October 18, 2019

3250 words 16 mins read

Paper Group ANR 619

Deep Learning on Key Performance Indicators for Predictive Maintenance in SAP HANA. A Hybrid Distribution Feeder Long-Term Load Forecasting Method Based on Sequence Prediction. REGMAPR - Text Matching Made Easy. A Semantic Model for Historical Manuscripts. What might matter in autonomous cars adoption: first person versus third person scenarios. A …

Deep Learning on Key Performance Indicators for Predictive Maintenance in SAP HANA


Title	Deep Learning on Key Performance Indicators for Predictive Maintenance in SAP HANA
Authors	Jaekoo Lee, Byunghan Lee, Jongyoon Song, Jaesik Yoon, Yongsik Lee, Donghun Lee, Sungroh Yoon
Abstract	With a new era of cloud and big data, Database Management Systems (DBMSs) have become more crucial in numerous enterprise business applications in all the industries. Accordingly, the importance of their proactive and preventive maintenance has also increased. However, detecting problems by predefined rules or stochastic modeling has limitations, particularly when analyzing the data on high-dimensional Key Performance Indicators (KPIs) from a DBMS. In recent years, Deep Learning (DL) has opened new opportunities for this complex analysis. In this paper, we present two complementary DL approaches to detect anomalies in SAP HANA. A temporal learning approach is used to detect abnormal patterns based on unlabeled historical data, whereas a spatial learning approach is used to classify known anomalies based on labeled data. We implement a system in SAP HANA integrated with Google TensorFlow. The experimental results with real-world data confirm the effectiveness of the system and models.
Tasks
Published	2018-04-16
URL	http://arxiv.org/abs/1804.05497v1
PDF	http://arxiv.org/pdf/1804.05497v1.pdf
PWC	https://paperswithcode.com/paper/deep-learning-on-key-performance-indicators
Repo
Framework

A Hybrid Distribution Feeder Long-Term Load Forecasting Method Based on Sequence Prediction


Title	A Hybrid Distribution Feeder Long-Term Load Forecasting Method Based on Sequence Prediction
Authors	Ming Dong
Abstract	Distribution feeder long-term load forecast (LTLF) is a critical task many electric utility companies perform on an annual basis. The goal of this task is to forecast the annual load of distribution feeders. The previous top-down and bottom-up LTLF methods are unable to incorporate different levels of information. This paper proposes a hybrid modeling method using sequence prediction for this classic and important task. The proposed method can seamlessly integrate top-down, bottom-up and sequential information hidden in multi-year data. Two advanced sequence prediction models Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks are investigated in this paper. They successfully solve the vanishing and exploding gradient problems a standard recurrent neural network has. This paper firstly explains the theories of LSTM and GRU networks and then discusses the steps of feature selection, feature engineering and model implementation in detail. In the end, a real-world application example for a large urban grid in West Canada is provided. LSTM and GRU networks under different sequential configurations and traditional models including bottom-up, ARIMA and feed-forward neural network are all implemented and compared in detail. The proposed method demonstrates superior performance and great practicality.
Tasks	Feature Engineering, Feature Selection, Load Forecasting, Time Series, Time Series Forecasting
Published	2018-12-09
URL	http://arxiv.org/abs/1812.04480v2
PDF	http://arxiv.org/pdf/1812.04480v2.pdf
PWC	https://paperswithcode.com/paper/a-hybrid-long-term-load-forecasting-model-for
Repo
Framework

REGMAPR - Text Matching Made Easy


Title	REGMAPR - Text Matching Made Easy
Authors	Siddhartha Brahma
Abstract	Text matching is a fundamental problem in natural language processing. Neural models using bidirectional LSTMs for sentence encoding and inter-sentence attention mechanisms perform remarkably well on several benchmark datasets. We propose REGMAPR - a simple and general architecture for text matching that does not use inter-sentence attention. Starting from a Siamese architecture, we augment the embeddings of the words with two features based on exact and para- phrase match between words in the two sentences. We train the model using three types of regularization on datasets for textual entailment, paraphrase detection and semantic related- ness. REGMAPR performs comparably or better than more complex neural models or models using a large number of handcrafted features. REGMAPR achieves state-of-the-art results for paraphrase detection on the SICK dataset and for textual entailment on the SNLI dataset among models that do not use inter-sentence attention.
Tasks	Natural Language Inference, Text Matching
Published	2018-08-13
URL	http://arxiv.org/abs/1808.04343v3
PDF	http://arxiv.org/pdf/1808.04343v3.pdf
PWC	https://paperswithcode.com/paper/regmapr-text-matching-made-easy
Repo
Framework

A Semantic Model for Historical Manuscripts


Title	A Semantic Model for Historical Manuscripts
Authors	Sahar Aljalbout, Gilles Falquet
Abstract	The study and publication of historical scientific manuscripts are com- plex tasks that involve, among others, the explicit representation of the text mean- ings and reasoning on temporal entities. In this paper we present the first results of an interdisciplinary project dedicated to the study of Saussure’s manuscripts. These results aim to fulfill requirements elaborated with Saussurean humanists. They comprise a model for the representation of time-varying statements and time-varying domain knowledge (in particular terminologies) as well as imple- mentation techniques for the semantic indexing of manuscripts and for temporal reasoning on knowledge extracted from the manuscripts.
Tasks
Published	2018-01-31
URL	http://arxiv.org/abs/1802.00295v2
PDF	http://arxiv.org/pdf/1802.00295v2.pdf
PWC	https://paperswithcode.com/paper/a-semantic-model-for-historical-manuscripts
Repo
Framework

What might matter in autonomous cars adoption: first person versus third person scenarios


Title	What might matter in autonomous cars adoption: first person versus third person scenarios
Authors	Eva Zackova, Jan Romportl
Abstract	The discussion between the automotive industry, governments, ethicists, policy makers and general public about autonomous cars’ moral agency is widening, and therefore we see the need to bring more insight into what meta-factors might actually influence the outcomes of such discussions, surveys and plebiscites. In our study, we focus on the psychological (personality traits), practical (active driving experience), gender and rhetoric/framing factors that might impact and even determine respondents’ a priori preferences of autonomous cars’ operation. We conducted an online survey (N=430) to collect data that show that the third person scenario is less biased than the first person scenario when presenting ethical dilemma related to autonomous cars. According to our analysis, gender bias should be explored in more extensive future studies as well. We recommend any participatory technology assessment discourse to use the third person scenario and to direct attention to the way any autonomous car related debate is introduced, especially in terms of linguistic and communication aspects and gender.
Tasks
Published	2018-10-17
URL	http://arxiv.org/abs/1810.07460v1
PDF	http://arxiv.org/pdf/1810.07460v1.pdf
PWC	https://paperswithcode.com/paper/what-might-matter-in-autonomous-cars-adoption
Repo
Framework

A Variational Image Segmentation Model based on Normalized Cut with Adaptive Similarity and Spatial Regularization


Title	A Variational Image Segmentation Model based on Normalized Cut with Adaptive Similarity and Spatial Regularization
Authors	Faqiang Wang, Cuicui Zhao, Jun Liu, Haiyang Huang
Abstract	Image segmentation is a fundamental research topic in image processing and computer vision. In the last decades, researchers developed a large number of segmentation algorithms for various applications. Amongst these algorithms, the Normalized cut (Ncut) segmentation method is widely applied due to its good performance. The Ncut segmentation model is an optimization problem whose energy is defined on a specifically designed graph. Thus, the segmentation results of the existing Ncut method are largely dependent on a pre-constructed similarity measure on the graph since this measure is usually given empirically by users. This flaw will lead to some undesirable segmentation results. In this paper, we propose a Ncut-based segmentation algorithm by integrating an adaptive similarity measure and spatial regularization. The proposed model combines the Parzen-Rosenblatt window method, non-local weights entropy, Ncut energy, and regularizer of phase field in a variational framework. Our method can adaptively update the similarity measure function by estimating some parameters. This adaptive procedure enables the proposed algorithm finding a better similarity measure for classification than the Ncut method. We provide some mathematical interpretation of the proposed adaptive similarity from multi-viewpoints such as statistics and convex optimization. In addition, the regularizer of phase field can guarantee that the proposed algorithm has a robust performance in the presence of noise, and it can also rectify the similarity measure with a spatial priori. The well-posed theory such as the existence of the minimizer for the proposed model is given in the paper. Compared with some existing segmentation methods such as the traditional Ncut-based model and the classical Chan-Vese model, the numerical experiments show that our method can provide promising segmentation results.
Tasks	Graph Clustering, Semantic Segmentation, Spectral Graph Clustering
Published	2018-06-06
URL	https://arxiv.org/abs/1806.01977v3
PDF	https://arxiv.org/pdf/1806.01977v3.pdf
PWC	https://paperswithcode.com/paper/normalized-cut-with-adaptive-similarity-and
Repo
Framework

A Recipe for Arabic-English Neural Machine Translation


Title	A Recipe for Arabic-English Neural Machine Translation
Authors	Abdullah Alrajeh
Abstract	In this paper, we present a recipe for building a good Arabic-English neural machine translation. We compare neural systems with traditional phrase-based systems using various parallel corpora including UN, ISI and Ummah. We also investigate the importance of special preprocessing of the Arabic script. The presented results are based on test sets from NIST MT 2005 and 2012. The best neural system produces a gain of +13 BLEU points compared to an equivalent simple phrase-based system in NIST MT12 test set. Unexpectedly, we find that tuning a model trained on the whole data using a small high quality corpus like Ummah gives a substantial improvement (+3 BLEU points). We also find that training a neural system with a small Arabic-English corpus is competitive to a traditional phrase-based system.
Tasks	Machine Translation
Published	2018-08-18
URL	http://arxiv.org/abs/1808.06116v1
PDF	http://arxiv.org/pdf/1808.06116v1.pdf
PWC	https://paperswithcode.com/paper/a-recipe-for-arabic-english-neural-machine
Repo
Framework

Runtime Concurrency Control and Operation Scheduling for High Performance Neural Network Training


Title	Runtime Concurrency Control and Operation Scheduling for High Performance Neural Network Training
Authors	Jiawen Liu, Dong Li, Gokcen Kestor, Jeffrey Vetter
Abstract	Training neural network often uses a machine learning framework such as TensorFlow and Caffe2. These frameworks employ a dataflow model where the NN training is modeled as a directed graph composed of a set of nodes. Operations in neural network training are typically implemented by the frameworks as primitives and represented as nodes in the dataflow graph. Training NN models in a dataflow-based machine learning framework involves a large number of fine-grained operations. Those operations have diverse memory access patterns and computation intensity. How to manage and schedule those operations is challenging, because we have to decide the number of threads to run each operation (concurrency control) and schedule those operations for good hardware utilization and system throughput. In this paper, we extend an existing runtime system (the TensorFlow runtime) to enable automatic concurrency control and scheduling of operations. We explore performance modeling to predict the performance of operations with various thread-level parallelism. Our performance model is highly accurate and lightweight. Leveraging the performance model, our runtime system employs a set of scheduling strategies that co-run operations to improve hardware utilization and system throughput. Our runtime system demonstrates a big performance benefit. Comparing with using the recommended configurations for concurrency control and operation scheduling in TensorFlow, our approach achieves 33% performance (execution time) improvement on average (up to 49%) for three neural network models, and achieves high performance closing to the optimal one manually obtained by the user.
Tasks
Published	2018-10-21
URL	http://arxiv.org/abs/1810.08955v2
PDF	http://arxiv.org/pdf/1810.08955v2.pdf
PWC	https://paperswithcode.com/paper/runtime-concurrency-control-and-operation
Repo
Framework

Discourse in Multimedia: A Case Study in Information Extraction


Title	Discourse in Multimedia: A Case Study in Information Extraction
Authors	Mrinmaya Sachan, Kumar Avinava Dubey, Eduard H. Hovy, Tom M. Mitchell, Dan Roth, Eric P. Xing
Abstract	To ensure readability, text is often written and presented with due formatting. These text formatting devices help the writer to effectively convey the narrative. At the same time, these help the readers pick up the structure of the discourse and comprehend the conveyed information. There have been a number of linguistic theories on discourse structure of text. However, these theories only consider unformatted text. Multimedia text contains rich formatting features which can be leveraged for various NLP tasks. In this paper, we study some of these discourse features in multimedia text and what communicative function they fulfil in the context. We examine how these multimedia discourse features can be used to improve an information extraction system. We show that the discourse and text layout features provide information that is complementary to lexical semantic information commonly used for information extraction. As a case study, we use these features to harvest structured subject knowledge of geometry from textbooks. We show that the harvested structured knowledge can be used to improve an existing solver for geometry problems, making it more accurate as well as more explainable.
Tasks
Published	2018-11-13
URL	http://arxiv.org/abs/1811.05546v1
PDF	http://arxiv.org/pdf/1811.05546v1.pdf
PWC	https://paperswithcode.com/paper/discourse-in-multimedia-a-case-study-in
Repo
Framework

Answering Science Exam Questions Using Query Rewriting with Background Knowledge


Title	Answering Science Exam Questions Using Query Rewriting with Background Knowledge
Authors	Ryan Musa, Xiaoyan Wang, Achille Fokoue, Nicholas Mattei, Maria Chang, Pavan Kapanipathi, Bassem Makni, Kartik Talamadupula, Michael Witbrock
Abstract	Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques. Much of the progress in open-domain QA systems has been realized through advances in information retrieval methods and corpus construction. In this paper, we focus on the recently introduced ARC Challenge dataset, which contains 2,590 multiple choice questions authored for grade-school science exams. These questions are selected to be the most challenging for current QA systems, and current state of the art performance is only slightly better than random chance. We present a system that rewrites a given question into queries that are used to retrieve supporting text from a large corpus of science-related text. Our rewriter is able to incorporate background knowledge from ConceptNet and – in tandem with a generic textual entailment system trained on SciTail that identifies support in the retrieved results – outperforms several strong baselines on the end-to-end QA task despite only being trained to identify essential terms in the original source question. We use a generalizable decision methodology over the retrieved evidence and answer candidates to select the best answer. By combining query rewriting, background knowledge, and textual entailment our system is able to outperform several strong baselines on the ARC dataset.
Tasks	Information Retrieval, Natural Language Inference, Open-Domain Question Answering, Question Answering
Published	2018-09-15
URL	http://arxiv.org/abs/1809.05726v2
PDF	http://arxiv.org/pdf/1809.05726v2.pdf
PWC	https://paperswithcode.com/paper/answering-science-exam-questions-using-query
Repo
Framework

Identity Preserving Generative Adversarial Network for Cross-Domain Person Re-identification


Title	Identity Preserving Generative Adversarial Network for Cross-Domain Person Re-identification
Authors	Jialun Liu
Abstract	Person re-identification is to retrieval pedestrian images from no-overlap camera views detected by pedestrian detectors. Most existing person re-identification (re-ID) models often fail to generalize well from the source domain where the models are trained to a new target domain without labels, because of the bias between the source and target domain. This issue significantly limits the scalability and usability of the models in the real world. Providing a labeled source training set and an unlabeled target training set, the aim of this paper is to improve the generalization ability of re-ID models to the target domain. To this end, we propose an image generative network named identity preserving generative adversarial network (IPGAN). The proposed method has two excellent properties: 1) only a single model is employed to translate the labeled images from the source domain to the target camera domains in an unsupervised manner; 2) The identity information of images from the source domain is preserved before and after translation. Furthermore, we propose IBN-reID model for the person re-identification task. It has better generalization ability than baseline models, especially in the cases without any domain adaptation. The IBN-reID model is trained on the translated images by supervised methods. Experimental results on Market-1501 and DukeMTMC-reID show that the images generated by IPGAN are more suitable for cross-domain person re-identification. Very competitive re-ID accuracy is achieved by our method.
Tasks	Domain Adaptation, Person Re-Identification
Published	2018-11-28
URL	http://arxiv.org/abs/1811.11510v1
PDF	http://arxiv.org/pdf/1811.11510v1.pdf
PWC	https://paperswithcode.com/paper/identity-preserving-generative-adversarial
Repo
Framework

Diachronic word embeddings and semantic shifts: a survey


Title	Diachronic word embeddings and semantic shifts: a survey
Authors	Andrey Kutuzov, Lilja Øvrelid, Terrence Szymanski, Erik Velldal
Abstract	Recent years have witnessed a surge of publications aimed at tracing temporal changes in lexical semantics using distributional methods, particularly prediction-based word embedding models. However, this vein of research lacks the cohesion, common terminology and shared practices of more established areas of natural language processing. In this paper, we survey the current state of academic research related to diachronic word embeddings and semantic shifts detection. We start with discussing the notion of semantic shifts, and then continue with an overview of the existing methods for tracing such time-related shifts with word embedding models. We propose several axes along which these methods can be compared, and outline the main challenges before this emerging subfield of NLP, as well as prospects and possible applications.
Tasks	Word Embeddings
Published	2018-06-09
URL	http://arxiv.org/abs/1806.03537v2
PDF	http://arxiv.org/pdf/1806.03537v2.pdf
PWC	https://paperswithcode.com/paper/diachronic-word-embeddings-and-semantic
Repo
Framework

Relevance in Structured Argumentation


Title	Relevance in Structured Argumentation
Authors	AnneMarie Borg, Christian Straßer
Abstract	We study properties related to relevance in non-monotonic consequence relations obtained by systems of structured argumentation. Relevance desiderata concern the robustness of a consequence relation under the addition of irrelevant information. For an account of what (ir)relevance amounts to we use syntactic and semantic considerations. Syntactic criteria have been proposed in the domain of relevance logic and were recently used in argumentation theory under the names of non-interference and crash-resistance. The basic idea is that the conclusions of a given argumentative theory should be robust under adding information that shares no propositional variables with the original database. Some semantic relevance criteria are known from non-monotonic logic. For instance, cautious monotony states that if we obtain certain conclusions from an argumentation theory, we may expect to still obtain the same conclusions if we add some of them to the given database. In this paper we investigate properties of structured argumentation systems that warrant relevance desiderata.
Tasks
Published	2018-09-13
URL	http://arxiv.org/abs/1809.04861v1
PDF	http://arxiv.org/pdf/1809.04861v1.pdf
PWC	https://paperswithcode.com/paper/relevance-in-structured-argumentation
Repo
Framework

Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce


Title	Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce
Authors	Minghui Qiu, Liu Yang, Feng Ji, Weipeng Zhao, Wei Zhou, Jun Huang, Haiqing Chen, W. Bruce Croft, Wei Lin
Abstract	Building multi-turn information-seeking conversation systems is an important and challenging research topic. Although several advanced neural text matching models have been proposed for this task, they are generally not efficient for industrial applications. Furthermore, they rely on a large amount of labeled data, which may not be available in real-world applications. To alleviate these problems, we study transfer learning for multi-turn information seeking conversations in this paper. We first propose an efficient and effective multi-turn conversation model based on convolutional neural networks. After that, we extend our model to adapt the knowledge learned from a resource-rich domain to enhance the performance. Finally, we deployed our model in an industrial chatbot called AliMe Assist (https://consumerservice.taobao.com/online-help) and observed a significant improvement over the existing online model.
Tasks	Chatbot, Text Matching, Transfer Learning
Published	2018-06-14
URL	http://arxiv.org/abs/1806.05434v1
PDF	http://arxiv.org/pdf/1806.05434v1.pdf
PWC	https://paperswithcode.com/paper/transfer-learning-for-context-aware-question
Repo
Framework

Signal Reconstruction from Modulo Observations


Title	Signal Reconstruction from Modulo Observations
Authors	Viraj Shah, Chinmay Hegde
Abstract	We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by a (relatively) less well-known imaging mechanism called modulo imaging, which can be used to extend the dynamic range of imaging systems; variations of this model have also been studied under the category of phase unwrapping. Signal reconstruction in the under-determined regime with modulo observations is a challenging ill-posed problem, and existing reconstruction methods cannot be used directly. In this paper, we propose a novel approach to solving the inverse problem limited to two modulo periods, inspired by recent advances in algorithms for phase retrieval under sparsity constraints. We show that given a sufficient number of measurements, our algorithm perfectly recovers the underlying signal and provides improved performance over other existing algorithms. We also provide experiments validating our approach on both synthetic and real data to depict its superior performance.
Tasks
Published	2018-12-03
URL	https://arxiv.org/abs/1812.00557v2
PDF	https://arxiv.org/pdf/1812.00557v2.pdf
PWC	https://paperswithcode.com/paper/signal-reconstruction-from-modulo
Repo
Framework