bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving Maximum k-plex Solver via Second-Order Reduction and Graph Color Bounding
Authors: Zhou Yi , Hu Shan , Xiao Mingyu , Fu Zhang-Hua
Published: September 2022

In a graph, a k-plex is a vertex set in which every vertex is not adjacent to at most k vertices of this set. The maximum k-plex problem, which asks for the largest k-plex from the given graph, is a key primitive in a variety of real-world applications like community detection and so on. In the paper, we develop an exact algorithm, Maplex, for solving this problem in real world graphs practically. Based on the existing first-order and the novel second-order reduction rules, we design a powerful preprocessing method which efficiently removes redundant vertices and edges for Maplex. Also, the graph color heuristic is widely used for overestimating the maximum clique of a graph. For the first time, we generalize this technique for bounding the size of maximum k-plex in Maplex. Experiments are carried out to compare our algorithm with other state-of-the-art solvers on a wide range of publicly available graphs. Maplex outperforms all other algorithms on large real world graphs and is competitive with existing solvers on artificial dense graphs. Finally, we shed light on the effectiveness of each key component of Maplex.
Benchmarking Knowledge-Enhanced Commonsense Question Answering via Knowledge-to-Text Transformation
Authors: Bian Ning , Han Xianpei , Chen Bo , Sun Le
Published: September 2022

A fundamental ability of humans is to utilize commonsense knowledge in language understanding and question answering. In recent years, many knowledge-enhanced Commonsense Question Answering (CQA) approaches have been proposed. However, it remains unclear: (1) How far can we get by exploiting external knowledge for CQA? (2) How much potential of knowledge has been exploited in current CQA models? (3) Which are the most promising directions for future CQA? To answer these questions, we benchmark knowledge-enhanced CQA by conducting extensive experiments on multiple standard CQA datasets using a simple and effective knowledge-to-text transformation framework. Experiments show that: (1) Our knowledge-to-text framework is effective and achieves state-of-the-art performance on CommonsenseQA dataset, providing a simple and strong knowledge-enhanced baseline for CQA; (2) The potential of knowledge is still far from being fully exploited in CQA — there is a significant performance gap from current models to our models with golden knowledge; and (3) Context-sensitive knowledge selection, heterogeneous knowledge exploitation, and commonsense-rich language models are promising CQA directions.
Fake it Till You Make it: Self-Supervised Semantic Shifts for Monolingual Word Embedding Tasks
Authors: Gruppi Maurício , Chen Pin-Yu , Adali Sibel
Published: September 2022

The use of language is subject to variation over time as well as across social groups and knowledge domains, leading to differences even in the monolingual scenario. Such variation in word usage is often called lexical semantic change (LSC). The goal of LSC is to characterize and quantify language variations with respect to word meaning, to measure how distinct two language sources are (that is, people or language models). Because there is hardly any data available for such a task, most solutions involve unsupervised methods to align two embeddings and predict semantic change with respect to a distance measure. To that end, we propose a self-supervised approach to model lexical semantic change based on the perturbation of word vectors in the input corpora. We show that our method can be used for the detection of semantic change with any alignment method. Furthermore, it can be used to choose the landmark words to use in alignment and can lead to substantial improvements over the existing techniques for alignment. We illustrate the utility of our techniques using experimental results on three different datasets, involving words with the same or different meanings. Our methods not only provide significant improvements but also can lead to novel findings for the LSC problem.
Synchronous Interactive Decoding for Multilingual Neural Machine Translation
Authors: He Hao , Wang Qian , Yu Zhipeng , Zhao Yang , Zhang Jiajun , Zong Chengqing
Published: September 2022

To simultaneously translate a source language into multiple different target languages is one of the most common scenarios of multilingual translation. However, existing methods cannot make full use of translation model information during decoding, such as intra-lingual and inter-lingual future information, and therefore may suffer from some issues like the unbalanced outputs. In this paper, we present a new approach for synchronous interactive multilingual neural machine translation (SimNMT), which predicts each target language output simultaneously and interactively using historical and future information of all target languages. Specifically, we first propose a synchronous cross-interactive decoder in which generation of each target output does not only depend on its generated sequences, but also relies on its future information, as well as history and future contexts of other target languages. Then, we present a new interactive multilingual beam search algorithm that enables synchronous interactive decoding of all target languages in a single model. We take two target languages as an example to illustrate and evaluate the proposed SimNMT model on IWSLT datasets. The experimental results demonstrate that our method achieves significant improvements over several advanced NMT and MNMT models.
FIXMYPOSE: Pose Correctional Captioning and Retrieval
Authors: Kim Hyounghun , Zala Abhay , Burri Graham , Bansal Mohit
Published: September 2022

Interest in physical therapy and individual exercises such as yoga/dance has increased alongside the well-being trend, and people globally enjoy such exercises at home/office via video streaming platforms. However, such exercises are hard to follow without expert guidance. Even if experts can help, it is almost impossible to give personalized feedback to every trainee remotely. Thus, automated pose correction systems are required more than ever, and we introduce a new captioning dataset named FixMyPose to address this need. We collect natural language descriptions of correcting a “current” pose to look like a “target” pose. To support a multilingual setup, we collect descriptions in both English and Hindi. The collected descriptions have interesting linguistic properties such as egocentric relations to the environment objects, analogous references, etc., requiring an understanding of spatial relations and commonsense knowledge about postures. Further, to avoid ML biases, we maintain a balance across characters with diverse demographics, who perform a variety of movements in several interior environments (e.g., homes, offices). From our FixMyPose dataset, we introduce two tasks: the pose-correctional-captioning task and its reverse, the target-pose-retrieval task. During the correctional-captioning task, models must generate the descriptions of how to move from the current to the target pose image, whereas in the retrieval task, models should select the correct target pose given the initial pose and the correctional description. We present strong cross-attention baseline models (uni/multimodal, RL, multilingual) and also show that our baselines are competitive with other models when evaluated on other image-difference datasets. We also propose new task-specific metrics (object-match, body-part-match, direction-match) and conduct human evaluation for more reliable evaluation, and we demonstrate a large human-model performance gap suggesting room for promising future work. Finally, to verify the sim-to-real transfer of our FixMyPose dataset, we collect a set of real images and show promising performance on these images. Data and code are available: https://fixmypose-unc.github.io.
The Style-Content Duality of Attractiveness: Learning to Write Eye-Catching Headlines via Disentanglement
Authors: Li Mingzhe , Chen Xiuying , Yang Min , Gao Shen , Zhao Dongyan , Yan Rui
Published: September 2022

Eye-catching headlines function as the first device to trigger more clicks, bringing reciprocal effect between producers and viewers. Producers can obtain more traffic and profits, and readers can have access to outstanding articles. When generating attractive headlines, it is important to not only capture the attractive content but also follow an eye-catching writtenstyle. In this paper, we propose a Disentanglement-based Attractive Headline Generator (DAHG) that generates headline which captures the attractive content following the attractive style. Concretely, we first devise a disentanglement module to divide the style and content of an attractive prototype headline into latent spaces, with two auxiliary constraints to ensure the two spaces are indeed disentangled. The latent content information is then used to further polish the document representation and help capture the salient part. Finally, the generator takes the polished document as input to generate headline under the guidance of the attractive style. Extensive experiments on the public Kuaibao dataset show that DAHG achieves state-of-the-art performance. Human evaluation also demonstrates that DAHG triggers 22% more clicks than existing models.
Converse, Focus and Guess - Towards Multi-Document Driven Dialogue
Authors: Liu Han , Yuan Caixia , Wang Xiaojie , Yang Yushu , Jiang Huixing , Wang Zhongyuan
Published: September 2022

We propose a novel task, Multi-Document Driven Dialogue (MD3), in which an agent can guess the target document that the user is interested in by leading a dialogue. To benchmark progress, we introduce a new dataset of GuessMovie, which contains 16,881 documents, each describing a movie, and associated 13,434 dialogues. Further, we propose the MD3 model. Keeping guessing the target document in mind, it converses with the user conditioned on both document engagement and user feedback. In order to incorporate large-scale external documents into the dialogue, it pretrains a document representation which is sensitive to attributes it talks about an object. Then it tracks dialogue state by detecting evolvement of document belief and attribute belief, and finally optimizes dialogue policy in principle of entropy decreasing and reward increasing, which is expected to successfully guess the user's target in a minimum number of turns. Experiments show that our method significantly outperforms several strong baseline methods and is very close to human's performance.
Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text
Authors: Madaan Nishtha , Padhi Inkit , Panwar Naveen , Saha Diptikalyan
Published: September 2022

Machine Learning has seen tremendous growth recently, which has led to a larger adaptation of ML systems for educational assessments, credit risk, healthcare, employment, criminal justice, to name a few. The trustworthiness of ML and NLP systems is a crucial aspect and requires a guarantee that the decisions they make are fair and robust. Aligned with this, we propose a novel framework GYC, to generate a set of exhaustive counterfactual text, which are crucial for testing these ML systems. Our main contributions include a) We introduce GYC, a framework to generate counterfactual samples such that the generation is plausible, diverse, goal-oriented, and effective, b) We generate counterfactual samples, that can direct the generation towards a corresponding \texttt{condition} such as named-entity tag, semantic role label, or sentiment. Our experimental results on various domains show that GYC generates counterfactual text samples exhibiting the above four properties. GYC generates counterfactuals that can act as test cases to evaluate a model and any text debiasing algorithm.
On the Softmax Bottleneck of Recurrent Language Models
Authors: Parthiban Dwarak Govind , Mao Yongyi , Inkpen Diana
Published: September 2022

Recent research has pointed to a limitation of word-level neural language models with softmax outputs. This limitation, known as the softmax bottleneck refers to the inability of these models to produce high-rank log probability (log P) matrices. Various solutions have been proposed to break this bottleneck, including Mixture of Softmaxes, SigSoftmax, and Linear Monotonic Softmax with Piecewise Linear Increasing Functions. They were reported to offer better performance in terms of perplexity on test data. A natural perception from these results is a strong positive correlation between the rank of the log P matrix and the model's performance. In this work, we show via an extensive empirical study that such a correlation is fairly weak and that the high-rank of the log P matrix is neither necessary nor sufficient for better test perplexity. Although our results are empirical, they are established in part via the construction of a rich family of models, which we call Generalized SigSoftmax. They are able to create diverse ranks for the log P matrices. We also present an investigation as to why the proposed solutions achieve better performance.
Multi-Document Transformer for Personality Detection
Authors: Yang Feifan , Quan Xiaojun , Yang Yunyi , Yu Jianxing
Published: September 2022

Personality detection aims to identify the personality traits implied in social media posts. The core of this task is to put together information in multiple scattered posts to depict an overall personality profile for each user. Existing approaches either encode each post individually or assemble posts arbitrarily into a new document that can be encoded sequentially or hierarchically. While the first approach ignores the connection between posts, the second tends to introduce unnecessary post-order bias into posts. In this paper, we propose a multi-document Transformer, namely Transformer-MD, to tackle the above issues. When encoding each post, Transformer-MD allows access to information in the other posts of the user through Transformer-XL’s memory tokens which share the same position embedding.Besides, personality is usually defined along different traits and each trait may need to attend to different post information, which has rarely been touched by existing research. To address this concern, we propose a dimension attention mechanism on top of Transformer-MD to obtain trait-specific representations for multi-trait personality detection. We evaluate the proposed model on the Kaggle and Pandora MBTI datasets and the experimental results show that it compares favorably with baseline methods.
Unanswerable Question Correction in Question Answering over Personal Knowledge Base
Authors: Yen An-Zi , Huang Hen-Hsen , Chen Hsin-Hsi
Published: September 2022

People often encounter situations where they need to recall past experiences from their daily life. In this paper, we aim to construct a question answering system that enables human to query their past experiences over personal knowledge base. Previous works on knowledge base question answering focus on finding answers for answerable questions. In the real world applications, however, people often muddle up facts and ask those questions that cannot be answered with knowledge base. This work presents a novel system consisting of question answering model and question generation model. It not only answers answerable questions, but also corrects unanswerable questions if necessary. Our question answering model recognizes the question that is inconsistent with the state of the personal knowledge base and suggests facts that can form a feasible question. Then, the facts are converted to an answerable question by the question generation model. For refining question, we propose a question generation model based on the reinforcement learning (RL) with question editing mechanism. Experimental results show that our proposed system is effective for correcting unanswerable questions in personal knowledge base question answering.
Discovering New Intents with Deep Aligned Clustering
Authors: Zhang Hanlei , Xu Hua , Lin Ting-En , Lyu Rui
Published: September 2022

Discovering new intents is a crucial task in dialogue systems. Most existing methods are limited in transferring the prior knowledge from known intents to new intents. These methods also have difficulties in providing high-quality supervised signals to learn clustering-friendly features for grouping unlabeled intents. In this work, we propose an effective method (Deep Aligned Clustering) to discover new intents with the aid of limited known intent data. Firstly, we leverage a few labeled known intent samples as prior knowledge to pre-train the model. Then, we perform k-means to produce cluster assignments as pseudo-labels. Moreover, we propose an alignment strategy to tackle the label inconsistency problem during clustering assignments. Finally, we learn the intent representations under the supervision of the aligned pseudo-labels. With an unknown number of new intents, we predict the number of intent categories by eliminating low-confidence intent-wise clusters. Extensive experiments on two benchmark datasets show that our method is more robust and achieves substantial improvements over the state-of-the-art methods.
News Content Completion with Location-Aware Image Selection
Authors: Zhang Zhengkun , Wang Jun , Jatowt Adam , Sun Zhe , Lu Shao-Ping , Yang Zhenglu
Published: September 2022

News, as one of the fundamental social media types, typically contains both texts and images. Image selection, which involves choosing appropriate images according to some specified contexts, is crucial for formulating good news. However, it presents two challenges: where to place images and which images to use. The difficulties associated with this where-which problem lie in the fact that news typically contains linguistically rich text that delivers complex information and more than one image. In this paper, we propose a novel end-to-end two-stage framework to address these issues comprehensively. In the first stage, we identify key information in news by using location embeddings, which represent the local contextual information of each candidate location for image insertion. Then, in the second stage, we thoroughly examine the candidate images and select the most context-related ones to insert into each location identified in the first stage. We also introduce three insertion strategies to formulate different scenarios influencing the image selection procedure. Extensive experiments demonstrate the consistent superiority of the proposed framework in image selection.
Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference
Authors: Zhou Yichao , Yan Yu , Han Rujun , Caufield J. Harry , Chang Kai-Wei , Sun Yizhou , Ping Peipei , Wang Wei
Published: September 2022

There has been a steady need in the medical community to precisely extract the temporal relations between clinical events. In particular, temporal information can facilitate a variety of downstream applications such as case report retrieval and medical question answering. Existing methods either require expensive feature engineering or are incapable of modeling the global relational dependencies among the events. In this paper, we propose a novel method, Clinical Temporal ReLation Exaction with Probabilistic Soft Logic Regularization and Global Inference (CTRL-PG) to tackle the problem at the document level. Extensive experiments on two benchmark datasets, I2B2-2012 and TB-Dense, demonstrate that CTRL-PG significantly outperforms baseline methods for temporal relation extraction.
Multi-Layer Networks for Ensemble Precipitation Forecasts Postprocessing
Authors: Xu Fengyang , Li Guanbin , Du Yunfei , Chen Zhiguang , Lu Yutong
Published: September 2022

The postprocessing method of ensemble forecasts is usually used to find a more precise estimate of future precipitation, because dynamic meteorology models have limitations in fitting fine-grained atmospheric processes and precipitation is driven more often by smaller-scale processes, while ensemble forecasts can hit this precipitation at times. However, the pattern of these hits cannot be easily summarized. The existing objective postprocessing methods tend to extend the rain area or false alarm the precipitation intensity categories. In this work, we introduce a multi-layer structure to simultaneously reduce the bias in forecast ensembles output by meteorology models and merge them to a quality deterministic (single-valued) forecast using cross-grid information, which differs quite dramatically from the previous statistical postprocessing method. The multi-layer network is designed to model the spatial distribution of future precipitation of different intensity categories(IC-MLNet). We provide a comparison of IC-MLNet to simple average as well as another two state-of-the-art ensemble quantitative precipitation forecasts (QPFs) postprocessing approaches over both single-model and multi-model ensemble forecasts datasets from TIGGE. The experimental results indicate that our model achieves superior performance over the compared baselines in precipitation amount prediction as well as precipitation intensities categories prediction.
Towards a Unifying Framework for Formal Theories of Novelty
Authors: Boult Terrance , Grabowicz Przemyslaw , Prijatelj Derek , Stern Roni , Holder Lawrence , Alspector Joshua , M. Jafarzadeh Mohsen , Ahmad Toqueer , Dhamija Akshay , Li Chunchun , Cruz Steve , Shrivastava Abhinav , Vondrick Carl , Walter Scheirer
Published: September 2022

Managing inputs that are novel, unknown, or out-of-distribution is critical as an agent moves from the lab to the open world. Novelty-related problems include being tolerant to novel perturbations of the normal input, detecting when the input includes novel items, and adapting to novel inputs. While significant research has been undertaken in these areas, a noticeable gap exists in the lack of a formalized definition of novelty that transcends problem domains. As a team of researchers spanning multiple research groups and different domains, we have seen, first hand, the difficulties that arise from ill-specified novelty problems, as well as inconsistent definitions and terminology. Therefore, we present the first unified framework for formal theories of novelty and use the framework to formally define a family of novelty types. Our framework can be applied across a wide range of domains, from symbolic AI to reinforcement learning, and beyond to open world image recognition. Thus, it can be used to help kick-start new research efforts and accelerate ongoing work on these important novelty-related problems.
Improving Causal Inference by Increasing Model Expressiveness
Authors: Jensen David D.
Published: September 2022

The ability to learn and reason with causal knowledge is a key aspect of intelligent behavior. In contrast to mere statistical association, knowledge of causation enables reasoning about the effects of actions. Causal reasoning is vital for autonomous agents and for a range of applications in science, medicine, business, and government. However, current methods for causal inference are hobbled because they use relatively inexpressive models. Surprisingly, current causal models eschew nearly every major representational innovation common in a range of other fields both inside and outside of computer science, including representation of objects, relationships, time, space, and hierarchy. Even more surprisingly, a range of recent research provides strong evidence that more expressive representations make possible causal inferences that are otherwise impossible and remove key biases that would otherwise afflict more naive inferences. New research on causal inference should target increases in expressiveness to improve accuracy and effectiveness.
JEL: Applying End-to-End Neural Entity Linking in JPMorgan Chase
Authors: Ding Wanying , Chaudhri Vinay K. , Chittar Naren , Konakanchi Krihshna
Published: September 2022

Knowledge Graphs have emerged as a compelling abstraction for capturing key relationship among the entities of interest to enterprises and for integrating data from heterogeneous sources. JPMorgan Chase (JPMC) is leading this trend by leveraging knowledge graphs across the organization for multiple mission critical applications such as risk assessment, fraud detection, investment advice, etc. A core problem in leveraging a knowledge graph is to link mentions (e.g., company names) that are encountered in textual sources to entities in the knowledge graph. Although several techniques exist for entity linking, they are tuned for entities that exist in Wikipedia, and fail to generalize for the entities that are of interest to an enterprise. In this paper, we propose a novel end-to-end neural entity linking model (JEL) that uses minimal context information and a margin loss to generate entity embeddings, and a Wide & Deep Learning model to match character and semantic information respectively. We show that JEL achieves the state-of-the-art performance to link mentions of company names in financial news with entities in our knowledge graph. We report on our efforts to deploy this model in the company-wide system to generate alerts in response to financial news. The methodology used for JEL is directly applicable and usable by other enterprises who need entity linking solutions for data that are unique to their respective situations.
Predicting Mining Industry Accidents with a Multitask Learning Approach
Authors: Palma Rodolfo , Martí Luis , Sánchez-Pi Nayat
Published: September 2022

The mining sector is a very relevant part of the Chilean economy, representing more than 14% of the country’s GDP and more than 50% of its exports. However, mining is also a high-risk activity where health, safety, and environmental aspects are fundamental concerns to take into account to render it viable in the longer term. The Chilean National Geology and Mining Service (SERNAGEOMIN, after its name in Spanish) is in charge of ensuring the safe operation of mines. On-site inspections are their main tool in order to detect issues, propose corrective measures, and track the compliance of those measures. Consequently, it is necessary to create inspection programs relying on a data-based decision-making strategy. This paper reports the work carried out in one of the most relevant dimensions of said strategy: predicting the mining worksites accident risk. That is, how likely it is a mining worksite to have accidents in the future. This risk is then used to create a priority ranking that is used to devise the inspection program. Estimating this risk at the government regulator level is particularly challenging as there is a very limited and biased data. Our main contribution is to apply a multi-task learning approach to train the risk prediction model in such a way that is able to overcome the constraints of the limited availability of data by fusing different sources. As part of this work, we also implemented a human-experience-based model that captures the procedures currently used by the current experts in charge of elaborating the inspection priority ranking. The mining worksites risk rankings built by model achieve a 121.2% NDCG performance improvement over the rankings based on the currently used experts’ model and outperforms the non-multi-task learning alternatives.
A Heuristic Evaluation Function for Hand Strength Estimation in Gin Rummy
Authors: Ahmed Aqib , Leppo Joshua , Lesniewski Michal , Patel Riken , Perez Jonathan , Blum Jeremy
Published: September 2022

This paper describes a fast hand strength estimation mod-el for the game of Gin Rummy. The algorithm is computationally inexpensive, and it incorporates not only cards in the player’s hand but also cards known to be in the opponent’s hand, cards in the discard pile, and the current game stage. This algorithm is used in conjunction with counterfactual regret (CFR) minimization to develop a gin rummy bot. CFR strategies were developed for the knocking strategies. The hand strength estimation algorithm was used to select a discard that balances the goals of maximizing the utility of the player’s hand and minimizing the likelihood that a card will be useful to the opponent. A study of the parameterization of this estimation algorithm demonstrates the soundness of approach as well as good performance under a wide range of parameter values.
Heisenbot: A Rule-Based Game Agent for Gin Rummy
Authors: Eicholtz Matthew , Moss Savanna , Traino Matthew , Roberson Christian
Published: September 2022

Games are an excellent tool for undergraduate research in artificial intelligence because they typically have clear objectives, a limited action space, and well-defined constraints. Nonetheless, games involving chance and imperfect information offer unique challenges for optimizing gameplay. In this paper, we analyze one such card game, gin rummy, and propose an artificial intelligence player based on empirically driven strategies. Our approach separates gameplay into three disjoint policies for drawing, discarding, and knocking. On each turn, decisions are influenced by offensive considerations as well as defensive moves. Tournament-style simulations enable us to determine statistically which combination of policies achieves the highest win rate. Our resulting player, dubbed Heisenbot, is competitive against strong baseline strategies.
Estimating Card Fitness for Discard in Gin Rummy
Authors: Gallucci Jacob , Bowser Richard , Kettell Sarah , Overton Christian
Published: September 2022

Due to the computation time and resources required, there is no known optimal strategy for the game of Gin Rummy. Previous work in extensive games, such as Texas Hold ’em Poker, has found that hand fitness and information sets about the state of the game can be used to determine an improved strategy. These information sets, combined with algorithms for Counterfactual Regret Minimization, can arrive at a Nash Equilibrium strategy for smaller abstractions of extensive games. This paper builds on previous research by extending the premise of hand fitness to card fitness in the discard decision point of Gin Rummy. We argue that a card can be ranked based on whether it meets four specific characteristics at that stage in the game. These characteristics include its effect on deadwood points after one more turn, its utility to the opponent, and if it can contribute to a meld. An optimal discard choice can then be picked from the highest-ranked card by using a simplified Counterfactual regret minimization strategy that can be trained in less time due to its limited information set. While this does not look at every potential characteristic of card fitness, it outperformed other bots when evaluated in a large number of games. These bots did not consider card fitness as a whole, but rather considered characteristics separately. We argue that the characteristics defined are a part of the total information set that can determine the discard fitness of a card within a hand in the game of Gin Rummy.
Applied Machine Learning for Games: A Graduate School Course
Authors: Zeng Yilei , Shah Aayush , Thai Jameson , Zyda Michael
Published: September 2022

The game industry is moving into an era where old-style game engines are being replaced by re-engineered systems with embedded machine learning technologies for the operation, analysis and understanding of game play. In this paper, we describe our machine learning course designed for graduate students interested in applying recent advances of deep learning and reinforcement learning towards gaming. This course serves as a bridge to foster interdisciplinary collaboration among graduate schools and does not require prior experience designing or building games. Graduate students enrolled in this course apply different fields of machine learning techniques such as computer vision, natural language processing, computer graphics, human computer interaction, robotics and data analysis to solve open challenges in gaming. Student projects cover use-cases such as training AI-bots in gaming benchmark environments and competitions, understanding human decision patterns in gaming, and creating intelligent non-playable characters or environments to foster engaging gameplay. Projects demos can help students open doors for an industry career, aim for publications, or lay the foundations of a future product. Our students gained hands-on experience in applying state of the art machine learning techniques to solve real-life problems in gaming.
Towards Fair, Equitable, and Efficient Peer Review
Authors: Stelmakh Ivan
Published: September 2022

Peer review is the backbone of academia. The rapid growth of the number of submissions to leading publication venues has identified a need for automation of some parts of the peer-review pipeline and nowadays human referees are required to interact with various interfaces and technologies in this process. However, there exists evidence that if such interactions are not carefully designed, they can exacerbate various problems related to fairness and efficiency of the process. In my research, I aim to design a Human-AI collaboration pipeline in peer review to mitigate these issues and ensure that science progresses in a fair, equitable, and efficient manner.
NEAP-F: Network Epoch Accuracy Prediction Framework (Student Abstract)
Authors: Chauhan Arushi , Vatsa Mayank , Singh Richa
Published: September 2022

Recent work in neural architecture search has spawned interest in algorithms that can predict the performance of convolutional neural networks using minimum time and computation resources. We propose a new framework, Network Epoch Accuracy Prediction Framework (NEAP-F) which can predict the testing accuracy achieved by a convolutional neural network in one or more epochs. We introduce a novel approach to generate vector representations for networks, and encode ``ease" of classifying image datasets into a vector. For vector representations of networks, we focus on the layer parameters and connections between the network layers. A network achieves different accuracies on different image datasets; therefore, we use the image dataset characteristics to create a vector signifying the ``ease" of classifying the image dataset. After generating these vectors, the prediction models are trained with architectures having skip connections seen in current state-of-the-art architectures. The framework predicts accuracies in order of milliseconds, demonstrating its computational efficiency. It can be easily applied to neural architecture search methods to predict the performance of candidate networks and can work on unseen datasets as well.
Incorporating Curiosity into Personalized Ranking for Collaborative Filtering (Student Abstract)
Authors: Ding Qiqi , Cai Yi , Xu Ke , Zhang Huakui
Published: September 2022

Curiosity affects users' selections of items, and it motivates them to explore the items regardless of their interests. This phenomenon is particularly common in social networks.
However, the existing social-based recommendation methods neglect such feature in social network, and it may cause the accuracy decease in recommendation. What's more, only focusing on simulating the users' preferences can lead to information cocoons. In order to tackle the problem, we propose a novel Curiosity Enhanced Bayesian Personalized Ranking (CBPR) model. Our model makes full use of the theories of psychology to model the users' curiosity aroused when facing different opinions. The experimental results on two public datasets demonstrate the advantages of our CBPR model over the existing models.
Demonstrating the Equivalence of List Based and Aggregate Metrics to Measure the Diversity of Recommendations (Student Abstract)
Authors: Ferrari Dacrema Maurizio
Published: September 2022

The evaluation of recommender systems is frequently focused on accuracy metrics, but this is only part of the picture. The diversity of recommendations is another important dimension that has received renewed interest in recent years. It is known that accuracy and diversity can be conflicting goals and finding appropriate ways to combine them is still an open research question. Several ways have been proposed to measure the diversity of recommendations and to include its optimization in the loss function used to train the model. Methods optimizing list based diversity suffer from two drawbacks: the high computational cost of the loss function and the lack of an efficient way to optimize them.
In this paper we show the equivalence of the list based diversity metrics Hamming and Mean Inter-List diversity to the aggregate diversity metric measured with the Herfindahl index, providing a formulation that allows to compute and optimize them easily.
Text Embedding Bank for Detailed Image Paragraph Captioning
Authors: Gupta Arjun , Shen Zengming , Huang Thomas
Published: September 2022

Existing deep learning-based models for image captioning typically consist of an image encoder to extract visual features and a language model decoder, an architecture that has shown promising results in single high-level sentence generation. However, only the word-level guiding signal is available when the image encoder is optimized to extract visual features. The inconsistency between the parallel extraction of visual features and sequential text supervision limits its success when the length of the generated text is long (more than 50 words). We propose a new module, called the Text Embedding Bank (TEB), to address this problem for image paragraph captioning. This module uses the paragraph vector model to learn fixed-length feature representations from a variable-length paragraph. We refer to the fixed-length feature as the TEB. This TEB module plays two roles to benefit paragraph captioning performance. First, it acts as a form of global and coherent deep supervision to regularize visual feature extraction in the image encoder. Second, it acts as a distributed memory to provide features of the whole paragraph to the language model, which alleviates the long-term dependency problem. Adding this module to two existing state-of-the-art methods achieves a new state-of-the-art result on the paragraph captioning Stanford Visual Genome dataset.
Leveraging on Deep Reinforcement Learning for Autonomous Safe Decision-Making in Highway On-ramp Merging (Student Abstract)
Authors: Kherroubi Zine el abidine , Aknine Samir , Bacha Rebiha
Published: September 2022

High-speed highway on-ramp merging is one of the most difficult and critical tasks for any autonomous driving system. This work studies this problem by combining deep deterministic policy gradient (DDPG) reinforcement learning with drivers’ intentions prediction. Our proposed solution is based on an artificial neural network to predict drivers’ intentions, used as an input state to the DDPG agent that outputs the longitudinal acceleration to the merging vehicle. We show that this solution improves safety performances.
A Nested Named Entity Recognition Model Based on Multi-agent Communication Mechanism (Student Abstract)
Authors: Li Canguang , Wang Guohua , Cao Jin , Cai Yi
Published: September 2022

Traditional sequence tagging methods for named entity recognition (NER) face challenges when handling nested entities, where an entity is nested in another. Most previous methods for nested NER ignore the effect of entity boundary information or type information. Considering that entity boundary information and type information can be utilized to improve the performance of boundary detection, we propose a nested NER model with a multi-agent communication module. The type tagger and boundary tagger in the multi-agent communication module iteratively utilize the information from each other, which improves the boundary detection and the final performance of nested NER. Empirical experiments conducted on two nested NER datasets show the effectiveness of our model.
Source Separation and Depthwise Separable Convolutions for Computer Audition (Student Abstract)
Authors: Mersy Gabriel , Kuan Jin Hong
Published: September 2022

Given recent advances in deep music source separation, we propose a feature representation method that combines source separation with a state-of-the-art representation learning technique that is suitably repurposed for computer audition (i.e. machine listening). We train a depthwise separable convolutional neural network on a challenging electronic dance music (EDM) data set and compare its performance to convolutional neural networks operating on both source separated and standard spectrograms. It is shown that source separation improves classification performance in a limited-data setting compared to the standard single spectrogram approach.
Task Uncertainty Loss Reduce Negative Transfer in Asymmetric Multi-task Feature Learning (Student Abstract)
Authors: Peres da Silva Rafael , Suphavilai Chayaporn , Nagarajan Niranjan
Published: September 2022

Multi-task learning (MTL) is frequently used in settings where a target task has to be learnt based on limited training data, but knowledge can be leveraged from related auxiliary tasks. While MTL can improve task performance overall relative to single-task learning (STL), these improvements can hide negative transfer (NT), where STL may deliver better performance for many individual tasks. Asymmetric multi-task feature learning (AMTFL) is an approach that tries to address this by allowing tasks with higher loss values to have smaller influence on feature representations for learning other tasks. Task loss values do not necessarily indicate reliability of models for a specific task. We present examples of NT in two orthogonal datasets (image recognition and pharmacogenomics) and tackle this challenge by using aleatoric homoscedastic uncertainty to capture the relative confidence between tasks, and set weights for task loss. Our results show that this approach reduces NT providing a new approach to enable robust MTL.
LB-DESPOT: Efficient Online POMDP Planning Considering Lower Bound in Action Selection (Student Abstract)
Authors: Wu Chenyang , Kong Rui , Yang Guoyu , Kong Xianghan , Zhang Zongzhang , Yu Yang , Li Dong , Liu Wulong
Published: September 2022

Partially observable Markov decision process (POMDP) is an extension to MDP. It handles the state uncertainty by specifying the probability of getting a particular observation given the current state. DESPOT is one of the most popular scalable online planning algorithms for POMDPs, which manages to significantly reduce the size of the decision tree while deriving a near-optimal policy by considering only $K$ scenarios. Nevertheless, there is a gap in action selection criteria between planning and execution in DESPOT. During the planning stage, it keeps choosing the action with the highest upper bound, whereas when the planning ends, the action with the highest lower bound is chosen for execution. Here, we propose LB-DESPOT to alleviate this issue, which utilizes the lower bound in selecting an action branch to expand. Empirically, our method has attained better performance than DESPOT and POMCP, which is another state-of-the-art, on several challenging POMDP benchmark tasks.
Local Search for Diversified Top-k s-plex Search Problem (Student Abstract)
Authors: Wu Jun , Yin Minghao
Published: September 2022

The diversified top-k s-plex (DTKSP) search problem aims to find k maximal s-plexes that cover the maximum number of vertices with lower overlapping in a given graph. In this paper, we first formalize the diversified top-k s-plex search problem and prove the NP-hardness of it. Second, we proposed a local search algorithm for solving the diversified top-k s-plex search problem based on some novel ideas. Experiments on real-world massive graphs show the effectiveness of our algorithm.
A Double Phases Generation Network for Yes or No Question Generation (Student Abstract)
Authors: Xie Jiayuan , Chen Feng , Cai Yi , Lin Zehang
Published: September 2022

This paper aims to solve the task of generating yes or no questions, which generates yes/no questions based on given passages. These questions can be used for evaluation automatically. We propose a double phases generation network that can identify specific phrases related to facts from the input passage and use them as auxiliary information for generation. Specifically, the 1st-phase prediction uses the extracted phrases as assistance to generate an initial question. Then, the 2nd-phase prediction utilizes an attention network to focus on the relevant phrases related to the initial question in the passage to generate questions that are more relevant to the specific facts contained in the initial question. Extensive experiments we performed on BoolQ dataset demonstrate the effectiveness of our framework.
Modeling High-order Interactions across Multi-interests for Micro-video Reommendation (Student Abstract)
Authors: Yao Dong , Zhang Shengyu , Zhao Zhou , Fan Wenyan , Zhu Jieming , He Xiuqiang , Wu Fei
Published: September 2022

Personalized recommendation system has become pervasive in various video platform.Many effective methods have been proposed, but most of them didn’t capture the user’s multilevel interest trait and dependencies between their viewed micro-videos well. To solve these problems, we propose a Self-over-Co Attention module to enhance user’s interest representation. In particular, we ﬁrst use co-attention to model correlation patterns across different levels and then use self attention to modelcorrelation patterns within a speciﬁc level. Experimental results on ﬁltered public datasets verify that our presented module is useful.
The Price of Anarchy in ROSCAS with Risk Averse Agents
Authors: Ikeokwu Christian
Published: September 2022

Rotating Savings and Credit Associations (Roscas) are a widely documented informal financial organization that is often used in low-income communities with limited funding sources. Roscas have been shown to serve as a tool for economic empowerment and a way of mitigating adverse shocks to income for vulnerable communities. In this paper, I describe my contributions to a research project in which we study the allocative efficiency of different Rosca structures and formats in the presence of risk-averse agents.
Affect-Aware Machine Learning Models for Deception Detection
Authors: Mathur Leena
Published: September 2022

Automated deception detection systems can enhance societal well-being by helping humans detect deceivers and support people in high-stakes situations across health, social work, and legal domains. Existing computational approaches for detecting deception have not leveraged dimensional representations of affect, specifically valence and arousal, expressed during communication. My research presents a novel analysis of the potential for including affect in machine learning models for detecting deception. My work informs and motivates the development of affect-aware machine learning approaches for modeling deception and other social behaviors during human interactions in-the-wild. This research, independently defined and conducted by me, is from work-in-progress towards my undergraduate thesis in the Department of Computer Science at the University of Southern California.
Bison Hacks the Yard: Assisting Underrepresented Students Overcome Impostor Syndrome with Augmented Reality and Artificial Intelligence
Authors: Sullivan Nicole
Published: September 2022

The prevalence of impostor syndrome in computer science students from underrepresented backgrounds contributes to low retention rates. Bison Hacks the Yard is an augmented reality game that aims to reduce impostor syndrome in underrepresented students by presenting a novel way to strengthen their knowledge of fundamental data structures and providing specialized videos of Historically Black College or University alumni, sharing their struggles with impostor syndrome.