bluuebunny/crossref_metadata_2025 huggingface dataset:
MLE-Guided Parameter Search for Task Loss Minimization in Neural Sequence Modeling
Authors: Welleck Sean , Cho Kyunghyun
Published: September 2022

Neural autoregressive sequence models are used to generate sequences in a variety of natural language processing (NLP) tasks, where they are evaluated according to sequence-level task losses. These models are typically trained with maximum likelihood estimation, which ignores the task loss, yet empirically performs well as a surrogate objective. Typical approaches to directly optimizing the task loss such as policy gradient and minimum risk training are based around sampling in the sequence space to obtain candidate update directions that are scored based on the loss of a single sequence. In this paper, we develop an alternative method based on random search in the parameter space that leverages access to the maximum likelihood gradient. We propose maximum likelihood guided parameter search (MGS), which samples from a distribution over update directions that is a mixture of random search around the current parameters and around the maximum likelihood gradient, with each direction weighted by its improvement in the task loss. MGS shifts sampling to the parameter space, and scores candidates using losses that are pooled from multiple sequences. Our experiments show that MGS is capable of optimizing sequence-level losses, with substantial reductions in repetition and non-termination in sequence completion, and similar improvements to those of minimum risk training in machine translation.
What's the Best Place for an AI Conference, Vancouver or _______: Why Completing Comparative Questions is Difficult
Authors: Zagoury‬‏ ‪Avishai , Minkov Einat , Szpektor Idan , Cohen William W.
Published: September 2022

Although large neural language models (LMs) like BERT can be finetuned to yield state-of-the-art results on many NLP tasks, it is often unclear what these models actually learn. Here we study using such LMs to fill in entities in human-authored comparative questions, like ``Which country is older, India or _____?''---i.e., we study the ability of neural LMs to ask (not answer) reasonable questions. We show that accuracy in this fill-in-the-blank task is well-correlated with human judgements of whether a question is reasonable, and that these models can be trained to achieve nearly human-level performance in completing comparative questions in three different subdomains. However, analysis shows that what they learn fails to model any sort of broad notion of which entities are semantically comparable or similar---instead the trained models are very domain-specific, and performance is highly correlated with co-occurrences between specific entities observed in the training set. This is true both for models that are pretrained on general text corpora, as well as models trained on a large corpus of comparison questions. Our study thus reinforces recent results on the difficulty of making claims about a deep model's world knowledge or linguistic competence based on performance on specific benchmark problems. We make our evaluation datasets publicly available to foster future research on complex understanding and reasoning in such models at standards of human interaction.
Probing Product Description Generation via Posterior Distillation
Authors: Zhan Haolan , Zhang Hainan , Chen Hongshen , Shen Lei , Ding Zhuoye , Bao Yongjun , Yan Weipeng , Lan Yanyan
Published: September 2022

In product description generation (PDG), the user-cared aspect is critical for the recommendation system, which can not only improve user's experiences but also obtain more clicks. High-quality customer reviews can be considered as an ideal source to mine user-cared aspects. However, in reality, a large number of new products (known as long-tailed commodities) cannot gather sufficient amount of customer reviews, which brings a big challenge in the product description generation task. Existing works tend to generate the product description solely based on item information, i.e., product attributes or title words, which leads to tedious contents and cannot attract customers effectively. To tackle this problem, we propose an adaptive posterior network based on Transformer architecture that can utilize user-cared information from customer reviews. Specifically, we first extend the self-attentive Transformer encoder to encode product titles and attributes. Then, we apply an adaptive posterior distillation module to utilize useful review information, which integrates user-cared aspects to the generation process. Finally, we apply a Transformer-based decoding phase with copy mechanism to automatically generate the product description. Besides, we also collect a large-scare Chinese product description dataset to support our work and further research in this field. Experimental results show that our model is superior to traditional generative models in both automatic indicators and human evaluation.
Graph-Based Tri-Attention Network for Answer Ranking in CQA
Authors: Zhang Wei , Chen Zeyuan , Dong Chao , Wang Wen , Zha Hongyuan , Wang Jianyong
Published: September 2022

In community-based question answering (CQA) platforms, automatic answer ranking for a given question is critical for finding potentially popular answers in early times. The mainstream approaches learn to generate answer ranking scores based on the matching degree between question and answer representations as well as the influence of respondents. However, they encounter two main limitations: (1) Correlations between answers in the same question are often overlooked. (2) Question and respondent representations are built independently of specific answers before affecting answer representations. To address the limitations, we devise a novel graph-based tri-attention network, namely GTAN, which has two innovations. First, GTAN proposes to construct a graph for each question and learn answer correlations from each graph through graph neural networks (GNNs). Second, based on the representations learned from GNNs, an alternating tri-attention method is developed to alternatively build target-aware respondent representations, answer-specific question representations, and context-aware answer representations by attention computation. GTAN finally integrates the above representations to generate answer ranking scores. Experiments on three real-world CQA datasets demonstrate GTAN significantly outperforms state-of-the-art answer ranking methods, validating the rationality of the network architecture.
Retrospective Reader for Machine Reading Comprehension
Authors: Zhang Zhuosheng , Yang Junjie , Zhao Hai
Published: September 2022

Machine reading comprehension (MRC) is an AI challenge that requires machines to determine the correct answers to questions based on a given passage. MRC systems must not only answer questions when necessary but also tactfully abstain from answering when no answer is available according to the given passage. When unanswerable questions are involved in the MRC task, an essential verification module called verifier is especially required in addition to the encoder, though the latest practice on MRC modeling still mostly benefits from adopting well pre-trained language models as the encoder block by only focusing on the "reading". This paper devotes itself to exploring better verifier design for the MRC task with unanswerable questions. Inspired by how humans solve reading comprehension questions, we proposed a retrospective reader (Retro-Reader) that integrates two stages of reading and verification strategies: 1) sketchy reading that briefly investigates the overall interactions of passage and question, and yields an initial judgment; 2) intensive reading that verifies the answer and gives the final prediction. The proposed reader is evaluated on two benchmark MRC challenge datasets SQuAD2.0 and NewsQA, achieving new state-of-the-art results. Significance tests show that our model is significantly better than strong baselines.
Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling
Authors: Zhou Wenxuan , Huang Kevin , Ma Tengyu , Huang Jing
Published: September 2022

Document-level relation extraction (RE) poses new challenges compared to its sentence-level counterpart. One document commonly contains multiple entity pairs, and one entity pair occurs multiple times in the document associated with multiple possible relations. In this paper, we propose two novel techniques, adaptive thresholding and localized context pooling, to solve the multi-label and multi-entity problems. The adaptive thresholding replaces the global threshold for multi-label classification in the prior work with a learnable entities-dependent threshold. The localized context pooling directly transfers attention from pre-trained language models to locate relevant context that is useful to decide the relation. We experiment on three document-level RE benchmark datasets: DocRED, a recently released large-scale RE dataset, and two datasets CDRand GDA in the biomedical domain. Our ATLOP (Adaptive Thresholding and Localized cOntext Pooling) model achieves an F1 score of 63.4, and also significantly outperforms existing models on both CDR and GDA. We have released our code at https://github.com/wzhouad/ATLOP.
Intelligent Recommendations for Citizen Science
Authors: Ben Zaken Daniel , Gal Kobi , Shani Guy , Segal Avi , Cavalier Darlene
Published: September 2022

Citizen science refers to scientific research that is carried out by volunteers, often in collaboration with professional scientists.
The spread of the internet has allowed volunteers to contribute to citizen science projects in dramatically new ways while creating scientific value and gaining pedagogical and social benefits. Given the sheer size of available projects, finding the right project, which best suits the user preferences and capabilities, has become a major challenge and is essential for keeping volunteers motivated and active contributors. We address this challenge by developing a system for personalizing project recommendations which was fully deployed in the wild. We adapted several recommendation algorithms to the citizen science domain from the literature based on memory-based and model-based collaborative filtering approaches. The algorithms were trained on historical data of users' interactions in the SciStarter platform - a leading citizen science site -as well as their contributions to different projects.
The trained algorithms were evaluated in SciStarter and involved hundreds of users who were provided with personalized recommendations for new projects they had not contributed to before. The results show that using the new recommendation system led people to increased participation in new SciStarter projects when compared to groups that were recommended projects using non-personalized
recommendation approaches, and compared to behavior before recommendations. In particular, the group of volunteers receiving recommendations created by an SVD algorithm (matrix factorization) exhibited the highest levels of contributions to new projects, when compared to the other cohorts. A follow-up survey conducted with the SciStarter community confirmed that users felt that the recommendations matched their personal interests and goals. Based on these results, our recommendation system is now fully integrated into the SciStarter portal, positively affecting hundreds of users each week, and leading to social and educational benefits.
Using Radio Archives for Low-Resource Speech Recognition: Towards an Intelligent Virtual Assistant for Illiterate Users
Authors: Doumbouya Moussa , Einstein Lisa , Piech Chris
Published: September 2022

For many of the 700 million illiterate people around the world, speech recognition technology could provide a bridge to valuable information and services. Yet, those most in need of this technology are often the most underserved by it. In many countries, illiterate people tend to speak only low-resource languages, for which the datasets necessary for speech technology development are scarce. In this paper, we investigate the effectiveness of unsupervised speech representation learning on noisy radio broadcasting archives, which are abundant even in low-resource languages. We make three core contributions. First, we release two datasets to the research community. The first, West African Radio Corpus, contains 142 hours of audio in more than 10 languages with a labeled validation subset. The second, West African Virtual Assistant Speech Recognition Corpus, consists of 10K labeled audio clips in four languages. Next, we share West African wav2vec, a speech encoder trained on the noisy radio corpus, and compare it with the baseline Facebook speech encoder trained on six times more data of higher quality. We show that West African wav2vec performs similarly to the baseline on a multilingual speech recognition task, and significantly outperforms the baseline on a West African language identification task. Finally, we share the first-ever speech recognition models for Maninka, Pular and Susu, languages spoken by a combined 10 million people in over seven countries, including six where the majority of the adult population is illiterate. Our contributions offer a path forward for ethical AI research to serve the needs of those most disadvantaged by the digital divide.
Degree Planning with PLAN-BERT: Multi-Semester Recommendation Using Future Courses of Interest
Authors: Shao Erzhuo , Guo Shiyuan , Pardos Zachary A.
Published: September 2022

Planning scenarios involving user pre-specified items present themselves frequently in recommender system domains. Although next-item and next-basket recommendation has been a focus of prior research, multiple consecutive item or basket approaches are needed for planning. No prior work has leveraged pre-specified future reference items to improve this type of challenging consecutive prediction task at inference time. PLAN-BERT is the first to accommodate this general planning scenario. It does so by contributing novel modifications that take inspiration from the masked training and contextual embedding of self-attention models. To test the model, we use the domain of student academic degree planning, in which students’ past course histories and future pre-specified courses of interest are used to fill in the remainder of their curriculum. Our offline analyses consist of 15 million historic course enrollments at 20 institutions and an online evaluation conducted at one of the institutions. Our results show that PLAN-BERT outperforms existing models including BERT, BiLSTM, and a UserKNN baseline, with small numbers of future reference items substantially improving accuracy. Significant results from our online evaluation show PLAN-BERT to be strongest in students' perceptions of personalization.
Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection
Authors: Xi Dongbo , Song Bowen , Zhuang Fuzhen , Zhu Yongchun , Chen Shuai , Zhang Tianyi , Qi Yuan , He Qing
Published: September 2022

With the explosive growth of e-payment industry, online transaction fraud has become one of the biggest challenges for the business. The historical behavior information of users provides rich information for digging into the users' fraud risk. While considerable efforts have been made in this direction, a long-standing challenge is how to effectively exploit user's behavioral information and provide explainable prediction results. In fact, the value variations of same field from different events and the interactions of different fields within one event have proven to be strong indicators of fraudulent behaviors. In this paper, we propose the Dual Importance-aware Factorization Machines (DIFM), which exploits the inter- and intra-event information among users’ behavior sequence from dual perspectives, i.e., field value variations and field interactions simultaneously for fraud detection. The proposed model is deployed in Alipay's risk management system, which provides real-time fraud detection service for e-commerce platforms. Experimental results on industrial data under various scenarios in the platform clearly demonstrate that our model achieves significant improvements compared with various state-of-the-art baseline models. Moreover, the \model~could also give an insight into the explanation of the prediction results from dual perspectives.
Dual-Mandate Patrols: Multi-Armed Bandits for Green Security
Authors: Xu Lily , Bondi Elizabeth , Fang Fei , Perrault Andrew , Wang Kai , Tambe Milind
Published: September 2022

Conservation efforts in green security domains to protect wildlife and forests are constrained by the limited availability of defenders (i.e., patrollers), who must patrol vast areas to protect from attackers (e.g., poachers or illegal loggers). Defenders must choose how much time to spend in each region of the protected area, balancing exploration of infrequently visited regions and exploitation of known hotspots. We formulate the problem as a stochastic multi-armed bandit, where each action represents a patrol strategy, enabling us to guarantee the rate of convergence of the patrolling policy. However, a naive bandit approach would compromise short-term performance for long-term optimality, resulting in animals poached and forests destroyed. To speed up performance, we leverage smoothness in the reward function and decomposability of actions. We show a synergy between Lipschitz-continuity and decomposition as each aids the convergence of the other. In doing so, we bridge the gap between combinatorial and Lipschitz bandits, presenting a no-regret approach that tightens existing guarantees while optimizing for short-term performance. We demonstrate that our algorithm, LIZARD, improves performance on real-world poaching data from Cambodia.
HOT-VAE: Learning High-Order Label Correlation for Multi-Label Classification via Attention-Based Variational Autoencoders
Authors: Zhao Wenting , Kong Shufeng , Bai Junwen , Fink Daniel , Gomes Carla
Published: September 2022

Understanding how environmental characteristics affect biodiversity patterns, from individual species to communities of species, is critical for mitigating effects of global change. A central goal for conservation planning and monitoring is the ability to accurately predict the occurrence of species communities and how these communities change over space and time. This in turn leads to a challenging and long-standing problem in the field of computer science - how to perform accurate multi-label classification with hundreds of labels? The key challenge of this problem is its exponential-sized output space with regards to the number of labels to be predicted. Therefore, it is essential to facilitate the learning process by exploiting correlations (or dependency) among labels. Previous methods mostly focus on modelling the correlation on label pairs; however, complex relations between real-world objects often go beyond second order. In this paper, we propose a novel framework for multi-label classification, High-order Tie-in Variational Autoencoder (HOT-VAE), which performs adaptive high-order label correlation learning. We experimentally verify that our model outperforms the existing state-of-the-art approaches on a bird distribution dataset on both conventional F1 scores and a variety of ecological metrics. To show our method is general, we also perform empirical analysis on seven other public real-world datasets in several application domains, and Hot-VAE exhibits superior performance to previous methods.
Robust PDF Document Conversion using Recurrent Neural Networks
Authors: Livathinos Nikolaos , Berrospi Cesar , Lysak Maksym , Kuropiatnyk Viktor , Nassar Ahmed , Carvalho Andre , Dolfi Michele , Auer Christoph , Dinkla Kasper , Staar Peter
Published: September 2022

The number of published PDF documents in both the academic and commercial world has increased exponentially in recent decades. There is a growing need to make their rich content discoverable to information retrieval tools. Achieving high-quality semantic searches demands that a document's structural components such as title, section headers, paragraphs, (nested) lists, tables and figures (including their captions) are properly identified. Unfortunately, the PDF format is known to not conserve such structural information because it simply represents a document as a stream of low-level printing commands, in which one or more characters are placed in a bounding box with a particular styling. In this paper, we present a novel approach to document structure recovery in PDF using recurrent neural networks to process the low-level PDF data representation directly, instead of relying on a visual re-interpretation of the rendered PDF page, as has been proposed in previous literature. We demonstrate how a sequence of PDF printing commands can be used as input into a neural network and how the network can learn to classify each printing command according to its structural function in the page. This approach has three advantages: First, it can distinguish among more fine-grained labels (typically 10-20 labels as opposed to 1-5 with visual methods), which results in a more accurate and detailed document structure resolution. Second, it can take into account the text flow across pages more naturally compared to visual methods because it can concatenate the printing commands of sequential pages. Last, our proposed method needs less memory and it is computationally less expensive than visual methods. This allows us to deploy such models in production environments at a much lower cost. Through extensive architectural search in combination with advanced feature engineering, we were able to implement a model that yields a weighted average F1 score of 97% across 17 distinct structural labels. The best model we achieved is currently served in production environments on our Corpus Conversion Service (CCS), which was presented at KDD18. This model enhances the capabilities of CCS significantly, as it eliminates the need for human annotated label ground-truth for every unseen document layout. This proved particularly useful when applied to a huge corpus of PDF articles related to COVID-19.
Using Unsupervised Learning for Data-driven Procurement Demand Aggregation
Authors: Shaham Eran , Westerski Adam , Kanagasabai Rajaraman , Narayanan Amudha , Ong Samuel , Wong Jiayu , Singh Manjeet
Published: September 2022

Procurement is an essential operation of every organization regardless of its size or domain. As such, aggregating the demands could lead to better value-for-money due to: (1) lower bulk prices; (2) larger vendor tendering; (3) lower shipping and handling fees; and (4) reduced legal and administration overheads. This paper describes our experience in developing an AI solution for demand aggregation and deploying it in A*STAR, a large governmental research organization in Singapore with procurement expenditure to the scale of hundreds of millions of dollars annually. We formulate the demand aggregation problem using a bipartite graph model depicting the relationship between procured items and target vendors, and show that identifying maximal edge bicliques within that graph would reveal potential demand aggregation patterns. We propose an unsupervised learning methodology for efficiently mining such bicliques using a novel Monte Carlo subspace clustering approach. Based on this, a proof-of-concept prototype was developed and tested with the end users during 2017, and later trialed and iteratively refined, before being rolled out in 2019. The final performance was 71% of past cases transformed into bulk tenders correctly detected by the engine; for new opportunities pointed out by the engine 81% were deemed useful for potential bulk tender contracts in the future. Additionally, per each valid pattern identified, the engine achieved 100% precision (all aggregated purchase orders were correct), and 79% recall (the engine correctly identified 79% of orders that should have been put into the bulk tenders). Overall, the cost savings from the true positive contracts spotted so far are estimated to be S$7 million annually.
Tool for Automated Tax Coding of Invoices
Authors: Tater Tarun , Dechu Sampath , Gantayat Neelamadhav , Guptha Meena , Narayanan Sivakumar
Published: September 2022

Accounts payable refer to the practice where organizations procure goods and services on credit which need to be reimbursed to the vendors in due time. Once the vendor raises an invoice, it undergoes through a complex process before the final payment. In this process, tax code determination is one of the most challenging steps, which determines the tax to be levied and directly influences the amount payable to a vendor. This step is also very important from a regulatory compliance standpoint. However, it is error-prone, labor (resource) intensive, and needs regular training of the resources as it is done manually. Further, an error in the tax code determination induces penalties on the organization. Automatically arriving at a tax-code for a given product accurately and efficiently is a daunting task. To address this problem, we present an automated end-to-end system for tax code determination which can either be used as a standalone application or can be integrated into an existing invoice processing workflow. The proposed system determines the most relevant tax code for an invoice using attributes such as item description, vendor details, shipping and delivery location. The system has been deployed in production for a multinational consumer goods company for more than 6 months. It has already processed more than 22k items with an accuracy of more than 94% and high confidence prediction accuracy of around 99.54%. Using this system, approximately 73% of all the invoices require no human intervention.
Attr2Style: A Transfer Learning Approach for Inferring Fashion Styles via Apparel Attributes
Authors: Banerjee Rajdeep H , Ravi Abhinav , Dutta Ujjal Kr
Published: September 2022

Popular fashion e-commerce platforms mostly provide details about low-level attributes of an apparel (for example, neck type, dress length, collar type, print etc) on their product detail pages. However, customers usually prefer to buy apparel based on their style information, or simply put, occasion (for example, party wear, sports wear, casual wear etc). Application of a supervised image-captioning model to generate style-based image captions is limited because obtaining ground-truth annotations in the form of style-based captions is difficult. This is because annotating style-based captions requires a certain amount of fashion domain expertise, and also adds to the costs and manual effort. On the contrary, low-level attribute based annotations are much more easily available. To address this issue, we propose a transfer-learning based image captioning model that is trained on a source dataset with sufficient attribute-based ground-truth captions, and used to predict style-based captions on a target dataset. The target dataset has only a limited amount of images with style-based ground-truth captions. The main motivation of our approach comes from the fact that most often there are correlations among the low-level attributes and the higher-level styles for an apparel. We leverage this fact and train our model in an encoder-decoder based framework using attention mechanism. In particular, the encoder of the model is first trained on the source dataset to obtain latent representations capturing the low-level attributes. The trained model is fine-tuned to generate style-based captions for the target dataset. To highlight the effectiveness of our method, we qualitatively and quantitatively demonstrate that the captions generated by our approach are close to the actual style information for the evaluated apparel. A Proof Of Concept (POC) for our model is under pilot at Myntra (www.myntra.com) where it is exposed to some internal users for feedback.
Shape-based Feature Engineering for Solar Flare Prediction
Authors: Deshmukh Varad , Berger Thomas , Meiss James , Bradley Elizabeth
Published: September 2022

Solar flares are caused by magnetic eruptions in active
regions (ARs) on the surface of the sun. These events can have
significant impacts on human activity, many of which can be mitigated
with enough advance warning from good forecasts. To date, machine
learning-based flare-prediction methods have employed physics-based
attributes of the AR images as features; more recently, there has been
some work that uses features deduced automatically by deep learning
methods (such as convolutional neural networks). We describe a suite
of novel shape-based features extracted from magnetogram images of the
Sun using the tools of computational topology and computational
geometry. We evaluate these features in the context of a multi-layer
perceptron (MLP) neural network and compare their performance against
the traditional physics-based attributes. We show that these abstract
shape-based features outperform the features chosen by the human
experts, and that a combination of the two feature sets improves the
forecasting capability even further.
Device Fabrication Knowledge Extraction from Materials Science Literature
Authors: Wadhwa Neelanshi , S Sarath , Shah Sapan , Reddy Sreedhar , Mitra Pritwish , Jain Deepak , Rai Beena
Published: September 2022

Devices like solar cells, batteries etc. often comprise of a host of material types including organic, inorganic and hybrid materials. The fabrication procedures for these devices involve screening or designing the right set of materials and then subjecting them to a sequence of operations under very specific conditions. The performance characteristics of a device critically depend on the materials used in its fabrication, the specific operations carried out, their operating conditions and the specific sequence in which they are carried out. The space of potential materials, operations and operating conditions is vast, and selecting the right combination thereof to achieve the desired characteristics is a knowledge intensive activity. A large amount of such device fabrication knowledge is available in the form of publications, patents, company reports and so on. In this paper, we present a system that systematically extracts this knowledge from materials science literature. The extracted knowledge is represented as knowledge graphs conforming to an ontology that can be queried to make informed decisions in device fabrication procedures. The system first identifies the set of relevant paragraphs that contain fabrication knowledge. It then employs state of the art entity and relation extraction models to identify instances of operations, methods, materials, etc. and relations between them. The system then applies an unsupervised algorithm to identify sequences of operations representing fabrication procedures. We applied our system on solar cell fabrication knowledge extraction and achieved good performance. We believe our results provide much needed impetus for further work in this area.
Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database
Authors: McGregor Sean
Published: September 2022

Mature industrial sectors (e.g., aviation) collect their real world failures in incident databases to inform safety improvements. Intelligent systems currently cause real world harms without a collective memory of their failings. As a result, companies repeatedly make the same mistakes in the design, development, and deployment of intelligent systems. A collection of intelligent system failures experienced in the real world (i.e., incidents) is needed to ensure intelligent systems benefit people and society. The AI Incident Database is an incident collection initiated by an industrial/non-profit cooperative to enable AI incident avoidance and mitigation. The database supports a variety of research and development use cases with faceted and full text search on more than 1,000 incident reports archived to date.
Learning Artificial Intelligence: Insights into How Youth Encounter and Build Understanding of AI Concepts
Authors: Greenwald Eric , Leitner Maxyn , Wang Ning
Published: September 2022

Artificial Intelligence’s impact on society is increasingly pervasive. While innovative educational programs are being developed, there has been little understanding of how students, especially pre-college aged students, construct understanding and gain practice with core ideas about AI or what concepts are most appropriate for what age-levels. In this paper, we discuss a cognitive interview study with high school students to better understand how students learn AI concepts. We aim to shed light on questions including: what is the range of background knowledge and experiences students are able to apply in encountering AI concepts; what concepts are most readily accessible and which are more challenging; what misconceptions do students bring to bear on AI problems; and how to help students approach AI concepts by leveraging related concepts, such as mathematical and computational thinking). Results from the exploratory study have the potential to provide important insights into AI learning for pre-college youth. These initial findings can inform further investigations to ground the design of learning and assessment in evidence-based learning progressions and grade-level performance expectations.
Modeling Expert Knowledge in a Heuristic-Based Gin Rummy Agent
Authors: Larkin Sarah , Collicott William , Hiebel Jason
Published: September 2022

We developed a heuristic-based reflex agent, Tonic, for the EAAI 2021 Undergraduate Research Challenge, which tasks competitors to create an autonomous player to play the card game gin rummy. Tonic's heuristics originate in expert knowledge and inform decision making for the three actions comprising a turn: drawing a card, discarding a card, and deciding when to knock. However, because these strategies are based in human intuition, there is often a lack of specificity to directly model them as algorithms. We developed parameterized models describing that intuition based on factors such as the number of turns played and an estimation of the opponent hand. To hone their performance, we conducted both manual analysis and parameter optimization (grid search) using self-play and play against a simple baseline agent. These heuristic models enable Tonic to win against the baseline agent at least 68% of the time.
Opponent Hand Estimation in Gin Rummy Using Deep Neural Networks and Heuristic Strategies
Authors: Mishra Bhaskar , Aggarwal Ashish
Published: September 2022

A vital part of any good strategy for most imperfect-information games is making predictions about the information that is unavailable. For example, in card games like Poker and Gin Rummy, predicting the kinds of cards the opponent is holding is necessary for playing well. Specifically, it is useful for agents to be able to map the partial game states that are made available to them to the probabilities of each of the possible complete game states, given that they are playing against other rational player(s). Finding this relationship, however, is difficult, as it requires knowledge of how a rational player would play, which is the problem this relationship is being used to solve. In this paper, we attempt to find this relationship in the context of the card game Gin Rummy, though instead of predicting the complete game state, we focus on what is most useful to a player: the opponent's hand. We do this by using heuristic utility functions to create an agent that approximates how a rational player would play, and then using the resulting game data to train a Deep Neural Network mapping known information to predictions about the opponent's hand. This model is used to improve the existing agent and, in turn, to produce more data to create better models.
A Deterministic Neural Network Approach to Playing Gin Rummy
Authors: Nguyen Viet Dung , Doan Dung , Neller Todd W.
Published: September 2022

This paper describes a deterministic approach to building a fixed-strategy gin rummy player. In the paper, we develop and evaluate both heuristic and neural network models for informing draw, discard, and knock decisions in the game. In this empirical study, we test performance of the models through competitive game play, show which best inform strategy, and demonstrate statistical significance of the improvement over a simple strategy. Through this empirical study, we indicate features that we expect to be helpful in future improvements to Gin Rummy play.
Introduction to Machine Learning with Robots and Playful Learning
Authors: Olari Viktoriya , Cvejoski Kostadin , Eide Øyvind
Published: September 2022

Inspired by explanations of machine learning concepts in children’s books, we developed an approach to introduce supervised, unsupervised, and reinforcement learning using a block-based programming language in combination with the benefits of educational robotics. Instead of using blocks as high-end APIs to access AI cloud services or to reproduce the machine learning algorithms, we use them as a means to put the student “in the algorithm’s shoes.” We adapt the training of neural networks, Q-learning, and k-means algorithms to a design and format suitable for children and equip the students with hands-on tools for playful experimentation. The children learn about direct supervision by modifying the weights in the neural networks and immediately observing the effects on the simulated robot. Following the ideas of constructionism, they experience how the algorithms and underlying machine learning concepts work in practice. We conducted and evaluated this approach with students in primary, middle, and high school. All the age groups perceived the topics to be very easy to moderately hard to grasp. Younger students experienced direct supervision as challenging, whereas they found Q-learning and k-means algorithms much more accessible. Most high-school students could cope with all the topics without particular difficulties.
Web-based Platform for K-12 AI Education in China
Authors: Wu Chao , Li Yan , Li Junxiang , Zhang Qiongdan , Wu Fei
Published: September 2022

As human beings are entering the era in which AI be-comes the new engine to drive social, economic, and sci-entific advancement, education is intensively being re-quired to adapt to this trend, to equip current and next generation with necessary knowledge, skills, and think-ing. Although AI education has achieved relative success in universities and cultivated a large number of talents and companies in the past decade, it hasn't made signifi-cant progress in K-12 education. We identify the key challenges as two gaps, one is about transferring practice from university education to K-12 education, and the other is about the inequal distribution of AI educational resources. To fill these gaps and to efficiently facilitate K-12 AI education, especially in countries like China, we designed and implemented a Web-based platform, which as a focal and sharing point of K-12 educational resources to provide essential AI learning and exercising components to both students and instructors. With this platform, we've successfully conducted a series of initial trials and gained positive feedbacks. We believe a wider-range application of the platform will achieve promising results for K-12 AI education.
Model AI Assignments 2021
Authors: Neller Todd W. , Sprague Nathan , Maraist John , Zhang Lisa , Fewzee Pouria , Long Duri , Moon Jonathan , Magerko Brian , Leto Alex , Lefton Toni , Williams Tom
Published: September 2022

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of six AI assignments from the 2021 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.
Screening for Depressed Individuals by Using Multimodal Social Media Data
Authors: Mann Paulo , Paes Aline , Matsushima Elton H.
Published: September 2022

Depression has increased at alarming rates in the worldwide population. One alternative to finding depressed individuals is using social media data to train machine learning (ML) models to identify depressed cases automatically. Previous works have already relied on ML to solve this task with reasonably good F-measure scores. Still, several limitations prevent the full potential of these models. In this work, we show that the depression identification task through social media is better modeled as a Multiple Instance Learning (MIL) problem that can exploit the temporal dependencies between posts.
Relational Learning to Capture the Dynamics and Sparsity of Knowledge Graphs
Authors: Mirtaheri Mehrnoosh
Published: September 2022

The rapid growth of large scale event datasets with timestamps has given rise to the dynamically evolving multi-relational knowledge graphs. Temporal reasoning over such data brings on many challenges and is still not well understood. Most real-world knowledge graphs are characterized by a long-tail relation frequency distribution where a significant fraction of relations occurs only a handful of times. This observation has given rise to the recent interest in low-shot learning methods that are able to generalize from only a few examples. The existing approaches, however, are tailored to static knowledge graphs and not easily generalized to temporal settings, where data scarcity poses even bigger problems, due to the occurrence of new, previously unseen relations. The goal of my doctoral research is to introduce new approaches for learning meaningful representation that capture the dynamics of temporal knowledge graphs while tackling various existing challenges such as data scarcity.
Safety Assurance for Systems with Machine Learning Components
Authors: Sidrane Chelsea
Published: September 2022

The use of machine learning components in safety-critical systems creates reliability concerns. My thesis focuses on developing algorithms to address these concerns.
Because the assurance of a safety-critical system generally requires multiple types of validation, my research takes three directions: safe deep learning algorithms, formal verification of neural networks, and adaptive testing methods.
Distributed Situation Awareness for Multi-agent Mission in Dynamic Environments: A Case Study of Multi-UAVs Wildfires Searching
Authors: Yusuf Sagir Muhammad , Baber Chris
Published: September 2022

This thesis focuses attention on achieving Distributed Situation Awareness (DSA) with minimal resources (energy, processing cost, etc.) using small low-capacity agents (e.g., UAVs) coordinated in a decentralised fashion while conducting searching activity. This is in contrast to the existing works involving convoluted communication and information processing.
A Serverless Approach to Federated Learning Infrastructure Oriented for IoT/Edge Data Sources (Student Abstract)
Authors: Ahuja Anshul , Gupta Geetesh , Kundu Suman
Published: September 2022

The paper proposes a Serverless and Mobile relay based architecture for a highly scalable Federated Learning system for low power IoT and Edge Devices. The aim is an easily deployable infrastructure on a public cloud platform by the end user and democratize the use of federated learning.
Encoding Temporal and Spatial Vessel Context using Self-Supervised Learning Model (Student Abstract)
Authors: Bernabé Pierre , Spieker Helge , Legeard Bruno , Gotlieb Arnaud
Published: September 2022

Maritime surveillance is essential to avoid illegal activities and for environmental protection. However, the unlabeled, noisy, irregular time-series data and the large area to be covered make it challenging to detect illegal activities. Existing solutions focus only on trajectory reconstruction and probabilistic models that do ignore the context, such as the neighboring vessels. We propose a novel representation learning method that considers both temporal and spatial contexts learned in a self-supervised manner, using a selection of pretext tasks that do not require to be labeled manually. The underlying model encodes the representation of maritime vessel data compactly and effectively. This generic encoder can then be used as input for more complex tasks lacking labeled data.
Fair Stable Matchings Under Correlated Preferences (Student Abstract)
Authors: Brilliantova Angelina , Hosseini Hadi
Published: September 2022

Stable matching models are widely used in market design, school admission, and donor organ exchange. The classic Deferred Acceptance (DA) algorithm guarantees a stable matching that is optimal for one side (say men) and pessimal for the other (say women). A sex-equal stable matching aims at providing a fair solution to this problem. We demonstrate that under a class of correlated preferences, the DA algorithm either returns a sex-equal solution or has a very low sex-equality cost.
Robotic Manipulation with Reinforcement Learning, State Representation Learning, and Imitation Learning (Student Abstract)
Authors: Chen Hanxiao
Published: September 2022

Humans possess the advanced ability to grab, hold, and manipulate objects with dexterous hands. What about robots? Can they interact with the surrounding world intelligently to achieve certain goals (e.g., grasping, object-relocation)? Actually, robotic manipulation is central to achieving the premise of robotics and represents immense potential to be widely applied in various scenarios like industries, hospitals, and homes. In this work, we aim to address multiple robotic manipulation tasks like grasping, button-pushing, and door-opening with reinforcement learning (RL), state representation learning (SRL), and imitation learning. For diverse missions, we self-built the PyBullet or MuJoCo simulated environments and independently explored three different learning-style methods to successfully solve such tasks: (1) Normal reinforcement learning methods; (2) Combined state representation learning (SRL) and RL approaches; (3) Imitation learning bootstrapped RL algorithms.
Multi-modal User Intent Classification Under the Scenario of Smart Factory (Student Abstract)
Authors: Chiu Yu-Ching , Chang Bo-Hao , Chen Tzu-Yu , Yang Cheng-Fu , Bi Nanyi , Tsai Richard Tzong-Han , Lee Hung-yi , Hsu Jane Yung-jen
Published: September 2022

Question-answering systems are becoming increasingly popular in Natural Language Processing, especially when applied in smart factory settings. A common practice in designing those systems is through intent classification. However, in a multiple-stage task commonly seen in those settings, relying solely on intent classification may lead to erroneous answers, as questions rising from different work stages may share the same intent but have different contexts and therefore require different answers. To address this problem, we designed an interactive dialogue system that utilizes contextual information to assist intent classification in a multiple-stage task. Specifically, our system incorporates user’s utterances with real-time video feed to better situate users’ questions and analyze their intent.
Passive learning of Timed Automata from logs (Student Abstract)
Authors: Cornanguer Lénaïg
Published: September 2022

We propose a novel algorithm to passively learn deterministic Timed Automata from events sequences associated with the delay occurring between them. This algorithm produces models that are more specific than State-of-the-Art algorithms and that has a better identification of the temporal constraints applying on the systems.