bluuebunny/crossref_metadata_2025 huggingface dataset:
Detecting Suspicious Timber Trades
Authors: Datta Debanjan , Islam M. Raihanul , Self Nathan , Meadows Amelia , Simeone John , Outhwaite Willow , Hin Keong Chen , Smith Amy , Walker Linda , Ramakrishnan Naren
Published: June 2020

Developing algorithms that identify potentially illegal trade shipments is a non-trivial task, exacerbated by the size of shipment data as well as the unavailability of positive training data. In collaboration with conservation organizations, we develop a framework that incorporates machine learning and domain knowledge to tackle this challenge. Modeling the task as anomaly detection, we propose a simple and effective embedding-based anomaly detection approach for categorical data that provides better performance and scalability than the current state-of-art, along with a negative sampling approach that can efficiently train the proposed model. Additionally, we show how our model aids the interpretability of results which is crucial for the task. Domain knowledge, though sparse and scattered across multiple open data sources, is ingested with input of domain experts to create rules that highlight actionable results. The application framework demonstrates the applicability of our proposed approach on real world trade data. An interface combined with the framework presents a complete system that can ingest, detect and aid in the analysis of suspicious timber trades.
Analog Accelerator for Simulation and Diagnostics
Authors: Feldman Alexander , Matei Ion , Totev Emil , De Kleer Johan
Published: June 2020

We propose a new method for solving Initial Value Problems (IVPs). Our method is based on analog computing and has the potential to almost eliminate traditional switching time in digital computing. The approach can be used to simulate large systems longer, faster, and with higher accuracy. Many algorithms for Model-Based Diagnosis use numerical integration to simulate physical systems. The numerical integration process is often either computationally expensive or imprecise. We propose a new method, based on Field-Programmable Analog Arrays (FPAAs) that has the potential to overcome many practical problems. We envision a software/hardware framework for solving systems of simultaneous Ordinary Differential Equations (ODEs) in fraction of the time of traditional numerical algorithms. In this paper we describe the solving of an IVP with the help of an Analog Computing Unit (ACU). To do this we build a special calculus based on operational amplifiers (op-amps) with local feedback. We discuss the implementation of the ACU on an Integrated Circuit (IC). We analyze the working if the IC and simulate the dynamic Lotka-Volterra system with the de-facto standard tool for electrical simulation: Spice.
GRACE: Generating Summary Reports Automatically for Cognitive Assistance in Emergency Response
Authors: Rahman M Arif , Preum Sarah M. , Williams Ronald , Alemzadeh Homa , Stankovic John A.
Published: June 2020

EMS (emergency medical service) plays an important role in saving lives in emergency and accident situations. When first responders, including EMS providers and firefighters, arrive at an incident, they communicate with the patients (if conscious), family members and other witnesses, other first responders, and the command center. The first responders utilize a microphone and headset to support these communications. After the incident, the first responders are required to document the incident by filling out a form. Today, this is performed manually. Manual documentation of patient summary report is time-consuming, tedious, and error-prone. We have addressed these form filling problems by transcribing the audio from the scene, identifying the relevant information from all the conversations, and automatically filling out the form. Informal survey of first responders indicate that this application would be exceedingly helpful to them. Results show that we can fill out a model summary report form with an F1 score as high as 94%, 78%, 96%, and 83% when the data is noise-free audio, noisy audio, noise-free textual narratives, and noisy textual narratives, respectively.
Semi-Supervised Learning to Perceive Children's Affective States in a Tablet Tutor
Authors: Agarwal Mansi , Mostow Jack
Published: June 2020

Like good human tutors, intelligent tutoring systems should detect and respond to students' affective states. However, accuracy in detecting affective states automatically has been limited by the time and expense of manually labeling training data for supervised learning. To combat this limitation, we use semi-supervised learning to train an affective state detector on a sparsely labeled, culturally novel, authentic data set in the form of screen capture videos from a Swahili literacy and numeracy tablet tutor in Tanzania that shows the face of the child using it. We achieved 88% leave-1-child-out cross-validated accuracy in distinguishing pleasant, unpleasant, and neutral affective states, compared to only 61% for the best supervised learning method we tested. This work contributes toward using automated affect detection both off-line to improve the design of intelligent tutors, and at runtime to respond to student affect based on input from a user-facing tablet camera or webcam.
Making High-Performance Robots Safe and Easy to Use For an Introduction to Computing
Authors: Spitzer Joseph , Biswas Joydeep , Guha Arjun
Published: June 2020

Robots are a popular platform for introducing computing and artificial intelligence to novice programmers. However, programming state-of-the-art robots is very challenging, and requires knowledge of concurrency, operation safety, and software engineering skills, which can take years to teach. In this paper, we present an approach to introducing computing that allows students to safely and easily program high-performance robots. We develop a platform for students to program RoboCup Small Size League robots using JavaScript. The platform 1) ensures physical safety at several levels of abstraction, 2) allows students to program robots using JavaScript in the browser, without the need to install software, and 3) presents a simplified JavaScript semantics that shields students from confusing language features. We discuss our experience running a week-long workshop using this platform, and analyze over 3,000 student-written program revisions to provide empirical evidence that our approach does help students.
Coding in the Liberal Arts through Natural Language Processing and Machine Learning
Authors: Wolz Ursula , Wilson Jennifer
Published: June 2020

An initiative recently established at our institution is creating new opportunities for students to deepen their understanding of code and computational thinking, and to embrace questions of access, equity and social justice. In this short paper we report on two contextualized computing courses in this initiative that introduce coding and computational thinking through contextualizing two subfields of AI: Natural Language Processing and Machine Learning. The goal was two-fold: to help students gain foundational computational skills to further their own creative and critical practices; and more broadly, to help them develop better-informed critiques of the use of algorithmic systems, especially AI technology.
AI for Software Quality Assurance Blue Sky Ideas Talk
Authors: Kalech Meir , Stern Roni
Published: June 2020

Modern software systems are highly complex and often have multiple dependencies on external parts such as other processes or services. This poses new challenges and exacerbate existing challenges in different aspects of software Quality Assurance (QA) including testing, debugging and repair. The goal of this talk is to present a novel AI paradigm for software QA (AI4QA). A quality assessment AI agent uses machine-learning techniques to predict where coding errors are likely to occur. Then a test generation AI agent considers the error predictions to direct automated test generation. Then a test execution AI agent executes tests, that are passed to the root-cause analysis AI agent, which applies automatic debugging algorithms. The candidate root causes are passed to a code repair AI agent that tries to create a patch for correcting the isolated error.
Unveiling Hidden Intentions
Authors: Ocampo Diaz Gerardo , Ng Vincent
Published: June 2020

Recent years have seen significant advances in machine perception, which have enabled AI systems to become grounded in the world. While AI systems can now "read" and "see", they still cannot read between the lines and see through the lens, unlike humans. We propose the novel task of hidden message and intention identification: given some perceptual input (i.e., a text, an image), the goal is to produce a short description of the message the input transmits and the hidden intention of its author, if any. Not only will a solution to this task enable machine perception technologies to reach the next level of complexity, but it will be an important step towards addressing a task that has recently received a lot of public attention, political manipulation in social media.
Embedding High-Level Knowledge into DQNs to Learn Faster and More Safely
Authors: Gao Zihang , Lin Fangzhen , Zhou Yi , Zhang Hao , Wu Kaishun , Zhang Haodi
Published: June 2020

Deep reinforcement learning has been successfully applied in many decision making scenarios. However, the slow training process and difficulty in explaining limit its application. In this paper, we attempt to address some of these problems by proposing a framework of Rule-interposing Learning (RIL) that embeds knowledge into deep reinforcement learning. In this framework, the rules dynamically effect the training progress, and accelerate the learning. The embedded knowledge in form of rule not only improves learning efficiency, but also prevents unnecessary or disastrous explorations at early stage of training. Moreover, the modularity of the framework makes it straightforward to transfer high-level knowledge among similar tasks.
PulseSatellite: A Tool Using Human-AI Feedback Loops for Satellite Image Analysis in Humanitarian Contexts
Authors: Logar Tomaz , Bullock Joseph , Nemni Edoardo , Bromley Lars , Quinn John A. , Luengo-Oroz Miguel
Published: June 2020

Humanitarian response to natural disasters and conflicts can be assisted by satellite image analysis. In a humanitarian context, very specific satellite image analysis tasks must be done accurately and in a timely manner to provide operational support. We present PulseSatellite, a collaborative satellite image analysis tool which leverages neural network models that can be retrained on-the fly and adapted to specific humanitarian contexts and geographies. We present two case studies, in mapping shelters and floods respectively, that illustrate the capabilities of PulseSatellite.
LearnIt: On-Demand Rapid Customization for Event-Event Relation Extraction
Authors: Min Bonan , Srivastava Manaj , Qiu Haoling , Muthukumar Prasannakumar , Fasching Joshua
Published: June 2020

We present a system which allows a user to create event-event relation extractors on-demand with a small amount of effort. The system provides a suite of algorithms, flexible workflows, and a user interface (UI), to allow rapid customization of event-event relation extractors for new types and domains of interest. Experiments show that it enables users to create extractors for 6 types of causal and temporal relations, with less than 20 minutes of effort per type. Our system (source code, UI) is available at https://github.com/BBN-E/LearnIt. A demonstration video is available at https://vimeo.com/329950144.
Exploratory Navigation and Selective Reading
Authors: Modani Natwar , Maheshwari Paridhi , Deshpande Harsh , Sirpurkar Saurab , . Diviya , Aditya Somak
Published: June 2020

Navigating a collection of documents can be facilitated by obtaining a human-understandable concept hierarchy with links to the content. This is a non-trivial task for two reasons. First, defining concepts that are understandable by an average consumer and yet meaningful for a large variety of corpora is hard. Second, creating semantically meaningful yet intuitive hierarchical representation is hard, and can be task dependent. We present out system Navigation.ai which automatically processes a document collection, induces a concept hierarchy using Wikipedia and presents an interactive interface that helps user navigate to individual paragraphs using concepts.
PresentationTrainer: Oral Presentation Support System for Impression-Related Feedback
Authors: Yi Shengzhou , Yumoto Hiroshi , Wang Xueting , Yamasaki Toshihiko
Published: June 2020

In order to support the pratice of oral presentation, we developed PresentationTrainer which includes (1) a presentation impression prediction system and (2) a presentation slide analysis system. For the presentation impression prediction system, we proposed two methods, using Support Vector Machine and Markov Random Field, or using multimodal neural network, to predict audiences' impressions for speech videos. For the slide analysis system, we used Convolutional Neural Network and Global Average Pooling to evaluate the design of slides. We then used Class Activation Mapping to provide visual feedback for showing which areas should be modified.
A Commentary on the Unsupervised Learning of Disentangled Representations
Authors: Locatello Francesco , Bauer Stefan , Lucic Mario , Rätsch Gunnar , Gelly Sylvain , Schölkopf Bernhard , Bachem Olivier
Published: June 2020

The goal of the unsupervised learning of disentangled representations is to separate the independent explanatory factors of variation in the data without access to supervision. In this paper, we summarize the results of (Locatello et al. 2019b) and focus on their implications for practitioners. We discuss the theoretical result showing that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases and the practical challenges it entails. Finally, we comment on our experimental findings, highlighting the limitations of state-of-the-art approaches and directions for future research.
Ranking and Rating Rankings and Ratings
Authors: Wang Jingyan , Shah Nihar B.
Published: June 2020

Cardinal scores collected from people are well known to suffer from miscalibrations. A popular approach to address this issue is to assume simplistic models of miscalibration (such as linear biases) to de-bias the scores. This approach, however, often fares poorly because people's miscalibrations are typically far more complex and not well understood. It is widely believed that in the absence of simplifying assumptions on the miscalibration, the only useful information in practice from the cardinal scores is the induced ranking. In this paper we address the fundamental question of whether this widespread folklore belief is actually true. We consider cardinal scores with arbitrary (or even adversarially chosen) miscalibrations that is only required to be consistent with the induced ranking. We design rating-based estimators and prove that despite making no assumptions on the ratings, they strictly and uniformly outperform all possible estimators that rely on only the ranking. These estimators can be used as a plug-in to show the superiority of cardinal scores over ordinal rankings for a variety of applications, including A/B testing and ranking. This work thus provides novel fundamental insights in the eternal debate between cardinal and ordinal data: It ranks the approach of using ratings higher than that of using rankings, and rates both approaches in terms of their estimation errors.
Coalitional Strategic Behaviour in Collective Decision Making
Authors: Lisowski Grzegorz
Published: June 2020

In my PhD project I study the algorithmic aspects of strategic behaviour in collective decision making, with the special focus on voting mechanisms. I investigate two manners of manipulation: (1) strategic selection of candidates from groups of potential representatives and (2) influence on voters located in a social network.
A Reinforcement Learning Approach to Strategic Belief Revelation with Social Influence
Authors: Shepherd Patrick , Goldsmith Judy
Published: June 2020

The study of social networks has increased rapidly in the past few decades. Of recent interest are the dynamics of changing opinions over a network. Some research has investigated how interpersonal influence can affect opinion change, how to maximize/minimize the spread of opinion change over a network, and recently, if/how agents can act strategically to effect some outcome in the network's opinion distribution. This latter problem can be modeled and addressed as a reinforcement learning problem; we introduce an approach to help network agents find strategies that outperform hand-crafted policies. Our preliminary results show that our approach is promising in networks with dynamic topologies.
Modeling Dynamic Behaviors within Population
Authors: Tavabi Nazgol
Published: June 2020

The abundance of temporal data generated by mankind in recent years gives us the opportunity to better understand human behaviors along with the similarities and differences in groups of people. Better understanding of human behaviors could be very beneficial in choosing strategies, from group-level to society-level depending on the domain. This type of data could range from physiological data collected from sensors to activity patterns in social media. Identifying frequent behavioral patterns in sensor data could give more insight into the health of a community and provoke strategies towards improving it; By analyzing patterns of behaviors in social media, platform's attributes could be adjusted to the user's needs.This type of modeling introduces numerous challenges that varies depending on the data. The goal of my doctoral research is to introduce ways to better understand and capture human behavior by modeling individual's behaviors as time series and extracting interesting patterns within them.
An Iterative Approach for Identifying Complaint Based Tweets in Social Media Platforms (Student Abstract)
Authors: Anand Gyanesh , Gautam Akash , Mathur Puneet , Mahata Debanjan , Shah Rajiv Ratn , Sawhney Ramit
Published: June 2020

Twitter is a social media platform where users express opinions over a variety of issues. Posts offering grievances or complaints can be utilized by private/ public organizations to improve their service and promptly gauge a low-cost assessment. In this paper, we propose an iterative methodology which aims to identify complaint based posts pertaining to the transport domain. We perform comprehensive evaluations along with releasing a novel dataset for the research purposes1.
Complex Emotional Intelligence Learning Using Deep Neural Networks (Student Abstract)
Authors: Billal Belainine , Sadat Fatiha , Lounis Hakim
Published: June 2020

Emotion recognition and mining tasks are often limited by the availability of manually annotated data. Several researchers have used emojis and specific hashtags as forms of training and supervision.This research paper proposes a new textual and social corpus, the corpus labeled using basic emotions following Plutchik's theory. Thus, This paper propose a first study for the representation and interpretation of complex emotional interactions, using deep neural networks.
Hierarchical Average Reward Policy Gradient Algorithms (Student Abstract)
Authors: Dharmavaram Akshay , Riemer Matthew , Bhatnagar Shalabh
Published: June 2020

Option-critic learning is a general-purpose reinforcement learning (RL) framework that aims to address the issue of long term credit assignment by leveraging temporal abstractions. However, when dealing with extended timescales, discounting future rewards can lead to incorrect credit assignments. In this work, we address this issue by extending the hierarchical option-critic policy gradient theorem for the average reward criterion. Our proposed framework aims to maximize the long-term reward obtained in the steady-state of the Markov chain defined by the agent's policy. Furthermore, we use an ordinary differential equation based approach for our convergence analysis and prove that the parameters of the intra-option policies, termination functions, and value functions, converge to their corresponding optimal values, with probability one. Finally, we illustrate the competitive advantage of learning options, in the average reward setting, on a grid-world environment with sparse rewards.
Exploring Abstract Concepts for Image Privacy Prediction in Social Networks (Student Abstract)
Authors: Galfré Gabriele , Caragea Cornelia
Published: June 2020

Automatically detecting the private nature of images posted in social networks such as Facebook, Flickr, and Instagram, is a long-standing goal considering the pervasiveness of these networks. Several prior works to image privacy prediction showed that object tags from images are highly informative about images' privacy. However, we conjecture that other aspects of images captured by abstract concepts (e.g., religion, sikhism, spirituality) can improve the performance of models that use only the concrete objects from an image (e.g., temple and person). Experimental results on a Flickr dataset show that the abstract concepts and concrete object tags complement each other and yield the best performance when used in combination as features for image privacy prediction.
VECA: A Method for Detecting Overfitting in Neural Networks (Student Abstract)
Authors: Ge Liangzhu , Hou Yuexian , Jiang Yaju , Yao Shuai , Yang Chao
Published: June 2020

Despite their widespread applications, deep neural networks often tend to overfit the training data. Here, we propose a measure called VECA (Variance of Eigenvalues of Covariance matrix of Activation matrix) and demonstrate that VECA is a good predictor of networks' generalization performance during the training process. Experiments performed on fully-connected networks and convolutional neural networks trained on benchmark image datasets show a strong correlation between test loss and VECA, which suggest that we can calculate the VECA to estimate generalization performance without sacrificing training data to be used as a validation set.
Streaming Batch Gradient Tracking for Neural Network Training (Student Abstract)
Authors: Huang Siyuan , Hoskins Brian D. , Daniels Matthew W. , Stiles Mark D. , Adam Gina C.
Published: June 2020

Faster and more energy efficient hardware accelerators are critical for machine learning on very large datasets. The energy cost of performing vector-matrix multiplication and repeatedly moving neural network models in and out of memory motivates a search for alternative hardware and algorithms. We propose to use streaming batch principal component analysis (SBPCA) to compress batch data during training by using a rank-k approximation of the total batch update. This approach yields comparable training performance to minibatch gradient descent (MBGD) at the same batch size while reducing overall memory and compute requirements.
Third-Person Imitation Learning via Image Difference and Variational Discriminator Bottleneck (Student Abstract)
Authors: Jiang Chong , Zhang Zongzhang , Chen Zixuan , Zhu Jiacheng , Jiang Junpeng
Published: June 2020

Third-person imitation learning (TPIL) is a variant of generative adversarial imitation learning and can learn an expert-like policy from third-person expert demonstrations. Third-person expert demonstrations usually exist in the form of videos recorded in a third-person perspective, and there is a lack of direct correspondence with samples generated by agent. To alleviate this problem, we improve TPIL by applying image difference and variational discriminator bottleneck. Empirically, our new method has better performance than TPIL on two MuJoCo tasks, Reacher and Inverted Pendulum.
Automatic Text-Based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings (Student Abstract)
Authors: Jiang Hang , Zhang Xianzhe , Choi Jinho D.
Published: June 2020

Previous works related to automatic personality recognition focus on using traditional classification models with linguistic features. However, attentive neural networks with contextual embeddings, which have achieved huge success in text classification, are rarely explored for this task. In this project, we have two major contributions. First, we create the first dialogue-based personality dataset, FriendsPersona , by annotating 5 personality traits of speakers from Friends TV Show through crowdsourcing. Second, we present a novel approach to automatic personality recognition using pre-trained contextual embeddings (BERT and RoBERTa) and attentive neural networks. Our models largely improve the state-of-art results on the monologue Essays dataset by 2.49%, and establish a solid benchmark on our FriendsPersona. By comparing results in two datasets, we demonstrate the challenges of modeling personality in multi-party dialogue.
Exploring the Benefits of Depth Information in Object Pixel Masking (Student Abstract)
Authors: Kachinthaya Anish , Ding Yi , Hollerer Tobias
Published: June 2020

In this paper, we look at how depth data can benefit existing object masking methods applied in occluded scenes. Masking the pixel locations of objects within scenes helps computers get a spatial awareness of where objects are within images. The current state-of-the-art algorithm for masking objects in images is Mask R-CNN, which builds on the Faster R-CNN network to mask object pixels rather than just detecting their bounding boxes. This paper examines the weaknesses Mask R-CNN has in masking people when they are occluded in a frame. It then looks at how depth data gathered from an RGB-D sensor can be used. We provide a case study to show how simply applying thresholding methods on the depth information can aid in distinguishing occluded persons. The intention of our research is to examine how features from depth data can benefit object pixel masking methods in an explainable manner, especially in complex scenes with multiple objects.
Toward Operational Safety Verification of AI-Enabled CPS (Student Abstract)
Authors: Lamrani Imane , Banerjee Ayan , Gupta Sandeep K.S.
Published: June 2020

AI-enabled Cyber-physical systems (CPS) such as artificial pancreas (AP) or autonomous cars are using machine learning to make several critical decisions. The system is subject to inputs and scenarios which are not observed during training and the expected outputs are not known. Hence, popular model based verification techniques that characterize behavior of a control system before deployment using predictive models may be inaccurate and often result in incorrect safety analysis results. In addition, regulatory agencies are required to regulate safety-critical AI enabled CPS to ensure their operational safety. However, high complexity of the system result in myriad of safety concerns all of which may not only be comprehensively tested before deployment but also may not even be detected during design and testing phase. In this work, we propose a tool to help regulatory agencies compare the operation of the CPS with the specifications given by the manufacturer to ensure that the operation results conform with the safety assured design of a CPS.
BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)
Authors: Lee Donghyeon , Kim Man-Je , Ahn Chang Wook
Published: June 2020

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.
Adabot: Fault-Tolerant Java Decompiler (Student Abstract)
Authors: Li Zhiming , Wu Qing , Qian Kun
Published: June 2020

Reverse Engineering has been an extremely important field in software engineering, it helps us to better understand and analyze the internal architecture and interrealtions of executables. Classical Java reverse engineering task includes disassembly and decompilation. Traditional Abstract Syntax Tree (AST) based disassemblers and decompilers are strictly rule defined and thus highly fault intolerant when bytecode obfuscation were introduced for safety concern. In this work, we view decompilation as a statistical machine translation task and propose a decompilation framework which is fully based on self-attention mechanism. Through better adaption to the linguistic uniqueness of bytecode, our model fully outperforms rule-based models and previous works based on recurrence mechanism.
Bayesian Adversarial Attack on Graph Neural Networks (Student Abstract)
Authors: Liu Xiao , Zhao Jing , Sun Shiliang
Published: June 2020

Adversarial attack on graph neural network (GNN) is distinctive as it often jointly trains the available nodes to generate a graph as an adversarial example. Existing attacking approaches usually consider the case that all the training set is available which may be impractical. In this paper, we propose a novel Bayesian adversarial attack approach based on projected gradient descent optimization, called Bayesian PGD attack, which gets more general attack examples than deterministic attack approaches. The generated adversarial examples by our approach using the same partial dataset as deterministic attack approaches would make the GNN have higher misclassification rate on graph node classification. Specifically, in our approach, the edge perturbation Z is used for generating adversarial examples, which is viewed as a random variable with scale constraint, and the optimization target of the edge perturbation is to maximize the KL divergence between its true posterior distribution p(Z|D) and its approximate variational distribution qθ(Z). We experimentally find that the attack performance will decrease with the reduction of available nodes, and the effect of attack using different nodes varies greatly especially when the number of nodes is small. Through experimental comparison with the state-of-the-art attack approaches on GNNs, our approach is demonstrated to have better and robust attack performance.
Meta-Learning on Graph with Curvature-Based Analysis (Student Abstract)
Authors: Moon Tae Hong , Lim Sungsu
Published: June 2020

Learning latent representations in graphs is finding a mapping that embeds nodes or edges as data points in a low-dimensional vector space. This paper introduces a flexible framework to enhance existing methodologies that have difficulty capturing local proximity and global relationships at the same time. Our approach generates a virtual edge between non-adjacent nodes based on the Forman-Ricci curvature in network. By analyzing the network using topological information, global relationships structurally similar can easily be detected and successfully integrated with previous works.
Random Projections and α-Shape to Support the Kernel Design (Student Abstract)
Authors: Moreira Cestari Daniel , Mello Rodrigo Fernandes de
Published: June 2020

We demonstrate that projecting data points into hyperplanes is good strategy for general-purpose kernel design. We used three different hyperplanes generation schemes, random, convex hull and α-shape, and evaluated the results on two synthetic and three well known image-based datasets. The results showed considerable improvement in the classification performance in almost all scenarios, corroborating the claim that such an approach can be used as a general-purpose kernel transformation. Also, we discuss some connection with Convolutional Neural Networks and how such an approach could be used to understand such networks better.
KnowBias: Detecting Political Polarity in Long Text Content (Student Abstract)
Authors: Saligrama Aditya
Published: June 2020

We introduce a classification scheme for detecting political bias in long text content such as newspaper opinion articles. Obtaining long text data and annotations at sufficient scale for training is difficult, but it is relatively easy to extract political polarity from tweets through their authorship. We train on tweets and perform inference on articles. Universal sentence encoders and other existing methods that aim to address this domain-adaptation scenario deliver inaccurate and inconsistent predictions on articles, which we show is due to a difference in opinion concentration between tweets and articles. We propose a two-step classification scheme that uses a neutral detector trained on tweets to remove neutral sentences from articles in order to align opinion concentration and therefore improve accuracy on that domain. Our implementation is available for public use at https://knowbias.ml.
LGML: Logic Guided Machine Learning (Student Abstract)
Authors: Scott Joseph , Panju Maysum , Ganesh Vijay
Published: June 2020

We introduce Logic Guided Machine Learning (LGML), a novel approach that symbiotically combines machine learning (ML) and logic solvers to learn mathematical functions from data. LGML consists of two phases, namely a learning-phase and a logic-phase with a corrective feedback loop, such that, the learning-phase learns symbolic expressions from input data, and the logic-phase cross verifies the consistency of the learned expression with known auxiliary truths. If inconsistent, the logic-phase feeds back "counterexamples" to the learning-phase. This process is repeated until the learned expression is consistent with auxiliary truth. Using LGML, we were able to learn expressions that correspond to the Pythagorean theorem and the sine function, with several orders of magnitude improvements in data efficiency compared to an approach based on an out-of-the-box multi-layered perceptron (MLP).
Fairness Does Not Imply Satisfaction (Student Abstract)
Authors: Searns Andrew , Hosseini Hadi
Published: June 2020

Fair division is a subfield of multiagent systems that is concerned with object distribution. When objects are indivisible, the Maximin Share Guarantee (MMS) is a desirable fairness notion; however, it is not guaranteed to exist. While MMS allocations may not always exist, a relaxation of MMS is guaranteed to exist. We show that there exists a family of instances for which this relaxation fails to guarantee the MMS value for all but a small constant number of agents.
Structure-Based Drug-Drug Interaction Detection via Expressive Graph Convolutional Networks and Deep Sets (Student Abstract)
Authors: Sun Mengying , Wang Fei , Elemento Olivier , Zhou Jiayu
Published: June 2020

In this work, we proposed a DDI detection method based on molecular structures using graph convolutional networks and deep sets. We proposed a more discriminative convolutional layer compared to conventional GCN and achieved permutation invariant prediction without losing the capability of capturing complicated interactions.
Keyphrase Generation for Scientific Articles Using GANs (Student Abstract)
Authors: Swaminathan Avinash , Gupta Raj Kuwar , Zhang Haimin , Mahata Debanjan , Gosangi Rakesh , Shah Rajiv Ratn
Published: June 2020

In this paper, we present a keyphrase generation approach using conditional Generative Adversarial Networks (GAN). In our GAN model, the generator outputs a sequence of keyphrases based on the title and abstract of a scientific article. The discriminator learns to distinguish between machine-generated and human-curated keyphrases. We evaluate this approach on standard benchmark datasets. Our model achieves state-of-the-art performance in generation of abstractive keyphrases and is also comparable to the best performing extractive techniques. We also demonstrate that our method generates more diverse keyphrases and make our implementation publicly available1.
Emergence of Writing Systems through Multi-Agent Cooperation (Student Abstract)
Authors: Verma Shresth , Dhar Joydip
Published: June 2020

Learning to communicate is considered an essential task to develop a general AI. While recent literature in language evolution has studied emergent language through discrete or continuous message symbols, there has been little work in the emergence of writing systems in artificial agents. In this paper, we present a referential game setup with two agents, where the mode of communication is a written language system that emerges during the play. We show that the agents can learn to coordinate successfully using this mode of communication. Further, we study how the game rules affect the writing system taxonomy by proposing a consistency metric.
Neural Dynamics and Gamma Oscillation on a Hybrid Excitatory-Inhibitory Complex Network (Student Abstract)
Authors: Wang Yuan , Shi Xia , Cheng Bo , Chen Junliang
Published: June 2020

This paper investigates the neural dynamics and gamma oscillation on a complex network with excitatory and inhibitory neurons (E-I network), as such network is ubiquitous in the brain. The system consists of a small-world network of neurons, which are emulated by Izhikevich model. Moreover, mixed Regular Spiking (RS) and Chattering (CH) neurons are considered to imitate excitatory neurons, and Fast Spiking (FS) neurons are used to mimic inhibitory neurons. Besides, the relationship between synchronization and gamma rhythm is explored by adjusting the critical parameters of our model. Experiments visually demonstrate that the gamma oscillations are generated by synchronous behaviors of our neural network. We also discover that the Chattering(CH) excitatory neurons can make the system easier to synchronize.
A Multi-Task Learning Machine Reading Comprehension Model for Noisy Document (Student Abstract)
Authors: Wu Zhijing , Xu Hua
Published: June 2020

Current neural models for Machine Reading Comprehension (MRC) have achieved successful performance in recent years. However, the model is too fragile and lack robustness to tackle the imperceptible adversarial perturbations to the input. In this work, we propose a multi-task learning MRC model with a hierarchical knowledge enrichment to further improve the robustness for noisy document. Our model follows a typical encode-align-decode framework. Additionally, we apply a hierarchical method of adding background knowledge into the model from coarse-to-fine to enhance the language representations. Besides, we optimize our model by jointly training the answer span and unanswerability prediction, aiming to improve the robustness to noise. Experiment results on benchmark datasets confirm the superiority of our method, and our method can achieve competitive performance compared with other strong baselines.
Multi-Agent/Robot Deep Reinforcement Learning with Macro-Actions (Student Abstract)
Authors: Xiao Yuchen , Hoffman Joshua , Xia Tian , Amato Christopher
Published: June 2020

We consider the challenges of learning multi-agent/robot macro-action-based deep Q-nets including how to properly update each macro-action value and accurately maintain macro-action-observation trajectories. We address these challenges by first proposing two fundamental frameworks for learning macro-action-value function and joint macro-action-value function. Furthermore, we present two new approaches of learning decentralized macro-action-based policies, which involve a new double Q-update rule that facilitates the learning of decentralized Q-nets by using a centralized Q-net for action selection. Our approaches are evaluated both in simulation and on real robots.
Domain Knowledge-Assisted Automatic Diagnosis of Idiopathic Pulmonary Fibrosis (IPF) Using High Resolution Computed Tomography (HRCT) (Student Abstract)
Authors: Yu Wenxi , Zhou Hua , Goldin Jonathan G. , Kim Grace Hyun J.
Published: June 2020

Domain knowledge acquired from pilot studies is important for medical diagnosis. This paper leverages the population-level domain knowledge based on the D-optimal design criterion to judiciously select CT slices that are meaningful for the disease diagnosis task. As an illustrative example, the diagnosis of idiopathic pulmonary fibrosis (IPF) among interstitial lung disease (ILD) patients is used for this work. IPF diagnosis is complicated and is subject to inter-observer variability. We aim to construct a time/memory-efficient IPF diagnosis model using high resolution computed tomography (HRCT) with domain knowledge-assisted data dimension reduction methods. Four two-dimensional convolutional neural network (2D-CNN) architectures (MobileNet, VGG16, ResNet, and DenseNet) are implemented for an automatic diagnosis of IPF among ILD patients. Axial lung CT images are acquired from five multi-center clinical trials, which sum up to 330 IPF patients and 650 non-IPF ILD patients. Model performance is evaluated using five-fold cross-validation. Depending on the model setup, MobileNet achieved satisfactory results with overall sensitivity, specificity, and accuracy greater than 90%. Further evaluation of independent datasets is underway. Based on our knowledge, this is the first work that (1) uses population-level domain knowledge with optimal design criterion in selecting CT slices and (2) focuses on patient-level IPF diagnosis.
Spike‐associated networks and intracranial electrographic findings
Authors: Bear Joshua J. , Kirsch Heidi E. , Berman Brian D. , Chapman Kevin E. , Tregellas Jason R.
Published: June 2020

Aims. Functional connectivity is providing new insights into the network nature of epilepsy with growing clinical applications. Our objective was to validate a novel magnetoencephalography‐based method to non‐invasively measure the epileptic network.

Methods. We retrospectively identified pediatric and adult patients with refractory focal epilepsy who underwent pre‐surgical magnetoencephalography with subsequent intracranial electrographic monitoring. Magnetoencephalography tracings were visually reviewed, and interictal epileptiform discharges (“spikes”) were individually marked. We then evaluated differences in whole‐brain connectivity during brief epochs preceding the spikes and during the spikes using the Network‐Based Statistic to test differences at the network level.

Results. In six patients with statistically‐significant network differences, we observed substantial overlap between the spike‐associated networks and electrographically active areas identified during intracranial monitoring (the spike‐associated network was 78% and 83% sensitive for intracranial electroencephalography‐defined regions in the irritative and seizure onset zones, respectively).

Conclusion. These findings support the neurobiological validity of the spike‐associated network method. Assessment of spike‐associated networks has the potential to improve surgical planning in epilepsy surgery patients by identifying components of the epileptic network prior to implantation.
Persons with onchocerciasis‐associated epilepsy and nodding seizures have a more severe form of epilepsy with more cognitive impairment and higher levels of Onchocerca volvulus infection
Authors: Abd‐Elfarag Gasim , Carter Jane Y. , Raimon Stephen , Sebit Wilson , Suliman Abozer , Fodjo Joseph Nelson Siewe , Olore Peter Claver , Biel Kai Puok , Ojok Morrish , Logora Makoy Yibi , Colebunders Robert
Published: June 2020

Aims. Following previous reports of very high epilepsy prevalence in the onchocerciasis‐endemic villages in Maridi County, South Sudan, a study was conducted to investigate the association between the level of Onchocerca volvulus infection, epilepsy, and related outcomes.

Methods. In December 2018, persons with epilepsy (PWE) were recruited from villages where an epilepsy prevalence of 4.4% (range: 3.5–11.9%) was documented. We enrolled 318 participants from whom two skin snips were taken for microscopic detection of O. volvulus microfilariae (mf). Seizure history was obtained for all PWE and their degree of disability assessed using the modified Rankin scale.

Results. Almost all (84.9%) PWE had detectable mf in their skin snips. Onchocerciasis‐infected PWE experienced nodding seizures more often than uninfected PWE (p=0.034). Moreover, persons with nodding seizures had more frequent seizures (p<0.001) and higher disability scores (p<0.001), and were more often cognitively impaired and younger at the time of their first epileptic seizure (nine years vs 12 years, p<0.001) compared to PWE without nodding seizures. Based on multivariate models, nodding seizures were associated with higher mf densities (aOR: 1.022; 95% CI: 1.005–1.041). Epilepsy onset at a younger age was associated with a worse outcome. Higher frequency of seizures, longer duration of epilepsy and younger age were associated with increased disability. Regular antiepileptic drug use was associated with better cognitive and disability outcomes.

Conclusion. PWE with nodding seizures have a more severe form of onchocerciasis‐associated epilepsy, with earlier seizure onset and higher levels of O. volvulus infection. Younger PWE were prone to worse epilepsy outcomes, which would be prevented with regular antiepileptic treatment.
A multimodal diagnostic approach for lateralised rhythmic delta activity in the ictal‐interictal continuum∗
Authors: Spalletti Maddalena , Pescini Francesca , Gadda Davide , Piccardi Benedetta , Scarpino Maenia , Carrai Riccardo , Boccardi Cristina , Grippo Antonello , Amantini Aldo
Published: June 2020

The ictal‐interictal continuum represents a diagnostic challenge even for expert neurrophysiologists, often requiring an additional multimodal diagnostic workup to understand its clinical significance. Lateralised rhythmic delta activity (LRDA) is an ictal‐interictal continuum pattern that has only recently been investigated and recognised as potentially ictogenic or sometimes even ictal.

We describe a patient who presented with acute‐onset aphasia, initially suspected of having a stroke; advanced brain imaging with CT‐perfusion showed features suggesting regional left temporo‐parietal hyperperfusion and an EEG revealed LRDA with fluctuations and intermixed sharp waves in the same areas. Treatment with lacosamide caused both clinical and EEG improvement after a few hours, supporting the hypothesis that the EEG pattern represented an ictal/interictal phenomenon.

In the literature, a correlation between metabolic/perfusion imaging and ictal‐interictal continuum patterns is described regarding lateralised periodic discharges but less studied for LRDA.

In this case, we adopted a multimodal approach, integrating advanced imaging, EEG, clinical features, and response to therapy, to consider the overall clinical presentation as focal NCSE.
Eslicarbazepine for focal epilepsy and acute intermittent porphyria∗
Authors: Herrera‐Fortin Tamara , Pana Raluca , Wesolowska Ewa , Bourré‐Tessier Josiane , Nguyen Dang K.
Published: June 2020

Porphyrias are rare genetic disorders which cause a deficiency in the enzymes involved in the biosynthesis of heme. The treatment of epilepsy in patients with acute intermittent porphyria can be difficult since many anticonvulsants can increase heme synthesis and trigger porphyric attacks. We report a patient with focal epilepsy successfully treated with eslicarbazepine without exacerbation of porphyria.