pwc-archive/papers-with-abstracts huggingface dataset:
Select and Attend: Towards Controllable Content Selection in Text Generation
Authors: Xiaoyu Shen, Jun Suzuki, Kentaro Inui, Hui Su, Dietrich Klakow, Satoshi Sekine
Date: 2019-09-10 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Decoder, Headline Generation, Text Generation

Many text generation tasks naturally contain two steps: content selection and surface realization. Current neural encoder-decoder models conflate both steps into a black-box architecture. As a result, the content to be described in the text cannot be explicitly controlled. This paper tackles this problem by decoupling content selection from the decoder. The decoupled content selection is human interpretable, whose value can be manually manipulated to control the content of generated text. The model can be trained end-to-end without human annotations by maximizing a lower bound of the marginal likelihood. We further propose an effective way to trade-off between performance and controllability with a single adjustable hyperparameter. In both data-to-text and headline generation tasks, our model achieves promising results, paving the way for controllable content selection in text generation.
A Corpus-free State2Seq User Simulator for Task-oriented Dialogue
Authors: Yutai Hou, Meng Fang, Wanxiang Che, Ting Liu
Date: 2019-09-10 00:00:00
Tasks: Diversity, Reinforcement Learning

Recent reinforcement learning algorithms for task-oriented dialogue system absorbs a lot of interest. However, an unavoidable obstacle for training such algorithms is that annotated dialogue corpora are often unavailable. One of the popular approaches addressing this is to train a dialogue agent with a user simulator. Traditional user simulators are built upon a set of dialogue rules and therefore lack response diversity. This severely limits the simulated cases for agent training. Later data-driven user models work better in diversity but suffer from data scarcity problem. To remedy this, we design a new corpus-free framework that taking advantage of their benefits. The framework builds a user simulator by first generating diverse dialogue data from templates and then build a new State2Seq user simulator on the data. To enhance the performance, we propose the State2Seq user simulator model to efficiently leverage dialogue state and history. Experiment results on an open dataset show that our user simulator helps agents achieve an improvement of 6.36% on success rate. State2Seq model outperforms the seq2seq baseline for 1.9 F-score.
Learning Priors for Adversarial Autoencoders
Authors: Hui-Po Wang, Wen-Hsiao Peng, Wei-Jan Ko
Date: 2019-09-10 00:00:00
Proceeding: ICLR 2018 1
Tasks: Image Generation, Translation

Most deep latent factor models choose simple priors for simplicity, tractability or not knowing what prior to use. Recent studies show that the choice of the prior may have a profound effect on the expressiveness of the model,especially when its generative network has limited capacity. In this paper, we propose to learn a proper prior from data for adversarial autoencoders(AAEs). We introduce the notion of code generators to transform manually selected simple priors into ones that can better characterize the data distribution. Experimental results show that the proposed model can generate better image quality and learn better disentangled representations than AAEs in both supervised and unsupervised settings. Lastly, we present its ability to do cross-domain translation in a text-to-image synthesis task.
The Prevalence of Errors in Machine Learning Experiments
Authors: Martin Shepperd, Yuchen Guo, Ning li, Mahir Arzoky, Andrea Capiluppi, Steve Counsell, Giuseppe Destefanis, Stephen Swift, Allan Tucker, Leila Yousefi
Date: 2019-09-10 00:00:00
Tasks: BIG-bench Machine Learning

Context: Conducting experiments is central to research machine learning research to benchmark, evaluate and compare learning algorithms. Consequently it is important we conduct reliable, trustworthy experiments. Objective: We investigate the incidence of errors in a sample of machine learning experiments in the domain of software defect prediction. Our focus is simple arithmetical and statistical errors. Method: We analyse 49 papers describing 2456 individual experimental results from a previously undertaken systematic review comparing supervised and unsupervised defect prediction classifiers. We extract the confusion matrices and test for relevant constraints, e.g., the marginal probabilities must sum to one. We also check for multiple statistical significance testing errors. Results: We find that a total of 22 out of 49 papers contain demonstrable errors. Of these 7 were statistical and 16 related to confusion matrix inconsistency (one paper contained both classes of error). Conclusions: Whilst some errors may be of a relatively trivial nature, e.g., transcription errors their presence does not engender confidence. We strongly urge researchers to follow open science principles so errors can be more easily be detected and corrected, thus as a community reduce this worryingly high error rate with our computational experiments.
An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue
Authors: Norman Packard, Mark A. Bedau, Alastair Channon, Takashi Ikegami, Steen Rasmussen, Kenneth O. Stanley, Tim Taylor
Date: 2019-09-10 00:00:00
Tasks: Articles, Artificial Life, Diversity

Nature's spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongly from nonliving. It is, therefore, not surprising that this aspect of life should become a central focus of artificial life. We have known since Darwin that the diversity is produced dynamically, through the process of evolution; this has led life's creative productivity to be called Open-Ended Evolution (OEE) in the field. This article introduces the second of two special issues on current research in OEE and provides an overview of the contents of both special issues. Most of the work was presented at a workshop on open-ended evolution that was held as a part of the 2018 Conference on Artificial Life in Tokyo, and much of it had antecedents in two previous workshops on open-ended evolution at artificial life conferences in Cancun and York. We present a simplified categorization of OEE and summarize progress in the field as represented by the articles in this special issue.
Novel tracking approach based on fully-unsupervised disentanglement of the geometrical factors of variation
Authors: Mykhailo Vladymyrov, Akitaka Ariga
Date: 2019-09-10 00:00:00
Tasks: Disentanglement

Efficient tracking algorithms are a crucial part of particle tracking detectors. While a lot of work has been done in designing a plethora of algorithms, these usually require tedious tuning for each use case. (Weakly) supervised Machine Learning-based approaches can leverage the actual raw data for maximal performance. Yet in realistic scenarios, sufficient high-quality labeled data is not available. While training might be performed on simulated data, the reproduction of realistic signal and noise in the detector requires substantial effort, compromising this approach. Here we propose a novel, fully unsupervised, approach to track reconstruction. The introduced model for learning to disentangle the factors of variation in a geometrically meaningful way employs geometrical space invariances. We train it through constraints on the equivariance between the image space and the latent representation in a Deep Convolutional Autoencoder. Using experimental results on synthetic data we show that a combination of different space transformations is required for meaningful disentanglement of factors of variation. We also demonstrate the performance of our model on real data from tracking detectors.
Automatic detection of estuarine dolphin whistles in spectrogram images
Authors: O. M. Serra, F. P. R. Martins, L. R. Padovese
Date: 2019-09-10 00:00:00

An algorithm for detecting tonal vocalizations from estuarine dolphin (Sotalia guianensis) specimens without interference of a human operator is developed. The raw audio data collected from a passive monitoring sensor in the Canan\'eia underwater soundscape is converted to spectrogram images, containing the desired acoustic event (whistle) as a linear pattern in the images. Detection is a four-step method: first, ridge maps are obtained from the spectrogram images; second, a probabilistic Hough transform algorithm is applied to detect roughly linear ridges, which are adjusted to the true corresponding shape of the whistles via an active contour algorithm; third, feature vectors are built from the geometry of each detected curve; and fourth, the detections are fed to a random forest classifier to parse out false positives. We develop a system capable of reliably classifying roughly 97% of the characteristic patterns detected as Sotalia guianensis whistles or random empty detections.
The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale
Authors: Christian Ertler, Jerneja Mislej, Tobias Ollmann, Lorenzo Porzi, Gerhard Neuhold, Yubin Kuang
Date: 2019-09-10 00:00:00
Proceeding: ECCV 2020 8
Tasks: Autonomous Driving, Classification, Diversity, General Classification, Traffic Sign Detection, Transfer Learning

Traffic signs are essential map features globally in the era of autonomous driving and smart cities. To develop accurate and robust algorithms for traffic sign detection and classification, a large-scale and diverse benchmark dataset is required. In this paper, we introduce a traffic sign benchmark dataset of 100K street-level images around the world that encapsulates diverse scenes, wide coverage of geographical locations, and varying weather and lighting conditions and covers more than 300 manually annotated traffic sign classes. The dataset includes 52K images that are fully annotated and 48K images that are partially annotated. This is the largest and the most diverse traffic sign dataset consisting of images from all over world with fine-grained annotations of traffic sign classes. We have run extensive experiments to establish strong baselines for both the detection and the classification tasks. In addition, we have verified that the diversity of this dataset enables effective transfer learning for existing large-scale benchmark datasets on traffic sign detection and classification. The dataset is freely available for academic research: https://www.mapillary.com/dataset/trafficsign.
Privacy-Preserving Bandits
Authors: Mohammad Malekzadeh, Dimitrios Athanasakis, Hamed Haddadi, Benjamin Livshits
Date: 2019-09-10 00:00:00
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Privacy Preserving

Contextual bandit algorithms~(CBAs) often rely on personal data to provide recommendations. Centralized CBA agents utilize potentially sensitive data from recent interactions to provide personalization to end-users. Keeping the sensitive data locally, by running a local agent on the user's device, protects the user's privacy, however, the agent requires longer to produce useful recommendations, as it does not leverage feedback from other users. This paper proposes a technique we call Privacy-Preserving Bandits (P2B); a system that updates local agents by collecting feedback from other local agents in a differentially-private manner. Comparisons of our proposed approach with a non-private, as well as a fully-private (local) system, show competitive performance on both synthetic benchmarks and real-world data. Specifically, we observed only a decrease of 2.6% and 3.6% in multi-label classification accuracy, and a CTR increase of 0.0025 in online advertising for a privacy budget $\epsilon \approx 0.693$. These results suggest P2B is an effective approach to challenges arising in on-device privacy-preserving personalization.
Cross-X Learning for Fine-Grained Visual Categorization
Authors: Wei Luo, Xitong Yang, Xianjie Mo, Yuheng Lu, Larry S. Davis, Jun Li, Jian Yang, Ser-Nam Lim
Date: 2019-09-10 00:00:00
Proceeding: ICCV 2019 10
Tasks: Fine-Grained Image Classification, Fine-Grained Visual Categorization

Recognizing objects from subcategories with very subtle differences remains a challenging task due to the large intra-class and small inter-class variation. Recent work tackles this problem in a weakly-supervised manner: object parts are first detected and the corresponding part-specific features are extracted for fine-grained classification. However, these methods typically treat the part-specific features of each image in isolation while neglecting their relationships between different images. In this paper, we propose Cross-X learning, a simple yet effective approach that exploits the relationships between different images and between different network layers for robust multi-scale feature learning. Our approach involves two novel components: (i) a cross-category cross-semantic regularizer that guides the extracted features to represent semantic parts and, (ii) a cross-layer regularizer that improves the robustness of multi-scale features by matching the prediction distribution across multiple layers. Our approach can be easily trained end-to-end and is scalable to large datasets like NABirds. We empirically analyze the contributions of different components of our approach and demonstrate its robustness, effectiveness and state-of-the-art performance on five benchmark datasets. Code is available at \url{https://github.com/cswluo/CrossX}.
U-net super-neural segmentation and similarity calculation to realize vegetation change assessment in satellite imagery
Authors: Chunxue Wu, Bobo Ju, Naixue Xiong, Guisong Yang, Yan Wu, Hongming Yang, Jiaying Huang, Zhiyong Xu
Date: 2019-09-10 00:00:00
Tasks: Segmentation, Semantic Segmentation

Vegetation is the natural linkage connecting soil, atmosphere and water. It can represent the change of land cover to a certain extent and serve as an indicator for global change research. Methods for measuring coverage can be divided into two types: surface measurement and remote sensing. Because vegetation cover has significant spatial and temporal differentiation characteristics, remote sensing has become an important technical means to estimate vegetation coverage. This paper firstly uses U-net to perform remote sensing image semantic segmentation training, then uses the result of semantic segmentation, and then uses the integral progressive method to calculate the forestland change rate, and finally realizes automated valuation of woodland change rate.
Subspace clustering without knowing the number of clusters: A parameter free approach
Authors: Vishnu Menon, Gokularam M, Sheetal Kalyani
Date: 2019-09-10 00:00:00
Tasks: Clustering

Subspace clustering, the task of clustering high dimensional data when the data points come from a union of subspaces is one of the fundamental tasks in unsupervised machine learning. Most of the existing algorithms for this task require prior knowledge of the number of clusters along with few additional parameters which need to be set or tuned apriori according to the type of data to be clustered. In this work, a parameter free method for subspace clustering is proposed, where the data points are clustered on the basis of the difference in statistical distribution of the angles subtended by the data points within a subspace and those by points belonging to different subspaces. Given an initial fine clustering, the proposed algorithm merges the clusters until a final clustering is obtained. This, unlike many existing methods, does not require the number of clusters apriori. Also, the proposed algorithm does not involve the use of an unknown parameter or tuning for one. %through cross validation. A parameter free method for producing a fine initial clustering is also discussed, making the whole process of subspace clustering parameter free. The comparison of proposed algorithm's performance with that of the existing state-of-the-art techniques in synthetic and real data sets, shows the significance of the proposed method.
Hierarchical Planning in the IPC
Authors: D. Höller, G. Behnke, P. Bercher, S. Biundo, H. Fiorino, D. Pellier, R. Alford
Date: 2019-09-10 00:00:00

Over the last year, the amount of research in hierarchical planning has increased, leading to significant improvements in the performance of planners. However, the research is diverging and planners are somewhat hard to compare against each other. This is mostly caused by the fact that there is no standard set of benchmark domains, nor even a common description language for hierarchical planning problems. As a consequence, the available planners support a widely varying set of features and (almost) none of them can solve (or even parse) any problem developed for another planner. With this paper, we propose to create a new track for the IPC in which hierarchical planners will compete. This competition will result in a standardised description language, broader support for core features of that language among planners, a set of benchmark problems, a means to fairly and objectively compare HTN planners, and for new challenges for planners.
Compositional Generalization in Image Captioning
Authors: Mitja Nikolaus, Mostafa Abdou, Matthew Lamm, Rahul Aralikatte, Desmond Elliott
Date: 2019-09-10 00:00:00
Proceeding: CONLL 2019 11
Tasks: Caption Generation, Image Captioning, Sentence

Image captioning models are usually evaluated on their ability to describe a held-out set of images, not on their ability to generalize to unseen concepts. We study the problem of compositional generalization, which measures how well a model composes unseen combinations of concepts when describing images. State-of-the-art image captioning models show poor generalization performance on this task. We propose a multi-task model to address the poor performance, that combines caption generation and image--sentence ranking, and uses a decoding mechanism that re-ranks the captions according their similarity to the image. This model is substantially better at generalizing to unseen combinations of concepts compared to state-of-the-art captioning models.
Extending the Service Composition Formalism with Relational Parameters
Authors: Paul Diac, Liana Tucar, Radu Mereuta
Date: 2019-09-10 00:00:00
Tasks: Service Composition

Web Service Composition deals with the (re)use of Web Services to provide complex functionality, inexistent in any single service. Over the state-of-the-art, we introduce a new type of modeling, based on ontologies and relations between objects, which allows us to extend the expressiveness of problems that can be solved automatically.
JSI-GAN: GAN-Based Joint Super-Resolution and Inverse Tone-Mapping with Pixel-Wise Task-Specific Filters for UHD HDR Video
Authors: Soo Ye Kim, Jihyong Oh, Munchurl Kim
Date: 2019-09-10 00:00:00
Tasks: Image Reconstruction, Inverse-Tone-Mapping, Super-Resolution, Tone Mapping

Joint learning of super-resolution (SR) and inverse tone-mapping (ITM) has been explored recently, to convert legacy low resolution (LR) standard dynamic range (SDR) videos to high resolution (HR) high dynamic range (HDR) videos for the growing need of UHD HDR TV/broadcasting applications. However, previous CNN-based methods directly reconstruct the HR HDR frames from LR SDR frames, and are only trained with a simple L2 loss. In this paper, we take a divide-and-conquer approach in designing a novel GAN-based joint SR-ITM network, called JSI-GAN, which is composed of three task-specific subnets: an image reconstruction subnet, a detail restoration (DR) subnet and a local contrast enhancement (LCE) subnet. We delicately design these subnets so that they are appropriately trained for the intended purpose, learning a pair of pixel-wise 1D separable filters via the DR subnet for detail restoration and a pixel-wise 2D local filter by the LCE subnet for contrast enhancement. Moreover, to train the JSI-GAN effectively, we propose a novel detail GAN loss alongside the conventional GAN loss, which helps enhancing both local details and contrasts to reconstruct high quality HR HDR results. When all subnets are jointly trained well, the predicted HR HDR results of higher quality are obtained with at least 0.41 dB gain in PSNR over those generated by the previous methods.
Classifying the Valence of Autobiographical Memories from fMRI Data
Authors: Alex Frid, Larry M. Manevitz, Norberto Eiji Nawa
Date: 2019-09-10 00:00:00
Tasks: Classification, feature selection, General Classification

We show that fMRI analysis using machine learning tools are sufficient to distinguish valence (i.e., positive or negative) of freely retrieved autobiographical memories in a cross-participant setting. Our methodology uses feature selection (ReliefF) in combination with boosting methods, both applied directly to data represented in voxel space. In previous work using the same data set, Nawa and Ando showed that whole-brain based classification could achieve above-chance classification accuracy only when both training and testing data came from the same individual. In a cross-participant setting, classification results were not statistically significant. Additionally, on average the classification accuracy obtained when using ReliefF is substantially higher than previous results - 81% for the within-participant classification, and 62% for the cross-participant classification. Furthermore, since features are defined in voxel space, it is possible to show brain maps indicating the regions of that are most relevant in determining the results of the classification. Interestingly, the voxels that were selected using the proposed computational pipeline seem to be consistent with current neurophysiological theories regarding the brain regions actively involved in autobiographical memory processes.
A Crowd-based Evaluation of Abuse Response Strategies in Conversational Agents
Authors: Amanda Cercas Curry, Verena Rieser
Date: 2019-09-10 00:00:00
Proceeding: WS 2019 9

How should conversational agents respond to verbal abuse through the user? To answer this question, we conduct a large-scale crowd-sourced evaluation of abuse response strategies employed by current state-of-the-art systems. Our results show that some strategies, such as "polite refusal" score highly across the board, while for other strategies demographic factors, such as age, as well as the severity of the preceding abuse influence the user's perception of which response is appropriate. In addition, we find that most data-driven models lag behind rule-based or commercial systems in terms of their perceived appropriateness.
Attesting Biases and Discrimination using Language Semantics
Authors: Xavier Ferrer Aran, Jose M. Such, Natalia Criado
Date: 2019-09-10 00:00:00

AI agents are increasingly deployed and used to make automated decisions that affect our lives on a daily basis. It is imperative to ensure that these systems embed ethical principles and respect human values. We focus on how we can attest to whether AI agents treat users fairly without discriminating against particular individuals or groups through biases in language. In particular, we discuss human unconscious biases, how they are embedded in language, and how AI systems inherit those biases by learning from and processing human language. Then, we outline a roadmap for future research to better understand and attest problematic AI biases derived from language.