OpenRaiser/Intern-Atlas huggingface dataset:
Fast Human Action Recognition via Millimeter Wave Radar Point Cloud Sequences Learning
Authors: Tongfei Shao, Zheyu Du, Chuanyou Li, Tianxing Wu, Meng Wang
Venue: CIKM
Year: 2024

Human action recognition using commercial millimeter wave radar is gaining significant attention in smart elderly care and smart homes. Due to privacy concerns, the sensing data often needs to be processed locally on embedded systems with restricted computational resources, necessitating a balance between recognition accuracy and efficiency. In this paper, we propose a fast human action recognition framework based on 3D point cloud sequences generated by commercial 4D millimeter wave imaging radar systems. The framework comprises two primary phases: data preprocessing and spatial-temporal feature extraction. During the data preprocessing phase, we employ a sliding window approach for frame fusion to enhance the spatial information of the sparse point cloud while retaining its temporal features. Additionally, Morton coding is used to address the disorderliness in the point cloud sequence. For spatial-temporal feature extraction, we introduce an innovative two-stage algorithm. In the spatial feature extraction stage, we initially extract local spatial features for each point, utilizing self-attention to construct a local graph and circumvent the limitations of using Euclidean distance in sparse point clouds. Subsequently, 3D frame fusion convolution is applied to extract spatial features at the frame level, reducing the length of the spatial feature map sequence and lowering computational requirements for subsequent temporal feature extraction. In the temporal feature extraction stage, we employ a modified Transformer encoder with fine-grained feature fusion to extract temporal features. We conducted comprehensive experiments using both our collected dataset and the open dataset RadHar. The experimental outcomes demonstrate that our framework not only improves inference accuracy but also maintains satisfactory real-time performance on embedded platforms with constrained computational resources. When compared with state-of-the-art (SOTA) methods, our framework significantly enhances inference speed while retaining competitive inference accuracy. Codes and dataset are available at https://github.com/Feiyuyu0503/FastHAR.
A Scalable Tool for Democratizing Variant Calling on Human Genomes Using Commodity Clusters
Authors: Khawar Shehzad, Ajay Kumar, Matthew Schutz, Chase Webb, Polycarp Nalela, Manas Jyoti Das, Praveen Rao
Venue: CIKM
Year: 2024

Variant calling is a fundamental task that involves identifying variants in an individual's genome compared to the reference genome. Knowing these variants is critical for assessing an individual's risk for diseases such as cancer and developing new treatments. Due to the large size of human genome sequences, processing and analyzing them requires significant compute and storage resources. Cluster computing is an attractive solution for processing a large workload of human genomes. In this paper, we present a scalable tool for democratizing variant calling on human genome sequences using testbeds that are available for academic research at no charge. Our tool can (a) execute two types of variant calling pipelines in a commodity cluster with CPUs and graphics processing units (GPUs); (b) enable improved cluster utilization and faster execution via asynchronous computations, minimal synchronization, and mutual exclusion when employing GPUs; and (c) execute variant calling pipelines of multiple users concurrently. Using publicly available human genome sequences, users can interactively experience the unique features of our tool, which has a low barrier to entry for large-scale variant calling.
Learning Links for Adaptable and Explainable Retrieval
Authors: Jianqiang Shen, Yuchin Juan, Ping Liu, Wen Pu, Shaobo Zhang, Qianqi Shen, Liangjie Hong, Wenjing Zhang
Venue: CIKM
Year: 2024

Web-scale search systems typically tackle the scalability challenge with a two-step paradigm: retrieval and ranking. The retrieval step, also known as candidate selection, often involves extracting entities, creating an inverted index, and performing term matching for retrieval. Such traditional methods require manual and time-consuming development of retrieval models. In this paper, we propose a framework for constructing a graph that integrates human knowledge with user activity data analysis. The learned links are utilized for retrieval purposes. The model is easy to explain, debug, and tune. The system implementation is straightforward and can directly leverage existing inverted index systems. We applied this retrieval framework to enhance the job search and recommendation systems on a large professional networking portal, resulting in significant performance improvements.
Robust Federated Unlearning
Authors: Xinyi Sheng, Wei Bao, Liming Ge
Venue: CIKM
Year: 2024

Federated unlearning (FU) algorithms offer participants in federated learning (FL) the "right to be forgotten'' for their individual data and its impact on a collaboratively trained model. Existing FU algorithms primarily focus on accelerating the retraining process and enhancing the utility of the retrained models following data removal requests. However, these approaches generally lack consideration for the robustness of FU algorithms in potential adversarial environments, where adversaries can craft malicious data removal requests to compromise the retrained model. In this work, we introduce a robust federated unlearning framework (robustFU) which notably enhances the resilience of FU algorithms against a wide range of adversarial attacks. In robustFU, we design a novel dynamic conflict sample compensation algorithm that dynamically reintroduces randomly generated samples with significant information gain to the participating clients during retraining. Additionally, robustFU employs an innovative global reweighting mechanism which adjusts the weight of each model update during the global aggregation, based on its degree of misalignment with the trained model prior to unlearning. Extensive experiments demonstrates the effectiveness and robustness of the proposed robustFU framework under adversarial environments. Furthermore, robustFU significantly accelerates the retraining process, achieving a 2.53× speed-up compared to the retrain from the scratch baseline.
Ads Supply Personalization via Doubly Robust Learning
Authors: Wei Shi, Chen Fu, Qi Xu, Sanjian Chen, Jizhe Zhang, Qinqin Zhu, Zhigang Hua, Shuang Yang
Venue: CIKM
Year: 2024

Ads supply personalization aims to balance the revenue and user engagement, two long-term objectives in social media ads, by tailoring the ad quantity and density. In the industry-scale system, the challenge for ads supply lies in modeling the counterfactual effects of a conservative supply treatment (e.g., a small density change) over an extended duration. In this paper, we present a streamlined framework for personalized ad supply. This framework optimally utilizes information from data collection policies through the doubly robust learning. Consequently, it significantly improves the accuracy of long-term treatment effect estimates. Additionally, its low-complexity design not only results in computational cost savings compared to existing methods, but also makes it scalable for billion-scale applications. Through both offline experiments and online production tests, the framework consistently demonstrated significant improvements in top-line business metrics over months. The framework has been fully deployed to live traffic in one of the world's largest social media platforms.
Discovering Graph Generating Dependencies for Property Graph Profiling
Authors: Larissa Capobianco Shimomura, Nikolay Yakovets, George Fletcher
Venue: CIKM
Year: 2024

Knowledge graphs have soared in popularity by supporting different types of applications and domains. In this context, the property graph data model has become an emerging standard in industry and academia. With its widespread use, there is also an increasing interest in investigating constraints for property graph data and their applications in data profiling. Graph Generating Dependencies (GGDs) are a class of property graph data dependencies that can express constraints on topology and properties of nodes and edges of the graph, making them a suitable candidate to expose an overview of the property graph to the user (profile graph data). However, GGDs can be difficult to set manually. To solve this issue, we propose a framework for discovering GGDs automatically from the property graph to profile graph data. Our framework has three main steps: (1) pre-processing, (2) candidate generation, and, (3) GGD extraction. Our results show that the discovered set of GGDs can give an overview of the input graph, including schema-level information between the graph patterns and attributes.
Self-supervised One-Stage Learning for RF-based Multi-Person Pose Estimation
Authors: Seunghwan Shin, Yusung Kim
Venue: CIKM
Year: 2024

In the field of Multi-Person Pose Estimation (MPPE), Radio Frequency (RF)-based methods can operate effectively regardless of lighting conditions and obscured line-of-sight situations. Existing RF-based MPPE methods typically involve either 1) converting RF signals into heatmap images through complex preprocessing, or 2) applying a deep embedding network directly to raw RF signals. The first approach, while delivering decent performance, is computationally intensive and time-consuming. The second method, though simpler in preprocessing, results in lower MPPE accuracy and generalization performance. This paper proposes an efficient and lightweight one-stage MPPE model based on raw RF signals. By sub-grouping RF signals and embedding them using a shared single-layer CNN followed by multi-head attention, this model outperforms previous methods that embed all signals at once through a large and deep CNN. Additionally, we propose a new self-supervised learning (SSL) method that takes inputs from both one unmasked subgroup and the remaining masked subgroups to predict the latent representations of the masked data. Empirical results demonstrate that our model improves MPPE accuracy by up to 15 in PCKh@0.5 compared to previous methods using raw RF signals. Especially, the proposed SSL method has shown to significantly enhance performance improvements when placed in new locations or in front of obstacles at RF antennas, contributing to greater performance gains as the number of people increases. Our code and dataset is open at Github.
Effectively Capturing Label Correlation for Tabular Multi-Label Classification
Authors: Sajjad Kamali Siahroudi, Zahra Ahmadi, Daniel Kudenko
Venue: CIKM
Year: 2024

Multi-label data is prevalent across various applications, where instances can be annotated with a set of classes. Although multi-label data can take various forms, such as images and text, tabular multi-label data stands out as the predominant data type in many real-world scenarios. Over the past decades, numerous methods have been proposed for tabular multi-label classification. Effectively addressing challenges like class imbalance, correlation among labels and features, and scalability is crucial for a high-performance multi-label classifier. However, many existing methods fall short of fully considering the correlation between labels and features. In cases where attempts are made, they often encounter high computational costs, rendering them impractical for large datasets. This paper in- troduces an innovative classification method for tabular multi-label data, utilizing a fusion of transformers and graph convolutional networks (GCN). The central concept of the proposed approach involves transforming tabular data into images, leveraging state-of-the-art methods in image processing, including image-based transformers and pre-trained models to capture correlation among labels effectively. Our approach jointly learns the representation of feature space and the correlation among labels within a unified network. To substantiate the performance of our proposed method, we conducted a rigorous series of experiments across diverse multi-label datasets1. The results underscore the superior performance and scalability of our approach compared to other existing state-of-the-art methods. This work not only contributes a novel perspective to the field of tabular multi-label classification but also showcases advancements in both accuracy and scalability.
Moving Region Representations on the Spread of a Forest Fire
Authors: Henrique Macías da Silva, Tiago F. R. Ribeiro, Rogério Luís de C. Costa, José Manuel Moreira
Venue: CIKM
Year: 2024

This paper focuses on the generation of spatiotemporal data from real-world observations to represent the evolution of phenomena of interest as moving regions. The case study is the creation of a dataset to represent the spread of a controlled forest fire from aerial images captured using a drone. We present an overview of the data acquisition and preparation steps and describe the optimization strategy implemented to establish a vertex correspondence between the regions that delimit the burned region at discrete time instants. The resulting dataset is used to create a continuous representation of the evolution of the burned region over time.
Tabularis Revilio: Converting Text to Tables
Authors: Mukul Singh, Gust Verbruggen, Vu Le, Sumit Gulwani
Venue: CIKM
Year: 2024

Copying tables from documents and applications without proper tabular support, like PDF documents, web pages or images, surprisingly remains a challenge. In this paper, we present Revilio, a novel neurosymbolic system for reconstructing tables when their column boundaries have been lost. Revilio addresses this task by detecting headers, generating an initial table sketch using a large language model, and using that sketch as a guiding representation during an enumerate-and-test strategy that evaluates syntactic and semantic table structures. We evaluate Revilio on a diverse set of datasets, demonstrating significant improvements over existing table parsing methods. Revilio outperforms traditional techniques in both accuracy and scalability, handling large tables with over 100,000 rows. Our experiments find an increase in reconstruction accuracy by 5.8-11.3% over both neural and symbolic baseline systems
XCrowd: Combining Explainability and Crowdsourcing to Diagnose Models in Relation Extraction
Authors: Alisa Smirnova, Jie Yang, Philippe Cudré-Mauroux
Venue: CIKM
Year: 2024

Relation extraction methods are currently dominated by deep neural models, which capture complex statistical patterns while being brittle and vulnerable to perturbations in data and distribution. Explainability techniques offer a means for understanding such vulnerabilities, and thus represent an opportunity to mitigate future errors; yet, existing methods are limited to describing what the model 'knows', while totally failing at explaining what the model does not know. This paper presents a new method for diagnosing model predictions and detecting potential inaccuracies. Our approach involves breaking down the problem into two components: (i) determining the necessary knowledge the model should possess for accurate prediction, through human annotations, and (ii) assessing the actual knowledge possessed by the model, using explainable AI methods (XAI). We apply our method to several relation extraction tasks and conduct an empirical study leveraging human specifications of what a model should know and does not know. Results show that human workers are capable of accurately specifying the model should-knows, despite variations in the specification, that the alignment between what a model really knows and what it should know is indeed indicative of model accuracy, and that the unknowns identified through our methods allow to foresee future errors that may or may not have been observed otherwise.
DFLStar: A Decentralized Federated Learning Framework with Self-Knowledge Distillation and Participant Selection
Authors: Behnaz Soltani, Venus Haghighi, Yipeng Zhou, Quan Z. Sheng, Lina Yao
Venue: CIKM
Year: 2024

Federated learning (FL) is a distributed machine learning paradigm in which clients collaboratively train models in a privacy-preserving manner. While centralized FL (CFL) suffers from single points of failure and performance bottlenecks, decentralized FL (DFL), which depends on inter-client communication, has emerged to eliminate the need of a central entity. However, due to lack of the coordination of a central server, heterogeneous data distribution across clients makes local models in DFL inclined to diverge towards their local objectives, resulting in poor model accuracy. Moreover, each client in DFL needs to communicate with multiple neighbors, yielding a heavy communication load. To tackle these challenges, we propose a novel DFL framework called DFLStar, which can improve DFL from two perspectives. First, to avoid significantly diverging towards local data, DFLStar incorporates self-knowledge distillation to enhance the local model training by assimilating knowledge from the aggregated model. Second, clients in DFLStar identify and only select the most informative neighbors (based on the last layer model similarity) for parameter exchange, thereby minimizing the communication overhead. Our experimental results on two real datasets demonstrate that DFLStar significantly improves both communication overhead and training time compared to traditional DFL algorithms while achieving a specific target accuracy. Furthermore, within a fixed training duration, DFLStar constantly obtains the highest model accuracy compared to the baselines.
Breaking the Bottleneck on Graphs with Structured State Spaces
Authors: Yunchong Song, Siyuan Huang, Jiacheng Cai, Xinbing Wang, Chenghu Zhou, Zhouhan Lin
Venue: CIKM
Year: 2024

The majority of GNNs are based on message-passing mechanisms. However, Message Passing Neural Networks (MPNNs) have inherent limitations in capturing long-range interactions. The exponentially growing node information is compressed into fixed-size representations through multiple rounds of message passing, leading to the over-squashing problem. This issue severely hinders the flow of information across the graph and creates a bottleneck in graph learning. The natural idea of introducing global attention to point-to-point communication, as adopted in Graph Transformers (GTs), lacks inductive biases on graph structures and relies on complex positional encodings to enhance their performance in practical tasks. In this paper, we observe that the sensitivity between nodes in MPNNs decreases exponentially with the shortest path distance. In contrast, GTs have constant sensitivity, which leads to a loss of inductive bias. To address these issues, we introduce structured state spaces to capture the hierarchy of rooted trees, achieving linear sensitivity with theoretical guarantees. We further propose a novel state-space model-based graph convolution, resulting in a new paradigm that retains both the strong inductive biases from MPNNs and the long-range modeling capabilities from GTs. Extensive experimental results on long-range and general graph benchmarks demonstrate the superiority of our approach.
MultiLoRA: Multi-Directional Low Rank Adaptation for Multi-Domain Recommendation
Authors: Zijian Song, Wenhan Zhang, Lifang Deng, Jiandong Zhang, Kaigui Bian, Bin Cui
Venue: CIKM
Year: 2024

To address the business needs of industrial recommendation systems, an increasing number of Multi-Domain Recommendation (MDR) methods are designed to improve recommendation performance on multiple domains simultaneously. Most MDR methods follow a multi-task learning paradigm, suffering from poor deployability and negative transfer. Due to the great success of large pre-trained models, the pre-train & fine-tune paradigm is attracting increasing attention. The latest methods introduce parameter-efficient fine-tuning techniques like prompt-tuning, showcasing high efficiency and effectiveness. However, these methods neglect the fundamental differences between recommendation and NLP tasks. The inadequate capacity of recommendation models restricts the effectiveness of prompts and adapters. Worse still, traditional natural domain division may group non-identically distributed samples into the same domain, violating the assumption of independent and identically distributed (i.i.d.) data. In this paper, we propose MultiLoRA, a Multi-directional Low Rank Adaptation paradigm for multi-domain recommendation. First we pre-train a universal model using all data samples. Then we conduct multiple domain divisions on the sample space. Under each division, we fine-tune the pre-trained model to obtain a set of domain-specific LoRAs. Finally, we learn a LoRA fusion module to integrate domain-specific preference patterns across multiple divisions. Experimental results on real-world datasets demonstrate notable advantages of MultiLoRA: (1) achieving SOTA performance, (2) showcasing remarkable compatibility, and (3) proving highly efficient, featuring only 2% trainable parameters compared to the backbone.
HTFabric: A Fast Re-ordering and Parallel Re-execution Method for a High-Throughput Blockchain
Authors: Jaeyub Song, Juyeong Jeong, Jemin Lee, Inju Na, Min-Soo Kim
Venue: CIKM
Year: 2024

The Execute-Order-Validate (EOV) model of blockchain has significantly improved throughput compared to the Order-Execute (OE) model. However, existing systems following the EOV model still struggle to meet the required throughput levels in many applications. We address two critical performance bottlenecks that hinder their throughput: the high cost of transaction re-ordering and the high cost of re-execution of invalid transactions. To address these challenges, we propose HTFabric, a method that combines fast re-ordering and parallel re-execution to achieve exceptionally high successful throughput. We have implemented HTFabric based on Hyperledger Fabric. Through extensive experiments, we demonstrate that HTFabric outperforms SOTA systems by 2.34 to 10.51 times, achieving a successful throughput of up to 8,930 TPS.
Enhancing SPARQL Generation by Triplet-order-sensitive Pre-training
Authors: Chang Su, Jiexing Qi, He Yan, Kai Zou, Zhouhan Lin
Venue: CIKM
Year: 2024

Semantic parsing that translates natural language queries to SPARQL is of great importance for Knowledge Graph Question Answering (KGQA) systems. Although pre-trained language models like T5 have achieved significant success in the Text-to-SPARQL task, their generated outputs still exhibit notable errors specific to the SPARQL language, such as triplet flips. To address this challenge and further improve the performance, we propose an additional pre-training stage with a new objective, Triplet Order Correction (TOC), along with the commonly used Masked Language Modeling (MLM), to collectively enhance the model's sensitivity to triplet order and SPARQL syntax. We also propose to verbalize the Internationalized Resource Identifiers (IRIs) during training. Our method achieves state-of-the-art performances on three widely-used benchmarks.