bluuebunny/crossref_metadata_2025 huggingface dataset:
Day-Ahead Forecasting of Losses in the Distribution Network
Authors: Dalal Nisha , Mølnå Martin , Herrem Mette , Røen Magne , Gundersen Odd Erik
Published: June 2020

We present a commercially deployed machine learning system that automates the day-ahead nomination of the expected grid loss for a Norwegian utility company. It meets several practical constraints and issues related to, among other things, delayed, missing and incorrect data and a small data set. The system incorporates a total of 24 different models that performs forecasts for three sub-grids. Each day one model is selected for making the hourly day-ahead forecasts for each sub-grid. The deployed system reduces the MAE with 41% from 3.68 MW to 2.17 MW per hour from mid July to mid October. It is robust and reduces manual work.
How Machine Learning Is Improving U.S. Navy Customer Support
Authors: Powell Michael , Rotz Jamison A , O’Malley Kevin D
Published: June 2020

The U.S. Navy is successfully using natural language processing (NLP) and common machine-learning (ML) algorithms to categorize and automatically route plain text support requests at a Navy fleet support center. The algorithms enhance routine IT support tasks with automation and reduce the workload of service desk agents. The ML pipeline works in a five-step process. First, an archive of documents is created from various sources, including standard operating procedure (SOP) memos, frequently asked questions (FAQs), knowledge articles, Wikipedia articles, encyclopedia articles, previously closed support requests, and other relevant documents. Next, a library of words and phrases is generated from the archive. Then, this library is used to vectorize an incoming support request, producing a term frequency inverse document frequency (TF-IDF) vector. Following, the TF-IDF vector is used to compute similarity scores between the support request and the documents in the previously-created archive. Finally, the similarity scores are processed by support vector machine (SVM) classifiers to categorize and route the incoming support request to the correct support provider. This algorithm was deployed at a U.S. Navy customer support center as part of a pilot study, where it decreased the amount of time agents spend on tickets by 35%; the amount of time required to assign tickets by 74%; and the amount of time to close tickets by 60%. Our internal tests show that, with an error rate of 2%, a 35% reduction in ticket volume could be achieved by fully deploying these algorithms.
Question Quality Improvement: Deep Question Understanding for Incident Management in Technical Support Domain
Authors: Ray Anupama , Aggarwal Pooja , Hadhazi Csaba , Dasgupta Gargi , Paradkar Amit
Published: June 2020

Technical support domain involves solving problems from user queries through various channels: voice, web and chat, and is both time-consuming and labour intensive. The textual queries in web or chat mode are unstructured and often incomplete. This affects information retrieval and increases the difficulty level for agents to solve it. Such cases require multiple rounds of interaction between user and agent/chatbot in order to better understand the user query. This paper presents a deployed system called Question Quality Improvement (QQI), that aims to improve the quality of user utterance by understanding and extracting important parts of an utterance and gamifying the user interface, prompting them to enter the remaining relevant information. QQI is guided by an ontology designed for the technical support domain and uses co-reference resolution and deep parsing to understand the sentences. Using the syntactics and semantics in the deep parse tree structure various attributes in the ontology are extracted. The system has been in production for over two years supporting around 800 products resulting in a reduction in the time-to-resolve cases by around 29%, leading to huge cost savings. QQI being a core natural language understanding and metadata extraction technology, directly affects more than 8K tickets everyday. These cases are submitted after 50K edits done on the case based on QQI feedback. QQI outputs are used by other technologies such as search and retrieval, case routing for automated dispatch, case-difficulty-prediction, and by the chatbots supported in each product page.
Combining Real-Time Segmentation and Classification of Rehabilitation Exercises with LSTM Networks and Pointwise Boosting
Authors: Bevilacqua Antonio , Ciampi Giovanni , Argent Rob , Caulfield Brian , Kechadi Tahar
Published: June 2020

Autonomous biofeedback tools in support of rehabilitation patients are commonly built as multi-tier pipelines, where a segmentation algorithm is first responsible for isolating motion primitives, and then classification can be performed on each primitive. In this paper, we present a novel segmentation technique that integrates on-the-fly qualitative classification of physical movements in the process. We adopt Long Short-Term Memory (LSTM) networks to model the temporal patterns of a streaming multivariate time series, obtained by sampling acceleration and angular velocity of the limb in motion, and then we aggregate the pointwise predictions of each isolated movement using different boosting methods. We tested our technique against a dataset composed of four common lower-limb rehabilitation exercises, collected from heterogeneous populations (clinical and healthy). Experimental results are promising and show that combining segmentation and classification of orthopaedic movements is a valid method with many potential real-world applications.
Automatic Building and Labeling of HD Maps with Deep Learning
Authors: Elhousni Mahdi , Lyu Yecheng , Zhang Ziming , Huang Xinming
Published: June 2020

In a world where autonomous driving cars are becoming increasingly more common, creating an adequate infrastructure for this new technology is essential. This includes building and labeling high-definition (HD) maps accurately and efficiently. Today, the process of creating HD maps requires a lot of human input, which takes time and is prone to errors. In this paper, we propose a novel method capable of generating labelled HD maps from raw sensor data. We implemented and tested our methods on several urban scenarios using data collected from our test vehicle. The results show that the proposed deep learning based method can produce highly accurate HD maps. This approach speeds up the process of building and labeling HD maps, which can make meaningful contribution to the deployment of autonomous vehicles.
A System for Medical Information Extraction and Verification from Unstructured Text
Authors: Juric Damir , Stoilos Giorgos , Melo Andre , Moore Jonathan , Khodadadi Mohammad
Published: June 2020

A wealth of medical knowledge has been encoded in terminologies like SNOMED CT, NCI, FMA, and more. However, these resources are usually lacking information like relations between diseases, symptoms, and risk factors preventing their use in diagnostic or other decision making applications. In this paper we present a pipeline for extracting such information from unstructured text and enriching medical knowledge bases. Our approach uses Semantic Role Labelling and is unsupervised. We show how we dealt with several deficiencies of SRL-based extraction, like copula verbs, relations expressed through nouns, and assigning scores to extracted triples. The system have so far extracted about 120K relations and in-house doctors verified about 5k relationships. We compared the output of the system with a manually constructed network of diseases, symptoms and risk factors build by doctors in the course of a year. Our results show that our pipeline extracts good quality and precise relations and speeds up the knowledge acquisition process considerably.
Can Eruptions Be Predicted? Short-Term Prediction of Volcanic Eruptions via Attention-Based Long Short-Term Memory
Authors: Le Hiep V. , Murata Tsuyoshi , Iguchi Masato
Published: June 2020

Short-term prediction of volcanic eruptions is one of the ultimate objectives of volcanology. At Sakurajima volcano, an active volcano in Japan, experts monitor the volcanic sensor data and analyze the prior signal to predict the eruptions. Even though experts derived some patterns, it is hard to make a good prediction due to handcrafted features. To address this issue, we propose to predict eruptions using machine learning. In this paper, we attempt to predict the eruptions hourly by adapting several machine learning methods including traditional and deep learning approaches. As recurrent neural network is well-known for extracting the time-sensitive features, we propose the model especially for volcanic eruption prediction named VepNet. The assumption is based on domain knowledge that some specific triggers are the main causes of future eruptions. To take this advantage, VepNet deploys an attention layer to locate and prioritize these triggers in decision making. The extensive experiments ever conducted using data from Sakurajima volcano showed the effectiveness of deep learning approach over the traditional approach. On top of that, VepNet showed its effectiveness on prediction with AUC-score up to 0.8665. Moreover, an attempt has been made to explain the mechanism of the eruptions by analyzing the attention layer of VepNet. Lastly, to support volcano expert in issuing warnings and the safety of living people around Sakurajima, a warning system named 3LWS is proposed. The system predicted the eruptions hourly with high accuracy and reliability with the eruption rate up to 68.97% in the High-Risk level.
Machine-Learning-Based Functional Microcirculation Analysis
Authors: Mahmoud Ossama , Janssen GH , El-Sakka Mahmoud R
Published: June 2020

Analysis of microcirculation is an important clinical and research task. Functional analysis of the microcirculation allows researchers to understand how blood flowing in a tissues’ smallest vessels affects disease progression, organ function, and overall health. Current methods of manual analysis of microcirculation are tedious and time-consuming, limiting the quick turnover of results. There has been limited research on automating functional analysis of microcirculation. As such, in this paper, we propose a two-step machine-learning-based algorithm to functionally assess microcirculation videos. The first step uses a modified vessel segmentation algorithm to extract the location of vessel-like structures. While the second step uses a 3D-CNN to assess whether the vessel-like structures contained flowing blood. To our knowledge, this is the first application of machine learning for functional analysis of microcirculation. We use real-world labelled microcirculation videos to train and test our algorithm and assess its performance. More precisely, we demonstrate that our two-step algorithm can efficiently analyze real data with high accuracy (90%).
Automated Utterance Generation
Authors: Parikh Soham , Vohra Quaizar , Tiwari Mitul
Published: June 2020

Conversational AI assistants are becoming popular and question-answering is an important part of any conversational assistant. Using relevant utterances as features in question-answering has shown to improve both the precision and recall for retrieving the right answer by a conversational assistant. Hence, utterance generation has become an important problem with the goal of generating relevant utterances (sentences or phrases) from a knowledge base article that consists of a title and a description. However, generating good utterances usually requires a lot of manual effort, creating the need for an automated utterance generation. In this paper, we propose an utterance generation system which 1) uses extractive summarization to extract important sentences from the description, 2) uses multiple paraphrasing techniques to generate a diverse set of paraphrases of the title and summary sentences, and 3) selects good candidate paraphrases with the help of a novel candidate selection algorithm.
A Natural Language Processing System for Extracting Evidence of Drug Repurposing from Scientific Publications
Authors: Subramanian Shivashankar , Baldini Ioana , Ravichandran Sushma , Katz-Rogozhnikov Dmitriy A. , Natesan Ramamurthy Karthikeyan , Sattigeri Prasanna , Varshney Kush R. , Wang Annmarie , Mangalath Pradeep , Kleiman Laura B.
Published: June 2020

More than 200 generic drugs approved by the U.S. Food and Drug Administration for non-cancer indications have shown promise for treating cancer. Due to their long history of safe patient use, low cost, and widespread availability, repurposing of these drugs represents a major opportunity to rapidly improve outcomes for cancer patients and reduce healthcare costs. In many cases, there is already evidence of efficacy for cancer, but trying to manually extract such evidence from the scientific literature is intractable. In this emerging applications paper, we introduce a system to automate non-cancer generic drug evidence extraction from PubMed abstracts. Our primary contribution is to define the natural language processing pipeline required to obtain such evidence, comprising the following modules: querying, filtering, cancer type entity extraction, therapeutic association classification, and study type classification. Using the subject matter expertise on our team, we create our own datasets for these specialized domain-specific tasks. We obtain promising performance in each of the modules by utilizing modern language processing techniques and plan to treat them as baseline approaches for future improvement of individual components.
Teaching Constraint Programming Using Fable-Based Learning
Authors: Chan Mavis , Chun Cecilia , Fung Holly , Lee Jimmy H.M. , Stuckey Peter J.
Published: June 2020

The paper presents the pedagogical innovations and experience of the co-development of three MOOCs on the subject of “Modeling and Solving Discrete Optimization Problems” by two universities. In a nutshell, the MOOCs feature the Fable-Based Learning approach, which is a form of problem-based learning encapsulated in a coherent story plot. Each lecture video begins with an animation that tells a story following a novel. The protagonists of the story encounter a problem requiring technical assistance from the two professors from modern time via a magical tablet granted to them by a fairy god. The new pedagogy aims at increasing learners' motivation and interests as well as situating the learners in a coherent learning context. In addition to scriptwriting, animation production and situating the teaching materials in the story plot, another challenge of the project is the remote distance between the two institutions as well as the need to produce all teaching materials in both (Mandarin) Chinese and English to cater for different geographic learning needs. The MOOCs have been running recurrently on Coursera since 2017. We present learner statistics and feedback, and discuss our experience with and preliminary observations of adopting the online materials in a Flipped Classroom setting.
Lessons Learned from Teaching Machine Learning and Natural Language Processing to High School Students
Authors: Norouzi Narges , Chaturvedi Snigdha , Rutledge Matthew
Published: June 2020

This paper describes an experience in teaching Machine Learning (ML) and Natural Language Processing (NLP) to a group of high school students over an intense one-month period. In this work, we provide an outline of an AI course curriculum we designed for high school students and then evaluate its effectiveness by analyzing student's feedback and student outcomes. After closely observing students, evaluating their responses to our surveys, and analyzing their contribution to the course project, we identified some possible impediments in teaching AI to high school students and propose some measures to avoid them. These measures include employing a combination of objectivist and constructivist pedagogies, reviewing/introducing basic programming concepts at the beginning of the course, and addressing gender discrepancies throughout the course.
Minecraft as a Platform for Project-Based Learning in AI
Authors: Singh Sameer
Published: June 2020

Undergraduate courses that focus on open-ended, project-based learning teach students how to define concrete goals, transfer conceptual understanding of algorithms to code, and evaluate/analyze/present their solution. However, AI, along with machine learning, is getting increasingly varied in terms of both the approaches and applications, making it challenging to design project courses that span a sufficiently wide spectrum of AI. For these reasons, existing AI project courses are restricted to a narrow set of approaches (e.g. only reinforcement learning) or applications (e.g. only computer vision).In this paper, we propose to use Minecraft as the platform for teaching AI via project-based learning. Minecraft is an open-world sandbox game with elements of exploration, resource gathering, crafting, construction, and combat, and is supported by the Malmo library that provides a programmatic interface to the player observations and actions at various levels of granularity. In Minecraft, students can design projects to use approaches like search-based AI, reinforcement learning, supervised learning, and constraint satisfaction, on data types like text, audio, images, and tabular data. We describe our experience with an open-ended, undergraduate AI projects course using Minecraft that includes 82 different projects, covering themes that ranged from navigation, instruction following, object detection, combat, and music/image generation.
Open-World Learning for Radically Autonomous Agents
Authors: Langley Pat
Published: June 2020

In this paper, I pose a new research challenge – to develop intelligent agents that exhibit radical autonomy by responding to sudden, long-term changes in their environments. I illustrate this idea with examples, identify abilities that support it, and argue that, although each ability has been studied in isolation, they have not been combined into integrated systems. In addition, I propose a framework for characterizing environments in which goal-directed physical agents operate, along with specifying the ways in which those environments can change over time. In closing, I outline some approaches to the empirical study of such open-world learning.
Developments in Multi-Agent Fair Allocation
Authors: Aziz Haris
Published: June 2020

Fairness is becoming an increasingly important concern when designing markets, allocation procedures, and computer systems. I survey some recent developments in the field of multi-agent fair allocation.
Software Testing for Machine Learning
Authors: Marijan Dusica , Gotlieb Arnaud
Published: June 2020

Machine learning has become prevalent across a wide variety of applications. Unfortunately, machine learning has also shown to be susceptible to deception, leading to errors, and even fatal failures. This circumstance calls into question the widespread use of machine learning, especially in safety-critical applications, unless we are able to assure its correctness and trustworthiness properties. Software verification and testing are established technique for assuring such properties, for example by detecting errors. However, software testing challenges for machine learning are vast and profuse - yet critical to address. This summary talk discusses the current state-of-the-art of software testing for machine learning. More specifically, it discusses six key challenge areas for software testing of machine learning systems, examines current approaches to these challenges and highlights their limitations. The paper provides a research agenda with elaborated directions for making progress toward advancing the state-of-the-art on testing of machine learning.
On the Robustness of Face Recognition Algorithms Against Attacks and Bias
Authors: Singh Richa , Agarwal Akshay , Singh Maneet , Nagpal Shruti , Vatsa Mayank
Published: June 2020

Face recognition algorithms have demonstrated very high recognition performance, suggesting suitability for real world applications. Despite the enhanced accuracies, robustness of these algorithms against attacks and bias has been challenged. This paper summarizes different ways in which the robustness of a face recognition algorithm is challenged, which can severely affect its intended working. Different types of attacks such as physical presentation attacks, disguise/makeup, digital adversarial attacks, and morphing/tampering using GANs have been discussed. We also present a discussion on the effect of bias on face recognition models and showcase that factors such as age and gender variations affect the performance of modern algorithms. The paper also presents the potential reasons for these challenges and some of the future research directions for increasing the robustness of face recognition models.
Generalized Arc Consistency Algorithms for Table Constraints: A Summary of Algorithmic Ideas
Authors: Yap Roland H. C. , Xia Wei , Wang Ruiwei
Published: June 2020

Constraint Programming is a powerful paradigm to model and solve combinatorial problems. While there are many kinds of constraints, the table constraint (also called a CSP) is perhaps the most significant—being the most well-studied and has the ability to encode any other constraints defined on finite variables. Thus, designing efficient filtering algorithms on table constraints has attracted significant research efforts. In turn, there have been great improvements in efficiency over time with the evolution and development of AC and GAC algorithms. In this paper, we survey the existing filtering algorithms for table constraint focusing on historically important ideas and recent successful techniques shown to be effective.
PARTNER: Human-in-the-Loop Entity Name Understanding with Deep Learning
Authors: Qian Kun , Chozhiyath Raman Poornima , Li Yunyao , Popa Lucian
Published: June 2020

Entity name disambiguation is an important task for many text-based AI tasks. Entity names usually have internal semantic structures that are useful for resolving different variations of the same entity. We present, PARTNER, a deep learning-based interactive system for entity name understanding. Powered by effective active learning and weak supervision, PARTNER can learn deep learning-based models for identifying entity name structure with low human effort. PARTNER also allows the user to design complex normalization and variant generation functions without coding skills.
Data-Driven Ranking and Visualization of Products by Competitiveness
Authors: Usmani Sheema , Bernagozzi Mariana , Huang Yufeng , Morales Michelle , Sabet Sarvestani Amir , Srivastava Biplav
Published: June 2020

Competitive analysis is a critical part of any business. Product managers, sellers, and marketers spend time and resources scouring through a huge volume of online and offline content, aiming to discover what their competitors are doing in the marketplace and to understand what type of threat they pose to their business' financial well-being. Currently, this process is slow, costly and labor-intensive. We demonstrate Clarity, a data-driven unsupervised system for assessment of products, which is currently in deployment at IBM. Clarity has been running for more than a year and is used by over 1,500 people to perform over 160 competitive analyses involving over 800 products. The system considers multiple factors from a collection of online content: numeric ratings by users, sentiment towards key product drivers, content volume, and recency of content. The results and explanations of factors leading to the results are visualized in an interactive dashboard that allows users to track the performance of their products as well as understand the main contributing factors. main contributing factors.
Restraining Bolts for Reinforcement Learning Agents
Authors: De Giacomo Giuseppe , Iocchi Luca , Favorito Marco , Patrizi Fabio
Published: June 2020

In this work we have investigated the concept of “restraining bolt”, inspired by Science Fiction. We have two distinct sets of features extracted from the world, one by the agent and one by the authority imposing some restraining specifications on the behaviour of the agent (the “restraining bolt”). The two sets of features and, hence the model of the world attainable from them, are apparently unrelated since of interest to independent parties. However they both account for (aspects of) the same world. We have considered the case in which the agent is a reinforcement learning agent on a set of low-level (subsymbolic) features, while the restraining bolt is specified logically using linear time logic on finite traces f/f over a set of high-level symbolic features. We show formally, and illustrate with examples, that, under general circumstances, the agent can learn while shaping its goals to suitably conform (as much as possible) to the restraining bolt specifications.1
Identifiability from a Combination of Observations and Experiments
Authors: Lee Sanghack , Correa Juan D. , Bareinboim Elias
Published: June 2020

We study the problem of causal identification from an arbitrary collection of observational and experimental distributions, and substantive knowledge about the phenomenon under investigation, which usually comes in the form of a causal graph. We call this problem g-identifiability, or gID for short. In this paper, we introduce a general strategy to prove non-gID based on thickets and hedgelets, which leads to a necessary and sufficient graphical condition for the corresponding decision problem. We further develop a procedure for systematically computing the target effect, and prove that it is sound and complete for gID instances. In other words, the failure of the algorithm in returning an expression implies that the target effect is not computable from the available distributions. Finally, as a corollary of these results, we show that do-calculus is complete for the task of g-identifiability.
Energy and Policy Considerations for Modern Deep Learning Research
Authors: Strubell Emma , Ganesh Ananya , McCallum Andrew
Published: June 2020

The field of artificial intelligence has experienced a dramatic methodological shift towards large neural networks trained on plentiful data. This shift has been fueled by recent advances in hardware and techniques enabling remarkable levels of computation, resulting in impressive advances in AI across many applications. However, the massive computation required to obtain these exciting results is costly both financially, due to the price of specialized hardware and electricity or cloud compute time, and to the environment, as a result of non-renewable energy used to fuel modern tensor processing hardware. In a paper published this year at ACL, we brought this issue to the attention of NLP researchers by quantifying the approximate financial and environmental costs of training and tuning neural network models for NLP (Strubell, Ganesh, and McCallum 2019). In this extended abstract, we briefly summarize our findings in NLP, incorporating updated estimates and broader information from recent related publications, and provide actionable recommendations to reduce costs and improve equity in the machine learning and artificial intelligence community.
Interpreting Multimodal Machine Learning Models Trained for Emotion Recognition to Address Robustness and Privacy Concerns
Authors: Jaiswal Mimansa
Published: June 2020

Many mobile applications and virtual conversational agents now aim to recognize and adapt to emotions. These predicted emotions are used in variety of downstream applications: (a) generating more human like dialogues, (b) predicting mental health issues, and (c) hate speech detection and intervention. To enable this, data are transmitted from users' devices and stored on central servers. These data are then processed further, either annotated or used as inputs for training a model for a specific task. Yet, these data contain sensitive information that could be used by mobile applications without user's consent or, maliciously, by an eavesdropping adversary. My work focuses on two major issues that are faced while training emotion recognition algorithms: (a) privacy of the generated representations and, (b) explaining and ensuring that the predictions are robust to various situations. Tackling these issues would lead to emotion based algorithms that are deployable and helpful at a larger scale, thus enabling more human like experience when interacting with AI.
Partial Correlation-Based Attention for Multivariate Time Series Forecasting
Authors: Lee Won Kyung
Published: June 2020

A multivariate time-series forecasting has great potentials in various domains. However, it is challenging to find dependency structure among the time-series variables and appropriate time-lags for each variable, which change dynamically over time. In this study, I suggest partial correlation-based attention mechanism which overcomes the shortcomings of existing pair-wise comparisons-based attention mechanisms. Moreover, I propose data-driven series-wise multi-resolution convolutional layers to represent the input time-series data for domain agnostic learning.
Abstract Constraints for Safe and Robust Robot Learning from Demonstration
Authors: Mueller Carl L.
Published: June 2020

My thesis research incorporates high-level abstract behavioral requirements, called ‘conceptual constraints’, into the modeling processes of robot Learning from Demonstration (LfD) techniques. My most recent work introduces an LfD algorithm called Concept Constrained Learning from Demonstration. This algorithm encodes motion planning constraints as temporal Boolean operators that enforce high-level constraints over portions of the robot's motion plan during learned skill execution. This results in more easily trained, more robust, and safer learned skills. Future work will incorporate conceptual constraints into human-aware motion planning algorithms. Additionally, my research will investigate how these concept constrained algorithms and models are best incorporated into effective interfaces for end-users.
Sample Complexity Bounds for RNNs with Application to Combinatorial Graph Problems (Student Abstract)
Authors: Akpinar Nil-Jana , Kratzwald Bernhard , Feuerriegel Stefan
Published: June 2020

Learning to predict solutions to real-valued combinatorial graph problems promises efficient approximations. As demonstrated based on the NP-hard edge clique cover number, recurrent neural networks (RNNs) are particularly suited for this task and can even outperform state-of-the-art heuristics. However, the theoretical framework for estimating real-valued RNNs is understood only poorly. As our primary contribution, this is the first work that upper bounds the sample complexity for learning real-valued RNNs. While such derivations have been made earlier for feed-forward and convolutional neural networks, our work presents the first such attempt for recurrent neural networks. Given a single-layer RNN with a rectified linear units and input of length b, we show that a population prediction error of ε can be realized with at most Õ(a4b/ε2) samples.1 We further derive comparable results for multi-layer RNNs. Accordingly, a size-adaptive RNN fed with graphs of at most n vertices can be learned in Õ(n6/ε2), i.,e., with only a polynomial number of samples. For combinatorial graph problems, this provides a theoretical foundation that renders RNNs competitive.
Analysis of Parliamentary Debate Transcripts Using Community-Based Graphical Approaches (Student Abstract)
Authors: Bhavan Anjali , Sharma Mohit , Sawhney Ramit , Ratn Shah Rajiv
Published: June 2020

Gauging political sentiments and analyzing stances of elected representatives pose an important challenge today, and one with wide-ranging ramifications. Community-based analysis of parliamentary debate sentiments could pave a way for better insights into the political happenings of a nation and help in keeping the voters informed. Such analysis could be given another dimension by studying the underlying connections and networks in such data. We present a sentiment classification method for UK Parliament debate transcripts, which is a combination of a graphical method based on DeepWalk embeddings and text-based analytical methods. We also present proof for our hypothesis that parliamentarians with similar voting patterns tend to deliver similar speeches. We also provide some further avenues and future work towards the end.
SATNet: Symmetric Adversarial Transfer Network Based on Two-Level Alignment Strategy towards Cross-Domain Sentiment Classification (Student Abstract)
Authors: Cao Yu , Xu Hua
Published: June 2020

In recent years, domain adaptation tasks have attracted much attention, especially, the task of cross-domain sentiment classification (CDSC). In this paper, we propose a novel domain adaptation method called Symmetric Adversarial Transfer Network (SATNet). Experiments on the Amazon reviews dataset demonstrate the effectiveness of SATNet.
I Am Guessing You Can't Recognize This: Generating Adversarial Images for Object Detection Using Spatial Commonsense (Student Abstract)
Authors: Garg Anurag , Tandon Niket , Varde Aparna S.
Published: June 2020

Can we automatically predict failures of an object detection model on images from a target domain? We characterize errors of a state-of-the-art object detection model on the currently popular smart mobility domain, and find that a large number of errors can be identified using spatial commonsense. We propose øurmodel , a system that automatically identifies a large number of such errors based on commonsense knowledge. Our system does not require any new annotations and can still find object detection errors with high accuracy (more than 80% when measured by humans). This work lays the foundation to answer exciting research questions on domain adaptation including the ability to automatically create adversarial datasets for target domain.
Trimodal Attention Module for Multimodal Sentiment Analysis (Student Abstract)
Authors: Harish Anirudh Bindiganavale , Sadat Fatiha
Published: June 2020

In our research, we propose a new multimodal fusion architecture for the task of sentiment analysis. The 3 modalities used in this paper are text, audio and video. Most of the current methods deal with either a feature level or a decision level fusion. In contrast, we propose an attention-based deep neural network and a training approach to facilitate both feature and decision level fusion. Our network effectively leverages information across all three modalities using a 2 stage fusion process. We test our network on the individual utterance based contextual information extracted from the CMU-MOSI Dataset. A comparison is drawn between the state-of-the-art and our network.
Multi-View Deep Attention Network for Reinforcement Learning (Student Abstract)
Authors: Hu Yueyue , Sun Shiliang , Xu Xin , Zhao Jing
Published: June 2020

The representation approximated by a single deep network is usually limited for reinforcement learning agents. We propose a novel multi-view deep attention network (MvDAN), which introduces multi-view representation learning into the reinforcement learning task for the first time. The proposed model approximates a set of strategies from multiple representations and combines these strategies based on attention mechanisms to provide a comprehensive strategy for a single-agent. Experimental results on eight Atari video games show that the MvDAN has effective competitive performance than single-view reinforcement learning methods.
Incremental Sense Weight Training for In-Depth Interpretation of Contextualized Word Embeddings (Student Abstract)
Authors: Jiang Xinyi , Yang Zhengzhe , Choi Jinho D.
Published: June 2020

We present a novel online algorithm that learns the essence of each dimension in word embeddings. We first mask dimensions determined unessential by our algorithm, apply the masked word embeddings to a word sense disambiguation task (WSD), and compare its performance against the one achieved by the original embeddings. Our results show that the masked word embeddings do not hurt the performance and can improve it by 3%.
Learning Directional Sentence-Pair Embedding for Natural Language Reasoning (Student Abstract)
Authors: Jiang Yuchen , Xiao Zhenxin , Chang Kai-Wei
Published: June 2020

Enabling the models with the ability of reasoning and inference over text is one of the core missions of natural language understanding. Despite deep learning models have shown strong performance on various cross-sentence inference benchmarks, recent work has shown that they are leveraging spurious statistical cues rather than capturing deeper implied relations between pairs of sentences. In this paper, we show that the state-of-the-art language encoding models are especially bad at modeling directional relations between sentences by proposing a new evaluation task: Cause-and-Effect relation prediction task. Back by our curated Cause-and-Effect Relation dataset (Cℰℛ), we also demonstrate that a mutual attention mechanism can guide the model to focus on capturing directional relations between sentences when added to existing transformer-based models. Experiment results show that the proposed approach improves the performance on downstream applications, such as the abductive reasoning task.
Suicide Risk Assessment via Temporal Psycholinguistic Modeling (Student Abstract)
Authors: Mathur Puneet , Sawhney Ramit , Shah Rajiv Ratn
Published: June 2020

Social media platforms are increasingly being used for studying psycho-linguistic phenomenon to model expressions of suicidal intent in tweets. Most recent work in suicidal ideation detection doesn't leverage contextual psychological cues. In this work, we hypothesize that the contextual information embedded in the form of historical activities of users and homophily networks formed between like-minded individuals in Twitter can substantially improve existing techniques for automated identification of suicidal tweets. This premise is extensively tested to yield state of the art results as compared to linguistic only models, and the state-of-the-art model.
A Simple Deconvolutional Mechanism for Point Clouds and Sparse Unordered Data (Student Abstract)
Authors: Paniagua Thomas , Lagergren John , Foderaro Greg
Published: June 2020

This paper presents a novel deconvolution mechanism, called the Sparse Deconvolution, that generalizes the classical transpose convolution operation to sparse unstructured domains, enabling the fast and accurate generation and upsampling of point clouds and other irregular data. Specifically, the approach uses deconvolutional kernels, which each map an input feature vector and set of trainable scalar weights to the feature vectors of multiple child output elements. Unlike previous approaches, the Sparse Deconvolution does not require any voxelization or structured formulation of data, it is scalable to a large number of elements, and it is capable of utilizing local feature information. As a result, these capabilities allow for the practical generation of unstructured data in unsupervised settings. Preliminary experiments are performed here, where Sparse Deconvolution layers are used as a generator within an autoencoder trained on the 3D MNIST dataset.
Personalized Prediction of Trust Links in Social Networks (Student Abstract)
Authors: Parmentier Alexandre , Cohen Robin
Published: June 2020

In this paper we show how integrating both domain specific and generic trust indicators into a prediction of trust links between users in social networks can improve upon methods for recommending content to users and how clustering of users to deliver personalized solutions offers even greater advantages.
Video Person Re-ID: Fantastic Techniques and Where to Find Them (Student Abstract)
Authors: Pathak Priyank , Eshratifar Amir Erfan , Gormish Michael
Published: June 2020

The ability to identify the same person from multiple camera views without the explicit use of facial recognition is receiving commercial and academic interest. The current status-quo solutions are based on attention neural models. In this paper, we propose Attention and CL loss, which is a hybrid of center and Online Soft Mining (OSM) loss added to the attention loss on top of a temporal attention-based neural network. The proposed loss function applied with bag-of-tricks for training surpasses the state of the art on the common person Re-ID datasets, MARS and PRID 2011. Our source code is publicly available on github1.
Link Prediction between Group Entities in Knowledge Graphs (Student Abstract)
Authors: Su Jialin , Wang Yuanzhuo , Jin Xiaolong , Jia Yantao , Cheng Xueqi
Published: June 2020

Link prediction in knowledge graphs (KGs) aims at predicting potential links between entities in KGs. Existing knowledge graph embedding (KGE) based methods represent individual entities and links in KGs as vectors in low-dimension space. However, these methods focus mainly on the link prediction of individual entities, yet neglect that between group entities, which exist widely in real-world KGs. In this paper, we propose a KGE based method, called GTransA, for link prediction between group entities in a heterogeneous network by integrating individual entity links into group entity links during prediction. Experiments show that GTransA decreases mean rank by 5.4%, compared to TransA.
HGMAN: Multi-Hop and Multi-Answer Question Answering Based on Heterogeneous Knowledge Graph (Student Abstract)
Authors: Wang Xu , Zhao Shuai , Cheng Bo , Han Jiale , Li Yingting , Yang Hao , Nan Guoshun
Published: June 2020

Multi-hop question answering models based on knowledge graph have been extensively studied. Most existing models predict a single answer with the highest probability by ranking candidate answers. However, they are stuck in predicting all the right answers caused by the ranking method. In this paper, we propose a novel model that converts the ranking of candidate answers into individual predictions for each candidate, named heterogeneous knowledge graph based multi-hop and multi-answer model (HGMAN). HGMAN is capable of capturing more informative representations for relations assisted by our heterogeneous graph, which consists of multiple entity nodes and relation nodes. We rely on graph convolutional network for multi-hop reasoning and then binary classification for each node to get multiple answers. Experimental results on MetaQA dataset show the performance of our proposed model over all baselines.
Multi-Channel Convolutional Neural Networks with Adversarial Training for Few-Shot Relation Classification (Student Abstract)
Authors: Xie Yuxiang , Xu Hua , Yang Congcong , Gao Kai
Published: June 2020

The distant supervised (DS) method has improved the performance of relation classification (RC) by means of extending the dataset. However, DS also brings the problem of wrong labeling. Contrary to DS, the few-shot method relies on few supervised data to predict the unseen classes. In this paper, we use word embedding and position embedding to construct multi-channel vector representation and use the multi-channel convolutional method to extract features of sentences. Moreover, in order to alleviate few-shot learning to be sensitive to overfitting, we introduce adversarial learning for training a robust model. Experiments on the FewRel dataset show that our model achieves significant and consistent improvements on few-shot RC as compared with baselines.
Deep Ranking for Style-Aware Room Recommendations (Student Abstract)
Authors: Yıldız İlkay , Ataer-Cansızoğlu Esra , Liu Hantian , Golbus Peter , Tezcan Ozan , Choi Jae-Woo
Published: June 2020

We present a deep learning based room image retrieval framework that is based on style understanding. Given a dataset of room images labeled by interior design experts, we map the noisy style labels to comparison labels. Our framework learns the style spectrum of each image from the generated comparisons and makes significantly more accurate recommendations compared to discrete classification baselines.
Interactive Neural Network: Leveraging Part-of-Speech Window for Aspect Term Extraction (Student Abstract)
Authors: Yin Da , Wu Xiuyu , Chang Baobao
Published: June 2020

Aspect term extraction is a fundamental task for aspect-level sentiment analysis. Previous methods tend to extract noun aspect terms due to the large quantities of them, and perform badly on extracting aspect terms containing words with other POS tags, according to experimental results. In addition, few works focus on the POS tags of adjacent words which are critical to aspect term extraction. We propose a novel model which combines POS and word features in an interactive way, and makes full use of the POS tags of adjacent words by POS window. We conduct experiments on two datasets, and prove the effectiveness of our model.
Cancer Treatment Classification with Electronic Medical Health Records (Student Abstract)
Authors: Zeng Jiaming , Banerjee Imon , Gensheimer Michael , Rubin Daniel
Published: June 2020

We built a natural language processing (NLP) language model that can be used to extract cancer treatment information using structured and unstructured electronic medical records (EMR). Our work appears to be the first that combines EMR and NLP for treatment identification.
Effects of fines content and plasticity on liquefaction resistance of sands
Authors: Ranga Swamy K. , Akhila M. , Sankar N.
Published: June 2020

A detailed experimental programme was conducted to evaluate the liquefaction susceptibility of non-plastic and low-plasticity soils subjected to cyclic loading under undrained triaxial loading conditions. The study mainly focused on examining the influence of the amount of fines and plasticity indices on the liquefaction resistance of sands. After mixing silt and clay fractions into fine sand, 16 soil combinations were prepared. The silty sands contained up to 40% non-plastic fines and the low-plasticity soils contained a clay fraction of 5–40%. Each cylindrical soil specimen was constituted to a medium relative density and saturated specimens were subjected to a confinement pressure of 100 kPa. The consolidated specimens were then subjected to various levels of cyclic stress amplitudes using a sinusoidal wave load form at a frequency of 1 Hz. The results showed that both the non-plastic and low-plasticity clay soils were less resistant to liquefaction than the fine sand. The soils belonging to categories SM and SC (silty sand and clayey sand, respectively, as per Indian standard soil classifications) were susceptible to liquefaction if the fines passing through a 75 μm sieve were ≤ 40%, the liquid limit was ≤ 40%, the plasticity index was < 15 and the saturated water content was about 0.86 times the LL.
Assessment of undrained non-linear stiffness of clays using rigidity index
Authors: Ahmed Sayed M. , Agaiby Shehab S.
Published: June 2020

Due consideration of non-linear soil stiffness is one of the essential requisites in many geotechnical applications. A considerable amount of the geotechnical literature, as well as numerous recent studies, have been directed towards enhancing our ability to formulate mathematical relationships expressing the stiffness non-linearity of fine-grained soils and to determine the controlling parameters of such relationships. At present, the parameters of the non-linear clay stiffness formulations are commonly correlated to Atterberg's plasticity index (PI), even though this is obtained from testing remoulded samples, whereas the looked-for parameters are distinctly related to the undisturbed clay condition. In this paper, a new approach is proposed to establish the link between the non-linear stiffness parameters and the undrained rigidity index, as a more pertinent factor for the undisturbed compressibility of clays than the PI. The proposed approach was used to predict the undrained non-linear behaviour of several well-reported formations (London Clay, San Francisco Bay Mud, Skå-Edeby Clay and Hachirougata Clay). Good agreement between the predicted and the measured non-linear stiffnesses was found, implying that the presented methodology can be broadly applicable to different clay formations.