OpenRaiser/Intern-Atlas huggingface dataset:
Advmind: Inferring adversary intent of black-box attacks
Authors: Ren Pang, Xinyang Zhang 0001, Shouling Ji, Xiapu Luo, Ting Wang 0006
Venue: Conference KDD 2020
Year: 2020

Deep neural networks (DNNs) are inherently susceptible to adversarial attacks even under black-box settings, in which the adversary only has query access to the target models. In practice, while it may be possible to effectively detect such attacks (e.g., observing massive similar but non-identical queries), it is often challenging to exactly infer the adversary intent (e.g., the target class of the adversarial example the adversary attempts to craft) especially during early stages of the attacks, which is crucial for performing effective deterrence and remediation of the threats in many scenarios. In this paper, we present AdvMind, a new class of estimation models that infer the adversary intent of black-box adversarial attacks in a robust and prompt manner. Specifically, to achieve robust detection, AdvMind accounts for the adversary adaptiveness such that her attempt to conceal the target will significantly increase the attack cost (e.g., in terms of the number of queries); to achieve prompt detection, AdvMind proactively synthesizes plausible query results to solicit subsequent queries from the adversary that maximally expose her intent. Through extensive empirical evaluation on benchmark datasets and state-of-the-art black-box attacks, we demonstrate that on average AdvMind detects the adversary intent with over 75% accuracy after observing less than 3 query batches and meanwhile increases the cost of adaptive attacks by over 60%. We further discuss the possible synergy between AdvMind and other defense methods against black-box adversarial attacks, pointing to several promising research directions.
Communication motifs: a tool to characterize social communications
Authors: Qiankun Zhao, Yuan Tian, Qi He, Nuria Oliver, Ruoming Jin, Wang-Chien Lee
Venue: Conference CIKM 2010
Year: 2010

Social networks mediate not only the relations between entities, but also the patterns of information propagation among them and their communication behavior. In this paper, we extensively study the temporal annotations (e.g., time stamps and duration) of historical communications in social networks and propose two novel tools -- communication motifs and maximum-flow communication motifs -- for characterizations of the patterns of information propagation in social networks. Using these motifs, we verify the following hypothesis in social communication network: 1) the functional behavioral patterns of information propagation within both social networks are stable over time; 2) the patterns of information propagation in synchronous and asynchronous social networks are different and sensitive to the cost of communication; and 3) the speed and the amount of information that is propagated through a network are correlated and dependent on individual profiles.
Communication in human-agent teams for tasks with joint action
Authors: Sirui Li, Weixing Sun, Tim Miller 0001
Venue: Conference IJCAI 2015
Year: 2015

© Springer International Publishing Switzerland 2016. In many scenarios, humans must team with agents to achieve joint aims. When working collectively in a team of human and artificial agents, communication is important to establish a shared situation of the task at hand. With no human in the loop and little cost for communication, information about the task can be easily exchanged. However, when communication becomes expensive, or when there are humans in the loop, the strategy for sharing information must be carefully designed: too little information leads to lack of shared situation awareness, while too much overloads the human team members, decreasing performance overall. This paper investigates the effects of sharing beliefs and goals in agent teams and in human-agent teams. We performed a set of experiments using the BlocksWorlds for Teams (BW4T) testbed to assess different strategies for information sharing. In previous experimental studies using BW4T, explanations about agent behaviour were shown to have no effect on team performance. One possible reason for this is because the existing scenarios in BW4T contained joint tasks, but not joint actions. That is, atomic actions that required interdependent and simultaneous action between more than one agent. We implemented new scenarios in BW4T in which some actions required two agents to complete. Our results showed an improvement in artificial-agent team performance when communicating goals and sharing beliefs, but with goals contributing more to team performance, and that in human-agent teams, communicating only goals was more effective than communicating both goals and beliefs.
Forecasting seasonal influenza fusing digital indicators and a mechanistic disease model
Authors: Qian Zhang, Nicola Perra, Daniela Perrotta, Michele Tizzoni, Daniela Paolotti, Alessandro Vespignani
Venue: Conference WWW 2017
Year: 2017

The availability of novel digital data streams that can be used as proxy for monitoring infectious disease incidence is ushering in a new era for real-time forecast approaches to disease spreading. Here, we propose the first seasonal influenza forecast framework based on a stochastic, spatially structured mechanistic model (individual level microsimulation) initialized with geo-localized microblogging data. The framework provides for more than 600 census areas in the United States, Italy and Spain, the initial conditions for a stochastic epidemic computational model that generates an ensemble of forecasts for the main indicators of the epidemic season: peak time and intensity. We evaluate the forecasts accuracy and reliability by comparing the results with the data from the official influenza surveillance systems in the US, Italy and Spain in the seasons 2014/15 and 2015/16. In all countries studied, the proposed framework provides reliable results with leads of up to 6 weeks that became more stable and accurate with progression of the season. The results for the United States have been generated in real-time in the context of the Centers for Disease Control and Prevention ``Forecasting the Influenza Season Challenge''. A characteristic feature of the mechanistic modeling approach is in the explicit estimate of key epidemiological parameters relevant for public health decision-making that cannot be achieved with statistical models that do not consider the disease dynamic. Furthermore, the presented framework allows the fusion of multiple data streams in the initialization stage and can be enriched with census, weather and socioeconomic data.
When recommendation goes wrong: Anomalous link discovery in recommendation networks
Authors: Bryan Perozzi, Michael Schueppert, Jack Saalweachter, Mayur Thakur
Venue: Conference KDD 2016
Year: 2016

We present a secondary ranking system to find and remove erroneous suggestions from a geospatial recommendation system. We discover such anomalous links by "double checking" the recommendation system's output to ensure that it is both structurally cohesive, and semantically consistent.
Gradient descent with sparsification: an iterative algorithm for sparse recovery with restricted isometry property
Authors: Rahul Garg 0001, Rohit Khandekar
Venue: Conference ICML 2009
Year: 2009

We present an algorithm for finding an s-sparse vector x that minimizes the square-error ∥y -- Φx∥2 where Φ satisfies the restricted isometry property (RIP), with isometric constant δ2s < 1/3. Our algorithm, called GraDeS (Gradient Descent with Sparsification) iteratively updates x as: [EQUATION]
Tweetboost: Influence of social media on nft valuation
Authors: Arnav Kapoor, Dipanwita Guhathakurta, Mehul Mathur, Rupanshu Yadav, Manish Gupta, Ponnurangam Kumaraguru
Venue: Conference WWW 2022
Year: 2022

NFT or Non-Fungible Token is a token that certifies a digital asset to be unique. A wide range of assets including, digital art, music, tweets, memes, are being sold as NFTs. NFT-related content has been widely shared on social media sites such as Twitter. We aim to understand the dominant factors that influence NFT asset valuation. Towards this objective, we create a first-of-its-kind dataset linking Twitter and OpenSea (the largest NFT marketplace) to capture social media profiles and linked NFT assets. Our dataset contains 245,159 tweets posted by 17,155 unique users, directly linking 62,997 NFT assets on OpenSea worth 19 Million USD. We have made the dataset publicly available.1 We analyze the growth of NFTs, characterize the Twitter users promoting NFT assets, and gauge the impact of Twitter features on the virality of an NFT. Further, we investigate the effectiveness of different social media and NFT platform features by experimenting with multiple machine learning and deep learning models to predict an asset’s value. We model the problem as a binary classification as well as an ordinal classification task. Our results show that social media features improve the ordinal classification accuracy by 6% over baseline models that use only NFT platform features. Among social media features, count of user membership lists, number of likes and replies are important features. On the other hand, OpenSea features like offer entered, bids withdrawn, bid entered and is presale turn out to be important predictors.
Voiceloop: Voice fitting and synthesis via a phonological loop
Authors: Yaniv Taigman, Lior Wolf, Adam Polyak, Eliya Nachmani
Venue: Conference ICLR 2018
Year: 2018

We present a new neural text to speech (TTS) method that is able to transform text to speech in voices that are sampled in the wild. Unlike other systems, our solution is able to deal with unconstrained voice samples and without requiring aligned phonemes or linguistic features. The network architecture is simpler than those in the existing literature and is based on a novel shifting buffer working memory. The same buffer is used for estimating the attention, computing the output audio, and for updating the buffer itself. The input sentence is encoded using a context-free lookup table that contains one entry per character or phoneme. The speakers are similarly represented by a short vector that can also be fitted to new identities, even with only a few samples. Variability in the generated speech is achieved by priming the buffer prior to generating the audio. Experimental results on several datasets demonstrate convincing capabilities, making TTS accessible to a wider range of applications. In order to promote reproducibility, we release our source code and models.
Improving adversarial transferability via frequency-based stationary point search
Authors: Zhiyu Zhu, Huaming Chen, Jiayu Zhang, Xinyi Wang, Zhibo Jin, Qinghua Lu, Jun Shen, Kim-Kwang Raymond Choo
Venue: Conference CIKM 2023
Year: 2023

Deep neural networks (DNNs) have been shown vulnerable to interference from adversarial samples, leading to erroneous predictions. Investigating adversarial attacks can effectively improve the reliability as well as the performance of deep neural models in real-world applications. Since it is generally challenging to infer the parameters in black-box models, high transferability becomes an important factor for the success rate of an attack method. Recently, the Spectrum Simulation Attack method exhibits promising results based on the frequency domain. In light of SSA, we propose a novel attack approach in this paper, which achieves the best results among diverse state-of-the-art transferable adversarial attack methods. Our method aims to find a stationary point, which extends the ability to find multiple local optima with the optimal local attack effect. After finding the stationary point, a frequency-based search is employed to explore the best adversarial samples in the neighbouring space, utilmately determining the final adversarial direction. We compare our method against a variety of cutting-edge transferable adversarial methods. Extensive experiments validate that our method improves the attack success rate by 4.7% for conventionally trained models and 53.1% for adversarially trained models. Our code is available at https://github.com/LMBTough/FSPS
Fantastic four: Differentiable and efficient bounds on singular values of convolution layers
Authors: Sahil Singla 0002, Soheil Feizi
Venue: Conference ICLR 2021
Year: 2021

In deep neural networks, the spectral norm of the Jacobian of a layer bounds the factor by which the norm of a signal changes during forward/backward propagation. Spectral norm regularizations have been shown to improve generalization, robustness and optimization of deep learning methods. Existing methods to compute the spectral norm of convolution layers either rely on heuristics that are efficient in computation but lack guarantees or are theoretically-sound but computationally expensive. In this work, we obtain the best of both worlds by deriving {\it four} provable upper bounds on the spectral norm of a standard 2D multi-channel convolution layer. These bounds are differentiable and can be computed efficiently during training with negligible overhead. One of these bounds is in fact the popular heuristic method of Miyato et al. (multiplied by a constant factor depending on filter sizes). Each of these four bounds can achieve the tightest gap depending on convolution filters. Thus, we propose to use the minimum of these four bounds as a tight, differentiable and efficient upper bound on the spectral norm of convolution layers. Moreover, our spectral bound is an effective regularizer and can be used to bound either the lipschitz constant or curvature values (eigenvalues of the Hessian) of neural networks. Through experiments on MNIST and CIFAR-10, we demonstrate the effectiveness of our spectral bound in improving generalization and robustness of deep networks.
Explaining aviation safety incidents using deep temporal multiple instance learning
Authors: Vijay Manikandan Janakiraman
Venue: Conference KDD 2018
Year: 2018

Although aviation accidents are rare, safety incidents occur more frequently and require a careful analysis to detect and mitigate risks in a timely manner. Analyzing safety incidents using operational data and producing event-based explanations is invaluable to airline companies as well as to governing organizations such as the Federal Aviation Administration (FAA) in the United States. However, this task is challenging because of the complexity involved in mining multi-dimensional heterogeneous time series data, the lack of time-step-wise annotation of events in a flight, and the lack of scalable tools to perform analysis over a large number of events. In this work, we propose a precursor mining algorithm that identifies events in the multidimensional time series that are correlated with the safety incident. Precursors are valuable to systems health and safety monitoring and in explaining and forecasting safety incidents. Current methods suffer from poor scalability to high dimensional time series data and are inefficient in capturing temporal behavior. We propose an approach by combining multiple-instance learning (MIL) and deep recurrent neural networks (DRNN) to take advantage of MIL's ability to learn using weakly supervised data and DRNN's ability to model temporal behavior. We describe the algorithm, the data, the intuition behind taking a MIL approach, and a comparative analysis of the proposed algorithm with baseline models. We also discuss the application to a real-world aviation safety problem using data from a commercial airline company and discuss the model's abilities and shortcomings, with some final remarks about possible deployment directions.
Deeptagger: Knowledge enhanced named entity recognition for web-based ads queries
Authors: Simiao Zuo, Pengfei Tang, Xinyu Hu, Qiang Lou, Jian Jiao, Denis Charles
Venue: Conference CIKM 2023
Year: 2023

Named entity recognition (NER) is a crucial task for online advertisement. State-of-the-art solutions leverage pre-trained language models for this task. However, three major challenges remain unresolved: web queries differ from natural language, on which pre-trained models are trained; web queries are short and lack contextual information; and labeled data for NER is scarce. We propose DeepTagger, a knowledge-enhanced NER model for web-based ads queries. The proposed knowledge enhancement framework leverages both model-free and model-based approaches. For model-free enhancement, we collect unlabeled web queries to augment domain knowledge; and we collect web search results to enrich the information of ads queries. We further leverage effective prompting methods to automatically generate labels using large language models such as ChatGPT. Additionally, we adopt a model-based knowledge enhancement method based on adversarial data augmentation. We employ a three-stage training framework to train DeepTagger models. Empirical results in various NER tasks demonstrate the effectiveness of the proposed framework.
Searching for embeddings in a haystack: link prediction on knowledge graphs with subgraph pruning
Authors: Unmesh Joshi, Jacopo Urbani
Venue: Conference WWW 2020
Year: 2020

Embedding-based models of Knowledge Graphs (KGs) can be used to predict the existence of missing links by ranking the entities according to some likelihood scores. An exhaustive computation of all likelihood scores is very expensive if the KG is large. To counter this problem, we propose a technique to reduce the search space by identifying smaller subsets of promising entities. Our technique first creates embeddings of subgraphs using the embeddings from the model. Then, it ranks the subgraphs with some proposed ranking functions and considers only the entities in the top k subgraphs. Our experiments show that our technique is able to reduce the search space significantly while maintaining a good recall.
Backdoor in seconds: Unlocking vulnerabilities in large pre-trained models via model editing
Authors: Dongliang Guo, Mengxuan Hu, Zihan Guan, Junfeng Guo, Thomas Hartvigsen, Sheng Li
Venue: Conference CIKM 2025
Year: 2025

Large pre-trained models have achieved notable success across a range of downstream tasks. However, recent research shows that a type of adversarial attack (i.e., backdoor attack) can manipulate the behavior of machine learning models through contaminating their training dataset, posing significant threat in the real-world application of large pre-trained model, especially for those customized models. Therefore, addressing the unique challenges for exploring vulnerability of pre-trained models is of paramount importance. Through empirical studies on the capability for performing backdoor attack in large pre-trained models (e.g., ViT), we find the following unique challenges of attacking large pre-trained models: 1) the inability to manipulate or even access large training datasets, and 2) the substantial computational resources required for training or fine-tuning these models. To address these challenges, we establish new standards for an effective and feasible backdoor attack in the context of large pre-trained models. In line with these standards, we introduce our EDT model, an Efficient, Data-free, Training-free backdoor attack method. Inspired by model editing techniques, EDT injects an editing-based lightweight codebook into the backdoor of large pre-trained models, which replaces the embedding of the poisoned image with the target image without poisoning the training dataset or training the victim model. Our experiments, conducted across various pre-trained models such as ViT, CLIP, BLIP, and stable diffusion, and on downstream tasks including image classification, image captioning, and image generation, demonstrate the effectiveness of our method. Our code is available at https://github.com/donglgcn/Editing/
Medlink: De-identified patient health record linkage
Authors: Zhenbang Wu, Cao Xiao, Jimeng Sun 0001
Venue: Conference KDD 2023
Year: 2023

A comprehensive patient health history is essential for patient care and healthcare research. However, due to the distributed nature of healthcare services, patient health records are often scattered across multiple systems. Existing record linkage approaches primarily rely on patient identifiers, which have inherent limitations such as privacy invasion and identifier discrepancies. To tackle this problem, we propose linking de-identified patient health records by matching health patterns without strictly relying on sensitive patient identifiers. Our model MedLink solves two challenges faced with the patient linkage task: (1) the challenge of identifying the same patients based on data collected in different timelines as disease progression makes the record matching difficult, and (2) the challenge of identifying distinct health patterns as common medical codes dominate health records and overshadow the more informative low-prevalence codes. To address these challenges, MedLink utilizes bi-directional health prediction to predict future codes forwardly and past codes backwardly, thus accounting for the health progression. MedLink also has a prevalence-aware retrieval design to focus more on the low-prevalence but informative codes during learning. MedLink can be trained end-to-end and is lightweight for efficient inference on large patient databases. We evaluate MedLink against leading baselines on real-world patient datasets, including the critical care dataset MIMIC-III and a large health claims dataset. Results show that MedLink outperforms the best baseline by 4% in top-1 accuracy with only 8% memory cost. Additionally, when combined with existing identifier-based linkage approaches, MedLink can improve their performance by up to 15%.
Semi-supervised node importance estimation with informative distribution modeling for uncertainty regularization
Authors: Yankai Chen, Taotao Wang, Yixiang Fang, Yunyu Xiao
Venue: Conference WWW 2025
Year: 2025

Node importance estimation, a classical problem in network analysis, underpins various web applications. Previous methods either exploit intrinsic topological characteristics, e.g., graph centrality, or leverage additional information, e.g., data heterogeneity, for node feature enhancement. However, these methods follow the supervised learning setting, overlooking the fact that ground-truth node-importance data are usually partially labeled in practice. In this work, we propose the first semi-supervised node importance estimation framework, i.e., EASING, to improve learning quality for unlabeled data in heterogeneous graphs. Different from previous approaches, EASING explicitly captures uncertainty to reflect the confidence of model predictions. To jointly estimate the importance values and uncertainties, EASING incorporates DJE, a deep encoder-decoder neural architecture. DJE introduces distribution modeling for graph nodes, where the distribution representations derive both importance and uncertainty estimates. Additionally, DJE facilitates effective pseudo-label generation for the unlabeled data to enrich the training samples. Based on labeled and pseudo-labeled data, EASING develops effective semi-supervised heteroscedastic learning with the varying node uncertainty regularization. Extensive experiments on three real-world datasets highlight the superior performance of EASING compared to competing methods. Codes are available via https://github.com/yankai-chen/EASING.