Calton Pu

Nonprofit organizations are important contributors to the US economy and social well-being. The Nonprofit Organization Research Panel Project (NORPP) Manager has been developing and sharing datasets and software tools to facilitate data-driven research on nonprofit organizations. The project has two major thrusts: (1) large-scale survey panels, e.g., the Annual National Survey of Nonprofit Trends and Impacts, from 2021 to the current (2024); and (2) NORPP Analytics Integration Platform (NAIP) to collect, process, and query a wide range of socioeconomic indicator datasets, such as IRS 990 form and census data. Technical challenges that include data heterogeneity, data quality, and protection of sensitive data have made the expansion and maintenance of NAIP datasets both labor-intensive and time-consuming. We are currently exploring new technologies, including Large Language Models such as GPT series, to automate database query generation, schema adaptation, and data quality assurance processes.

NAIP is a collaborative project between social scientists and computer scientists to facilitate and promote datadriven research on nonprofit organizations. A major thrust of NAIP is the treatment of socioeconomic datasets such as IRS from 990 and census data for statistical analysis on nonprofits. This paper describes recent advances and experiences using Large Language Models (LLMs) to facilitate data cleaning, modeling, and automated SQL query generation on NAIP datasets. With non-trivial prompt engineering, LLMs become capable of generating useful SQL queries, but significant efforts are still needed for evaluation and validation.

This demo presents KimBilet.com, an educational platform that utilizes generative AI to create personalized educational content on demand. Catering to high-school and college students, instructors, job seekers, and lifelong learners, the system generates customized courses based on user prompts, covering any topic of interest. Each course may include a sequence of AI-created lessons and quizzes, providing detailed feedback for every quiz option to enhance understanding. The platform supports intuitive navigation through keyboard shortcuts and allows users to jump between course items seamlessly. It also maintains a history of completed quizzes to help users track their learning progress. Future enhancements include topic suggestions based on past interests, support for coding exercises, and multilingual support. This demo will showcase how KimBilet.com leverages AI to offer adaptive learning experiences, engage attendees through interactive exploration, and discuss its potential applications in educational settings. Participants will gain insights into integrating AI-driven tools into teaching and learning processes to address diverse educational needs.

The modern web services landscape is characterized by numerous fine-grained, loosely coupled microservices with increasingly stringent low-latency requirements. However, this architecture also brings new performance vulnerabilities. In this paper, we introduce a novel low-volume application layer DDoS attack called the Sync-Millibottleneck (SyncM) attack, specifically targeting microservices. The goal of this attack is to cause a long-tail latency problem that violates the service-level agreement (SLA) while evading state-of-the-art DDoS detection/defense mechanisms. The SyncM attack exploits two unique features of microservices architecture: (1) the shared frontend gateway that directs user requests to mid-tier/backend microservices, and (2) the co-existence of multiple logically independent execution paths, each with its own bottleneck resource. By creating synchronized millibottlenecks (i.e., sub-second duration bottlenecks) on multiple independent execution paths, SyncM attack can cause the queuing effect in each execution path to be propagated and superimposed in the shared frontend gateway. As a result, SyncM triggers surprisingly high latency spikes in the system, even when all system resources are far from saturation, making it challenging to trace the cause of performance instability. To evaluate the practicality of the SyncM attack, we conduct extensive experiments on real cloud systems such as EC2 and Azure, which are equipped with state-of-the-art IDS/IPS systems. We also conduct a large-scale simulation using a production Alibaba trace to show the scalability of our attack. Our results demonstrate that the SyncM attack is highly effective, as it only consumes less than 15% of additional CPU resources of the target system while increasing its 95th percentile response time by more than 20 times.

The physical world evolves. The cyber world evolves and grows with big data, with social media as a major component of information growth. Classic ML models are limited by their static training data with implicit Complete and Timeless Knowledge assumptions. In an evolving world, static training data suffer from knowledge obsolescence due to truly novel timely information. Knowledge obsolescence introduces a widening distance between static ML models and the evolving world, called cyber-physical gap. Periodic retraining of new models may restore their accuracy temporarily, but subsequently their performance will deteriorate with widening cyber-physical gap. Knowledge obsolescence affects statically trained models of any size, including LLMs. Two major research challenges arise from cyber-physical gap: (1) collection and incorporation of space-time aware ground truth training data, and (2) understanding and capturing of the varying speed of information and knowledge evolution when the physical and cyber worlds evolve.

Deep learning shows great promise in providing more intelligence to the cross-platform web. However, insufficient infrastructure, heavy models, and intensive computation limit the use of deep learning with low-performing web browsers. We propose DeepAdapter, an integrated cloud-edge-device framework that ties the edge, the remote cloud, with the device by cross-platform web technology for adaptive deep learning services towards lower latency, lower mobile energy, and higher system throughput. DeepAdapter consists of context-aware pruning, service updating, and online scheduling. First, the offline pruning module provides a context-aware pruning algorithm that incorporates the latency, the network condition, and the device's computing capability to fit various contexts. Second, the service updating module optimizes branch model cache on the edge for massive mobile users and updates the new model pruning requirements. Third, the online scheduling module matches optimal branch models for mobile users. Also, a two-stage DRL-based online scheduling method named DeepScheduler can handle high concurrent requests between edge centers and remote cloud by designing the reward prediction model. Extensive experiments show that DeepAdapter can decrease average latency by 1.33x, reduce average mobile energy by 1.4x, and improve system throughput by 2.1x with considerable accuracy.