Proceedings of the 7th ACM IKDD CoDS and 25th COMAD最新文献

{"title":"A Benchmark for OWL 2 DL Reasoners","authors":"Gunjan Singh, S. Bhatia, Raghava Mutharaju","doi":"10.1145/3371158.3371222","DOIUrl":"https://doi.org/10.1145/3371158.3371222","url":null,"abstract":"","PeriodicalId":360747,"journal":{"name":"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128213129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tutorial on Software Testing & Quality Assurance for Machine Learning Applications from research bench to real world","authors":"Sandya Mannarswamy, Shourya Roy, Saravanan Chidambaram","doi":"10.1145/3371158.3371233","DOIUrl":"https://doi.org/10.1145/3371158.3371233","url":null,"abstract":"Rapid progress in Machine Learning (ML) has seen a swift translation to real world commercial deployment. While research and development of ML applications have progressed at an exponential pace, the required software engineering process for ML applications and the corresponding eco-system of testing and quality assurance tools which enable software reliable, trustworthy and safe and easy to deploy, have sadly lagged behind. Specifically, the challenges and gaps in quality assurance (QA) and testing of AI applications have largely remained unaddressed contributing to a poor translation rate of ML applications from research to real world. Unlike traditional software, which has a well-defined software testing methodology, ML applications have largely taken an ad-hoc approach to testing. ML researchers and practitioners either fall back to traditional software testing approaches, which are inadequate for this domain, due to its inherent probabilistic and data dependent nature, or rely largely on non-rigorous self-defined QA methodologies. These issues have driven the ML and Software Engineering research communities to develop of newer tools and techniques designed specifically for ML. These research advances need to be publicized and practiced in real world in ML development and deployment for enabling successful translation of ML from research prototypes to real world. This tutorial intends to address this need. This tutorial aims to: [1] Provide a comprehensive overview of testing of ML applications [2] Provide practical insights and share community best practices for testing ML software Besides scientific literature, we derive our insights from our conversations with industry experts in ML.","PeriodicalId":360747,"journal":{"name":"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128995050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Kauwa-Kaate Fake News Detection System: Demo","authors":"A. Bagade, Ashwini Pale, Shreyans Sheth, Megha Agarwal, Soumen Chakrabarti, Kameswari Chebrolu, S. Sudarshan","doi":"10.1145/3371158.3371402","DOIUrl":"https://doi.org/10.1145/3371158.3371402","url":null,"abstract":"Fake news spread via social media is a major problem today. It is not easy with current-generation tools to check if a particular article is genuine or contains fake news. While there are many Web sites today that debunk viral fake news, checking if a particular article has been debunked or is true is not easy for an end-user. Search engines like Google do not make it easy to check a complete article since they limit the number of query keywords. In this paper, we outline the architecture of the Kauwa-Kaate system for fact-checking articles. Queried articles are searched against articles crawled from fact-checking sites, as well as against articles crawled from trusted news sites. Our system supports querying based on text as well as on images and video; the latter features are very important since many fake news articles are based on images and videos. We also describe the user interfaces which we will use to demonstrate the Kauwa-Kaate system.","PeriodicalId":360747,"journal":{"name":"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133040315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A segmentation based similarity measure for time series data","authors":"Kakuli Mishra, Srinka Basu, U. Maulik","doi":"10.1145/3371158.3371221","DOIUrl":"https://doi.org/10.1145/3371158.3371221","url":null,"abstract":"Focusing on the objectives of Demand Side Management (DSM), we propose a novel time series distance metric that better capture the information related to similar peaks/off-peaks. The proposed metric uses autocorrelation based segmentation and similar segment identification for computation of overall distance. Experiment shows the proposed distance advances the state-of-the-art.","PeriodicalId":360747,"journal":{"name":"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133533398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Neural Learning for Automated Diagnostic Code Group Prediction Using Unstructured Nursing Notes","authors":"Aditya Jayasimha, Tushaar Gangavarapu, Sowmya S Kamath, G. Krishnan","doi":"10.1145/3371158.3371176","DOIUrl":"https://doi.org/10.1145/3371158.3371176","url":null,"abstract":"Disease prediction, a central problem in clinical care and management, has gained much significance over the last decade. Nursing notes documented by caregivers contain valuable information concerning a patient's state, which can aid in the development of intelligent clinical prediction systems. Moreover, due to the limited adaptation of structured electronic health records in developing countries, the need for disease prediction from such clinical text has garnered substantial interest from the research community. The availability of large, publicly available databases such as MIMIC-III, and advancements in machine and deep learning models with high predictive capabilities have further facilitated research in this direction. In this work, we model the latent knowledge embedded in the unstructured clinical nursing notes, to address the clinical task of disease prediction as a multi-label classification of ICD-9 code groups. We present EnTAGS, which facilitates aggregation of the data in the clinical nursing notes of a patient, by modeling them independent of one another. To handle the sparsity and high dimensionality of clinical nursing notes effectively, our proposed EnTAGS is built on the topics extracted using Non-negative matrix factorization. Furthermore, we explore the applicability of deep learning models for the clinical task of disease prediction, and assess the reliability of the proposed models using standard evaluation metrics. Our experimental evaluation revealed that the proposed approach consistently exceeded the state-of-the-art prediction model by 1.87% in accuracy, 12.68% in AUPRC, and 11.64% in MCC score.","PeriodicalId":360747,"journal":{"name":"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130527537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lessons and Insights from Super-Resolution of Energy Data","authors":"Rithwik Kukunuri, Nipun Batra, Hongning Wang","doi":"10.1145/3371158.3371224","DOIUrl":"https://doi.org/10.1145/3371158.3371224","url":null,"abstract":"Motivation Studies have shown that consumers of electricity can save up 15% of their bills when provided with a detailed appliance wise feedback [1]. Energy super-resolution refers to estimating energy usage at a higher-sampling rate from the lower sampling rate. We mainly focus on predicting the hourly reading of a home, using the daily usage (which can be noted down by the users from the meter). This predicted usage can be used by the consumers to identify the times of the day, which are contributing more to electricity usage and help them optimize their usage. This is analogous to image superresolution, where the zooming out factor equals 24. Problem definition Throughout the paper we will be using the following notation: H Number of homes; D Number of days; X ∈ RH×D Denotes low resolution matrix (Aggregate); Y ∈ RH×D×24 Denotes high resolution matrix; P ∈ RH×D×24 Denotes weights matrix; Weights matrix is same as the matrix which stores the proportion of electricity consumed on a particular day. For the hth home and the dth day, the matrix ∀iPh,d,i = Yh,d,i Xh,d Approach Triplet learning Let L(i, j) denoteX [i, j −K : j +K] , which is a vector of length 2K+1. It stores the K past and K future neighbor aggregate readings in a home i during day j. We can refer to this a neighborhood vector for the ith home for jth day. An embedding network takes 2K+1 dimension vector as input and outputs an vector of dimensionN . The embedding network can be configured with various options such as normalization of output and positive activation of output.Consider (i,x),(j,y),(k, z), where each tuple denotes a home and day pairs. Let V (i,x) denote the embedding vector generated using L(i,x) . We define similarity functions which are specified in Equation(1). The functions in Equation(1) denote the similarity of the given tuples in the super-resolution usage. The losses in Table 1 ensure that tuples that are similar in the weights space are also similar in the embedding space. After the embedding network finished training, we generate the embeddings for each of the test samples. Then we find k nearest training samples using the embeddings and use the weights of the closest samples as the weights for the test sample.","PeriodicalId":360747,"journal":{"name":"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131223419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Keep Your Dimensions on a Leash: True Cognate Detection using Siamese Deep Neural Networks","authors":"Diptesh Kanojia, Sravan Munukutla, Sayali Ghodekar, P. Bhattacharyya, Malhar A. Kulkarni","doi":"10.1145/3371158.3371207","DOIUrl":"https://doi.org/10.1145/3371158.3371207","url":null,"abstract":"Automatic Cognate Detection helps NLP tasks of Machine Translation, Information Retrieval, and Phylogenetics. Cognate words are defined as word pairs across languages which exhibit partial or full lexical similarity and mean the same (e.g., hund-hound in German-English). In this paper, we use a Siamese Feed-forward neural network with word-embeddings to detect such word pairs. Our experiments with various embedding dimensions show larger embedding dimensions can only be used for large corpora sizes for this task. On a dataset built using linked Indian Wordnets, our approach beats the baseline approach with a significant margin (up to 71%) with the best F-score of 0.85% on the Hindi-Gujarati language pair.","PeriodicalId":360747,"journal":{"name":"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129628053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PlantDoc: A Dataset for Visual Plant Disease Detection","authors":"D. Singh, Naman Jain, Pranjali Jain, Pratik Kayal, Sudhakar Kumawat, Nipun Batra","doi":"10.1145/3371158.3371196","DOIUrl":"https://doi.org/10.1145/3371158.3371196","url":null,"abstract":"India loses 35% of the annual crop yield due to plant diseases. Early detection of plant diseases remains difficult due to the lack of lab infrastructure and expertise. In this paper, we explore the possibility of computer vision approaches for scalable and early plant disease detection. The lack of availability of sufficiently large-scale non-lab data set remains a major challenge for enabling vision based plant disease detection. Against this background, we present PlantDoc: a dataset for visual plant disease detection. Our dataset contains 2,598 data points in total across 13 plant species and up to 17 classes of diseases, involving approximately 300 human hours of effort in annotating internet scraped images. To show the efficacy of our dataset, we learn 3 models for the task of plant disease classification. Our results show that modelling using our dataset can increase the classification accuracy by up to 31%. We believe that our dataset can help reduce the entry barrier of computer vision techniques in plant disease detection.","PeriodicalId":360747,"journal":{"name":"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115345075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph-based Deep Learning in Natural Language Processing","authors":"Shikhar Vashishth, N. Yadati, Partha P. Talukdar","doi":"10.1145/3371158.3371232","DOIUrl":"https://doi.org/10.1145/3371158.3371232","url":null,"abstract":"This tutorial aims to introduce recent advances in graph-based deep learning techniques such as Graph Convolutional Networks (GCNs) for Natural Language Processing (NLP). It provides a brief introduction to deep learning methods on non-Euclidean domains such as graphs and justifies their relevance in NLP. It then covers recent advances in applying graph-based deep learning methods for various NLP tasks, such as semantic role labeling, machine translation, relationship extraction, and many more.","PeriodicalId":360747,"journal":{"name":"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD","volume":"399 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124718164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Weakly-Supervised Deep Learning for Domain Invariant Sentiment Classification","authors":"Pratik Kayal, M. Singh, Pawan Goyal","doi":"10.1145/3371158.3371194","DOIUrl":"https://doi.org/10.1145/3371158.3371194","url":null,"abstract":"The task of learning a sentiment classification model that adapts well to any target domain, different from the source domain, is a challenging problem. Majority of the existing approaches focus on learning a common representation by leveraging both source and target data during training. In this paper, we introduce a two-stage training procedure that leverages weakly supervised datasets for developing simple lift-and-shift-based predictive models without being exposed to the target domain during the training phase. Experimental results show that transfer with weak supervision from a source domain to various target domains provides performance very close to that obtained via supervised training on the target domain itself.","PeriodicalId":360747,"journal":{"name":"Proceedings of the 7th ACM IKDD CoDS and 25th COMAD","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130432878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}