolutional networks model for student's performance prediction. We conduct extensive experiments on a real-world dataset obtained from a large public university. The experimental results show that our proposed model outperforms state-of-the-art approaches in terms of grade prediction. The proposed model also shows strong accuracy in identifying students who are at-risk of failing or dropping out so that timely intervention and feedback can be provided to the student.
Computing L1 Straight-Line Fits to Data (Part 1)
Venue: arXiv cs.LG 2019
Year: 2019

The initial remarks in this technical report are primarily for those not familiar with the properties of L1 approximation, but the remainder of the report should also interest readers who are already acquainted with the inner workings of L1 algorithms.
Glottal Closure and Opening Instant Detection from Speech Signals
Venue: arXiv cs.SD 2019
Year: 2019

This paper proposes a new procedure to detect Glottal Closure and Opening Instants (GCIs and GOIs) directly from speech waveforms. The procedure is divided into two successive steps. First a mean-based signal is computed, and intervals where speech events are expected to occur are extracted from it. Secondly, at each interval a precise position of the speech event is assigned by locating a discontinuity in the Linear Prediction residual. The proposed method is compared to the DYPSA algorithm on the CMU ARCTIC database. A significant improvement as well as a better noise robustness are reported. Besides, results of GOI identification accuracy are promising for the glottal source characterization.
Black Box Algorithm Selection by Convolutional Neural Network
Venue: arXiv cs.NE 2019
Year: 2019

Although a large number of optimization algorithms have been proposed for black box optimization problems, the no free lunch theorems inform us that no algorithm can beat others on all types of problems. Different types of optimization problems need different optimization algorithms. To deal with this issue, researchers propose algorithm selection to suggest the best optimization algorithm from the algorithm set for a given unknown optimization problem. Usually, algorithm selection is treated as a classification or regression task. Deep learning, which has been shown to perform well on various classification and regression tasks, is applied to the algorithm selection problem in this paper. Our deep learning architecture is based on convolutional neural network and follows the main architecture of visual geometry group. This architecture has been applied to many different types of 2-D data. Moreover, we also propose a novel method to extract landscape information from the optimization problems and save the information as 2-D images. In the experimental section, we conduct three experiments to investigate the classification and optimization capability of our approach on the BBOB functions. The results indicate that our new approach can effectively solve the algorithm selection problem.
A Deep Neuro-Fuzzy Network for Image Classification
Venue: arXiv cs.NE 2019
Year: 2019

The combination of neural network and fuzzy systems into neuro-fuzzy systems integrates fuzzy reasoning rules into the connectionist networks. However, the existing neuro-fuzzy systems are developed under shallow structures having lower generalization capacity. We propose the first end-to-end deep neuro-fuzzy network and investigate its application for image classification. Two new operations are developed based on definitions of Takagi-Sugeno-Kang (TSK) fuzzy model namely fuzzy inference operation and fuzzy pooling operations; stacks of these operations comprise the layers in this network. We evaluate the network on MNIST, CIFAR-10 and CIFAR-100 datasets, finding that the network has a reasonable accuracy in these benchmarks.
Shareable Representations for Search Query Understanding
Venue: arXiv cs.IR 2019
Year: 2019

Understanding search queries is critical for shopping search engines to deliver a satisfying customer experience. Popular shopping search engines receive billions of unique queries yearly, each of which can depict any of hundreds of user preferences or intents. In order to get the right results to customers it must be known queries like "inexpensive prom dresses" are intended to not only surface results of a certain product type but also products with a low price. Referred to as query intents, examples also include preferences for author, brand, age group, or simply a need for customer service. Recent works such as BERT have demonstrated the success of a large transformer encoder architecture with language model pre-training on a variety of NLP tasks. We adapt such an architecture to learn intents for search queries and describe methods to account for the noisiness and sparseness of search query data. We also describe cost effective ways of hosting transformer encoder models in context with low latency requirements. With the right domain-specific training we can build a shareable deep learning model whose internal representation can be reused for a variety of query understanding tasks including query intent identification. Model sharing allows for fewer large models needed to be served at inference time and provides a platform to quickly build and roll out new search query classifiers.
In-pipe Robotic System for Pipe-joint Rehabilitation in Fresh Water Pipes
Venue: arXiv cs.RO 2019
Year: 2019

The robot's objective is to rehabilitate the pipe joints of fresh water supply systems by crawling into water canals and applying a restoration material to repair the pipes. The robot's structure consists of six wheeled-legs, three on the front separated 120° and three on the back in the same configuration, supporting the structure along the centre of the pipe. In this configuration the robot is able to clean and seal with a rotating tool, similar to a cylindrical robot, covering the entire 3D in-pipe space.