bluuebunny/crossref_metadata_2025 huggingface dataset:
Sentiment and Emotion-Aware Multi-Modal Complaint Identification
Authors: SINGH APOORVA , Dey Soumyodeep , Singha Anamitra , Saha Sriparna
Published: July 2022

The expression of displeasure on a consumer's behalf towards an organization, product, or event is denoted via the speech act known as complaint. Customers typically post reviews on retail websites and various social media platforms about the products or services they purchase, and the reviews may include complaints about the products or services. Automatic detection of consumers' complaints about items or services they buy can be critical for organizations and online merchants since they can use this insight to meet the customers' requirements, including handling and addressing the complaints. Previous studies on Complaint Identification (CI) are limited to text. Images posted with the reviews can provide cues to identify complaints better, thus emphasizing the importance of incorporating multi-modal inputs into the process. Furthermore, the customer's emotional state significantly impacts the complaint expression since emotions generally influence any speech act. As a result, the impact of emotion and sentiment on automatic complaint identification must also be investigated. One of the major contributions of this work is the creation of a new dataset- Complaint, Emotion, and Sentiment Annotated Multi-modal Amazon Reviews Dataset (CESAMARD), a collection of opinionated texts (reviews) and images of the products posted on the website of the retail giant Amazon. We present an attention-based multi-modal, adversarial multi-task deep neural network model for complaint detection to demonstrate the utility of the multi-modal dataset. Experimental results indicate that the multi-modality and multi-tasking complaint identification outperforms uni-modal and single-task variants.
Toward a New Science of Common Sense
Authors: Brachman Ronald J. , Levesque Hector J.
Published: July 2022

Common sense has always been of interest in AI, but has rarely taken center stage. Despite its mention in one of John McCarthy's earliest papers and years of work by dedicated researchers, arguably no AI system with a serious amount of general common sense has ever emerged. Why is that? What's missing? Examples of AI systems' failures of common sense abound, and they point to AI's frequent focus on expertise as the cause. Those attempting to break the brittleness barrier, even in the context of modern deep learning, have tended to invest their energy in large numbers of small bits of commonsense knowledge. But all the commonsense knowledge fragments in the world don't add up to a system that actually demonstrates common sense in a human-like way. We advocate examining common sense from a broader perspective than in the past. Common sense is more complex than it has been taken to be and is worthy of its own scientific exploration.
Subjective Attributes in Conversational Recommendation Systems: Challenges and Opportunities
Authors: Radlinski Filip , Boutilier Craig , Ramachandran Deepak , Vendrov Ivan
Published: July 2022

The ubiquity of recommender systems has increased the need for higher-bandwidth, natural and efficient communication with users. This need is increasingly filled by recommenders that support natural language interaction, often conversationally. Given the inherent semantic subjectivity present in natural language, we argue that modeling subjective attributes in recommenders is a critical, yet understudied, avenue of AI research. We propose a novel framework for understanding different forms of subjectivity, examine various recommender tasks that will benefit from a systematic treatment of subjective attributes, and outline a number of research challenges.
Consent as a Foundation for Responsible Autonomy
Authors: Singh Munindar P.
Published: July 2022

This paper focuses on a dynamic aspect of responsible autonomy, namely, to make intelligent agents be responsible at run time. That is, it considers settings where decision making by agents impinges upon the outcomes perceived by other agents. For an agent to act responsibly, it must accommodate the desires and other attitudes of its users and, through other agents, of their users.

The contribution of this paper is twofold. First, it provides a conceptual analysis of consent, its benefits and misuses, and how understanding consent can help achieve responsible autonomy. Second, it outlines challenges for AI (in particular, for agents and multiagent systems) that merit investigation to form as a basis for modeling consent in multiagent systems and applying consent to achieve responsible autonomy.
Latent Space Simulation for Carbon Capture Design Optimization
Authors: Bartoldson Brian , Wang Rui , Fu Yucheng , Widemann David , Nguyen Sam , Bao Jie , Xu Zhijie , Ng Brenda
Published: July 2022

The CO2 capture efficiency in solvent-based carbon capture systems (CCSs) critically depends on the gas-solvent interfacial area (IA), making maximization of IA a foundational challenge in CCS design. While the IA associated with a particular CCS design can be estimated via a computational fluid dynamics (CFD) simulation, using CFD to derive the IAs associated with numerous CCS designs is prohibitively costly. Fortunately, previous works such as Deep Fluids (DF) (Kim et al., 2019) show that large simulation speedups are achievable by replacing CFD simulators with neural network (NN) surrogates that faithfully mimic the CFD simulation process. This raises the possibility of a fast, accurate replacement for a CFD simulator and therefore efficient approximation of the IAs required by CCS design optimization. Thus, here, we build on the DF approach to develop surrogates that can successfully be applied to our complex carbon-capture CFD simulations. Our optimized DF-style surrogates produce large speedups (4000x) while obtaining IA relative errors as low as 4% on unseen CCS configurations that lie within the range of training configurations. This hints at the promise of NN surrogates for our CCS design optimization problem. Nonetheless, DF has inherent limitations with respect to CCS design (e.g., limited transferability of trained models to new CCS packings). We conclude with ideas to address these challenges.
Using Public Data to Predict Demand for Mobile Health Clinics
Authors: Chen Haipeng , Ghosh Susobhan , Fan Gregory , Behari Nikhil , Biswas Arpita , Williams Mollie , Oriol Nancy E. , Tambe Milind
Published: July 2022

Improving health equity is an urgent task for our society. The advent of mobile clinics plays an important role in enhancing health equity, as they can provide easier access to preventive healthcare for patients from disadvantaged populations. For effective functioning of mobile clinics, accurate prediction of demand (expected number of individuals visiting mobile clinic) is the key to their daily operations and staff/resource allocation. Despite its importance, there have been very limited studies on predicting demand of mobile clinics. To the best of our knowledge, we are among the first to explore this area, using AI-based techniques. A crucial challenge in this task is that there are no known existing data sources from which we can extract useful information to account for the exogenous factors that may affect the demand, while considering protection of client privacy. We propose a novel methodology that completely uses public data sources to extract the features, with several new components that are designed to improve the prediction. Empirical evaluation on a real-world dataset from the mobile clinic The Family Van shows that, by leveraging publicly available data (which introduces no extra monetary cost to the mobile clinics), our AI-based method achieves 26.4% - 51.8% lower Root Mean Squared Error (RMSE) than the historical average-based estimation (which is presently employed by mobile clinics like The Family Van). Our algorithm makes it possible for mobile clinics to plan proactively, rather than reactively, as what has been doing.
TCN: Pioneering Topological-Based Convolutional Networks for Planetary Terrain Learning
Authors: Chen Yuzhou , Marchetti Yuliya , Sizikova Elena , Gel Yulia R.
Published: July 2022

Implementations of artificial intelligence (AI) based on deep learning (DL) have proven to be highly successful in many domains, from biomedical imaging to natural language processing, but are still rarely applied in the space industry, particularly for onboard learning of planetary surfaces. In this project, we discuss the utility and limitations of DL, enhanced with topological footprints of the sensed objects, for multi-class classification of planetary surface patterns, in conjunction with tactile and embedded sensing in rover exploratory missions. We consider a Topological Convolutional Network (TCN) model with a persistence-based attention mechanism for supervised classification of various landforms. We study TCN's performance on the Barefoot surface pattern dataset, a novel surface pressure dataset from a prototype tactile rover wheel, known as the Barefoot Rover tactile wheel. Multi-class pattern recognition in the Barefoot data has neither been ever tackled before with DL nor assessed with topological methods. We provide insights into advantages and restrictions of topological DL as the early-stage concept for onboard learning and planetary exploration.
Domain Reconstruction for UWB Car Key Localization Using Generative Adversarial Networks
Authors: Kuvshinov Aleksei , Knobloch Daniel , Külzer Daniel , Vardanyan Elen , Günnemann Stephan
Published: July 2022

We consider the car key localization task using ultra-wideband (UWB) signal measurements. Given labeled data for a certain car, we train a deep classifier to make the prediction about the new points. However, due to the differences in car models and possible environmental effects that might alter the signal propagation, data collection requires considerable effort for each car. In particular, we consider a situation where the data for the new car is collected only in one environment, so we have to utilize the measurements in other environments from a different car. We propose a framework based on generative adversarial networks (GANs) to generate missing parts of the data and train the classifier on it, mitigating the necessity to collect the real data. We show that the model trained on the synthetic data performs better than the baseline trained on the collected measurements only. Furthermore, our model closes the gap to the level of performance achieved when we would have the information about the new car in multiple environments by 35%.
Combating Sampling Bias: A Self-Training Method in Credit Risk Models
Authors: Liao Jingxian , Wang Wei , Xue Jason , Lei Anthony , Han Xue , Lu Kun
Published: July 2022

A significant challenge in credit risk models for underwriting is the presence of bias in model training data. When most credit risk models are built using only applicants who had been funded for credit, such non-random sampling predominantly influenced by credit policymakers and previous loan performances may introduce sampling bias to the models, and thus alter their prediction of default on loan repayment when screening applications from prospective borrowers. In this paper, we propose a novel data augmentation method that aims to identify and pseudo-label parts of the historically declined loan applications to mitigate sampling bias in the training data. We also introduce a new measure to assess the performance from the business perspective, loan application approval rates at various loan default rate levels. Our proposed methods were compared to the original supervised learning model and the traditional sampling issue remedy techniques in the industry. The experiment and early production results from deployed model show that self-training method with calibrated probability as data augmentation selection criteria improved the ability of credit scoring to differentiate default loan applications and, more importantly, can increase loan approval rate up to 8.8\%, while keeping similar default rate comparing to baselines. The results demonstrate practical implications on how future underwriting model development processes should follow.
Inferring Multiple Tissue Properties from Magnetic Resonance Fingerprinting Images
Authors: Nallapareddy Naren , Ray Soumya
Published: July 2022

Magnetic Resonance Imaging (MRI) is a non-invasive imaging modality that is a cornerstone of diagnostic radiology. Clinical MRI scans capture a single image to highlight a single tissue property. The intensity difference between different regions of this image shows disease states that a radiologist can interpret. Magnetic Resonance Fingerprinting (MRF) is a recently proposed novel MRI technique. MRF allows the capture of multiple MR images in a single scan. This enables clinicians to analyze multiple tissue properties, potentially increasing the sensitivity of diagnosis and also allowing for the diagnosis of novel diseases. However, it is more challenging to analyze MRF images, because MRF produces much larger and noisier data than MRI. In this paper, we show how AI techniques can help solve this problem. Using a hybrid search strategy combining simulated annealing with pattern search, we show it is possible to tractably reconstruct multiple tissue properties from a single MRF image. This is a key step towards the deployment of MRF for radiological diagnosis.
A Simulation-Based Evaluation Framework for Interactive AI Systems and Its Application
Authors: Hanafi Maeda F. , Katsis Yannis , Santillán Cooper Martín , Li Yunyao
Published: July 2022

Interactive AI (IAI) systems are increasingly popular as the human-centered AI design paradigm is gaining strong traction. However, evaluating IAI systems, a key step in building such systems, is particularly challenging, as their output highly depends on the performed user actions. Developers often have to rely on limited and mostly qualitative data from ad-hoc user testing to assess and improve their systems. In this paper, we present InteractEva; a systematic evaluation framework for IAI systems. We also describe how we have applied InteractEva to evaluate a commercial IAI system, leading to both quality improvements and better data-driven design decisions.
Towards an AI-Infused Interdisciplinary Curriculum for Middle-Grade Classrooms
Authors: Akram Bita , Yoder Spencer , Tatar Cansu , Boorugu Sankalp , Aderemi Ifeoluwa , Jiang Shiyan
Published: July 2022

As AI becomes more widely used across a variety of disciplines, it is increasingly important to teach AI concepts to K-12 students in order to prepare them for an AI-driven future workforce. Hence, educators and researchers have been working to develop curricula that make these concepts accessible to K-12 students. We are designing and developing a comprehensive AI curriculum delivered through a series of carefully crafted activities in an adapted \emph{Snap!} environment for middle-grade students. In this work, we lay out the proposed content of our curriculum and present the design, development, and implementation results of the first unit of our curriculum that focuses on teaching the breadth-first search algorithm. The activities in this unit have been revised after being piloted with a single high-school student. These activities were further refined after a group of K-12 teachers examined and critiqued them during a two-week professional development workshop. Our teachers created a lesson plan around the activities and implemented that lesson in a summer workshop with 14 middle school students. Our results demonstrated that our activities were successful in helping many of the students in understanding and implementing the algorithm through block-based programming while extra supplementary material was needed to assist some other students. In this paper, we explain our curriculum and technology, the results of implementing the first unit of our curriculum in a summer camp, and lessons learned for future developments.
Fast Heuristic Detection of Offensive Words in Wordwheel Puzzles
Authors: Blum Anand D. , Parry R. Mitchell
Published: July 2022

Offensive words appear in Wordwheel-type puzzles with a high frequency. Previous approaches to eliminating these words have focused largely on eliminating puzzles that might give rise to an offensive word. This work presents a fast, heuristic approach to detecting an offensive word within a puzzle. After a preprocessing stage, the detection occurs with a single bitwise operation on a 64-bit word. Tests show that as long as there are at least 3 taboo words possible in a puzzle, the heuristic approach is faster than a depth-first search of the puzzle. In addition to being fast, the approach is guaranteed to detect all offensive words, and has a low false positive rate.
Predictive Student Modelling in an Online Reading Platform
Authors: Farhana Effat , Rutherford Teomara , Lynch Collin F.
Published: July 2022

Use of technology-enhanced education and online learning systems has become more popular, especially after COVID-19. These systems capture a rich array of data as students interact with them. Predicting student performance is an essential part of technology-enhanced education systems to enable the generation of hints and provide recommendations to students. Typically, this is done through use of data on student interactions with questions without utilizing important data on the temporal ordering of students’ other interaction behavior, (e.g., reading, video watching). In this paper, we hypothesize that to predict students’ question performance, it is necessary to (i) consider other learning activities beyond question-answering and (ii) understand how these activities are related to question-solving behavior. We collected middle school physical science students’ data within a K12 reading platform, Actively Learn. This platform provides reading-support to students and collects trace data on their use of the system. We propose a transformer-based model to predict students' question scores utilizing question interaction and reading-related behaviors. Our findings show that integrating question attempts and reading-related behaviors results in better predictive power compared to using only question attempt features. The interpretable visualization of the transformer’s attention can be helpful for teachers to make tailored interventions in students’ learning.
Paving the Way for Novices: How to Teach AI for K-12 Education in China
Authors: Song Jiachen , Zhang Linan , Yu Jinglei , Peng Yan , Ma Anyao , Lu Yu
Published: July 2022

In response to the trend that artificial intelligence (AI) is becoming the main driver for social and economic development, enhancing the readiness of learners in AI is significant and important. The state council and the ministry of education of China put AI education for K-12 schools on a high priority in order to foster local AI talents and reduce educational disparities. However, the AI knowledge and technical skills are still limited for not only students but also the school teachers. Furthermore, many local schools in China, especially in the rural areas, are lack of the necessary software and hardware for teaching AI. Hence, we designed and implemented a structured series of AI courses, built on an online block-based visual programming platform. The AI courses are free and easily accessible for all. We have conducted the experimental classes in a local school and collected the results. The results show that the learners in general gained significant learning progress on AI knowledge comprehension, aroused strong interests in AI, and increased the degree of satisfaction towards the course. Especially, our practices significantly increased computational thinking of the students who were initially staying at a lower level.
Interpretable Privacy Preservation of Text Representations Using Vector Steganography
Authors: Bihani Geetanjali
Published: July 2022

Contextual word representations generated by language models learn spurious associations present in the training corpora. Adversaries can exploit these associations to reverse-engineer the private attributes of entities mentioned in the training corpora. These findings have led to efforts towards minimizing the privacy risks of language models. However, existing approaches lack interpretability, compromise on data utility and fail to provide privacy guarantees. Thus, the goal of my doctoral research is to develop interpretable approaches towards privacy preservation of text representations that maximize data utility retention and guarantee privacy. To this end, I aim to study and develop methods to incorporate steganographic modifications within the vector geometry to obfuscate underlying spurious associations and retain the distributional semantic properties learnt during training.
Equilibrium Learning in Auction Markets
Authors: Heidekrüger Stefan
Published: July 2022

My dissertation investigates the computation of Bayes-Nash equilibria in auctions via multiagent learning. A particular focus lies on the game-theoretic analysis of learned gradient dynamics in such markets. This requires overcoming several technical challenges like non-differentiable utility functions and infinite-dimensional strategy spaces. Positive results may open the door for wide-ranging applications in Market Design and the economic sciences.
Creating Interactive Crowds with Reinforcement Learning
Authors: Kwiatkowski Ariel
Published: July 2022

The entertainment industry, as well as the field of Computer Graphics, frequently faces the issue of creating large virtual crowds that would populate a scene. One of the ways to achieve that, particularly with modern rendering techniques, is by using simulation -- this, however, is nontrivial to design and control. The main goal of my PhD work is working towards the creation of a tool enabling the creation of virtual crowds that one can interact with, and we believe the best way to that is through Multiagent Reinforcement Learning techniques. These animated crowds can then be used both in movies and video games. Especially for the latter, it is highly desirable that both the crowd as a whole, as well as the individual characters, can react to the user's input in real time.
PESTO: Switching Point Based Dynamic and Relative Positional Encoding for Code-Mixed Languages (Student Abstract)
Authors: Ali Mohsin , Kandukuri Sai Teja , Manduru Sumanth , Patwa Parth , Das Amitava
Published: July 2022

NLP applications for code-mixed (CM) or mix-lingual text have gained a significant momentum recently, the main reason being the prevalence of language mixing in social media communications in multi-lingual societies like India, Mexico, Europe, parts of USA etc. Word embeddings are basic building blocks of any NLP system today, yet, word embedding for CM languages is an unexplored territory. The major bottleneck for CM word embeddings is switching points, where the language switches. These locations lack in contextually and statistical systems fail to model this phenomena due to high variance in the seen examples. In this paper we present our initial observations on applying switching point based positional encoding techniques for CM language, specifically Hinglish (Hindi - English). Results are only marginally better than SOTA, but it is evident that positional encoding could be an effective way to train position sensitive language models for CM text.
Deep Learning Based Side Channel Attacks on Lightweight Cryptography (Student Abstract)
Authors: Benjamin Alexander , Herzoff Jack , Babinkostova Liljana , Serra Edoardo
Published: July 2022

Computing devices continue to be increasingly spread out within our everyday environments. Computers are embedded into everyday devices in order to serve the functionality of electronic components or to enable new services in their own right. Existing Substitution-Permutation Network (SPN) ciphers, such as the Advanced Encryption Standard (AES), are not suitable for devices where memory, power consumption or processing power is limited. Lightweight SPN ciphers, such as GIFT-128 provide a solution for running cryptography on low resource devices. The GIFT-128 cryptographic scheme is a building block for GIFT-COFB (Authenticated Encryption with Associated Data), one of the finalists in the ongoing NIST lightweight cryptography standardization process (NISTIR 8369). Determination of an adequate level of security and providing subsequent mechanisms to achieve it, is one of the most pressing problems regarding embedded computing devices. In this paper we present experimental results and comparative study of Deep Learning (DL) based Side Channel Attacks on lightweight GIFT-128. To our knowledge, this is the first study of the security of GIFT-128 against DL-based SCA attacks.
Learning Contrastive Multi-View Graphs for Recommendation (Student Abstract)
Authors: Cheng Zhangtao , Zhong Ting , Zhang Kunpeng , Walker Joojo , Zhou Fan
Published: July 2022

This paper exploits self-supervised learning (SSL) to learn more accurate and robust representations from the user-item interaction graph. Particularly, we propose a novel SSL model that effectively leverages contrastive multi-view learning and pseudo-siamese network to construct a pre-training and post-training framework. Moreover, we present three graph augmentation techniques during the pre-training stage and explore the effects of combining different augmentations, which allow us to learn general and robust representations for the GNN-based recommendation. Simple experimental evaluations on real-world datasets show that the proposed solution significantly improves the recommendation accuracy, especially for sparse data, and is also noise resistant.
Contrastive Personalization Approach to Suspect Identification (Student Abstract)
Authors: Gupta Devansh , Bhasin Drishti , Bhagat Sarthak , Uppal Shagun , Kumaraguru Ponnurangam , Shah Rajiv Ratn
Published: July 2022

Targeted image retrieval has long been a challenging problem since each person has a different perception of different features leading to inconsistency among users in describing the details of a particular image. Due to this, each user needs a system personalized according to the way they have structured the image in their mind. One important application of this task is suspect identification in forensic investigations where a witness needs to identify the suspect from an existing criminal database. Existing methods require the attributes for each image or suffer from poor latency during training and inference. We propose a new approach to tackle this problem through explicit relevance feedback by introducing a novel loss function and a corresponding scoring function. For this, we leverage contrastive learning on the user feedback to generate the next set of suggested images while improving the level of personalization with each user feedback iteration.
Gerrymandering under Uncertain Preferences (Student Abstract)
Authors: Kelly Benjamin
Published: July 2022

Gerrymandering is the manipulating of redistricting for political gain. While many attempts to formalize and model gerrymandering have been made, the assumption of known voter preference, or perfect information, limits the applicability of these works to model real world scenarios. To more accurately reason about gerrymandering we investigate how to adapt existing models of the problem to work with imperfect information. In our work, we formalize a definition of the gerrymandering problem under probabilistic voter preferences, reason about its complexity compared to the deterministic version, and propose a greedy algorithm to approximate the problem in polynomial time under certain conditions.
How to Reduce Action Space for Planning Domains? (Student Abstract)
Authors: Kokel Harsha , Lee Junkyu , Katz Michael , Sohrabi Shirin , Srinivas Kavitha
Published: July 2022

While AI planning and Reinforcement Learning (RL) solve sequential decision-making problems, they are based on different formalisms, which leads to a significant difference in their action spaces. When solving planning problems using RL algorithms, we have observed that a naive translation of the planning action space incurs severe degradation in sample complexity. In practice, those action spaces
are often engineered manually in a domain-specific manner. In this abstract, we present a method that reduces the parameters of operators in AI planning domains by introducing a parameter seed set problem and casting it as a classical planning task. Our experiment shows that our proposed method significantly reduces the number of actions in the RL environments originating from AI planning domains.
Social Aware Assignment of Passengers in Ridesharing (Student Abstract)
Authors: Levinger Chaya , Hazon Noam , Azaria Amos
Published: July 2022

We analyze the assignment of passengers in a shared ride, which considers the social relationship among the passengers. Namely, there is a fixed number of passengers in each vehicle, and the goal is to recommend an assignment of the passengers such that the number of friendship relations is maximized. We show that the problem is computationally hard, and we provide an approximation algorithm.
Exploring Entity Interactions for Few-Shot Relation Learning (Student Abstract)
Authors: Liang Yi , Zhao Shuai , Cheng Bo , Yin Yuwei , Yang Hao
Published: July 2022

Few-shot relation learning refers to infer facts for relations with a few observed triples. Existing metric-learning methods mostly neglect entity interactions within and between triples. In this paper, we explore this kind of fine-grained semantic meaning and propose our model TransAM. Specifically, we serialize reference entities and query entities into sequence and apply transformer structure with local-global attention to capture intra- and inter-triple entity interactions. Experiments on two public datasets with 1-shot setting prove the effectiveness of TransAM.
CL-NERIL: A Cross-Lingual Model for NER in Indian Languages (Student Abstract)
Authors: Prabhakar Akshara , Majumder Gouri Sankar , Anand Ashish
Published: July 2022

Developing Named Entity Recognition (NER) systems for Indian languages has been a long-standing challenge, mainly owing to the requirement of a large amount of annotated clean training instances. This paper proposes an end-to-end framework for NER for Indian languages in a low-resource setting by exploiting parallel corpora of English and Indian languages and an English NER dataset. The proposed framework includes an annotation projection method that combines word alignment score and NER tag prediction confidence score on source language (English) data to generate weakly labeled data in a target Indian language. We employ a variant of the Teacher-Student model and optimize it jointly on the pseudo labels of the Teacher model and predictions on the generated weakly labeled data. We also present manually annotated test sets for three Indian languages: Hindi, Bengali, and Gujarati. We evaluate the performance of the proposed framework on the test sets of the three Indian languages. Empirical results show a minimum 10% performance improvement compared to the zero-shot transfer learning model on all languages. This indicates that weakly labeled data generated using the proposed annotation projection method in target Indian languages can complement well-annotated source language data to enhance performance. Our code is publicly available at https://github.com/aksh555/CL-NERIL.
Optimizing Global Influenza Surveillance for Locations with Deficient Data (Student Abstract)
Authors: Shan Songwei , Tan Qi , Lau Yiu Chung , Du Zhanwei , Lau Eric H.Y. , Wu Peng , Cowling Benjamin J.
Published: July 2022

For better monitoring and controlling influenza, WHO has launched FluNet (recently integrated to FluMART) to provide a unified platform for participating countries to routinely collect influenza-related syndromic, epidemiological and virological data. However, the reported data were incomplete.We propose a novel surveillance system based on data from multiple sources to accurately assess the epidemic status of different countries, especially for those with missing surveillance data in some periods. The proposed method can automatically select a small set of reliable and informative indicators for assessing the underlying epidemic status and proper supporting data to train the predictive model. Our proactive selection method outperforms three other out-of-box methods (linear regression, multilayer perceptron, and long-short term memory) to make accurate predictions.
Modeling Constraints Can Identify Winning Arguments in Multi-Party Interactions (Student Abstract)
Authors: Sia Suzanna , Jaidka Kokil , Chayya Niyati , Duh Kevin
Published: July 2022

In contexts where debate and
deliberation is the norm, participants are regularly presented with new information that conflicts with their original beliefs. When required to update their beliefs (belief alignment), they may choose arguments that align with their worldview (confirmation bias). We test this and competing hypotheses in a constraint-based modeling approach to predict the winning arguments in multi-party interactions in the Reddit ChangeMyView dataset. We impose structural constraints that reflect competing hypotheses on a hierarchical generative Variational Auto-encoder. Our findings suggest that when arguments are further from the initial belief state of the target, they are more likely to succeed.
VeNAS: Versatile Negotiating Agent Strategy via Deep Reinforcement Learning (Student Abstract)
Authors: Takahashi Toki , Higa Ryota , Fujita Katsuhide , Nakadai Shinji
Published: July 2022

Existing research in the field of automated negotiation considers a negotiation architecture in which some of the negotiation components are designed separately by reinforcement learning (RL), but comprehensive negotiation strategy design has not been achieved.
In this study, we formulated an RL model based on a Markov decision process (MDP) for bilateral multi-issue negotiations. We propose a versatile negotiating agent that can effectively learn various negotiation strategies and domains through comprehensive strategies using deep RL. We show that the proposed method can achieve the same or better utility than existing negotiation agents.
Hybrid Deep Learning Model for Fake News Detection in Social Networks (Student Abstract)
Authors: Upadhayay Bibek , Behzadan Vahid
Published: July 2022

The proliferation of fake news has grown into a global concern with adverse socio-political and economical impact. In recent years, machine learning has emerged as a promising approach to the automation of detecting and tracking fake news at scale. Current state of the art in the identification of fake news is generally focused on semantic analysis of the text, resulting in promising performance in automated detection of fake news. However, fake news campaigns are also evolving in response to such new technologies by mimicking semantic features of genuine news, which can significantly affect the performance of fake news classifiers trained on contextually limited features. In this work, we propose a novel hybrid deep learning model for fake news detection that augments the semantic characteristics of the news with features extracted from the structure of the dissemination network. To this end, we first extend the LIAR dataset by integrating sentiment and affective features to the data, and then use a BERT-based model to obtain a representation of the text. Moreover, we propose a novel approach for fake news detection based on Graph Attention Networks to leverage the user-centric features and graph features of news residing social network in addition to the features extracted in the previous steps. Experimental evaluation of our approach shows classification accuracy of 97% on the Politifact dataset. We also examined the generalizability of our proposed model on the BuzzFeed dataset, resulting in an accuracy 89.50%.
Reducing Catastrophic Forgetting in Self Organizing Maps with Internally-Induced Generative Replay (Student Abstract)
Authors: Vaidya Hitesh , Desell Travis , Ororbia Alexander G.
Published: July 2022

A lifelong learning agent is able to continually learn from potentially infinite streams of pattern sensory data. One major historic difficulty in building agents that adapt in this way is that neural systems struggle to retain previously-acquired knowledge when learning from new samples. This problem is known as catastrophic forgetting (interference) and remains an unsolved problem in the domain of machine learning to this day. While forgetting in the context of feedforward networks has been examined extensively over the decades, far less has been done in the context of alternative architectures such as the venerable self-organizing map (SOM), an unsupervised neural model that is often used in tasks such as clustering and dimensionality reduction. Although the competition among its internal neurons might carry the potential to improve memory retention, we observe that a fixed-sized SOM trained on task incremental data, i.e., it receives data points related to specific classes at certain temporal increments, it experiences severe interference. In this study, we propose the c-SOM, a model that is capable of reducing its own forgetting when processing information.
Conditional Collaborative Filtering Process for Top-K Recommender System (Student Abstract)
Authors: Wang Guanyu , Xu Xovee , Zhong Ting , Zhou Fan
Published: July 2022

Conditional neural process (CNP) has been extensively applied into data analyzing tasks due to its excellent ability to make accurate predictions for incomplete data points. However, in literature there are only few works that studied the CNPin recommendation systems. In this work, we propose CCFP, which is a collaborative filtering method that differs from other CF models by incorporating CNP into encoder-decoder architecture. By analyzing the complete user-item interaction data, our model fits a global representation that can better rep-resenting the features of users and items. CCFP can significantly improve the recommendation performance compared to baselines by predicting items for the target users with their incomplete observation data.
Efficient Attribute (α,β)-Core Detection in Large Bipartite Graphs (Student Abstract)
Authors: Wu Yanping , Sun Renjie , Chen Chen , Wang Xiaoyang
Published: July 2022

In this paper, we propose a novel problem, named rational (alpha, beta)-core detection in attribute bipartite graphs (RCD-ABG), which retrieves the connected (alpha, beta)-core with the largest rational score. A basic greedy framework with an optimized strategy is developed and extensive experiments are conducted to evaluate the performance of the techniques.
Automatic Slides Generation for Scholarly Papers: A Fine-Grained Dataset and Baselines (Student Abstract)
Authors: Xu Sheng , Wan Xiaojun
Published: July 2022

Slides are broadly used to present the research works and there are several studies on the problem of automatic slides generation. However, the lack of dataset hinders further research. In this paper, we construct a benchmark dataset for the problem of slides generation from scholarly papers. The dataset is fine-grained and consists of aligned pairs of single slide and specific region of a paper. Then we deploy several baseline models and conduct preliminary experiments. The results show that this task is challenging and awaits more exploration. The dataset and code will be released.
Learning to Ask for Data-Efficient Event Argument Extraction (Student Abstract)
Authors: Ye Hongbin , Zhang Ningyu , Bi Zhen , Deng Shumin , Tan Chuanqi , Chen Hui , Huang Fei , Chen Huajun
Published: July 2022

Event argument extraction (EAE) is an important task for information extraction to discover specific argument roles. In this study, we cast EAE as a question-based cloze task and empirically analyze fixed discrete token template performance. As generating human-annotated question templates is often time-consuming and labor-intensive, we further propose a novel approach called “Learning to Ask,” which can learn optimized question templates for EAE without human annotations. Experiments using the ACE-2005 dataset demonstrate that our method based on optimized questions achieves state-of-the-art performance in both the few-shot and supervised settings.
Linking Transformer to Hawkes Process for Information Cascade Prediction (Student Abstract)
Authors: Yu Liu , Xu Xovee , Zhong Ting , Trajcevski Goce , Zhou Fan
Published: July 2022

Information cascade is typically formalized as a process of (simplified) discrete sequence of events, and recent approaches have tackled its prediction via variants of recurrent neural networks. However, the information diffusion process is essentially an evolving directed acyclic graph (DAG) in the continuous-time domain. In this paper, we propose a transformer enhanced Hawkes process (Hawkesformer), which links the hierarchical attention mechanism with Hawkes process to model the arrival stream of discrete events continuously. A two-level attention architecture is used to parameterize the intensity function of Hawkesformer, which captures the long-term dependencies between nodes in graph and better embeds the cascade evolution rate for modeling short-term outbreaks. Experimental results demonstrate the significant improvements of Hawkesformer over the state-of-the-art.
Understanding Stochastic Optimization Behavior at the Layer Update Level (Student Abstract)
Authors: Zhang Jack , Qiao Guan Xiong , Lopotenco Alexandru , Pan Ian Tong
Published: July 2022

Popular first-order stochastic optimization methods for deep neural networks (DNNs) are usually either accelerated schemes (e.g. stochastic gradient descent (SGD) with momentum) or adaptive step-size methods (e.g. Adam/AdaMax, AdaBelief). In many contexts, including image classification with DNNs, adaptive methods tend to generalize poorly compared to SGD, i.e. get stuck in non-robust local minima; however, SGD typically converges slower. We analyze possible reasons for this behavior by modeling gradient updates as vectors of random variables and comparing them to probabilistic bounds to identify "meaningful" updates. Through experiments, we observe that only layers close to the output have "definitely non-random" update behavior. In the future, the tools developed here may be useful in rigorously quantifying and analyzing intuitions about why some optimizers and particular DNN architectures perform better than others.
From “Dynamics on Graphs” to “Dynamics of Graphs”: An Adaptive Echo-State Network Solution (Student Abstract)
Authors: Zhang Lei , Chen Zhiqian , Lu Chang-Tien , Zhao Liang
Published: July 2022

Many real-world networks evolve over time, which results in dynamic graphs such as human mobility networks and brain networks. Usually, the “dynamics on graphs” (e.g., node attribute values evolving) are observable, and may be related to and indicative of the underlying “dynamics of graphs” (e.g., evolving of the graph topology). Traditional RNN-based methods are not adaptive or scalable for learn- ing the unknown mappings between two types of dynamic graph data. This study presents a AD-ESN, and adaptive echo state network that can automatically learn the best neural net- work architecture for certain data while keeping the efficiency advantage of echo state networks. We show that AD-ESN can successfully discover the underlying pre-defined map- ping function and unknown nonlinear map-ping between time series and graphs.
A Multi-Factor Classification Framework for Completing Users’ Fuzzy Queries (Student Abstract)
Authors: Zhang Yaning , Wu Liangqing , Wang Yangyang , Wang Jia , Yu Xiaoguang , Song Shuangyong , Wu Youzheng , He Xiaodong
Published: July 2022

Intent identification is the key technology in dialogue system. However, not all online queries are clear or complete. To identify users' intents from those fuzzy queries accurately, this paper proposes a multi-factor classification framework on the query level. Experimental results on our online serving system JIMI demonstrate the effectiveness of our proposed framework.
Capsule Graph Neural Network for Multi-Label Image Recognition (Student Abstract)
Authors: Zheng Xiangping , Liang Xun , Wu Bo
Published: July 2022

This paper studies the problem of learning complex relationships between multi-labels for image recognition. Its challenges come from the rich and diverse semantic information in images. However, current methods cannot fully explore the mutual interactions among labels and do not explicitly model the label co-occurrence. To overcome these shortcomings, we innovatively propose CGML that consists of two crucial modules: 1) an image representation learning module that aims to complete the feature extraction of an image whose features are expressed in the form of primary capsules; 2) a label adaptive graph convolutional network module that leverages the popular graph convolutional networks with an adaptive label correlation graph to model label dependencies. Experiments show that our approach obviously outperforms the existing state-of-the-art methods.
Gradient and Mangitude Based Pruning for Sparse Deep Neural Networks
Authors: Belay Kaleab
Published: July 2022

Deep Neural Networks have memory and computational demands that often render them difficult to use in low-resource environments. Also, highly dense networks are over-parameterized and thus prone to overfitting. To address these problems, we introduce a novel algorithm that prunes (sparsifies) weights from the network by taking into account their magnitudes and gradients taken against a validation dataset. Unlike existing pruning methods, our method does not require the network model to be retrained once initial training is completed. On the CIFAR-10 dataset, our method reduced the number of paramters of MobileNet by a factor of 9X, from 14 million to 1.5 million, with just a 3.8% drop in accuracy.
Measuring Students’ Engagement with Digital Interactive Textbooks by Analyzing Clickstream Data
Authors: Crockett Breanne
Published: July 2022

This paper provides an overview of my contributions to a project to measure and predict student’s mental workload when using digital interactive textbooks. The current work focuses on analysis of clickstream data from the textbook in search of viewing patterns among students. It was found that students typically fit one of three viewing patterns. These patterns can be used in further research to inform creation of new interactive texts for improved student success.
Unsupervised Identification of Materials with Hyperspectral Images
Authors: Welner Mira
Published: July 2022

We introduce a novel technique to identify three spectra representing the three primary materials in a hyperspectral image of a scene. We accomplish this using a modified autoencoder. Further research will be conducted to verify the accuracy of these spectra.
An Extraction and Representation Pipeline for Literary Characters
Authors: Yang Funing
Published: July 2022

Readers of novels need to identify and learn about the characters as they develop an understanding of the plot. The paper presents an end-to-end automated pipeline for literary character identification and ongoing work for extracting and comparing character representations for full-length English novels. The character identification pipeline involves a named entity recognition (NER) module with F1 score of 0.85, a coreference resolution module with F1 score of 0.76, and a disambiguation module using both heuristic and algorithmic approaches. Ongoing work compares event extraction as well as speech extraction pipelines for literary characters representations with case studies. The paper is the first to my knowledge that combines a modular pipeline for automated character identification, representation extraction and comparisons for full-length English novels.