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"Topic": models, code, and papers

Finding Social Media Trolls: Dynamic Keyword Selection Methods for Rapidly-Evolving Online Debates

Nov 16, 2019
Anqi Liu, Maya Srikanth, Nicholas Adams-Cohen, R. Michael Alvarez, Anima Anandkumar

Online harassment is a significant social problem. Prevention of online harassment requires rapid detection of harassing, offensive, and negative social media posts. In this paper, we propose the use of word embedding models to identify offensive and harassing social media messages in two aspects: detecting fast-changing topics for more effective data collection and representing word semantics in different domains. We demonstrate with preliminary results that using the GloVe (Global Vectors for Word Representation) model facilitates the discovery of new and relevant keywords to use for data collection and trolling detection. Our paper concludes with a discussion of a research agenda to further develop and test word embedding models for identification of social media harassment and trolling.

* AI for Social Good workshop at NeurIPS (2019) 

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Facial Emotion Recognition using Convolutional Neural Networks

Oct 12, 2019
Akash Saravanan, Gurudutt Perichetla, Dr. K. S. Gayathri

Facial expression recognition is a topic of great interest in most fields from artificial intelligence and gaming to marketing and healthcare. The goal of this paper is to classify images of human faces into one of seven basic emotions. A number of different models were experimented with, including decision trees and neural networks before arriving at a final Convolutional Neural Network (CNN) model. CNNs work better for image recognition tasks since they are able to capture spacial features of the inputs due to their large number of filters. The proposed model consists of six convolutional layers, two max pooling layers and two fully connected layers. Upon tuning of the various hyperparameters, this model achieved a final accuracy of 0.60.

* AICV '18: International Symposium on Artificial Intelligence and Computer Vision. College of Engineering, Guindy. Chennai, India (September 2018) 

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"Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding

Sep 06, 2019
Ben Zhou, Daniel Khashabi, Qiang Ning, Dan Roth

Understanding time is crucial for understanding events expressed in natural language. Because people rarely say the obvious, it is often necessary to have commonsense knowledge about various temporal aspects of events, such as duration, frequency, and temporal order. However, this important problem has so far received limited attention. This paper systematically studies this temporal commonsense problem. Specifically, we define five classes of temporal commonsense, and use crowdsourcing to develop a new dataset, MCTACO, that serves as a test set for this task. We find that the best current methods used on MCTACO are still far behind human performance, by about 20%, and discuss several directions for improvement. We hope that the new dataset and our study here can foster more future research on this topic.

* EMNLP 2019 (short paper). arXiv admin note: text overlap with arXiv:1908.04926 

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Combining Q&A Pair Quality and Question Relevance Features on Community-based Question Retrieval

Jul 03, 2019
Dong Li, Lin Li

The Q&A community has become an important way for people to access knowledge and information from the Internet. However, the existing translation based on models does not consider the query specific semantics when assigning weights to query terms in question retrieval. So we improve the term weighting model based on the traditional topic translation model and further considering the quality characteristics of question and answer pairs, this paper proposes a communitybased question retrieval method that combines question and answer on quality and question relevance (T2LM+). We have also proposed a question retrieval method based on convolutional neural networks. The results show that Compared with the relatively advanced methods, the two methods proposed in this paper increase MAP by 4.91% and 6.31%.

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CRH: A Simple Benchmark Approach to Continuous Hashing

Oct 10, 2018
Miao Cheng, Ah Chung Tsoi

In recent years, the distinctive advancement of handling huge data promotes the evolution of ubiquitous computing and analysis technologies. With the constantly upward system burden and computational complexity, adaptive coding has been a fascinating topic for pattern analysis, with outstanding performance. In this work, a continuous hashing method, termed continuous random hashing (CRH), is proposed to encode sequential data stream, while ignorance of previously hashing knowledge is possible. Instead, a random selection idea is adopted to adaptively approximate the differential encoding patterns of data stream, e.g., streaming media, and iteration is avoided for stepwise learning. Experimental results demonstrate our method is able to provide outstanding performance, as a benchmark approach to continuous hashing.

* 6 pages 

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Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models

Jun 13, 2018
Jiuxiang Gu, Jianfei Cai, Shafiq Joty, Li Niu, Gang Wang

Textual-visual cross-modal retrieval has been a hot research topic in both computer vision and natural language processing communities. Learning appropriate representations for multi-modal data is crucial for the cross-modal retrieval performance. Unlike existing image-text retrieval approaches that embed image-text pairs as single feature vectors in a common representational space, we propose to incorporate generative processes into the cross-modal feature embedding, through which we are able to learn not only the global abstract features but also the local grounded features. Extensive experiments show that our framework can well match images and sentences with complex content, and achieve the state-of-the-art cross-modal retrieval results on MSCOCO dataset.

* 10 pages, 6 figures, Accepted as spotlight at CVPR 2018 

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Quality assessment metrics for edge detection and edge-aware filtering: A tutorial review

Jan 01, 2018
Diana Sadykova, Alex Pappachen James

The quality assessment of edges in an image is an important topic as it helps to benchmark the performance of edge detectors, and edge-aware filters that are used in a wide range of image processing tasks. The most popular image quality metrics such as Mean squared error (MSE), Peak signal-to-noise ratio (PSNR) and Structural similarity (SSIM) metrics for assessing and justifying the quality of edges. However, they do not address the structural and functional accuracy of edges in images with a wide range of natural variabilities. In this review, we provide an overview of all the most relevant performance metrics that can be used to benchmark the quality performance of edges in images. We identify four major groups of metrics and also provide a critical insight into the evaluation protocol and governing equations.

* 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, 2017, pp. 2366-2369 

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Capturing Hand Motion with an RGB-D Sensor, Fusing a Generative Model with Salient Points

Apr 03, 2017
Dimitrios Tzionas, Abhilash Srikantha, Pablo Aponte, Juergen Gall

Hand motion capture has been an active research topic in recent years, following the success of full-body pose tracking. Despite similarities, hand tracking proves to be more challenging, characterized by a higher dimensionality, severe occlusions and self-similarity between fingers. For this reason, most approaches rely on strong assumptions, like hands in isolation or expensive multi-camera systems, that limit the practical use. In this work, we propose a framework for hand tracking that can capture the motion of two interacting hands using only a single, inexpensive RGB-D camera. Our approach combines a generative model with collision detection and discriminatively learned salient points. We quantitatively evaluate our approach on 14 new sequences with challenging interactions.

* German Conference on Pattern Recognition (GCPR) 2014, 

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Classification and its applications for drug-target interaction identification

Mar 12, 2015
Jian-Ping Mei, Chee-Keong Kwoh, Peng Yang, Xiao-Li Li

Classification is one of the most popular and widely used supervised learning tasks, which categorizes objects into predefined classes based on known knowledge. Classification has been an important research topic in machine learning and data mining. Different classification methods have been proposed and applied to deal with various real-world problems. Unlike unsupervised learning such as clustering, a classifier is typically trained with labeled data before being used to make prediction, and usually achieves higher accuracy than unsupervised one. In this paper, we first define classification and then review several representative methods. After that, we study in details the application of classification to a critical problem in drug discovery, i.e., drug-target prediction, due to the challenges in predicting possible interactions between drugs and targets.

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