pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Urdu Caption Generation using Attention based LSTM
Authors: Inaam Ilahi, Hafiz Muhammad Abdullah Zia, Muhammad Ahtazaz Ahsan, Rauf Tabassam, Armaghan Ahmed
Date: 2020-08-02 00:00:00
Tasks: Caption Generation

Recent advancements in deep learning have created many opportunities to solve real-world problems that remained unsolved for more than a decade. Automatic caption generation is a major research field, and the research community has done a lot of work on it in most common languages like English. Urdu is the national language of Pakistan and also much spoken and understood in the sub-continent region of Pakistan-India, and yet no work has been done for Urdu language caption generation. Our research aims to fill this gap by developing an attention-based deep learning model using techniques of sequence modeling specialized for the Urdu language. We have prepared a dataset in the Urdu language by translating a subset of the "Flickr8k" dataset containing 700 'man' images. We evaluate our proposed technique on this dataset and show that it can achieve a BLEU score of 0.83 in the Urdu language. We improve on the previous state-of-the-art by using better CNN architectures and optimization techniques. Furthermore, we provide a discussion on how the generated captions can be made correct grammar-wise.
Towards Deep Clustering of Human Activities from Wearables
Authors: Alireza Abedin, Farbod Motlagh, Qinfeng Shi, Seyed Hamid Rezatofighi, Damith Chinthana Ranasinghe
Date: 2020-08-02 00:00:00
Tasks: Activity Recognition, Clustering, Deep Clustering, Human Activity Recognition, Semantic correspondence

Our ability to exploit low-cost wearable sensing modalities for critical human behaviour and activity monitoring applications in health and wellness is reliant on supervised learning regimes; here, deep learning paradigms have proven extremely successful in learning activity representations from annotated data. However, the costly work of gathering and annotating sensory activity datasets is labor-intensive, time consuming and not scalable to large volumes of data. While existing unsupervised remedies of deep clustering leverage network architectures and optimization objectives that are tailored for static image datasets, deep architectures to uncover cluster structures from raw sequence data captured by on-body sensors remains largely unexplored. In this paper, we develop an unsupervised end-to-end learning strategy for the fundamental problem of human activity recognition (HAR) from wearables. Through extensive experiments, including comparisons with existing methods, we show the effectiveness of our approach to jointly learn unsupervised representations for sensory data and generate cluster assignments with strong semantic correspondence to distinct human activities.
Deep Multi-modality Soft-decoding of Very Low Bit-rate Face Videos
Authors: Yanhui Guo, Xi Zhang, Xiaolin Wu
Date: 2020-08-02 00:00:00
Tasks: Quantization, Video Compression, Video Restoration

We propose a novel deep multi-modality neural network for restoring very low bit rate videos of talking heads. Such video contents are very common in social media, teleconferencing, distance education, tele-medicine, etc., and often need to be transmitted with limited bandwidth. The proposed CNN method exploits the correlations among three modalities, video, audio and emotion state of the speaker, to remove the video compression artifacts caused by spatial down sampling and quantization. The deep learning approach turns out to be ideally suited for the video restoration task, as the complex non-linear cross-modality correlations are very difficult to model analytically and explicitly. The new method is a video post processor that can significantly boost the perceptual quality of aggressively compressed talking head videos, while being fully compatible with all existing video compression standards.
A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction
Authors: Takanori Fujiwara, Shilpika, Naohisa Sakamoto, Jorji Nonaka, Keiji Yamamoto, Kwan-Liu Ma
Date: 2020-08-02 00:00:00
Tasks: Contrastive Learning, Dimensionality Reduction, Time Series, Time Series Analysis

Data-driven problem solving in many real-world applications involves analysis of time-dependent multivariate data, for which dimensionality reduction (DR) methods are often used to uncover the intrinsic structure and features of the data. However, DR is usually applied to a subset of data that is either single-time-point multivariate or univariate time-series, resulting in the need to manually examine and correlate the DR results out of different data subsets. When the number of dimensions is large either in terms of the number of time points or attributes, this manual task becomes too tedious and infeasible. In this paper, we present MulTiDR, a new DR framework that enables processing of time-dependent multivariate data as a whole to provide a comprehensive overview of the data. With the framework, we employ DR in two steps. When treating the instances, time points, and attributes of the data as a 3D array, the first DR step reduces the three axes of the array to two, and the second DR step visualizes the data in a lower-dimensional space. In addition, by coupling with a contrastive learning method and interactive visualizations, our framework enhances analysts' ability to interpret DR results. We demonstrate the effectiveness of our framework with four case studies using real-world datasets.
Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization
Authors: Rui Hu, Yanmin Gong, Yuanxiong Guo
Date: 2020-08-01 00:00:00
Tasks: Federated Learning

Federated learning (FL) enables distributed agents to collaboratively learn a centralized model without sharing their raw data with each other. However, data locality does not provide sufficient privacy protection, and it is desirable to facilitate FL with rigorous differential privacy (DP) guarantee. Existing DP mechanisms would introduce random noise with magnitude proportional to the model size, which can be quite large in deep neural networks. In this paper, we propose a new FL framework with sparsification-amplified privacy. Our approach integrates random sparsification with gradient perturbation on each agent to amplify privacy guarantee. Since sparsification would increase the number of communication rounds required to achieve a certain target accuracy, which is unfavorable for DP guarantee, we further introduce acceleration techniques to help reduce the privacy cost. We rigorously analyze the convergence of our approach and utilize Renyi DP to tightly account the end-to-end DP guarantee. Extensive experiments on benchmark datasets validate that our approach outperforms previous differentially-private FL approaches in both privacy guarantee and communication efficiency.
Tense, aspect and mood based event extraction for situation analysis and crisis management
Authors: Ali Hürriyetoğlu
Date: 2020-08-01 00:00:00
Tasks: Event Extraction, Humanitarian, Management

Nowadays event extraction systems mainly deal with a relatively small amount of information about temporal and modal qualifications of situations, primarily processing assertive sentences in the past tense. However, systems with a wider coverage of tense, aspect and mood can provide better analyses and can be used in a wider range of text analysis applications. This thesis develops such a system for Turkish language. This is accomplished by extending Open Source Information Mining and Analysis (OPTIMA) research group's event extraction software, by implementing appropriate extensions in the semantic representation format, by adding a partial grammar which improves the TAM (Tense, Aspect and Mood) marker, adverb analysis and matching functions of ExPRESS, and by constructing an appropriate lexicon in the standard of CORLEONE. These extensions are based on iv the theory of anchoring relations (Tem\"urc\"u, 2007, 2011) which is a crosslinguistically applicable semantic framework for analyzing tense, aspect and mood related categories. The result is a system which can, in addition to extracting basic event structures, classify sentences given in news reports according to their temporal, modal and volitional/illocutionary values. Although the focus is on news reports of natural disasters, disease outbreaks and man-made disasters in Turkish language, the approach can be adapted to other languages, domains and genres. This event extraction and classification system, with further developments, can provide a basis for automated browsing systems for preventing environmental and humanitarian risk.
Adaptive Chemotaxis for improved Contour Tracking using Spiking Neural Networks
Authors: Shashwat Shukla, Rohan Pathak, Vivek Saraswat, Udayan Ganguly
Date: 2020-08-01 00:00:00
Tasks: Autonomous Navigation

In this paper we present a Spiking Neural Network (SNN) for autonomous navigation, inspired by the chemotaxis network of the worm Caenorhabditis elegans. In particular, we focus on the problem of contour tracking, wherein the bot must reach and subsequently follow a desired concentration setpoint. Past schemes that used only klinokinesis can follow the contour efficiently but take excessive time to reach the setpoint. We address this shortcoming by proposing a novel adaptive klinotaxis mechanism that builds upon a previously proposed gradient climbing circuit. We demonstrate how our klinotaxis circuit can autonomously be configured to perform gradient ascent, gradient descent and subsequently be disabled to seamlessly integrate with the aforementioned klinokinesis circuit. We also incorporate speed regulation (orthokinesis) to further improve contour tracking performance. Thus for the first time, we present a model that successfully integrates klinokinesis, klinotaxis and orthokinesis. We demonstrate via contour tracking simulations that our proposed scheme achieves an 2.4x reduction in the time to reach the setpoint, along with a simultaneous 8.7x reduction in average deviation from the setpoint.
A Self-Assessing Compilation Based Search Approach for Analytical Research and Data Retrieval
Authors: Ananth Goyal
Date: 2020-07-30 00:00:00
Tasks: Retrieval, Specificity

While meta-analytic research is performed, it becomes time-consuming to filter through the sheer amount of sources made available by individual databases and search engines and therefore degrades the specificity of source analysis. This study sought to predict the feasibility of a research-oriented searching algorithm across all topics and a search technique to combat flaws in dealing with large datasets by automating three key components of meta-analysis: a query-based search associated with the intended research topic, selecting given sources and determining their relevance to the original query, and extracting applicable information including excerpts and citations. The algorithm was evaluated using 5 key historical topics, and results were broken down into 4 categories: the total number of relevant sources retrieved, the efficiency given a particular search, the total time it takes to finish a complete cycle, and the quality of the extracted sources when compared to results from current searching methods. Although results differed through several searches, on average, the program collected a total of 126 sources per search with an average efficiency of 19.55 sources per second which, when compared and qualitatively evaluated for definitive results, indicates that an algorithm developed across all subject areas will make progress in future research methods.
Musical Word Embedding: Bridging the Gap between Listening Contexts and Music
Authors: Seungheon Doh, Jongpil Lee, Tae Hong Park, Juhan Nam
Date: 2020-07-23 00:00:00
Tasks: Information Retrieval, Music Information Retrieval, Retrieval

Word embedding pioneered by Mikolov et al. is a staple technique for word representations in natural language processing (NLP) research which has also found popularity in music information retrieval tasks. Depending on the type of text data for word embedding, however, vocabulary size and the degree of musical pertinence can significantly vary. In this work, we (1) train the distributed representation of words using combinations of both general text data and music-specific data and (2) evaluate the system in terms of how they associate listening contexts with musical compositions.
Deep Learning Techniques for Future Intelligent Cross-Media Retrieval
Authors: Sadaqat ur Rehman, Muhammad Waqas, Shanshan Tu, Anis Koubaa, Obaid ur Rehman, Jawad Ahmad, Muhammad Hanif, Zhu Han
Date: 2020-07-21 00:00:00
Tasks: Deep Learning, Retrieval

With the advancement in technology and the expansion of broadcasting, cross-media retrieval has gained much attention. It plays a significant role in big data applications and consists in searching and finding data from different types of media. In this paper, we provide a novel taxonomy according to the challenges faced by multi-modal deep learning approaches in solving cross-media retrieval, namely: representation, alignment, and translation. These challenges are evaluated on deep learning (DL) based methods, which are categorized into four main groups: 1) unsupervised methods, 2) supervised methods, 3) pairwise based methods, and 4) rank based methods. Then, we present some well-known cross-media datasets used for retrieval, considering the importance of these datasets in the context in of deep learning based cross-media retrieval approaches. Moreover, we also present an extensive review of the state-of-the-art problems and its corresponding solutions for encouraging deep learning in cross-media retrieval. The fundamental objective of this work is to exploit Deep Neural Networks (DNNs) for bridging the "media gap", and provide researchers and developers with a better understanding of the underlying problems and the potential solutions of deep learning assisted cross-media retrieval. To the best of our knowledge, this is the first comprehensive survey to address cross-media retrieval under deep learning methods.
Presentation of a Recommender System with Ensemble Learning and Graph Embedding: A Case on MovieLens
Authors: Saman Forouzandeh, Mehrdad Rostami, Kamal Berahmand
Date: 2020-07-15 00:00:00
Tasks: Decision Making, Ensemble Learning, General Classification, Graph Embedding, Recommendation Systems

Information technology has spread widely, and extraordinarily large amounts of data have been made accessible to users, which has made it challenging to select data that are in accordance with user needs. For the resolution of the above issue, recommender systems have emerged, which much help users go through the process of decision-making and selecting relevant data. A recommender system predicts users behavior to be capable of detecting their interests and needs, and it often uses the classification technique for this purpose. It may not be sufficiently accurate to employ individual classification, where not all cases can be examined, which makes the method inappropriate to specific problems. In this research, group classification and the ensemble learning technique were used for increasing prediction accuracy in recommender systems. Another issue that is raised here concerns user analysis. Given the large size of the data and a large number of users, the process of user needs analysis and prediction (using a graph in most cases, representing the relations between users and their selected items) is complicated and cumbersome in recommender systems. Graph embedding was also proposed for resolution of this issue, where all or part of user behavior can be simulated through the generation of several vectors, resolving the problem of user behavior analysis to a large extent while maintaining high efficiency. In this research, individuals most similar to the target user were classified using ensemble learning, fuzzy rules, and the decision tree, and relevant recommendations were then made to each user with a heterogeneous knowledge graph and embedding vectors. This study was performed on the MovieLens datasets, and the obtained results indicated the high efficiency of the presented method.
Hybrid Collaborative Filtering Models for Clinical Search Recommendation
Authors: Zhiyun Ren, Bo Peng, Titus K. Schleyer, Xia Ning
Date: 2020-07-19 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

With increasing and extensive use of electronic health records, clinicians are often under time pressure when they need to retrieve important information efficiently among large amounts of patients' health records in clinics. While a search function can be a useful alternative to browsing through a patient's record, it is cumbersome for clinicians to search repeatedly for the same or similar information on similar patients. Under such circumstances, there is a critical need to build effective recommender systems that can generate accurate search term recommendations for clinicians. In this manuscript, we developed a hybrid collaborative filtering model using patients' encounter and search term information to recommend the next search terms for clinicians to retrieve important information fast in clinics. For each patient, the model will recommend terms that either have high co-occurrence frequencies with his/her most recent ICD codes or are highly relevant to the most recent search terms on this patient. We have conducted comprehensive experiments to evaluate the proposed model, and the experimental results demonstrate that our model can outperform all the state-of-the-art baseline methods for top-N search term recommendation on different datasets.
Facets of Fairness in Search and Recommendation
Authors: Sahil Verma, Ruoyuan Gao, Chirag Shah
Date: 2020-07-16 00:00:00
Tasks: Diversity, Fairness, Recommendation Systems

Several recent works have highlighted how search and recommender systems exhibit bias along different dimensions. Counteracting this bias and bringing a certain amount of fairness in search is crucial to not only creating a more balanced environment that considers relevance and diversity but also providing a more sustainable way forward for both content consumers and content producers. This short paper examines some of the recent works to define relevance, diversity, and related concepts. Then, it focuses on explaining the emerging concept of fairness in various recommendation settings. In doing so, this paper presents comparisons and highlights contracts among various measures, and gaps in our conceptual and evaluative frameworks.
Including Images into Message Veracity Assessment in Social Media
Authors: Abderrazek Azri, Cécile Favre, Nouria Harbi, Jérôme Darmont
Date: 2020-07-20 00:00:00

The extensive use of social media in the diffusion of information has also laid a fertile ground for the spread of rumors, which could significantly affect the credibility of social media. An ever-increasing number of users post news including, in addition to text, multimedia data such as images and videos. Yet, such multimedia content is easily editable due to the broad availability of simple and effective image and video processing tools. The problem of assessing the veracity of social network posts has attracted a lot of attention from researchers in recent years. However, almost all previous works have focused on analyzing textual contents to determine veracity, while visual contents, and more particularly images, remains ignored or little exploited in the literature. In this position paper, we propose a framework that explores two novel ways to assess the veracity of messages published on social networks by analyzing the credibility of both their textual and visual contents.
Dynamically Extracting Outcome-Specific Problem Lists from Clinical Notes with Guided Multi-Headed Attention
Authors: Justin Lovelace, Nathan C. Hurley, Adrian D. Haimovich, Bobak J. Mortazavi
Date: 2020-07-25 00:00:00

Problem lists are intended to provide clinicians with a relevant summary of patient medical issues and are embedded in many electronic health record systems. Despite their importance, problem lists are often cluttered with resolved or currently irrelevant conditions. In this work, we develop a novel end-to-end framework that first extracts diagnosis and procedure information from clinical notes and subsequently uses the extracted medical problems to predict patient outcomes. This framework is both more performant and more interpretable than existing models used within the domain, achieving an AU-ROC of 0.710 for bounceback readmission and 0.869 for in-hospital mortality occurring after ICU discharge. We identify risk factors for both readmission and mortality outcomes and demonstrate that our framework can be used to develop dynamic problem lists that present clinical problems along with their quantitative importance. We conduct a qualitative user study with medical experts and demonstrate that they view the lists produced by our framework favorably and find them to be a more effective clinical decision support tool than a strong baseline.
Multi-Perspective Semantic Information Retrieval in the Biomedical Domain
Authors: Samarth Rawal
Date: 2020-07-17 00:00:00
Tasks: Articles, Information Retrieval, Language Modelling, Passage Re-Ranking, Re-Ranking, Retrieval, Sentence, Sentence Classification

Information Retrieval (IR) is the task of obtaining pieces of data (such as documents) that are relevant to a particular query or need from a large repository of information. IR is a valuable component of several downstream Natural Language Processing (NLP) tasks. Practically, IR is at the heart of many widely-used technologies like search engines. While probabilistic ranking functions like the Okapi BM25 function have been utilized in IR systems since the 1970's, modern neural approaches pose certain advantages compared to their classical counterparts. In particular, the release of BERT (Bidirectional Encoder Representations from Transformers) has had a significant impact in the NLP community by demonstrating how the use of a Masked Language Model trained on a large corpus of data can improve a variety of downstream NLP tasks, including sentence classification and passage re-ranking. IR Systems are also important in the biomedical and clinical domains. Given the increasing amount of scientific literature across biomedical domain, the ability find answers to specific clinical queries from a repository of millions of articles is a matter of practical value to medical professionals. Moreover, there are domain-specific challenges present, including handling clinical jargon and evaluating the similarity or relatedness of various medical symptoms when determining the relevance between a query and a sentence. This work presents contributions to several aspects of the Biomedical Semantic Information Retrieval domain. First, it introduces Multi-Perspective Sentence Relevance, a novel methodology of utilizing BERT-based models for contextual IR. The system is evaluated using the BioASQ Biomedical IR Challenge. Finally, practical contributions in the form of a live IR system for medics and a proposed challenge on the Living Systematic Review clinical task are provided.