论文检索

Shengming Wang, Xiaocai Zhang, Jing Li, Xiaoyang Wei, Hoong Chuin Lau, Bing Tian Dai, Binbin Huang, Zhe Xiao, Xiuju Fu, Zheng Qin

In recent years, there are trends toward cleaner port environments through enforcement by imposed legislation. Transit optimisation of fuel-based port service boats like harbour tugs has emerged as a critical task to reduce fuel consumption and carbon emission. In this paper, an innovative learning-based method, comprising a Reinforcement Learning (RL) model together with a fuel consumption prediction model, was proposed to formulate fuel-saving transit routes. Firstly, an ensemble model is established by combining a Long Short-Term Memory (LSTM) model with a Multilayer Perceptron (MLP) model, predicting fuel use based on tugboat movement and environment factors. Subsequently, an innovative RL based on Deep Deterministic Policy Gradient (DDPG) framework is developed considering the characteristics and obstructions of waterway in Singapore as well as the environmental factors to learn the optimal transit strategy that minimizes fuel consumption. We also demonstrate the efficacy of the solution to generate routes from origin to destination terminals, exhibiting significantly reduced fuel consumption in comparison to real-world transit scenarios.

Nguyen Thach, Patrick Habecker, Bergen Johnston, Lillianna Cervantes, Anika Eisenbraun, Alex Mason, Kimberly Tyler, Bilal Khan, Hau Chan

Substance use is a global issue that negatively impacts millions of persons who use drugs (PWUDs). In practice, identifying vulnerable PWUDs for efficient allocation of appropriate resources is challenging due to their complex use patterns (e.g., their tendency to change usage within months) and the high acquisition costs for collecting PWUD-focused substance use data. Thus, there has been a paucity of machine learning models for accurately predicting short-term substance use behaviors of PWUDs. In this paper, using longitudinal survey data of 258 PWUDs in the U.S. Great Plains collected by our team, we design a novel GAN that deals with high-dimensional low-sample-size tabular data and survey skip logic to augment existing data to improve classification models' prediction on (A) whether the PWUDs would increase usage and (B) at which ordinal frequency they would use a particular drug within the next 12 months. Our evaluation results show that, when trained on augmented data from our proposed GAN, the classification models improve their predictive performance (AUROC) by up to 13.4% in Problem (A) and 15.8% in Problem (B) for usage of marijuana, meth, amphetamines, and cocaine, which outperform state-of-the-art generative models.

Salam Michael Singh, Shubhmoy Kumar Garg, Amitesh Misra, Aaditeshwar Seth, Tanmoy Chakraborty

Sexual education aims to foster a healthy lifestyle in terms of emotional, mental and social well-being. In countries like India, where adolescents form the largest demographic group, they face significant vulnerabilities concerning sexual health. Unfortunately, sexual education is often stigmatized, creating barriers to providing essential counseling and information to this at-risk population. Consequently, issues such as early pregnancy, unsafe abortions, sexually transmitted infections, and sexual violence become prevalent. Our current proposal aims to provide a safe and trustworthy platform for sexual education to the vulnerable rural Indian population, thereby fostering the healthy and overall growth of the nation. In this regard, we strive towards designing SUKHSANDESH, a multi-staged AI-based Question Answering platform for sexual education tailored to rural India, adhering to safety guardrails and regional language support. By utilizing information retrieval techniques and large language models, SUKHSANDESH will deliver effective responses to user queries. We also propose to anonymise the dataset to mitigate safety measures and set AI guardrails against any harmful or unwanted response generation. Moreover, an innovative feature of our proposal involves integrating "avatar therapy" with SUKHSANDESH. This feature will convert AI-generated responses into real-time audio delivered by an animated avatar speaking regional Indian languages. This approach aims to foster empathy and connection, which is particularly beneficial for individuals with limited literacy skills. Partnering with Gram Vanni, an industry leader, we will deploy SUKHSANDESH to address sexual education needs in rural India.

Karandeep Singh, Chaeyoon Jeong, Naufal Shidqi, Sungwon Park, Arjun Nellikkattil, Elke Zeller, Meeyoung Cha

Climate change is one of the most critical challenges that our planet is facing today. Rising global temperatures are already affecting Earth's weather and climate patterns with an increased frequency of unpredictable and extreme events. Future projections for climate change research are based on computer models like Earth System Models (ESMs). Climate simulations typically run on a coarser grid due to the high computational resources required, and then undergo a lighter downscaling process to obtain data on a finer grid. This work presents a self-supervised deep learning model that does not require high resolution ground truth data for downscaling. This is realized by leveraging salient distribution patterns and the hidden dependencies between weather variables for an individual data point at runtime. We propose three climate-specific components that well represent the patterns of underlying weather variables and learn intricate inter-variable dependencies. Extensive evaluation with 2x, 3x, and 4x scaling factors demonstrates that our model obtains 8% to 47% performance gain over existing baselines while greatly reducing the overall runtime. The improved performance and no dependence on high resolution ground truth data make our method a valuable tool for future climate research.

Gopendra Vikram Singh, Soumitra Ghosh, Neil Dcruze, Asif Ekbal

In an era where language biases contribute to societal inequalities, this research focuses on gender bias in textual data, with profound implications for promoting inclusivity and equity, aligning with United Nations Sustainable Development Goals (SDGs) and upholding the principle of Leave No One Behind (LNOB). Leveraging advances in artificial intelligence, the study introduces the GEnder-NEutralizing Text Transformation (GENETT) framework, addressing gender bias in text through auto-encoders, vector quantization, and Neutrality-Infused Stylization. Furthermore, we present the first-of-its-kind corpus of GEnder Neutralized REvisions (GENRE) crafted from gender-stereotyped versions. This corpus serves a multifaceted utility, offering a resource for diverse downstream tasks in gender-bias analysis. Extensive experimentation on GENRE highlights the superiority of the proposed model over established baselines and state-of-the-art methods. Access the code and dataset at 1. https://www.iitp.ac.in/~ai-nlp-ml/resources.html#GNR, 2. https://github.com/Soumitra816/GNR. Note: Our research focuses on understanding cyber harassment conversations, especially in under-researched areas, with the exclusion of non-binary cases due to existing dataset limitations, not lack of sensitivity. We strive for inclusivity and plan to address this in future research with suitable datasets.

Riyad Bin Rafiq, Weishi Shi, Mark V. Albert

Hand gestures can provide a natural means of human-computer interaction and enable people who cannot speak to communicate efficiently. Existing hand gesture recognition methods heavily depend on pre-defined gestures, however, motor-impaired individuals require new gestures tailored to each individual's gesture motion and style. Gesture samples collected from different persons have distribution shifts due to their health conditions, the severity of the disability, motion patterns of the arms, etc. In this paper, we introduce the Latent Embedding Exploitation (LEE) mechanism in our replay-based Few-Shot Continual Learning (FSCL) framework that significantly improves the performance of fine-tuning a model for out-of-distribution data. Our method produces a diversified latent feature space by leveraging a preserved latent embedding known as gesture prior knowledge, along with intra-gesture divergence derived from two additional embeddings. Thus, the model can capture latent statistical structure in highly variable gestures with limited samples. We conduct an experimental evaluation using the SmartWatch Gesture and the Motion Gesture datasets. The proposed method results in an average test accuracy of 57.0%, 64.6%, and 69.3% by using one, three, and five samples for six different gestures. Our method helps motor-impaired persons leverage wearable devices, and their unique styles of movement can be learned and applied in human-computer interaction and social communication. Code is available at: https://github.com/riyadRafiq/wearable-latent-embedding-exploitation.

Sophie Pavia, David Rogers, Amutheezan Sivagnanam, Michael Wilbur, Danushka Edirimanna, Youngseo Kim, Philip Pugliese, Samitha Samaranayake, Aron Laszka, Ayan Mukhopadhyay 等

While on-demand ride-sharing services have become popular in recent years, traditional on-demand transit services cannot be used by everyone, e.g., people who use wheelchairs. Paratransit services, operated by public transit agencies, are a critical infrastructure that offers door-to-door transportation assistance for individuals who face challenges in using standard transit routes. However, with declining ridership and mounting financial pressure, public transit agencies in the USA struggle to operate existing services. We collaborate with a public transit agency from the southern USA, highlight the specific nuances of paratransit optimization, and present a vehicle routing problem formulation for optimizing paratransit. We validate our approach using real-world data from the transit agency, present results from an actual pilot deployment of the proposed approach in the city, and show how the proposed approach comprehensively outperforms existing approaches used by the transit agency. To the best of our knowledge, this work presents one of the first examples of using open-source algorithmic approaches for paratransit optimization.

Mira Moukheiber, Lama Moukheiber, Dana Moukheiber, Hyung-Chul Lee

In critical care settings, where precise and timely interventions are crucial for health outcomes, evaluating disparities in patient outcomes is important. Current approaches often fall short in comprehensively understanding and evaluating the impact of respiratory support interventions on individuals affected by social determinants of health. Attributes such as gender, race, and age are commonly assessed and essential, but provide only a partial view of the complexities faced by diverse populations. In this study, we focus on two clinically motivated tasks: prolonged mechanical ventilation and successful weaning. We also perform fairness audits on the models' predictions across demographic groups and social determinants of health to better understand the health inequities in respiratory interventions in the intensive care unit. We also release a temporal benchmark dataset, verified by clinical experts, to enable benchmarking of clinical respiratory intervention tasks.

Eleonora Misino, Roberta Calegari, Michele Lombardi, Michela Milano

Recent advancements in Artificial Intelligence in Education (AIEd) have revolutionized educational practices using machine learning to extract insights from students' activities and behaviours. Performance prediction, a key domain within AIEd, aims to enhance student achievement levels and address sustainable development goals related to education, health, gender equality, and economic growth. However, the potential of AIEd to contribute to these goals is hindered by the lack of attention to fairness in prediction algorithms, leading to educational inequality. To address this gap, we introduce FAiRDAS a general framework that models long-term fairness as an abstract dynamic system. Our approach, illustrated through a case study in AIEd with real data, offers a customizable solution to promote long-term fairness while promoting the stability of mitigation actions over time.

Siti Mariyah, Wayne Wobcke

Previous domain adaptation methods are designed to work for a single task, either classification or regression. In this paper, the task of the learner is to produce both an estimation and an ordinal classification of instances that are consistent in that the classification of instances into quantiles is derived from the estimated values. We propose an extension of the boosting for transfer method (TrAdaBoost), Joint Quantile Loss Boosting Domain Adaptation (TrAdaBoost.JQL) for regression transfer learning, that aims to jointly minimize regression and ordinal classification errors. Motivated by the real-world problem of poverty targeting using the Proxy Means Test, we empirically show that TrAdaBoost.JQL can consistently reduce RMSE and inclusion and exclusion errors for estimating per capita household expenditure, across a wide variety of districts in Indonesia, compared to other reweighting-based and invariant feature representation-based domain adaptation methods. We design TrAdaBoost.JQL to be flexible as to the chosen eligibility (poor) threshold used in poverty targeting practice and as to whether estimation or ordinal classification accuracy is prioritized.

Rui Mao, Qika Lin, Qiawen Liu, Gianmarco Mengaldo, Erik Cambria

Extreme weather can lead to weather-induced disasters. These have a profound impact on communities worldwide, causing loss of life, damage to properties and infrastructure, and disruption of daily activities. In alignment with the United Nations Sustainable Development Goals, addressing the increasing frequency and severity of these events, exacerbated by climate change, is imperative. Exploring public perception and responses to weather disasters becomes crucial for policymakers to formulate effective strategies that not only mitigate the impacts but also contribute to the goal of ensuring sustainable and resilient communities. Social media, as a pervasive and real-time communication platform, has gathered a large amount of public opinion. In this work, we analyze public perception towards weather disasters based on tweets and metaphors. Metaphor, as a linguistic device, plays a pivotal role in unraveling cognitive processes and understanding how individuals perceive and make sense of concepts. We focus on tweets related to four distinct types of weather disasters i.e., floods, hurricanes, tornadoes, and wildfires, aiming to extract nuanced insights regarding public perceptions, concerns, and attitudes towards these specific events. We also deliver constructive recommendations, based on the insights.

Zhaoyuan Lu, Taijun Li, Jingzhen Zhang, Moyang Liu, Xiang Li, Linyi Cui, Junqi Chen, Zhibin Niu

In the face of economic downturns, Small and Medium-sized Enterprises (SMEs) within interconnected networked-loans are vulnerable to cascading debt crises, exacerbated by factors like social media-induced financial shocks. Traditional risk assessment models, which mainly rely on financial data, inadequately predict such crises, as evidenced by the collapse of Silicon Valley Bank in 2023. To address this issue, we developed RisQNet, a model that uses temporal graph networks to incorporate diverse risks, including real-time media influences. This approach not only advances risk prediction through news feature extraction and large language models but also enhances risk management strategies with intuitive visualization tools. Validated on a dataset with a total loan volume of USD 3 trillion, RisQNet outperforms the state-of-the-art baseline and achieves 87.1% of AUC. Our collaborative effort with financial regulators and the SME community underpins the model's development, aligning with the UN SDG 8. RisQNet represents a significant step forward in leveraging AI for financial stability, offering a promising approach to combat the propagation of debt crises in financial networks.

Weizhen Liu, Jiayu Tan, Guangyu Lan, Ao Li, Dongye Li, Le Zhao, Xiaohui Yuan, Nanqing Dong

Accurate phenotypic analysis in aquaculture breeding necessitates the quantification of subtle morphological phenotypes. Existing datasets suffer from limitations such as small scale, limited species coverage, and inadequate annotation of keypoints for measuring refined and complex morphological phenotypes of fish body parts. To address this gap, we introduce FishPhenoKey, a comprehensive dataset comprising 23,331 high-resolution images spanning six fish species. Notably, FishPhenoKey includes 22 phenotype-oriented annotations, enabling the capture of intricate morphological phenotypes. Motivated by the nuanced evaluation of these subtle morphologies, we also propose a new evaluation metric, Percentage of Measured Phenotypes (PMP). It is designed to assess the accuracy of individual keypoint positions and is highly sensitive to the phenotype measured using the corresponding keypoints. To enhance keypoint detection accuracy, we further propose a novel loss, Anatomically-Calibrated Regularization (ACR), that can be integrated into keypoint detection models, leveraging biological insights to refine keypoint localization. Our contributions set a new benchmark in fish phenotype analysis, addressing the challenges of precise morphological quantification and opening new avenues for research in sustainable aquaculture and genetic studies. Our dataset and code are available at https://github.com/WeizhenLiuBioinform/FishPhenotype-Detect.

Weizhen Liu, Ao Li, Ze Wu, Yue Li, Baobin Ge, Guangyu Lan, Shilin Chen, Minghe Li, Yunfei Liu, Xiaohui Yuan 等

Hierarchical leaf vein segmentation is a crucial but under-explored task in agricultural sciences, where analysis of the hierarchical structure of plant leaf venation can contribute to plant breeding. While current segmentation techniques rely on data-driven models, there is no publicly available dataset specifically designed for hierarchical leaf vein segmentation. To address this gap, we introduce the HierArchical Leaf Vein Segmentation (HALVS) dataset, the first public hierarchical leaf vein segmentation dataset. HALVS comprises 5,057 real-scanned high-resolution leaf images collected from three plant species: soybean, sweet cherry, and London planetree. It also includes human-annotated ground truth for three orders of leaf veins, with a total labeling effort of 83.8 person-days. Based on HALVS, we further develop a label-efficient learning paradigm that leverages partial label information, i.e. missing annotations for tertiary veins. Empirical studies are performed on HALVS, revealing new observations, challenges, and research directions on leaf vein segmentation. Our dataset and code are available at https://github.com/WeizhenLiuBioinform/ HALVS-Hierarchical-Vein-Segment.

Anush Niranjan Lingamoorthy, Abhishek Kumar Mishra, Suman Kumar, David Gordon, Jacob Brenner, Nagarajan Kandasamy, Amanda Watson

Opioid overdose is an escalating global epidemic, affecting 16 million individuals. Lack of overdose detection and slower response times are the leading causes of overdose deaths. During a fatal opioid overdose, the user exhibits motionlessness, lack of breathing, and hypoxemia (oxygen saturation drops). In this paper, we discuss the development of a shoulder-based wearable overdose detection device that monitors hypoxemia, motion, and respiration. The device's design considers the underserved socio-economic population and their psychological contexts. However, conventional approaches to detecting an overdose typically focus on a single biomarker. To address this, we have developed a robust capsule networks based machine learning (ML) model, OxyCaps that integrates oxygen saturation, respiration rate, and motion to classify different levels of hypoxemia. This also helps improve patient adherence by decreasing the chances of false positive alerts. To determine a hypoxemic state, the model considers various features like skin tone, body physiology, motion, and photoplethysmography (PPG) signals. In the absence of real-world opioid overdose data, our research leverages data collected by our device from 19 patients experiencing sleep apnea, exploiting the parallels between overdose and apnea biomarkers. Our dataset provides a novel compilation of raw PPG and motion signals detected from the shoulder. Our model classifies 3 stages of hypoxemia with an average accuracy of 92%, specifically achieving a high recall of 0.98 for the critical hypoxemic state that is crucial in determining an overdose.

Yuming Lin, Xin Zhang, Yu Liu, Zhenyu Han, Qingmin Liao, Yong Li

Urban villages are areas filled with rural-like improvised structures in Chinese cities, usually housing the most vulnerable groups. Under the guidance of the Sustainable Development Goals (SDGs), the Chinese government initiated renewal and redevelopment projects, underscoring the meticulous mapping and segmentation of urban villages. Satellite imagery is advanced and efficient in identifying urban villages and monitoring changes, but traditional methods neglect the morphological diversity in season, shape, size, spacing, and layout of urban villages, which is not satisfying for long-term wide-range data. Here, we design a targeted approach based on Tobler’s First Law of Geography, using curriculum labeling to solve morphological diversity and semi-automatically generate segmentation for urban village boundaries. Specifically, we use manually labeled data as seeds for pre-trained SegFormer models and incrementally fine-tune the model based on geographical proximity. The rigorous experimentation across five diverse cities substantiates the commendable efficacy of our methodology. IoU metric demonstrates a noteworthy improvement of over 119% to baseline. Our final results cover 265,050 urban villages across 433 cities in China over the past 10 years, and the analysis reveals the uneven redevelopment by geography and city scale. We further examine the within-city distribution and verify the urban scaling law associated with several socio-economic factors. Our method can be used nationwide to decide redevelopment priority and resource tilt, contributing to SDG 11.1 on affordable housing and upgrading slums. The code and dataset are available at https://github.com/tsinghua-fib-lab/LtCUV.

ChungYi Lin, Shen-Lung Tung, Hung-Ting Su, Winston H. Hsu

Traditional traffic prediction, limited by the scope of sensor data, falls short in comprehensive traffic management. Mobile networks offer a promising alternative using network activity counts, but these lack crucial directionality. Thus, we present the TeltoMob dataset, featuring undirected telecom counts and corresponding directional flows, to predict directional mobility flows on roadways. To address this, we propose a two-stage spatio-temporal graph neural network (STGNN) framework. The first stage uses a pre-trained STGNN to process telecom data, while the second stage integrates directional and geographic insights for accurate prediction. Our experiments demonstrate the framework's compatibility with various STGNN models and confirm its effectiveness. We also show how to incorporate the framework into real-world transportation systems, enhancing sustainable urban mobility.

Haicheng Liao, Xuelin Li, Yongkang Li, Hanlin Kong, Chengyue Wang, Bonan Wang, Yanchen Guan, KaHou Tam, Zhenning Li

Trajectory prediction is a cornerstone in autonomous driving (AD), playing a critical role in enabling vehicles to navigate safely and efficiently in dynamic environments. To address this task, this paper presents a novel trajectory prediction model tailored for accuracy in the face of heterogeneous and uncertain traffic scenarios. At the heart of this model lies the Characterized Diffusion Module, an innovative module designed to simulate traffic scenarios with inherent uncertainty. This module enriches the predictive process by infusing it with detailed semantic information, thereby enhancing trajectory prediction accuracy. Complementing this, our Spatio-Temporal (ST) Interaction Module captures the nuanced effects of traffic scenarios on vehicle dynamics across both spatial and temporal dimensions with remarkable effectiveness. Demonstrated through exhaustive evaluations, our model sets a new standard in trajectory prediction, achieving state-of-the-art (SOTA) results on the Next Generation Simulation (NGSIM), Highway Drone (HighD), and Macao Connected Autonomous Driving (MoCAD) datasets across both short and extended temporal spans. This performance underscores the model's unparalleled adaptability and efficacy in navigating complex traffic scenarios, including highways, urban streets, and intersections.

Jinjin Li, Bin Liu, Chengyan Liu, Hongli Zhang

Urban migration is a significant aspect of a city's economy. The exploration of the underlying determinants of housing purchases among current residents contributes to the study of future trends in urban migration, enabling governments to formulate appropriate policies to guide future economic growth. This article employs a factor model to analyze data on residents' rentals, first-time home purchases, and subsequent housing upgrades. We decompose the factors influencing housing purchases into common drivers and specific drivers. Our hypothesis is that common drivers reflect universal social patterns, while personalized drivers represent stochastic elements. We construct a correlation matrix capturing the inter-resident relationships based on the common drivers of housing purchases. We then propose a graph neural network based on the correlation matrix to model housing predictions as a node classification problem. Our model addresses two critical questions. Firstly, we aim to identify which part of rental residents will engage in first-time home purchases in the future. Secondly, we seek to determine which group of residents, having completed rental and first-time home purchases, will opt for a second home purchase. The results of our testing on real-world datasets demonstrate that based solely on rental and home purchase records, we can achieve a sensitivity for housing predictions exceeding 80%.

Yun Sing Koh, Albert Bifet, Karin Bryan, Guilherme Cassales, Olivier Graffeuille, Nick Lim, Phil Mourot, Ding Ning, Bernhard Pfahringer, Varvara Vetrova 等

New Zealand's unique ecosystems face increasing threats from climate change, impacting biodiversity and posing challenges to safety, livelihoods, and well-being. To tackle these complex issues, advanced data science and artificial intelligence techniques can provide unique solutions. Currently, in its fourth year of a seven-year program, TAIAO focuses on methods for analyzing environmental datasets. Recognizing this urgency, the open-source TAIAO platform was developed. This platform enables new artificial intelligence research for environmental data and offers an open-access repository to enhance reproducibility in the field. This paper will showcase four environmental case studies, artificial intelligence research, platform implementation details, and future development plans.