OpenRaiser/Intern-Atlas huggingface dataset:
Linkage attack on skeleton-based motion visualization
Authors: Thomas Carr, Aidong Lu, Depeng Xu
Venue: Conference CIKM 2023
Year: 2023

Skeleton-based motion capture and visualization is an important computer vision task, especially in the virtual reality (VR) environment. It has grown increasingly popular due to the ease of gathering skeleton data and the high demand of virtual socialization. The captured skeleton data seems anonymous but can still be used to extract personal identifiable information (PII). This can lead to an unintended privacy leakage inside a VR meta-verse. We propose a novel linkage attack on skeleton-based motion visualization. It detects if a target and a reference skeleton are the same individual. The proposed model, called Linkage Attack Neural Network (LAN), is based on the principles of a Siamese Network. It incorporates deep neural networks to embed the relevant PII then uses a classifier to match the reference and target skeletons. We also employ classical and deep motion retargeting (MR) to cast the target skeleton onto a dummy skeleton such that the motion sequence is anonymized for privacy protection. Our evaluation shows that the effectiveness of LAN in the linkage attack and the effectiveness of MR in anonymization.
Extending the framework of equilibrium propagation to general dynamics
Authors: Benjamin Scellier, Anirudh Goyal, Jonathan Binas, Thomas Mesnard, Yoshua Bengio
Venue: Conference ICLR 2018
Year: 2018

The biological plausibility of the backpropagation algorithm has long been doubted by neuroscientists. Two major reasons are that neurons would need to send two different types of signal in the forward and backward phases, and that pairs of neurons would need to communicate through symmetric bidirectional connections. We present a simple two-phase learning procedure for fixed point recurrent networks that addresses both these issues. In our model, neurons perform leaky integration and synaptic weights are updated through a local mechanism. Our learning method extends the framework of Equilibrium Propagation to general dynamics, relaxing the requirement of an energy function. As a consequence of this generalization, the algorithm does not compute the true gradient of the objective function, but rather approximates it at a precision which is proven to be directly related to the degree of symmetry of the feedforward and feedback weights. We show experimentally that the intrinsic properties of the system lead to alignment of the feedforward and feedback weights, and that our algorithm optimizes the objective function.
The image torque operator: A new tool for mid-level vision
Authors: Morimichi Nishigaki, Cornelia Fermüller, Daniel DeMenthon
Venue: Conference CVPR 2012
Year: 2012

Contours are a powerful cue for semantic image understanding. Objects and parts of objects in the image are delineated from their surrounding by closed contours which make up their boundary. In this paper we introduce a new bottom-up visual operator to capture the concept of closed contours, which we call the `Torque' operator. Its computation is inspired by the mechanical definition of torque or moment of force, and applied to image edges. The torque operator takes as input edges and computes over regions of different size a measure of how well the edges are aligned to form a closed, convex contour. We explore fundamental properties of this measure and demonstrate that it can be made a useful tool for visual attention, segmentation, and boundary edge detection by verifying its benefits on these applications.
Friend story ranking with edge-contextual local graph convolutions
Authors: Xianfeng Tang, Yozen Liu, Xinran He, Suhang Wang, Neil Shah
Venue: Conference WSDM 2022
Year: 2022

Social platforms have paved the way in creating new, modern ways for users to communicate with each other. In recent years, multiple platforms have introduced ''Stories'' features, which enable broadcasting of ephemeral multimedia content. Specifically, ''Friend Stories,'' or Stories meant to be consumed by one's close friends, are a popular feature, promoting significant user-user interactions by allowing people to see (visually) what their friends and family are up to. A key challenge in surfacing Friend Stories for a given user, is in ranking over each viewing user's friends to efficiently prioritize and route limited user attention. In this work, we explore the novel problem of Friend Story Ranking from a graph representation learning perspective. More generally, our problem is a link ranking task, where inferences are made over existing links (relations), unlike common node or graph-based tasks, or link prediction tasks, where the goal is to make inferences about non-existing links. We propose ELR, an edge-contextual approach which carefully considers local graph structure, differences between local edge types and directionality, and rich edge attributes, building on the backbone of graph convolutions. ELR handles social sparsity challenges by considering and attending over neighboring nodes, and incorporating multiple edge types in local surrounding egonet structures. We validate ELR on two large country-level datasets with millions of users and tens of millions of links from Snapchat. ELR shows superior performance over alternatives by 8% and 5% error reduction measured by MSE and MAE correspondingly. Further generality, data efficiency and ablation experiments confirm the advantages of ELR.
Combining partitions by probabilistic label aggregation
Authors: Tilman Lange, Joachim M. Buhmann
Venue: Conference KDD 2005
Year: 2005

Data clustering represents an important tool in exploratory data analysis. The lack of objective criteria render model selection as well as the identification of robust solutions particularly difficult. The use of a stability assessment and the combination of multiple clustering solutions represents an important ingredient to achieve the goal of finding useful partitions. In this work, we propose a novel way of combining multiple clustering solutions for both, hard and soft partitions: the approach is based on modeling the probability that two objects are grouped together. An efficient EM optimization strategy is employed in order to estimate the model parameters. Our proposal can also be extended in order to emphasize the signal more strongly by weighting individual base clustering solutions according to their consistency with the prediction for previously unseen objects. In addition to that, the probabilistic model supports an out-of-sample extension that (i) makes it possible to assign previously unseen objects to classes of the combined solution and (ii) renders the efficient aggregation of solutions possible. In this work, we also shed some light on the usefulness of such combination approaches. In the experimental result section, we demonstrate the competitive performance of our proposal in comparison with other recently proposed methods for combining multiple classifications of a finite data set.
Deploying machine learning models for public policy: A framework
Authors: Klaus Ackermann, Joe Walsh, Adolfo De Unánue, Hareem Naveed, Andrea Navarrete Rivera, Sun-Joo Lee, Jason Bennett, Michael Defoe, Crystal Cody, Lauren Haynes, Rayid Ghani
Venue: Conference KDD 2018
Year: 2018

Machine learning research typically focuses on optimization and testing on a few criteria, but deployment in a public policy setting requires more. Technical and non-technical deployment issues get relatively little attention. However, for machine learning models to have real-world benefit and impact, effective deployment is crucial. In this case study, we describe our implementation of a machine learning early intervention system (EIS) for police officers in the Charlotte-Mecklenburg (North Carolina) and Metropolitan Nashville (Tennessee) Police Departments. The EIS identifies officers at high risk of having an adverse incident, such as an unjustified use of force or sustained complaint. We deployed the same code base at both departments, which have different underlying data sources and data structures. Deployment required us to solve several new problems, covering technical implementation, governance of the system, the cost to use the system, and trust in the system. In this paper we describe how we addressed and solved several of these challenges and provide guidance and a framework of important issues to consider for future deployments.
Semantic hierarchies for recognizing objects and parts
Authors: Boris Epshtein, Shimon Ullman
Venue: Conference CVPR 2007
Year: 2007

This paper describes the construction and use of a novel representation for the recognition of objects and their parts, the semantic hierarchy. Its advantages include improved classification performance, accurate detection and localization of object parts and sub-parts, and explicitly identifying the different appearances of each object part. The semantic hierarchy algorithm starts by constructing a minimal feature hierarchy and proceeds by adding semantically equivalent representatives to each node, using the entire hierarchy as a context for determining the identity and locations of added features. Part detection is obtained by a bottom-up top-down cycle. Unlike previous approaches, the semantic hierarchy learns to represent the set of possible appearances of object parts at all levels, and their statistical dependencies. The algorithm is fully automatic and is shown experimentally to substantially improve the recognition of objects and their parts.
Learning a latent simplex in input-sparsity time
Authors: Ainesh Bakshi, Chiranjib Bhattacharyya, Ravi Kannan, David P. Woodruff, Samson Zhou
Venue: Conference ICLR 2021
Year: 2021

We consider the problem of learning a latent $k$-vertex simplex $K\subset\mathbb{R}^d$, given access to $A\in\mathbb{R}^{d\times n}$, which can be viewed as a data matrix with $n$ points that are obtained by randomly perturbing latent points in the simplex $K$ (potentially beyond $K$). A large class of latent variable models, such as adversarial clustering, mixed membership stochastic block models, and topic models can be cast as learning a latent simplex. Bhattacharyya and Kannan (SODA, 2020) give an algorithm for learning such a latent simplex in time roughly $O(k\cdot\textrm{nnz}(A))$, where $\textrm{nnz}(A)$ is the number of non-zeros in $A$. We show that the dependence on $k$ in the running time is unnecessary given a natural assumption about the mass of the top $k$ singular values of $A$, which holds in many of these applications. Further, we show this assumption is necessary, as otherwise an algorithm for learning a latent simplex would imply an algorithmic breakthrough for spectral low rank approximation. At a high level, Bhattacharyya and Kannan provide an adaptive algorithm that makes $k$ matrix-vector product queries to $A$ and each query is a function of all queries preceding it. Since each matrix-vector product requires $\textrm{nnz}(A)$ time, their overall running time appears unavoidable. Instead, we obtain a low-rank approximation to $A$ in input-sparsity time and show that the column space thus obtained has small $\sinΘ$ (angular) distance to the right top-$k$ singular space of $A$. Our algorithm then selects $k$ points in the low-rank subspace with the largest inner product with $k$ carefully chosen random vectors. By working in the low-rank subspace, we avoid reading the entire matrix in each iteration and thus circumvent the $Θ(k\cdot\textrm{nnz}(A))$ running time.
Synthesis of dependent multichannel ecg using generative adversarial networks
Authors: Eoin Brophy
Venue: Conference CIKM 2020
Year: 2020

Access to medical data is highly regulated due to its sensitive nature, which can constrain communities' ability to utilise these data for research or clinical purposes. Common de-identification techniques to enable the sharing of data may not provide adequate protection for an individual's personal data in every circumstance. We investigate the ability of Generative Adversarial Networks (GANs) to generate realistic medical time series data to address these privacy and identification concerns. We generate synthetic, and more significantly, multichannel electrocardiogram (ECG) signals that are representative of waveforms observed in patients. Successful generation of high-quality synthetic time series data has the potential to act as an effective substitute for actual patient data. For the first time, we demonstrate a multivariate GAN architecture that can successfully generate dependent multichannel time series signals. We present the first application of multivariate dynamic time warping as a means of evaluating generated GAN samples. Quantitative evidence demonstrates our GAN can generate data that is structurally similar to the training set and diverse across generated samples, all whilst ensuring sufficient privacy guarantees for the underlying training data.
Repetition and exploration in sequential recommendation
Authors: Ming Li, Ali Vardasbi, Andrew Yates, Maarten de Rijke
Venue: Conference SIGIR 2023
Year: 2023

In several recommendation scenarios, including next basket recommendation, the importance of repetition and exploration has been discovered and studied. Sequential recommenders (SR) aim to infer a user's preferences and suggest the next item for them to interact with based on their historical interaction sequences. There has not been a systematic analysis of sequential recommenders from the perspective of repetition and exploration. As a result, it is unclear how these models, that are typically optimized for accuracy, perform in terms of repetition and exploration, as well as the potential drawbacks of deploying them in real applications. In this paper, we examine whether repetition and exploration are important dimensions in the sequential recommendation scenario. We consider this generalizability question both from a user-centered and an item-centered perspective. Towards the latter, we define item repeat exposure and item explore exposure and examine the recommendation performance of sequential recommendation models in terms of both accuracy and exposure from the perspective of repetition and exploration. We find that (i) there is an imbalance in accuracy and difficulty w.r.t. repetition and exploration in SR scenarios, (ii) using the conventional average overall accuracy with a significance test does not fully represent a model's recommendation accuracy, and (iii) accuracy-oriented sequential recommendation models may suffer from less/zero item explore exposure issue, where items are mostly (or even only) recommended to their repeat users and fail to reach their potential new users. To analyze our findings, we remove repeat samples from the dataset, that often act as easy shortcuts, and focus on a pure exploration SR scenario. We find that (i) removing the repetition shortcut increases the recommendation novelty and helps users who prefer to consume novel items next, (ii) neural-based models fail to learn the basic characteristics of this pure exploration scenario and suffer from an inherent repetitive bias issue, (iii) using shared item embeddings in the prediction layer may skew recommendations to repeat items, and (iv) removing all repeat items to post-processing recommendation results leads to a substantial improvement on top of several SR methods.
Mapping and Influencing the Political Ideology of Large Language Models using Synthetic Personas
Authors: Pietro Bernardelle, Leon Fröhling, Stefano Civelli, Riccardo Lunardi, Kevin Roitero, Gianluca Demartini
Venue: Conference WWW 2025
Year: 2025

The analysis of political biases in large language models (LLMs) has primarily examined these systems as single entities with fixed viewpoints. While various methods exist for measuring such biases, the impact of persona-based prompting on LLMs' political orientation remains unexplored. In this work we leverage PersonaHub, a collection of synthetic persona descriptions, to map the political distribution of persona-based prompted LLMs using the Political Compass Test (PCT). We then examine whether these initial compass distributions can be manipulated through explicit ideological prompting towards diametrically opposed political orientations: right-authoritarian and left-libertarian. Our experiments reveal that synthetic personas predominantly cluster in the left-libertarian quadrant, with models demonstrating varying degrees of responsiveness when prompted with explicit ideological descriptors. While all models demonstrate significant shifts towards right-authoritarian positions, they exhibit more limited shifts towards left-libertarian positions, suggesting an asymmetric response to ideological manipulation that may reflect inherent biases in model training.
Denoising time cycle modeling for recommendation
Authors: Sicong Xie, Qunwei Li, Weidi Xu, Kaiming Shen, Shaohu Chen, Wenliang Zhong
Venue: Conference SIGIR 2022
Year: 2022

Recently, modeling temporal patterns of user-item interactions have attracted much attention in recommender systems. We argue that existing methods ignore the variety of temporal patterns of user behaviors. We define the subset of user behaviors that are ir- relevant to the target item as noises, which limits the performance of target-related time cycle modeling and affect the recommendation performance. In this paper, we propose Denoising Time Cycle Modeling (DiCycle), a novel approach to denoise user behaviors and select the subset of user behaviors that are highly related to the target item. DiCycle is able to explicitly model diverse time cycle patterns for recommendation. Extensive experiments are conducted on both public benchmarks and a real-world dataset, demonstrating the superior performance of DiCycle over the state-of-the-art recommendation methods.
Weinfer: Unleashing the power of webGPU on LLM inference in web browsers
Authors: Zhiyang Chen, Yun Ma, Haiyang Shen, Mugeng Liu
Venue: Conference WWW 2025
Year: 2025

Web-based large language model (LLM) has garnered significant attention from both academia and industry as it combines the benefits of on-device computation with the accessibility and portability of Web applications. The advent of WebGPU, a modern browser API that enables Web applications to utilize a device's GPU, has opened up new possibilities for GPU-accelerated LLM inference within browsers. However, our experiment reveals that existing Web-based LLM inference frameworks exhibit inefficiencies in GPU utilization, limiting the inference speed. These inefficiencies primarily arise from underutilizing the full capabilities of WebGPU, particularly in resource management and execution synchronization. To address these limitations, we present WeInfer, an efficient Web-based LLM inference framework specifically designed to unleash the power of WebGPU. WeInfer incorporates two key innovations: 1) buffer reuse strategies that reduce the overhead associated with resource preparation, optimizing the lifecycle management of WebGPU buffers, and 2) an asynchronous pipeline that decouples resource preparation from GPU execution, enabling parallelized computation and deferred result fetching to improve overall efficiency. We conduct extensive evaluations across 9 different LLMs and 5 heterogeneous devices, covering a broad spectrum of model architectures and hardware configurations. The results demonstrate that WeInfer delivers substantial improvements in decoding speed, achieving up to a 3.76× performance boost compared with WebLLM, the state-of-the-art Web-based LLM inference framework.
Asynchronous vertical federated learning for kernelized auc maximization
Authors: Ke Zhang, Ganyu Wang, Han Li, Yulong Wang 0002, Hong Chen 0004, Bin Gu 0001
Venue: Conference KDD 2024
Year: 2024

Vertical Federated Learning (VFL) has garnered significant attention due to its applicability in multi-party collaborative learning and the increasing demand for privacy-preserving measures. Most existing VFL algorithms primarily focus on accuracy as the training model metric. However, the data we access is often imbalanced in the real world, making it difficult for models based on accuracy to correctly classify minority samples. The Area Under the Curve (AUC) serves as an effective metric to evaluate the performance of a model on imbalanced data. Therefore, optimizing AUC can enhance the model's ability to handle imbalanced data. Besides, computational resources within VFL systems are also imbalanced, which makes synchronous VFL algorithms are difficult to apply in the real world. To address the double imbalance issue, we propose Asynchronous Vertical Federated Kernelized AUC Maximization (AVFKAM). Specifically, AVFKAM asynchronously updates a kernel model based on triply stochastic gradients with respect to (w.r.t.) the pairwise loss and random feature approximation. To facilitate theoretical analysis, we transfer the asynchrony of model coefficients to the functional gradient through a dual relationship between coefficients and objective function. Furthermore, we demonstrate that AVFKAM converges to the optimal solution at a rate of O(1/t), where t represents the global iteration number, and discuss the security of the model. If t is denoted as the global iteration number, we provide that it converges to the optimal solution with the rate of O(1/t). Finally, experimental results on various benchmark datasets demonstrate that AVFKAM maintains high AUC performance and efficiency.
Predicting paper acceptance via interpretable decision sets
Authors: Peng Bao, Weihui Hong, Xuanya Li
Venue: Conference WWW 2021
Year: 2021

Measuring the quality of research work is an essential component of the scientific process. With the ever-growing rates of articles being submitted to top-tier conferences, and the potential consistency and bias issues in the peer review process identified by scientific community, it is thus of great necessary and challenge to automatically evaluate submissions. Existing works mainly focus on exploring relevant factors and applying machine learning models to simply be accurate at predicting the acceptance of a given academic paper, while ignoring the interpretability power which is required by a wide range of applications. In this paper, we propose a framework to construct decision sets that consist of unordered if-then rules for predicting paper acceptance. We formalize decision set learning problem via a joint objective function that simultaneously optimize accuracy and interpretability of the rules, rather than organizing them in a hierarchy. We evaluate the effectiveness of the proposed framework by applying it on a public scientific peer reviews dataset. Experimental results demonstrate that the learned interpretable decision sets by our framework performs on par with state-of-the-art classification algorithms which optimize exclusively for predictive accuracy and much more interpretable than rule-based methods.
Data quality-aware graph machine learning
Authors: Yu Wang, Kaize Ding, Xiaorui Liu, Jian Kang, Ryan A. Rossi, Tyler Derr
Venue: Conference CIKM 2024
Year: 2024

Recent years have seen a significant shift in Artificial Intelligence from model-centric to data-centric approaches, highlighted by the success of large foundational models. Following this trend, despite numerous innovations in graph machine learning model design, graph-structured data often suffers from data quality issues, jeopardizing the progress of Data-centric AI in graph-structured applications. Our proposed tutorial addresses this gap by raising awareness about data quality issues within the graph machine-learning community. We provide an overview of existing topology, imbalance, bias, limited data, and abnormality issues in graph data. Additionally, we highlight recent developments in foundational graph models that focus on identifying, investigating, mitigating, and resolving these issues.