GenAI4ELab/papercli-papers huggingface dataset:
Building a Personalized Messaging System for Health Intervention in Underprivileged Regions Using Reinforcement Learning
Authors: Sarah Kinsey; Jack Wolf; Nalini Saligram; Varun Ramesan; Meeta Walavalkar; Nidhi Jaswal; Sandhya Ramalingam; Arunesh Sinha; Thanh Nguyen
Venue: IJCAI 2023 (main)

Abstract: This work builds an effective AI-based message generation system for diabetes prevention in rural areas, where the diabetes rate has been increasing at an alarming rate. The messages contain information about diabetes causes and complications and the impact of nutrition and fitness on preventing diabetes. We propose to apply reinforcement learning (RL) to optimize our message selection policy over time, tailoring our messages to align with each individual participant's needs and preferences. We conduct an extensive field study in a large country in Asia which involves more than 1000 participants who are local villagers and they receive messages generated by our system, over a period of six months. Our analysis shows that with the use of AI, we can deliver significant improvements in the participants' diabetes-related knowledge, physical activity levels, and high-fat food avoidance, when compared to a static message set. Furthermore, we build a new neural network based behavior model to predict behavior changes of participants, trained on data collected during our study. By exploiting underlying characteristics of health-related behavior, we manage to significantly improve the prediction accuracy of our model compared to baselines.
Unified Model for Crystalline Material Generation
Authors: Astrid Klipfel; Yaël Frégier; Adlane Sayede; Zied Bouraoui
Venue: IJCAI 2023 (main)

Abstract: One of the greatest challenges facing our society is the discovery of new innovative crystal materials with specific properties. Recently, the problem of generating crystal materials has received increasing attention, however, it remains unclear to what extent, or in what way, we can develop generative models that consider both the periodicity and equivalence geometric of crystal structures. To alleviate this issue, we propose two unified models that act at the same time on crystal lattice and atomic positions using periodic equivariant architectures. Our models are capable to learn any arbitrary crystal lattice deformation by lowering the total energy to reach thermodynamic stability. Code and data are available at https://github.com/aklipf/GemsNet.
Machine Learning Driven Aid Classification for Sustainable Development
Authors: Junho Lee; Hyeonho Song; Dongjoon Lee; Sundong Kim; Jisoo Sim; Meeyoung Cha; Kyung-Ryul Park
Venue: IJCAI 2023 (main)

Abstract: This paper explores how machine learning can help classify aid activities by sector using the OECD Creditor Reporting System (CRS). The CRS is a key source of data for monitoring and evaluating aid flows in line with the United Nations Sustainable Development Goals (SDGs), especially SDG17 which calls for global partnership and data sharing. To address the challenges of current labor-intensive practices of assigning the code and the related human inefficiencies, we propose a machine learning solution that uses ELECTRA to suggest relevant five-digit purpose codes in CRS for aid activities, achieving an accuracy of 0.9575 for the top-3 recommendations. We also conduct qualitative research based on semi-structured interviews and focus group discussions with SDG experts who assess the model results and provide feedback. We discuss the policy, practical, and methodological implications of our work and highlight the potential of AI applications to improve routine tasks in the public sector and foster partnerships for achieving the SDGs.
Confidence-based Self-Corrective Learning: An Application in Height Estimation Using Satellite LiDAR and Imagery
Authors: Zhili Li; Yiqun Xie; Xiaowei Jia
Venue: IJCAI 2023 (main)

Abstract: Widespread, and rapid, environmental transformation is underway on Earth driven by human activities. Climate shifts such as global warming have led to massive and alarming loss of ice and snow in the high-latitude regions including the Arctic, causing many natural disasters due to sea-level rise, etc. Mitigating the impacts of climate change has also become a United Nations' Sustainable Development Goal for 2030. The recent launch of the ICESat-2 satellites target on heights in the polar regions. However, the observations are only available along very narrow scan lines, leaving large no-data gaps in-between. We aim to fill the gaps by combining the height observations with high-resolution satellite imagery that have large footprints (spatial coverage). The data expansion is a challenging task as the height data are often constrained on one or a few lines per image in real applications, and the images are highly noisy for height estimation. Related work on image-based height prediction and interpolation relies on specific types of images or does not consider the highly-localized height distribution. We propose a spatial self-corrective learning framework, which explicitly uses confidence-based pseudo-interpolation, recurrent self-refinement, and truth-based correction with a regression layer to address the challenges. We carry out experiments on different landscapes in the high-latitude regions and the proposed method shows stable improvements compared to the baseline methods.
DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback
Authors: Junfan Lin; Yuying Zhu; Lingbo Liu; Yang Liu; Guanbin Li; Liang Lin
Venue: IJCAI 2023 (main)

Abstract: Traffic Signal Control (TSC) aims to reduce the average travel time of vehicles in a road network, which in turn enhances fuel utilization efficiency, air quality, and road safety, benefiting society as a whole. Due to the complexity of long-horizon control and coordination, most prior TSC methods leverage deep reinforcement learning (RL) to search for a control policy and have witnessed great success. However, TSC still faces two significant challenges. 1) The travel time of a vehicle is delayed feedback on the effectiveness of TSC policy at each traffic intersection since it is obtained after the vehicle has left the road network. Although several heuristic reward functions have been proposed as substitutes for travel time, they are usually biased and not leading the policy to improve in the correct direction. 2) The traffic condition of each intersection is influenced by the non-local intersections since vehicles traverse multiple intersections over time. Therefore, the TSC agent is required to leverage both the local observation and the non-local traffic conditions to predict the long-horizontal traffic conditions of each intersection comprehensively. To address these challenges, we propose DenseLight, a novel RL-based TSC method that employs an unbiased reward function to provide dense feedback on policy effectiveness and a non-local enhanced TSC agent to better predict future traffic conditions for more precise traffic control. Extensive experiments and ablation studies demonstrate that DenseLight can consistently outperform advanced baselines on various road networks with diverse traffic flows. The code is available at https://github.com/junfanlin/DenseLight.
SUSTAINABLESIGNALS: An AI Approach for Inferring Consumer Product Sustainability
Authors: Tong Lin; Tianliang Xu; Amit Zac; Sabina Tomkins
Venue: IJCAI 2023 (main)

Abstract: The everyday consumption of household goods is a significant source of environmental pollution. The increase of online shopping affords an opportunity to provide consumers with actionable feedback on the social and environmental impact of potential purchases, at the exact moment when it is relevant. Unfortunately, consumers are inundated with ambiguous sustainability information. For example, greenwashing can make it difficult to identify environmentally friendly products. The highest-quality options, such as Life Cycle Assessment (LCA) scores or tailored impact certificates (e.g., environmentally friendly tags), designed for assessing the environmental impact of consumption, are ineffective in the setting of online shopping. They are simply too costly to provide a feasible solution when scaled up, and often rely on data from self-interested market players. We contribute an analysis of this online environment, exploring how the dynamic between sellers and consumers surfaces claims and concerns regarding sustainable consumption. In order to better provide information to consumers, we propose a machine learning method that can discover signals of sustainability from these interactions. Our method, SustainableSignals, is a first step in scaling up the provision of sustainability cues to online consumers.
Interpret ESG Rating’s Impact on the Industrial Chain Using Graph Neural Networks
Authors: Bin Liu; Jiujun He; Ziyuan Li; Xiaoyang Huang; Xiang Zhang; Guosheng Yin
Venue: IJCAI 2023 (main)

Abstract: We conduct a quantitative analysis of the development of the industry chain from the environmental, social, and governance (ESG) perspective, which is an overall measure of sustainability. Factors that may impact the performance of the industrial chain have been studied in the literature, such as government regulation, monetary policy, etc. Our interest lies in how the sustainability change (i.e., ESG shock) affects the performance of the industrial chain. To achieve this goal, we model the industrial chain with a graph neural network (GNN) and conduct node regression on two financial performance metrics, namely, the aggregated profitability ratios and operating margin. To quantify the effects of ESG, we propose to compute the interaction between ESG shocks and industrial chain features with a cross-attention module, and then filter the original node features in the graph regression. Experiments on two real datasets demonstrate that (i) there are significant effects of ESG shocks on the industrial chain, and (ii) model parameters including regression coefficients and the attention map can explain how ESG shocks affect the performance of the industrial chain.
Preventing Attacks in Interbank Credit Rating with Selective-aware Graph Neural Network
Authors: Junyi Liu; Dawei Cheng; Changjun Jiang
Venue: IJCAI 2023 (main)

Abstract: Accurately credit rating on Interbank assets is essential for a healthy financial environment and substantial economic development. But individual participants tend to provide manipulated information in order to attack the rating model to produce a higher score, which may conduct serious adverse effects on the economic system, such as the 2008 global financial crisis. To this end, in this paper, we propose a novel selective-aware graph neural network model (SA-GNN) for defense the Interbank credit rating attacks. In particular, we first simulate the rating information manipulating process by structural and feature poisoning attacks. Then we build a selective-aware defense graph neural model to adaptively prioritize the poisoning training data with Bernoulli distribution similarities. Finally, we optimize the model with weighed penalization on the objection function so that the model could differentiate the attackers. Extensive experiments on our collected real-world Interbank dataset, with over 20 thousand banks and their relations, demonstrate the superior performance of our proposed method in preventing credit rating attacks compared with the state-of-the-art baselines.
Customized Positional Encoding to Combine Static and Time-varying Data in Robust Representation Learning for Crop Yield Prediction
Authors: Qinqing Liu; Fei Dou; Meijian Yang; Ezana Amdework; Guiling Wang; Jinbo Bi
Venue: IJCAI 2023 (main)

Abstract: Accurate prediction of crop yield under the conditions of climate change is crucial to ensure food security. Transformers have shown remarkable success in modeling sequential data and hold the potential for improving crop yield prediction. To understand how weather and meteorological sequence variables affect crop yield, the positional encoding used in Transformers is typically shared across different sample sequences. We argue that it is necessary and beneficial to differentiate the positional encoding for distinct samples based on time-invariant properties of the sequences. Particularly, the sequence variables influencing crop yield vary according to static variables such as geographical locations. Sample data from southern areas may benefit from more tailored positional encoding different from that for northern areas. We propose a novel transformer based architecture for accurate and robust crop yield prediction, by introducing a Customized Positional Encoding (CPE) that encodes a sequence adaptively according to static information associated with the sequence. Empirical studies demonstrate the effectiveness of the proposed novel architecture and show that partially lin- earized attention better captures the bias introduced by side information than softmax re-weighting. The resultant crop yield prediction model is robust to climate change, with mean-absolute-error reduced by up to 26% compared to the best baseline model in extreme drought years.
GreenFlow: A Computation Allocation Framework for Building Environmentally Sound Recommendation System
Authors: Xingyu Lu; Zhining Liu; Yanchu Guan; Hongxuan Zhang; Chenyi Zhuang; Wenqi Ma; Yize Tan; Jinjie Gu; Guannan Zhang
Venue: IJCAI 2023 (main)

Abstract: Given the enormous number of users and items, industrial cascade recommendation systems (RS) are continuously expanded in size and complexity to deliver relevant items, such as news, services, and commodities, to the appropriate users. In a real-world scenario with hundreds of thousands requests per second, significant computation is required to infer personalized results for each request, resulting in a massive energy consumption and carbon emission that raises concern. This paper proposes GreenFlow, a practical computation allocation framework for RS, that considers both accuracy and carbon emission during inference. For each stage (e.g., recall, pre-ranking, ranking, etc.) of a cascade RS, when a user triggers a request, we define two actions that determine the computation: (1) the trained instances of models with different computational complexity; and (2) the number of items to be inferred in the stage. We refer to the combinations of actions in all stages as action chains. A reward score is estimated for each action chain, followed by dynamic primal-dual optimization considering both the reward and computation budget. Extensive experiments verify the effectiveness of the framework, reducing computation consumption by 41% in an industrial mobile application while maintaining commercial revenue. Moreover, the proposed framework saves approximately 5000kWh of electricity and reduces 3 tons of carbon emissions per day.
Coupled Point Process-based Sequence Modeling for Privacy-preserving Network Alignment
Authors: Dixin Luo; Haoran Cheng; Qingbin Li; Hongteng Xu
Venue: IJCAI 2023 (main)

Abstract: Network alignment aims at finding the correspondence of nodes across different networks, which is significant for many applications, e.g., fraud detection and crime network tracing across platforms. In practice, however, accessing the topological information of different networks is often restricted and even forbidden, considering privacy and security issues. Instead, what we observed might be the event sequences of the networks' nodes in the continuous-time domain. In this study, we develop a coupled neural point process-based (CPP) sequence modeling strategy, which provides a solution to privacy-preserving network alignment based on the event sequences. Our CPP consists of a coupled node embedding layer and a neural point process module. The coupled node embedding layer embeds one network's nodes and explicitly models the alignment matrix between the two networks. Accordingly, it parameterizes the node embeddings of the other network by the push-forward operation. Given the node embeddings, the neural point process module jointly captures the dynamics of the two networks' event sequences. We learn the CPP model in a maximum likelihood estimation framework with an inverse optimal transport (IOT) regularizer. Experiments show that our CPP is compatible with various point process backbones and is robust to the model misspecification issue, which achieves encouraging performance on network alignment. The code is available at https://github.com/Dixin-s-Lab/CNPP.
Group Sparse Optimal Transport for Sparse Process Flexibility Design
Authors: Dixin Luo; Tingting Yu; Hongteng Xu
Venue: IJCAI 2023 (main)

Abstract: As a fundamental problem in Operations Research, sparse process flexibility design (SPFD) aims to design a manufacturing network across industries that achieves a trade-off between the efficiency and robustness of supply chains. In this study, we propose a novel solution to this problem with the help of computational optimal transport techniques. Given a set of supply-demand pairs, we formulate the SPFD task approximately as a group sparse optimal transport (GSOT) problem, in which a group of couplings between the supplies and demands is optimized with a group sparse regularizer. We solve this optimization problem via an algorithmic framework of alternating direction method of multipliers (ADMM), in which the target network topology is updated by soft-thresholding shrinkage, and the couplings of the OT problems are updated via a smooth OT algorithm in parallel. This optimization algorithm has guaranteed convergence and provides a generalized framework for the SPFD task, which is applicable regardless of whether the supplies and demands are balanced. Experiments show that our GSOT-based method can outperform representative heuristic methods in various SPFD tasks. Additionally, when implementing the GSOT method, the proposed ADMM-based optimization algorithm is comparable or superior to the commercial software Gurobi. The code is available at https://github.com/Dixin-s-Lab/GSOT.
A Prediction-and-Scheduling Framework for Efficient Order Transfer in Logistics
Authors: Wenjun Lyu; Haotian Wang; Yiwei Song; Yunhuai Liu; Tian He; Desheng Zhang
Venue: IJCAI 2023 (main)

Abstract: Order Transfer from the transfer center to delivery stations is an essential and expensive part of the logistics service chain. In practice, one vehicle sends transferred orders to multiple delivery stations in one transfer trip to achieve a better trade-off between the transfer cost and time. A key problem is generating the vehicle’s route for efficient order transfer, i.e., minimizing the order transfer time. In this paper, we explore fine-grained delivery station features, i.e., downstream couriers’ remaining working times in last-mile delivery trips and the transferred order distribution to design a Prediction-and-Scheduling framework for efficient Order Transfer called PSOT, including two components: i) a Courier’s Remaining Working Time Prediction component to predict each courier’s working time for conducting heterogeneous tasks, i.e., order pickups and deliveries, with a context-aware location embedding and an attention-based neural network; ii) a Vehicle Scheduling component to generate the vehicle’s route to served delivery stations with an order-transfer-time-aware heuristic algorithm. The evaluation results with real-world data from one of the largest logistics companies in China show PSOT improves the courier’s remaining working time prediction by up to 35.6% and reduces the average order transfer time by up to 51.3% compared to the state-of-the-art methods.
Fighting against Organized Fraudsters Using Risk Diffusion-based Parallel Graph Neural Network
Authors: Jiacheng Ma; Fan Li; Rui Zhang; Zhikang Xu; Dawei Cheng; Yi Ouyang; Ruihui Zhao; Jianguang Zheng; Yefeng Zheng; Changjun Jiang
Venue: IJCAI 2023 (main)

Abstract: Medical insurance plays a vital role in modern society, yet organized healthcare fraud causes billions of dollars in annual losses, severely harming the sustainability of the social welfare system. Existing works mostly focus on detecting individual fraud entities or claims, ignoring hidden conspiracy patterns. Hence, they face severe challenges in tackling organized fraud. In this paper, we proposed RDPGL, a novel Risk Diffusion-based Parallel Graph Learning approach, to fighting against medical insurance criminal gangs. In particular, we first leverage a heterogeneous graph attention network to encode the local context from the beneficiary-provider graph. Then, we devise a community-aware risk diffusion model to infer the global context of organized fraud behaviors with the claim-claim relation graph. The local and global representations are parallel concatenated together and trained simultaneously in an end-to-end manner. Our approach is extensively evaluated on a real-world medical insurance dataset. The experimental results demonstrate the superiority of our proposed approach, which could detect more organized fraud claims with relatively high precision compared with state-of-the-art baselines.
Planning Multiple Epidemic Interventions with Reinforcement Learning
Authors: Anh Mai; Nikunj Gupta; Azza Abouzied; Dennis Shasha
Venue: IJCAI 2023 (main)

Abstract: Combating an epidemic entails finding a plan that describes when and how to apply different interventions, such as mask-wearing mandates, vaccinations, school or workplace closures. An optimal plan will curb an epidemic with minimal loss of life, disease burden, and economic cost. Finding an optimal plan is an intractable computational problem in realistic settings. Policy-makers, however, would greatly benefit from tools that can efficiently search for plans that minimize disease and economic costs especially when considering multiple possible interventions over a continuous and complex action space given a continuous and equally complex state space. We formulate this problem as a Markov decision process. Our formulation is unique in its ability to represent multiple continuous interventions over any disease model defined by ordinary differential equations. We illustrate how to effectively apply state-of-the-art actor-critic reinforcement learning algorithms (PPO and SAC) to search for plans that minimize overall costs. We empirically evaluate the learning performance of these algorithms and compare their performance to hand-crafted baselines that mimic plans constructed by policy-makers. Our method outperforms baselines. Our work confirms the viability of a computational approach to support policy-makers.