OpenRaiser/Intern-Atlas huggingface dataset:
Digital democracy at crossroads: A meta-analysis of web and ai influence on global elections
Authors: Zheng Wei, Xian Xu, Pan Hui
Venue: Conference WWW 2024
Year: 2024

2024 will be the largest election year in history involving over 50 countries and approximately 4.2 billion people. Since 1996, the Web has been instrumental in political campaigns, enhancing public engagement and creating new communication avenues for elections. Nevertheless, the proliferation of generative AI technologies has made false information dissemination simpler and quicker, posing a substantial threat to election integrity and democratic processes. The 2024 global elections underscore the need to comprehend and tackle the impact of such technologies on democracy. In this paper, we undertake a detailed meta-analysis, scrutinizing 44 papers published in The Web Conference, detailing the influence of the Web on elections. Our research reveals key historical trends on how the Web has impacted elections: first, social media has revolutionized election strategies through direct voter-candidate interactions. Second, big data and algorithm-driven campaigns are commonplace. Third, AI advancements have exacerbated the spread of fake news, risking election fairness. Predominantly from studies published since 2018 among 44 papers, we underscore the necessity for advanced detection tools, policy formulation, and responsible AI use to maintain electoral integrity. This analysis offers an insight into the Web and AI's impact on elections, presenting pointers for addressing challenges and leveraging opportunities in the 2024 and future elections.
Precision meets resilience: Cross-database generalization with uncertainty quantification for robust cost estimation
Authors: Shuhuan Fan, Mengshu Hou, Rui Xi, Wenwen Ma
Venue: Conference CIKM 2024
Year: 2024

Learning-based models have shown promise in addressing query optimization challenges in the database field, where the learned cost model plays a central role. While these models outperform traditional optimizers on static datasets, their resilience and reliability in real-world applications remain a concern, limiting their widespread adoption. In this paper, we take a step towards a practical cost estimation model, named Tosure, which can quantify the uncerT ainty for cost estimation and generalizes to unseen databases accurately and efficiently. It consists primarily of two modules: a Cross-Database Representation (CDR) module and a Cost Estimation with Uncertainty (CEU) module. The CDR module captures the transferable features by focusing the minimal set based on deep-learning network, thereby enhancing the model's generalization capabilities. The CEU module introduces a novel Neural Network Gaussian Process (NNGP) to quantify the uncertainty in cost estimation, ensuring more robust estimations with an upper bound. To improve the model's performance, we perform pre-training on diverse large-scale datasets. Furthermore, we implement the model and integrate it with traditional query optimizer to validate its usability and effectiveness in real-world scenarios. Extensive experimentation demonstrates that Tosure outperforms state-of-the-art methods, achieving a 20% improvement in cost estimation accuracy and twice of the robustness.
Smartindex: An index advisor with learned cost estimator
Authors: Jianling Gao, Nan Zhao, Ning Wang, Shuang Hao
Venue: Conference CIKM 2022
Year: 2022

As an important part of database optimization, index selection problem remains a hot topic. Existing methods tend to use the cost estimated by the DBMS optimizer to measure the benefit of an index. However, due to the limitations of the cost estimation model in database management system (DBMS), these methods may not find the optimal index configuration. To address this problem, we present SmartIndex, an index advisor for relational database with learned cost estimator. We first design a graph convolutional network (GCN) based cost estimation model to predict a query's execution time on certain indexes. After that, we use a greedy method for index selection under certain constraints including number of indexes and storage cost of indexes, which can find better solutions for a given workload.
Graph anomaly detection with bi-level optimization
Authors: Yuan Gao, Junfeng Fang, Yongduo Sui, Yangyang Li, Xiang Wang, Huamin Feng, Yongdong Zhang
Venue: Conference WWW 2024
Year: 2024

Graph anomaly detection (GAD) has various applications in finance, healthcare, and security. Graph Neural Networks (GNNs) are now the primary method for GAD, treating it as a task of semi-supervised node classification (normal vs. anomalous). However, most traditional GNNs aggregate and average embeddings from all neighbors, without considering their labels, which can hinder detecting actual anomalies. To address this issue, previous methods try to selectively aggregate neighbors. However, the same selection strategy is applied regardless of normal and anomalous classes, which does not fully solve this issue. This study discovers that nodes with different classes yet similar neighbor label distributions (NLD) tend to have opposing loss curves, which we term it as "loss rivalry". By introducing Contextual Stochastic Block Model (CSBM) and defining NLD distance, we explain this phenomenon theoretically and propose a Bi-level optimization Graph Neural Network (BioGNN), based on these observations. In a nutshell, the lower level of BioGNN segregates nodes based on their classes and NLD, while the upper level trains the anomaly detector using separation outcomes. Our experiments demonstrate that BioGNN outperforms state-of-the-art methods on four benchmarks and effectively mitigates "loss rivalry".
Spatio-temporal graph normalizing flow for probabilistic traffic prediction
Authors: Yang An, Zhibin Li, Wei Liu, Haoliang Sun, Meng Chen, Wenpeng Lu, Yongshun Gong
Venue: Conference CIKM 2024
Year: 2024

With the development of the Intelligent Transportation Systems, a great deal of work has been proposed to tackle traffic prediction tasks. Despite their good performance, most traffic prediction models are point estimation models, lacking the capability to estimate the uncertainties of future traffic data, which is crucial in practical traffic decision-making. Aiming at this problem, we combine the probabilistic estimation capabilities of conditional normalizing flows with the spatio-temporal relationship learning of spatio-temporal graphs, leading to a Spatio-Temporal Graph Normalizing Flow (STGNF) model to estimate the distribution of future traffic data. We are the first to employ the conditional normalizing flows as the backbone for probabilistic traffic prediction. Then we design a spatio-temporal graph conditional fusion network to learn the spatio-temporal relationships between future and historical traffic data, which are provided to the conditional normalizing flows as conditional information. Extensive experiments on two real-world traffic datasets demonstrate that our proposed model significantly outperforms the state-of-the-art baselines.
Paperlm: A pre-trained model for hierarchical examination paper representation learning
Authors: Minghui Shan, Yixiao Ma, Shulan Ruan, Zhi Cao, Shiwei Tong, Qi Liu, Yu Su, Shijin Wang
Venue: Conference CIKM 2023
Year: 2023

Representation learning of examination papers is significantly crucial for online education systems, as it benefits various applications such as estimating paper difficulty and examination paper retrieval. Previous works mainly explore the representation learning of individual questions in an examination paper, with limited attention given to the examination paper as a whole. In fact, the structure of examination papers is strongly correlated with paper properties such as paper difficulty, which existing paper representation methods fail to capture adequately. To this end, we propose a pre-trained model namely PaperLM to learn the representation of examination papers. Our model integrates both the text content and hierarchical structure of examination papers within a single framework by converting the path of the Examination Organization Tree (EOT) into embedding. Furthermore, we specially design three pre-training objectives for PaperLM, namely EOT Node Relationship Prediction (ENRP), Question Type Prediction (QTP) and Paper Contrastive Learning (PCL), aiming to capture features from text and structure effectively. We pre-train our model on a real-world examination paper dataset, and then evaluate the model with three down-stream tasks: paper difficulty estimation, examination paper retrieval, and paper clustering. The experimental results demonstrate the effectiveness of our method.
How to impute missing ratings? claims, solution, and its application to collaborative filtering
Authors: Youngnam Lee, Sang-Wook Kim, Sunju Park, Xing Xie
Venue: Conference WWW 2018
Year: 2018

Data sparsity is one of the biggest problems faced by collaborative filtering used in recommender systems. Data imputation alleviates the data sparsity problem by inferring missing ratings and imputing them to the original rating matrix. In this paper, we identify the limitations of existing data imputation approaches and suggest three new claims that all data imputation approaches should follow to achieve high recommendation accuracy. Furthermore, we propose a deep-learning based approach to compute imputed values that satisfies all three claims. Based on our hypothesis that most pre-use preferences (e.g., impressions) on items lead to their post-use preferences (e.g., ratings), our approach tries to understand via deep learning how pre-use preferences lead to post-use preferences differently depending on the characteristics of users and items. Through extensive experiments on real-world datasets, we verify our three claims and hypothesis, and also demonstrate that our approach significantly outperforms existing state-of-the-art approaches.
Efvae: Efficient federated variational autoencoder for collaborative filtering
Authors: Lu Zhang, Qian Rong, Xuanang Ding, Guohui Li, Ling Yuan
Venue: Conference CIKM 2024
Year: 2024

Federated recommender systems are used to address privacy issues in recommendations. Among them, FedVAE extends the representative non-linear recommendation method MultVAE. However, the bottleneck of FedVAE lies in its communication load during training, as the parameter volume of its first and last layers is correlated with the number of items. This leads to significant communication cost during the model's transmission phases (distribution and upload), making FedVAE's implementation extremely challenging. To address these challenges, we propose an Efficient Federated Variational AutoEncoder for collaborative filtering, EFVAE, which core is the Federated Collaborative Importance Sampling (FCIS) method. FCIS reduces communication costs through a client-to-server collaborative sampling mechanism and provides satisfactory recommendation performance through dynamic multi-stage approximation of the decoding distribution. Extensive experiments and analyses on real-world datasets confirm that EFVAE significantly reduces communication costs by up to 94.51% while maintaining the recommendation performance. Moreover, its recommendation performance is better on sparse datasets, with improvements reaching up to 13.79%.
LC: a flexible, extensible open-source toolkit for model compression
Authors: Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán
Venue: Conference CIKM 2021
Year: 2021

The continued increase in memory, runtime and energy consumption of deployed machine learning models on one side, and the trend to miniaturize intelligent devices and sensors on the other side, imply that model compression will remain a critical need for the foreseeable future. A scalable solution to this problem must be able to handle arbitrary choices of the reference model to be compressed (driven by the machine learning task), of the form of compression to use, and of the costs and constraints to obey (driven by the target device). We describe an open-source toolkit that is primarily designed to be flexible and extensible, but which is also efficient in compression time and achieves state-of-the-art accuracy-compression curves, as demonstrated empirically over a number of deep net architectures. Mathematically, this is achieved by formulating compression as a constrained optimization using auxiliary variables that facilitate separability, and solving it via a penalty method and alternating optimization, which results in a "learning-compression" (LC) algorithm. This alternates a "learning" step over the original model, independent of the compression, and a "compression" step over the compressed parameters, independent of the dataset and task. Each step can typically be solved by reusing well-known algorithms, such as SGD or EM in the learning step, or SVD or k-means in the compression step, and this makes the algorithm flexible and extensible. The toolkit is available at https://github.com/UCMerced-ML/LC-model-compression.
Multi-label relieff and f-statistic feature selections for image annotation
Authors: Deguang Kong, Chris H. Q. Ding, Heng Huang 0001, Haifeng Zhao
Venue: Conference CVPR 2012
Year: 2012

The classical ReliefF and F-statistic feature selections can not be directly applied into multi-label problems due to the ambiguity produced from a data point attributed to multiple classes simultaneously. In this paper, we present MReliefF and MF-statistic algorithms for multi-label feature selections. Discriminant features are selected to boost the multi-label classification accuracy. The proposed MReliefF and MF-statistic can be used in image categorization and annotation problems. Extensive experiments on image annotation tasks show the good performance of our approach. To our knowledge, this is the first work to generalize the ReliefF and F-statistic feature selection algorithms for multi-label image annotation tasks.
Applying deep learning based probabilistic forecasting to food preparation time for on-demand delivery service
Authors: Chengliang Gao, Fan Zhang, Yue Zhou, Ronggen Feng, Qiang Ru, Kaigui Bian, Renqing He, Zhizhao Sun
Venue: Conference KDD 2022
Year: 2022

On-demand food delivery service has widely served people's daily demands worldwide, e.g., customers place over 40 million online orders in Meituan food delivery platform per day in Q3 of 2021. Predicting the food preparation time (FPT) of each order accurately is very significant for the courier and customer experience over the platform. However, there are two challenges, namely incomplete label and huge uncertainty in FPT data, to make the prediction of FPT in practice. In this paper, we apply probabilistic forecasting to FPT for the first time and propose a non-parametric method based on deep learning. Apart from the data with precise label of FPT, we make full use of the lower/upper bound of orders without precise label, during feature extraction and model construction. A number of categories of meaningful features are extracted based on the detailed data analysis to produce sharp probability distribution. For probabilistic forecasting, we propose S-QL and prove its relationship with S-CRPS for interval-censored data for the first time, which serves the quantile discretization of S-CRPS and optimization for the constructed neural network model. Extensive offline experiments over the large-scale real-world dataset, and online A/B test both demonstrate the effectiveness of our proposed method.
Predicting age-related macular degeneration progression with contrastive attention and time-aware lstm
Authors: Changchang Yin, Sayoko E. Moroi, Ping Zhang 0016
Venue: Conference KDD 2022
Year: 2022

Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in developed countries. Identifying patients at high risk of progression to late AMD, the sight-threatening stage, is critical for clinical actions, including medical interventions and timely monitoring. Recently, deep-learning-based models have been developed and achieved superior performance for late AMD prediction. However, most existing methods are limited to the color fundus photography (CFP) from the last ophthalmic visit and do not include the longitudinal CFP history and AMD progression during the previous years' visits. Patients in different AMD subphenotypes might have various speeds of progression in different stages of AMD disease. Capturing the progression information during the previous years' visits might be useful for the prediction of AMD progression. In this work, we propose a <b>C</b>ontrastive-<b>A</b>ttention-based <b>T</b>ime-aware <b>L</b>ong <b>S</b>hort-<b>T</b>erm <b>M</b>emory network (<b>CAT-LSTM</b>) to predict AMD progression. First, we adopt a convolutional neural network (CNN) model with a contrastive attention module (CA) to extract abnormal features from CFPs. Then we utilize a time-aware LSTM (T-LSTM) to model the patients' history and consider the AMD progression information. The combination of disease progression, genotype information, demographics, and CFP features are sent to T-LSTM. Moreover, we leverage an auto-encoder to represent temporal CFP sequences as fixed-size vectors and adopt k-means to cluster them into subphenotypes. We evaluate the proposed model based on real-world datasets, and the results show that the proposed model could achieve 0.925 on area under the receiver operating characteristic (AUROC) for 5-year late-AMD prediction and outperforms the state-of-the-art methods by more than 3%, which demonstrates the effectiveness of the proposed CAT-LSTM. After analyzing patient representation learned by an auto-encoder, we identify 3 novel subphenotypes of AMD patients with different characteristics and progression rates to late AMD, paving the way for improved personalization of AMD management. The code of CAT-LSTM can be found at GitHub.
Flea: Addressing data scarcity and label skew in federated learning via privacy-preserving feature augmentation
Authors: Tong Xia, Abhirup Ghosh, Xinchi Qiu, Cecilia Mascolo
Venue: Conference KDD 2024
Year: 2024

Federated Learning (FL) enables model development by leveraging data distributed across numerous edge devices without transferring local data to a central server. However, existing FL methods still face challenges when dealing with scarce and label-skewed data across devices, resulting in local model overfitting and drift, consequently hindering the performance of the global model. In response to these challenges, we propose a pioneering framework called \textit{FLea}, incorporating the following key components: \textit{i)} A global feature buffer that stores activation-target pairs shared from multiple clients to support local training. This design mitigates local model drift caused by the absence of certain classes; \textit{ii)} A feature augmentation approach based on local and global activation mix-ups for local training. This strategy enlarges the training samples, thereby reducing the risk of local overfitting; \textit{iii)} An obfuscation method to minimize the correlation between intermediate activations and the source data, enhancing the privacy of shared features. To verify the superiority of \textit{FLea}, we conduct extensive experiments using a wide range of data modalities, simulating different levels of local data scarcity and label skew. The results demonstrate that \textit{FLea} consistently outperforms state-of-the-art FL counterparts (among 13 of the experimented 18 settings, the improvement is over $5\%$) while concurrently mitigating the privacy vulnerabilities associated with shared features. Code is available at https://github.com/XTxiatong/FLea.git
Extraction and tracking of the tongue surface from ultrasound image sequences
Authors: Yusuf Sinan Akgul, Chandra Kambhamettu, Maureen Stone 0001
Venue: Conference CVPR 1998
Year: 1998

This paper presents a system for automatic extraction and tracking of 2D contours of the tongue surfaces from digital ultrasound image sequences. The input to the system is provided by a Head and Transducer Support System (HATS), which is developed for use in ultrasound imaging of the tongue movement. We developed a novel active contour (snakes) model that uses several temporally adjacent images during the extraction of the tongue surface contour for an image frame. The user supplies an initial contour model for a single image frame in the whole sequence. Using optical flow and multi-resolution methods, this initial contour is then used to find the candidate contour points in the temporally immediate adjacent images. Subsequently, the new snake mechanism is applied to estimate optimal contours for each image frame using these candidate points. In turn, the extracted contours are used as models for the extraction process of new adjacent frames. Finally, the system uses a novel postprocessing technique to refine the positions of the contours. We tested the system on 11 different speech sequences, each containing about 25 images. Visual inspection of the detected contours by the speech experts shows that the results are very promising and this system can be effectively employed in speech and swallowing research.
A Phonotactic-Semantic Paradigm for Automatic Spoken Document Classification
Authors: Bin Ma, Haizhou Li
Venue: Conference SIGIR 2005
Year: 2005

We demonstrate a phonotactic-semantic paradigm for spoken document categorization. In this framework, we define a set of acoustic words instead of lexical words to represent acoustic activities in spoken languages. The strategy for acoustic vocabulary selection is studied by comparing different feature selection methods. With an appropriate acoustic vocabulary, a voice tokenizer converts a spoken document into a text-like document of acoustic words. Thus, a spoken document can be represented by a count vector, named a bag-of-sounds vector, which characterizes a spoken document's semantic domain. We study two phonotactic-semantic classifiers, the support vector machine classifier and the latent semantic analysis classifier, and their properties. The phonotactic-semantic framework constitutes a new paradigm in spoken document classification, as demonstrated by its success in the spoken language identification task. It achieves 18.2% error reduction over state-of-the-art benchmark performance on the 1996 NIST Language Recognition Evaluation database.
Neighborhood Preserving Embedding
Authors: Xiaofei He 0001, Deng Cai 0001, Shuicheng Yan, HongJiang Zhang
Venue: Conference ICCV 2005
Year: 2005

Recently there has been a lot of interest in geometrically motivated approaches to data analysis in high dimensional spaces. We consider the case where data is drawn from sampling a probability distribution that has support on or near a submanifold of Euclidean space. In this paper, we propose a novel subspace learning algorithm called neighborhood preserving embedding (NPE). Different from principal component analysis (PCA) which aims at preserving the global Euclidean structure, NPE aims at preserving the local neighborhood structure on the data manifold. Therefore, NPE is less sensitive to outliers than PCA. Also, comparing to the recently proposed manifold learning algorithms such as Isomap and locally linear embedding, NPE is defined everywhere, rather than only on the training data points. Furthermore, NPE may be conducted in the original space or in the reproducing kernel Hilbert space into which data points are mapped. This gives rise to kernel NPE. Several experiments on face database demonstrate the effectiveness of our algorithm.