OpenRaiser/Intern-Atlas huggingface dataset:
From Receptive to Productive: Learning to Use Confusing Words through Automatically Selected Example Sentences
Venue: arXiv cs.CL 2019
Year: 2019

Knowing how to use words appropriately has been a key to improving language proficiency. Previous studies typically discuss how students learn receptively to select the correct candidate from a set of confusing words in the fill-in-the-blank task where specific context is given. In this paper, we go one step further, assisting students to learn to use confusing words appropriately in a productive task: sentence translation. We leverage the GiveMeExample system, which suggests example sentences for each confusing word, to achieve this goal. In this study, students learn to differentiate the confusing words by reading the example sentences, and then choose the appropriate word(s) to complete the sentence translation task. Results show students made substantial progress in terms of sentence structure. In addition, highly proficient students better managed to learn confusing words. In view of the influence of the first language on learners, we further propose an effective approach to improve the quality of the suggested sentences.
Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks
Venue: arXiv cs.LG 2019
Year: 2019

As autonomous vehicles (AVs) need to interact with other road users, it is of importance to comprehensively understand the dynamic traffic environment, especially the future possible trajectories of surrounding vehicles. This paper presents an algorithm for long-horizon trajectory prediction of surrounding vehicles using a dual long short term memory (LSTM) network, which is capable of effectively improving prediction accuracy in strongly interactive driving environments. In contrast to traditional approaches which require trajectory matching and manual feature selection, this method can automatically learn high-level spatial-temporal features of driver behaviors from naturalistic driving data through sequence learning. By employing two blocks of LSTMs, the proposed method feeds the sequential trajectory to the first LSTM for driver intention recognition as an intermediate indicator, which is immediately followed by a second LSTM for future trajectory prediction. Test results from real-world highway driving data show that the proposed method can, in comparison to state-of-art methods, output more accurate and reasonable estimate of different future trajectories over 5s time horizon with root mean square error (RMSE) for longitudinal and lateral prediction less than 5.77m and 0.49m, respectively.
Black-boxing the user: internet protocol over xylophone players (IPoXP)
Venue: arXiv cs.HC 2019
Year: 2019

We introduce IP over Xylophone Players (IPoXP), a novel Internet protocol between two computers using xylophone-based Arduino interfaces. In our implementation, human operators are situated within the lowest layer of the network, transmitting data between computers by striking designated keys. We discuss how IPoXP inverts the traditional mode of human-computer interaction, with a computer using the human as an interface to communicate with another computer.
Figure Captioning with Reasoning and Sequence-Level Training
Venue: arXiv cs.CV 2019
Year: 2019

Figures, such as bar charts, pie charts, and line plots, are widely used to convey important information in a concise format. They are usually human-friendly but difficult for computers to process automatically. In this work, we investigate the problem of figure captioning where the goal is to automatically generate a natural language description of the figure. While natural image captioning has been studied extensively, figure captioning has received relatively little attention and remains a challenging problem. First, we introduce a new dataset for figure captioning, FigCAP, based on FigureQA. Second, we propose two novel attention mechanisms. To achieve accurate generation of labels in figures, we propose Label Maps Attention. To model the relations between figure labels, we propose Relation Maps Attention. Third, we use sequence-level training with reinforcement learning in order to directly optimizes evaluation metrics, which alleviates the exposure bias issue and further improves the models in generating long captions. Extensive experiments show that the proposed method outperforms the baselines, thus demonstrating a significant potential for the automatic captioning of vast repositories of figures.
Modeling financial analysts' decision making via the pragmatics and semantics of earnings calls
Venue: arXiv cs.CL 2019
Year: 2019

Every fiscal quarter, companies hold earnings calls in which company executives respond to questions from analysts. After these calls, analysts often change their price target recommendations, which are used in equity research reports to help investors make decisions. In this paper, we examine analysts' decision making behavior as it pertains to the language content of earnings calls. We identify a set of 20 pragmatic features of analysts' questions which we correlate with analysts' pre-call investor recommendations. We also analyze the degree to which semantic and pragmatic features from an earnings call complement market data in predicting analysts' post-call changes in price targets. Our results show that earnings calls are moderately predictive of analysts' decisions even though these decisions are influenced by a number of other factors including private communication with company executives and market conditions. A breakdown of model errors indicates disparate performance on calls from different market sectors.
Towards Non-I.I.D. Image Classification: A Dataset and Baselines
Venue: arXiv cs.CV 2019
Year: 2019

I.I.D. hypothesis between training and testing data is the basis of numerous image classification methods. Such property can hardly be guaranteed in practice where the Non-IIDness is common, causing instable performances of these models. In literature, however, the Non-I.I.D. image classification problem is largely understudied. A key reason is lacking of a well-designed dataset to support related research. In this paper, we construct and release a Non-I.I.D. image dataset called NICO, which uses contexts to create Non-IIDness consciously. Compared to other datasets, extended analyses prove NICO can support various Non-I.I.D. situations with sufficient flexibility. Meanwhile, we propose a baseline model with ConvNet structure for General Non-I.I.D. image classification, where distribution of testing data is unknown but different from training data. The experimental results demonstrate that NICO can well support the training of ConvNet model from scratch, and a batch balancing module can help ConvNets to perform better in Non-I.I.D. settings.
Rectifying Classifier Chains for Multi-Label Classification
Venue: arXiv cs.LG 2019
Year: 2019

Classifier chains have recently been proposed as an appealing method for tackling the multi-label classification task. In addition to several empirical studies showing its state-of-the-art performance, especially when being used in its ensemble variant, there are also some first results on theoretical properties of classifier chains. Continuing along this line, we analyze the influence of a potential pitfall of the learning process, namely the discrepancy between the feature spaces used in training and testing: While true class labels are used as supplementary attributes for training the binary models along the chain, the same models need to rely on estimations of these labels at prediction time. We elucidate under which circumstances the attribute noise thus created can affect the overall prediction performance. As a result of our findings, we propose two modifications of classifier chains that are meant to overcome this problem. Experimentally, we show that our variants are indeed able to produce better results in cases where the original chaining process is likely to fail.
Coding Theorems for Asynchronous Slepian-Wolf Coding Systems
Venue: arXiv cs.IT 2019
Year: 2019

The Slepian-Wolf (SW) coding system is a source coding system with two encoders and a decoder, where these encoders independently encode source sequences from two correlated sources into codewords, and the decoder reconstructs both source sequences from the codewords. In this paper, we consider the situation in which the SW coding system is asynchronous, i.e., each encoder samples a source sequence with some unknown delay. We assume that delays are unknown but maximum and minimum values of possible delays are known to encoders and the decoder. We also assume that sources are discrete stationary memoryless and the probability mass function (PMF) of the sources is unknown but the system knows that it belongs to a certain set of PMFs. For this asynchronous SW coding system, we clarify the achievable rate region which is the set of rate pairs of encoders such that the decoding error probability vanishes as the blocklength tends to infinity. We show that this region does not always coincide with that of the synchronous SW coding system in which each encoder samples a source sequence without any delay.
Artificial Intelligence helps making Quality Assurance processes leaner
Venue: arXiv cs.SE 2019
Year: 2019

Lean processes focus on doing only necessery things in an efficient way. Artificial intelligence and Machine Learning offer new opportunities to optimizing processes. The presented approach demonstrates an improvement of the test process by using Machine Learning as a support tool for test management. The scope is the semi-automation of the selection of regression tests. The proposed lean testing process uses Machine Learning as a supporting machine, while keeping the human test manager in charge of the adequate test case selection. 1 Introduction Many established long running projects and programs are execute regression tests during the release tests. The regression tests are the part of the release test to ensure that functionality from past releases still works fine in the new release. In many projects, a significant part of these regression tests are not automated and therefore executed manually. Manual tests are expensive and time intensive [1], which is why often only a relevant subset of all possible regression tests are executed in order to safe time and money. Depending on the software process, different approaches can be used to identify the right set of regression tests. The source code file level is a frequent entry point for this identification [2]. Advanced approaches combine different file level methods [3]. To handle black-box tests, methods like [4] or [5] can be used for test case prioritiza-tion. To decide which tests can be skipped, a relevance ranking of the tests in a regression test suite is needed. Based on the relevance a test is in or out of the regression test set for a specific release. This decision is a task of the test manager supported by experts. The task can be time-consuming in case of big (often a 4-to 5-digit number) regression test suites because the selection is specific to each release. Trends are going to continuous prioritization [6], which this work wants to support with the presented ML based approach for black box regression test case prioritization. Any regression test selection is made upon release specific changes. Changes can be new or deleted code based on refactoring or implementation of new features. But also changes on externals systems which are connected by interfaces have to be considered
Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift
Venue: arXiv cs.LG 2019
Year: 2019

A novel variational inference based resampling framework is proposed to evaluate the robustness and generalization capability of deep learning models with respect to distribution shift. We use Auto Encoding Variational Bayes to find a latent representation of the data, on which a Variational Gaussian Mixture Model is applied to deliberately create distribution shift by dividing the dataset into different clusters. Wasserstein distance is used to characterize the extent of distribution shift between the generated data splits. We compare several popular Convolutional Neural Network (CNN) architectures and Bayesian CNN models for image classification on the Fashion-MNIST dataset, to assess their robustness and generalization behavior under the deliberately created distribution shift, as well as under random Cross Validation. Our method of creating artificial domain splits of a single dataset can also be used to establish novel model selection criteria and assessment tools in machine learning, as well as benchmark methods for domain adaptation and domain generalization approaches.
An Artificial Intelligence-Based System for Nutrient Intake Assessment of Hospitalised Patients
Venue: arXiv cs.CV 2019
Year: 2019

Regular nutrient intake monitoring in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition (DRM). Although several methods to estimate nutrient intake have been developed, there is still a clear demand for a more reliable and fully automated technique, as this could improve the data accuracy and reduce both the participant burden and the health costs. In this paper, we propose a novel system based on artificial intelligence to accurately estimate nutrient intake, by simply processing RGB depth image pairs captured before and after a meal consumption. For the development and evaluation of the system, a dedicated and new database of images and recipes of 322 meals was assembled, coupled to data annotation using innovative strategies. With this database, a system was developed that employed a novel multi-task neural network and an algorithm for 3D surface construction. This allowed sequential semantic food segmentation and estimation of the volume of the consumed food, and permitted fully automatic estimation of nutrient intake for each food type with a 15% estimation error.
Prediction of Workplace Injuries
Venue: arXiv cs.CY 2019
Year: 2019

Workplace injuries result in substantial human and financial losses. As reported by the International Labour Organization (ILO), there are more than 374 million work-related injuries reported every year. In this study, we investigate the problem of injury risk prediction and prevention in a work environment. While injuries represent a significant number across all organizations, they are rare events within a single organization. Hence, collecting a sufficiently large dataset from a single organization is extremely difficult. In addition, the collected datasets are often highly imbalanced which increases the problem difficulty. Finally, risk predictions need to provide additional context for injuries to be prevented. We propose and evaluate the following for a complete solution: 1) several ensemble-based resampling methods to address the class imbalance issues, 2) a novel transfer learning approach to transfer the knowledge across organizations, and 3) various techniques to uncover the association and causal effect of different variables on injury risk, while controlling for relevant confounding factors.
Improving Relation Extraction by Pre-trained Language Representations
Venue: arXiv cs.CL 2019
Year: 2019

Current state-of-the-art relation extraction methods typically rely on a set of lexical, syntactic, and semantic features, explicitly computed in a pre-processing step. Training feature extraction models requires additional annotated language resources, which severely restricts the applicability and portability of relation extraction to novel languages. Similarly, pre-processing introduces an additional source of error. To address these limitations, we introduce TRE, a Transformer for Relation Extraction, extending the OpenAI Generative Pre-trained Transformer [Radford et al., 2018]. Unlike previous relation extraction models, TRE uses pre-trained deep language representations instead of explicit linguistic features to inform the relation classification and combines it with the self-attentive Transformer architecture to effectively model long-range dependencies between entity mentions. TRE allows us to learn implicit linguistic features solely from plain text corpora by unsupervised pre-training, before fine-tuning the learned language representations on the relation extraction task. TRE obtains a new state-of-the-art result on the TACRED and SemEval 2010 Task 8 datasets, achieving a test F1 of 67.4 and 87.1, respectively. Furthermore, we observe a significant increase in sample efficiency. With only 20% of the training examples, TRE matches the performance of our baselines and our model trained from scratch on 100% of the TACRED dataset. We open-source our trained models, experiments, and source code.
Validating IP Prefixes and AS-Paths with Blockchains
Venue: arXiv cs.NI 2019
Year: 2019

Networks (Autonomous Systems-AS) allocate or revoke IP prefixes with the intervention of official Internet resource number authorities, and select and advertise policy-compliant paths towards these prefixes using the inter-domain routing system and its primary enabler, the Border Gateway Protocol (BGP). Securing BGP has been a long-term objective of several research and industrial efforts during the last decades, that have culminated in the Resource Public Key Infrastructure (RPKI) for the cryptographic verification of prefix-to-AS assignments. However, there is still no widely adopted solution for securing IP prefixes and the (AS-)paths leading to them; approaches such as BGPsec have seen minuscule deployment. In this work, we design and implement a Blockchain-based system that (i) can be used to validate both of these resource types, (ii) can work passively and does not require any changes in the inter-domain routing system (BGP, RPKI), and (iii) can be combined with currently available systems for the detection and mitigation of routing attacks. We present early results and insights w.r.t. scalability.
POBA-GA: Perturbation Optimized Black-Box Adversarial Attacks via Genetic Algorithm
Venue: arXiv cs.CR 2019
Year: 2019

Most deep learning models are easily vulnerable to adversarial attacks. Various adversarial attacks are designed to evaluate the robustness of models and develop defense model. Currently, adversarial attacks are brought up to attack their own target model with their own evaluation metrics. And most of the black-box adversarial attack algorithms cannot achieve the expected success rate compared with white-box attacks. In this paper, comprehensive evaluation metrics are brought up for different adversarial attack methods. A novel perturbation optimized black-box adversarial attack based on genetic algorithm (POBA-GA) is proposed for achieving white-box comparable attack performances. Approximate optimal adversarial examples are evolved through evolutionary operations including initialization, selection, crossover and mutation. Fitness function is specifically designed to evaluate the example individual in both aspects of attack ability and perturbation control. Population diversity strategy is brought up in evolutionary process to promise the approximate optimal perturbations obtained. Comprehensive experiments are carried out to testify POBA-GA's performances. Both simulation and application results prove that our method is better than current state-of-art black-box attack methods in aspects of attack capability and perturbation control.
Lightweight Parallel Foundations: a model-compliant communication layer
Venue: arXiv cs.DC 2019
Year: 2019

We present the Lightweight Parallel Foundations (LPF), an interoperable and model-compliant communication layer adhering to a strict performance model of parallel computations. LPF consists of twelve primitives, each with strict performance guarantees, two of which enable interoperability. We argue that the principles of interoperability and model compliance suffice for the practical use of immortal algorithms: algorithms that are proven optimal once, and valid forever. These are ideally also implemented once, and usable from a wide range of sequential and parallel environments. This paradigm is evaluated by implementing an immortal fast Fourier transform (FFT) using LPF, and compared to state-of-the-art FFT implementations. We find it performs on par to Intel MKL FFT while consistently outperforming FFTW, thus showing model compliance can be achieved without sacrificing performance. Interoperability encourages the propagation of immortal algorithms as widely as possible. We evaluate this by integrating an LPF PageRank into Spark, without changing any PageRank nor Spark source codes, and while requiring only a minimal interface layer.
Learning Word Embeddings with Domain Awareness
Venue: arXiv cs.CL 2019
Year: 2019

Word embeddings are traditionally trained on a large corpus in an unsupervised setting, with no specific design for incorporating domain knowledge. This can lead to unsatisfactory performances when training data originate from heterogeneous domains. In this paper, we propose two novel mechanisms for domain-aware word embedding training, namely domain indicator and domain attention, which integrate domain-specific knowledge into the widely used SG and CBOW models, respectively. The two methods are based on a joint learning paradigm and ensure that words in a target domain are intensively focused when trained on a source domain corpus. Qualitative and quantitative evaluation confirm the validity and effectiveness of our models. Compared to baseline methods, our method is particularly effective in near-cold-start scenarios.
When and Why Metaheuristics Researchers Can Ignore "No Free Lunch" Theorems
Venue: arXiv cs.NE 2019
Year: 2019

The No Free Lunch (NFL) theorem for search and optimisation states that averaged across all possible objective functions on a fixed search space, all search algorithms perform equally well. Several refined versions of the theorem find a similar outcome when averaging across smaller sets of functions. This paper argues that NFL results continue to be misunderstood by many researchers, and addresses this issue in several ways. Existing arguments against real-world implications of NFL results are collected and re-stated for accessibility, and new ones are added. Specific misunderstandings extant in the literature are identified, with speculation as to how they may have arisen. This paper presents an argument against a common paraphrase of NFL findings -- that algorithms must be specialised to problem domains in order to do well -- after problematising the usually undefined term "domain". It provides novel concrete counter-examples illustrating cases where NFL theorems do not apply. In conclusion it offers a novel view of the real meaning of NFL, incorporating the anthropic principle and justifying the position that in many common situations researchers can ignore NFL.
Latent feature disentanglement for 3D meshes
Venue: arXiv cs.LG 2019
Year: 2019

Generative modeling of 3D shapes has become an important problem due to its relevance to many applications across Computer Vision, Graphics, and VR. In this paper we build upon recently introduced 3D mesh-convolutional Variational AutoEncoders which have shown great promise for learning rich representations of deformable 3D shapes. We introduce a supervised generative 3D mesh model that disentangles the latent shape representation into independent generative factors. Our extensive experimental analysis shows that learning an explicitly disentangled representation can both improve random shape generation as well as successfully address downstream tasks such as pose and shape transfer, shape-invariant temporal synchronization, and pose-invariant shape matching.