GenAI4ELab/papercli-papers huggingface dataset:
Industry-Scale Orchestrated Federated Learning for Drug Discovery
Authors: Martijn Oldenhof; Gergely Ács; Balázs Pejó; Ansgar Schuffenhauer; Nicholas Holway; Noé Sturm; Arne Dieckmann; Oliver Fortmeier; Eric Boniface; Clément Mayer; Arnaud Gohier; Peter Schmidtke; Ritsuya Niwayama; Dieter Kopecky; Lewis Mervin; Prakash Chandra Rathi; Lukas Friedrich; András Formanek; Peter Antal; Jordon Rahaman; Adam Zalewski; Wouter Heyndrickx; Ezron Oluoch; Manuel Stößel; Michal Vančo; David Endico; Fabien Gelus; Thaïs de Boisfossé; Adrien Darbier; Ashley Nicollet; Matthieu Blottière; Maria Telenczuk; Van Tien Nguyen; Thibaud Martinez; Camille Boillet; Kelvin Moutet; Alexandre Picosson; Aurélien Gasser; Inal Djafar; Antoine Simon; Ádám Arany; Jaak Simm; Yves Moreau; Ola Engkvist; Hugo Ceulemans; Camille Marini; Mathieu Galtier
Venue: AAAI 2023 (main)

Abstract: To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n°831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper.
THMA: Tencent HD Map AI System for Creating HD Map Annotations
Authors: Kun Tang; Xu Cao; Zhipeng Cao; Tong Zhou; Erlong Li; Ao Liu; Shengtao Zou; Chang Liu; Shuqi Mei; Elena Sizikova; Chao Zheng
Venue: AAAI 2023 (main)

Abstract: Nowadays, autonomous vehicle technology is becoming more and more mature. Critical to progress and safety, high-definition (HD) maps, a type of centimeter-level map collected using a laser sensor, provide accurate descriptions of the surrounding environment. The key challenge of HD map production is efficient, high-quality collection and annotation of large-volume datasets. Due to the demand for high quality, HD map production requires significant manual human effort to create annotations, a very time-consuming and costly process for the map industry. In order to reduce manual annotation burdens, many artificial intelligence (AI) algorithms have been developed to pre-label the HD maps. However, there still exists a large gap between AI algorithms and the traditional manual HD map production pipelines in accuracy and robustness. Furthermore, it is also very resource-costly to build large-scale annotated datasets and advanced machine learning algorithms for AI-based HD map automatic labeling systems. In this paper, we introduce the Tencent HD Map AI (THMA) system, an innovative end-to-end, AI-based, active learning HD map labeling system capable of producing and labeling HD maps with a scale of hundreds of thousands of kilometers. In THMA, we train AI models directly from massive HD map datasets via supervised, self-supervised, and weakly supervised learning to achieve high accuracy and efficiency required by downstream users. THMA has been deployed by the Tencent Map team to provide services to downstream companies and users, serving over 1,000 labeling workers and producing more than 30,000 kilometers of HD map data per day at most. More than 90 percent of the HD map data in Tencent Map is labeled automatically by THMA, accelerating the traditional HD map labeling process by more than ten times.
Increasing Impact of Mobile Health Programs: SAHELI for Maternal and Child Care
Authors: Shresth Verma; Gargi Singh; Aditya Mate; Paritosh Verma; Sruthi Gorantla; Neha Madhiwalla; Aparna Hegde; Divy Thakkar; Manish Jain; Milind Tambe; Aparna Taneja
Venue: AAAI 2023 (main)

Abstract: Underserved communities face critical health challenges due to lack of access to timely and reliable information. Nongovernmental organizations are leveraging the widespread use of cellphones to combat these healthcare challenges and spread preventative awareness. The health workers at these organizations reach out individually to beneficiaries; however such programs still suffer from declining engagement. We have deployed SAHELI, a system to efficiently utilize the limited availability of health workers for improving maternal and child health in India. SAHELI uses the Restless Multiarmed Bandit (RMAB) framework to identify beneficiaries for outreach. It is the first deployed application for RMABs in public health, and is already in continuous use by our partner NGO, ARMMAN. We have already reached ~100K beneficiaries with SAHELI, and are on track to serve 1 million beneficiaries by the end of 2023. This scale and impact has been achieved through multiple innovations in the RMAB model and its development, in preparation of real world data, and in deployment practices; and through careful consideration of responsible AI practices. Specifically, in this paper, we describe our approach to learn from past data to improve the performance of SAHELI’s RMAB model, the real-world challenges faced during deployment and adoption of SAHELI, and
the end-to-end pipeline.
MuMIC – Multimodal Embedding for Multi-Label Image Classification with Tempered Sigmoid
Authors: Fengjun Wang; Sarai Mizrachi; Moran Beladev; Guy Nadav; Gil Amsalem; Karen Lastmann Assaraf; Hadas Harush Boker
Venue: AAAI 2023 (main)

Abstract: Multi-label image classification is a foundational topic in various domains. Multimodal learning approaches have recently achieved outstanding results in image representation and single-label image classification. For instance, Contrastive Language-Image Pretraining (CLIP) demonstrates impressive image-text representation learning abilities and is robust to natural distribution shifts. This success inspires us to leverage multimodal learning for multi-label classification tasks, and benefit from contrastively learnt pretrained models.

We propose the Multimodal Multi-label Image Classification (MuMIC) framework, which utilizes a hardness-aware tempered sigmoid based Binary Cross Entropy loss function, thus enables the optimization on multi-label objectives and transfer learning on CLIP. MuMIC is capable of providing high classification performance, handling real-world noisy data, supporting zero-shot predictions, and producing domain-specific image embeddings.

In this study, a total of 120 image classes are defined, and more than 140K positive annotations are collected on approximately 60K Booking.com images. The final MuMIC model is deployed on Booking.com Content Intelligence Platform, and it outperforms other state-of-the-art models with 85.6% GAP@10 and 83.8% GAP on all 120 classes, as well as a 90.1% macro mAP score across 32 majority classes. We summarize the modelling choices which are extensively tested through ablation studies. To the best of our knowledge, we are the first to adapt contrastively learnt multimodal pretraining for real-world multi-label image classification problems, and the innovation can be transferred to other domains.
AHPA: Adaptive Horizontal Pod Autoscaling Systems on Alibaba Cloud Container Service for Kubernetes
Authors: Zhiqiang Zhou; Chaoli Zhang; Lingna Ma; Jing Gu; Huajie Qian; Qingsong Wen; Liang Sun; Peng Li; Zhimin Tang
Venue: AAAI 2023 (main)

Abstract: The existing resource allocation policy for application instances in Kubernetes cannot dynamically adjust according to the requirement of business, which would cause an enormous waste of resources during fluctuations. Moreover, the emergence of new cloud services puts higher resource management requirements. This paper discusses horizontal POD resources management in Alibaba Cloud Container Services with a newly deployed AI algorithm framework named AHPA - the adaptive horizontal pod auto-scaling system. Based on a robust decomposition forecasting algorithm and performance training model, AHPA offers an optimal pod number adjustment plan that could reduce POD resources
and maintain business stability. Since being deployed in April 2021, this system has expanded to multiple customer scenarios, including logistics, social networks, AI audio and video, e-commerce, etc. Compared with the previous algorithms, AHPA solves the elastic lag problem, increasing CPU usage by 10% and reducing resource cost by more than 20%. In addition, AHPA can automatically perform flexible planning according to the predicted business volume without manual intervention, significantly saving operation and maintenance costs.
eForecaster: Unifying Electricity Forecasting with Robust, Flexible, and Explainable Machine Learning Algorithms
Authors: Zhaoyang Zhu; Weiqi Chen; Rui Xia; Tian Zhou; Peisong Niu; Bingqing Peng; Wenwei Wang; Hengbo Liu; Ziqing Ma; Qingsong Wen; Liang Sun
Venue: AAAI 2023 (main)

Abstract: Electricity forecasting is crucial in scheduling and planning of future electric load, so as to improve the reliability and safeness of the power grid. Despite recent developments of forecasting algorithms in the machine learning community, there is a lack of general and advanced algorithms specifically considering requirements from the power industry perspective. In this paper, we present eForecaster, a unified AI platform including robust, flexible, and explainable machine learning algorithms for diversified electricity forecasting applications. Since Oct. 2021, multiple commercial bus load, system load, and renewable energy forecasting systems built upon eForecaster have been deployed in seven provinces of China. The deployed systems consistently reduce the average Mean Absolute Error (MAE) by 39.8% to 77.0%, with reduced manual work and explainable guidance. In particular, eForecaster also integrates multiple interpretation methods to uncover the working mechanism of the predictive models, which significantly improves forecasts adoption and user satisfaction.
Cosmic Microwave Background Recovery: A Graph-Based Bayesian Convolutional Network Approach
Authors: Jadie Adams; Steven Lu; Krzysztof M. Gorski; Graca Rocha; Kiri L. Wagstaff
Venue: AAAI 2023 (main)

Abstract: The cosmic microwave background (CMB) is a significant source of knowledge about the origin and evolution of our universe. However, observations of the CMB are contaminated by foreground emissions, obscuring the CMB signal and reducing its efficacy in constraining cosmological parameters. We employ deep learning as a data-driven approach to CMB cleaning from multi-frequency full-sky maps. In particular, we develop a graph-based Bayesian convolutional neural network based on the U-Net architecture that predicts cleaned CMB with pixel-wise uncertainty estimates. We demonstrate the potential of this technique on realistic simulated data based on the Planck mission. We show that our model ac- accurately recovers the cleaned CMB sky map and resulting angular power spectrum while identifying regions of uncertainty. Finally, we discuss the current challenges and the path forward for deploying our model for CMB recovery on real observations.
Phase-Informed Bayesian Ensemble Models Improve Performance of COVID-19 Forecasts
Authors: Aniruddha Adiga; Gursharn Kaur; Lijing Wang; Benjamin Hurt; Przemyslaw Porebski; Srinivasan Venkatramanan; Bryan Lewis; Madhav V. Marathe
Venue: AAAI 2023 (main)

Abstract: Despite hundreds of methods published in the literature, forecasting epidemic dynamics remains challenging yet important. The challenges stem from multiple sources, including: the need for timely data, co-evolution of epidemic dynamics with behavioral and immunological adaptations, and the evolution of new pathogen strains. The ongoing COVID-19 pandemic highlighted these challenges; in an important article, Reich et al. did a comprehensive analysis highlighting many of these challenges.

In this paper, we take another step in critically evaluating existing epidemic forecasting methods. Our methods are based on a simple yet crucial observation - epidemic dynamics go through a number of phases (waves). Armed with this understanding, we propose a modification to our deployed Bayesian ensembling case time series forecasting framework. We show that ensembling methods employing the phase information and using different weighting schemes for each phase can produce improved forecasts. We evaluate our proposed method with both the currently deployed model and the COVID-19 forecasthub models. The overall performance of the proposed model is consistent across the pandemic but more importantly, it is ranked third and first during two critical rapid growth phases in cases, regimes where the performance of most models from the CDC forecasting hub dropped significantly.
Towards Hybrid Automation by Bootstrapping Conversational Interfaces for IT Operation Tasks
Authors: Jayachandu Bandlamudi; Kushal Mukherjee; Prerna Agarwal; Sampath Dechu; Siyu Huo; Vatche Isahagian; Vinod Muthusamy; Naveen Purushothaman; Renuka Sindhgatta
Venue: AAAI 2023 (main)

Abstract: Process automation has evolved from end-to-end automation of repetitive process branches to hybrid automation where bots perform some activities and humans serve other activities. In the context of knowledge-intensive processes such as IT operations, implementing hybrid automation is a natural choice where robots can perform certain mundane functions, with humans taking over the decision of when and which IT systems need to act. Recently, ChatOps, which refers to conversation-driven collaboration for IT operations, has rapidly accelerated efficiency by providing a cross-organization and cross-domain platform to resolve and manage issues as soon as possible. Hence, providing a natural language interface to bots is a logical progression to enable collaboration between humans and bots. This work presents a no-code approach to provide a conversational interface that enables human workers to collaborate with bots executing automation scripts. The bots identify the intent of users' requests and automatically orchestrate one or more relevant automation tasks to serve the request. We further detail our process of mining the conversations between humans and bots to monitor performance and identify the scope for improvement in service quality.
Compressing Cross-Lingual Multi-Task Models at Qualtrics
Authors: Daniel Campos; Daniel Perry; Samir Joshi; Yashmeet Gambhir; Wei Du; Zhengzheng Xing; Aaron Colak
Venue: AAAI 2023 (main)

Abstract: Experience management is an emerging business area where organizations focus on understanding the feedback of customers and employees in order to improve their end-to-end experiences.
This results in a unique set of machine learning problems to help understand how people feel, discover issues they care about, and find which actions need to be taken on data that are different in content and distribution from traditional NLP domains.
In this paper, we present a case study of building text analysis applications that perform multiple classification tasks efficiently in 12 languages in the nascent business area of experience management.
In order to scale up modern ML methods on experience data, we leverage cross lingual and multi-task modeling techniques to consolidate our models into a single deployment to avoid overhead.
We also make use of model compression and model distillation to reduce overall inference latency and hardware cost to the level acceptable for business needs while maintaining model prediction quality.
Our findings show that multi-task modeling improves task performance for a subset of experience management tasks in both XLM-R and mBert architectures.
Among the compressed architectures we explored, we found that MiniLM achieved the best compression/performance tradeoff.
Our case study demonstrates a speedup of up to 15.61x with 2.60% average task degradation (or 3.29x speedup with 1.71% degradation) and estimated savings of 44% over using the original full-size model.
These results demonstrate a successful scaling up of text classification for the challenging new area of ML for experience management.
SolderNet: Towards Trustworthy Visual Inspection of Solder Joints in Electronics Manufacturing Using Explainable Artificial Intelligence
Authors: Hayden Gunraj; Paul Guerrier; Sheldon Fernandez; Alexander Wong
Venue: AAAI 2023 (main)

Abstract: In electronics manufacturing, solder joint defects are a common problem affecting a variety of printed circuit board components. To identify and correct solder joint defects, the solder joints on a circuit board are typically inspected manually by trained human inspectors, which is a very time-consuming and error-prone process. To improve both inspection efficiency and accuracy, in this work we describe an explainable deep learning-based visual quality inspection system tailored for visual inspection of solder joints in electronics manufacturing environments. At the core of this system is an explainable solder joint defect identification system called SolderNet which we design and implement with trust and transparency in mind. While several challenges remain before the full system can be developed and deployed, this study presents important progress towards trustworthy visual inspection of solder joints in electronics manufacturing.
MobilePTX: Sparse Coding for Pneumothorax Detection Given Limited Training Examples
Authors: Darryl Hannan; Steven C. Nesbit; Ximing Wen; Glen Smith; Qiao Zhang; Alberto Goffi; Vincent Chan; Michael J. Morris; John C. Hunninghake; Nicholas E. Villalobos; Edward Kim; Rosina O. Weber; Christopher J. MacLellan
Venue: AAAI 2023 (main)

Abstract: Point-of-Care Ultrasound (POCUS) refers to clinician-performed and interpreted ultrasonography at the patient's bedside. Interpreting these images requires a high level of expertise, which may not be available during emergencies. In this paper, we support POCUS by developing classifiers that can aid medical professionals by diagnosing whether or not a patient has pneumothorax. We decomposed the task into multiple steps, using YOLOv4 to extract relevant regions of the video and a 3D sparse coding model to represent video features. Given the difficulty in acquiring positive training videos, we trained a small-data classifier with a maximum of 15 positive and 32 negative examples. To counteract this limitation, we leveraged subject matter expert (SME) knowledge to limit the hypothesis space, thus reducing the cost of data collection. We present results using two lung ultrasound datasets and demonstrate that our model is capable of achieving performance on par with SMEs in pneumothorax identification. We then developed an iOS application that runs our full system in less than 4 seconds on an iPad Pro, and less than 8 seconds on an iPhone 13 Pro, labeling key regions in the lung sonogram to provide interpretable diagnoses.
Vessel-to-Vessel Motion Compensation with Reinforcement Learning
Authors: Sverre Herland; Kerstin Bach
Venue: AAAI 2023 (main)

Abstract: Actuation delay poses a challenge for robotic arms and cranes. This is especially the case in dynamic environments where the robot arm or the objects it is trying to manipulate are moved by exogenous forces. In this paper, we consider the task of using a robotic arm to compensate for relative motion between two vessels at sea. We construct a hybrid controller that combines an Inverse Kinematic (IK) solver with a Reinforcement Learning (RL) agent that issues small corrections to the IK input. The solution is empirically evaluated in a simulated environment under several sea states and actuation delays. We observe that more intense waves and larger actuation delays have an adverse effect on the IK controller's ability to compensate for vessel motion. The RL agent is shown to be effective at mitigating large parts of these errors, both in the average case and in the worst case. Its modest requirement for sensory information, combined with the inherent safety in only making small adjustments, also makes it a promising approach for real-world deployment.
Detecting VoIP Data Streams: Approaches Using Hidden Representation Learning
Authors: Maya Kapoor; Michael Napolitano; Jonathan Quance; Thomas Moyer; Siddharth Krishnan
Venue: AAAI 2023 (main)

Abstract: The use of voice-over-IP technology has rapidly expanded over the past several years, and has thus become a significant portion of traffic in the real, complex network environment. Deep packet inspection and middlebox technologies need to analyze call flows in order to perform network management, load-balancing, content monitoring, forensic analysis, and intelligence gathering. Because the session setup and management data can be sent on different ports or out of sync with VoIP call data over the Real-time Transport Protocol (RTP) with low latency, inspection software may miss calls or parts of calls. To solve this problem, we engineered two different deep learning models based on hidden representation learning. MAPLE, a matrix-based encoder which transforms packets into an image  representation, uses convolutional neural networks to determine RTP packets from data flow. DATE is a density-analysis based tensor encoder which transforms packet data into a three-dimensional point cloud representation. We then perform density-based clustering over the point clouds as latent representations of the data, and classify packets as RTP or non-RTP based on their statistical clustering features. In this research, we show that these tools may allow a data collection and analysis pipeline to begin detecting and buffering RTP streams for later session association, solving the initial drop problem. MAPLE achieves over ninety-nine percent accuracy in RTP/non-RTP detection. The results of our experiments show that both models can not only classify RTP versus non-RTP packet streams, but could extend to other network traffic classification problems in real deployments of network analysis pipelines.
Intuitive Access to Smartphone Settings Using Relevance Model Trained by Contrastive Learning
Authors: Joonyoung Kim; Kangwook Lee; Haebin Shin; Hurnjoo Lee; Sechun Kang; Byunguk Choi; Dong Shin; Joohyung Lee
Venue: AAAI 2023 (main)

Abstract: The more new features that are being added to smartphones, the harder it becomes for users to find them. This is because the feature names are usually short and there are just too many of them for the users to remember the exact words.  The users are more comfortable asking contextual queries that describe the features they are looking for, but the standard term frequency-based search cannot process them. This paper presents a novel retrieval system for mobile features that accepts intuitive and contextual search queries. We trained a relevance model via contrastive learning from a pre-trained language model to perceive the contextual relevance between a query embedding and indexed mobile features. Also, to make it efficiently run on-device using minimal resources, we applied knowledge distillation to compress the model without degrading much performance. To verify the feasibility of our method, we collected test queries and conducted comparative experiments with the currently deployed search baselines. The results show that our system outperforms the others on contextual sentence queries and even on usual keyword-based queries.