OpenRaiser/Intern-Atlas huggingface dataset:
Fast mining and forecasting of co-evolving epidemiological data streams
Authors: Tasuku Kimura, Yasuko Matsubara, Koki Kawabata, Yasushi Sakurai
Venue: Conference KDD 2022
Year: 2022

Given a large, semi-infinite collection of co-evolving epidemiological data containing the daily counts of cases/deaths/recovered in multiple locations, how can we incrementally monitor current dynamical patterns and forecast future behavior? The world faces the rapid spread of infectious diseases such as SARS-CoV-2 (COVID-19), where a crucial goal is to predict potential future outbreaks and pandemics, as quickly as possible, using available data collected throughout the world. In this paper, we propose a new streaming algorithm, EPICAST, which is able to model, understand and forecast dynamical patterns in large co-evolving epidemiological data streams. Our proposed method is designed as a dynamic and flexible system, and is based on a unified non-linear differential equation. Our method has the following properties: (a) Effective: it operates on large co-evolving epidemiological data streams, and captures important world-wide trends, as well as location-specific patterns. It also performs real-time and long-term forecasting; (b) Adaptive: it incrementally monitors current dynamical patterns, and also identifies any abrupt changes in streams; (c) Scalable: our algorithm does not depend on data size, and thus is applicable to very large data streams. In extensive experiments on real datasets, we demonstrate that EPICAST outperforms the best existing state-of-the-art methods as regards accuracy and execution speed.
Multistream classification for cyber threat data with heterogeneous feature space
Authors: Yi-Fan Li, Yang Gao, Gbadebo Ayoade, Hemeng Tao, Latifur Khan, Bhavani Thuraisingham
Venue: Conference WWW 2019
Year: 2019

Under a newly introduced setting of multistream classification, two data streams are involved, which are referred to as source and target streams. The source stream continuously generates data instances from a certain domain with labels, while the target stream does the same task without labels from another domain. Existing approaches assume that domains for both data streams are identical, which is not quite true in real world scenario, since data streams from different sources may contain distinct features. Furthermore, obtaining labels for every instance in a data stream is often expensive and time-consuming. Therefore, it has become an important topic to explore whether labeled instances from other related streams can be helpful to predict those unlabeled instances in a given stream. Note that domains of source and target streams may have distinct features spaces and data distributions. Our objective is to predict class labels of data instances in the target stream by using the classifiers trained by the source stream. We propose a framework of multistream classification by using projected data from a common latent feature space, which is embedded from both source and target domains. This framework is also crucial for enterprise system defenders to detect cross-platform attacks, such as Advanced Persistent Threats (APTs). Empirical evaluation and analysis on both real-world and synthetic datasets are performed to validate the effectiveness of our proposed algorithm, comparing to state-of-the-art techniques. Experimental results show that our approach significantly outperforms other existing approaches.
Resumeflow: An llm-facilitated pipeline for personalized resume generation and refinement
Authors: Saurabh Bhausaheb Zinjad, Amrita Bhattacharjee, Amey Bhilegaonkar, Huan Liu
Venue: Conference SIGIR 2024
Year: 2024

Crafting the ideal, job-specific resume is a challenging task for many job applicants, especially for early-career applicants. While it is highly recommended that applicants tailor their resume to the specific role they are applying for, manually tailoring resumes to job descriptions and role-specific requirements is often (1) extremely time-consuming, and (2) prone to human errors. Furthermore, performing such a tailoring step at scale while applying to several roles may result in a lack of quality of the edited resumes. To tackle this problem, in this demo paper, we propose ResumeFlow: a Large Language Model (LLM) aided tool that enables an end user to simply provide their detailed resume and the desired job posting, and obtain a personalized resume specifically tailored to that specific job posting in the matter of a few seconds. Our proposed pipeline leverages the language understanding and information extraction capabilities of state-of-the-art LLMs such as OpenAI's GPT-4 and Google's Gemini, in order to (1) extract details from a job description, (2) extract role-specific details from the user-provided resume, and then (3) use these to refine and generate a role-specific resume for the user. Our easy-to-use tool leverages the user-chosen LLM in a completely off-the-shelf manner, thus requiring no fine-tuning. We demonstrate the effectiveness of our tool via a https://www.youtube.com/watch?v=Agl7ugyu1N4 and propose novel task-specific evaluation metrics to control for alignment and hallucination. Our tool is available at https://job-aligned-resume.streamlit.app.
Time-efficient geo-obfuscation to protect worker location privacy over road networks in spatial crowdsourcing
Authors: Chenxi Qiu, Anna Cinzia Squicciarini, Zhuozhao Li, Ce Pang, Li Yan
Venue: Conference CIKM 2020
Year: 2020

To promote cost-effective task assignment in Spatial Crowdsourcing (SC), workers are required to report their location to servers, which raises serious privacy concerns. As a solution, geo-obfuscation has been widely used to protect the location privacy of SC workers, where workers are allowed to report perturbed location instead of the true location. Yet, most existing geo-obfuscation methods consider workers? mobility on a 2 dimensional (2D) plane, wherein workers can move in arbitrary directions. Unfortunately, 2D-based geo-obfuscation is likely to generate high traveling cost for task assignment over roads, as it cannot accurately estimate the traveling costs distortion caused by location obfuscation. In this paper, we tackle the SC worker location privacy problem over road networks. Considering the network-constrained mobility features of workers, we describe workers? mobility by a weighted directed graph, which considers the dynamic traffic condition and road network topology. Based on the graph model, we design a geo-obfuscation (GO) function for workers to maximize the workers? overall location privacy without compromising the task assignment efficiency. We formulate the problem of deriving the optimal GO function as a linear programming (LP) problem. By using the angular block structure of the LP's constraint matrix, we apply Dantzig-Wolfe decomposition to improve the time-efficiency of the GO function generation. Our experimental results in the real-trace driven simulation and the real-world experiment demonstrate the effectiveness of our approach in terms of both privacy and task assignment efficiency.
Multi-variate time series forecasting on variable subsets
Authors: Jatin Chauhan, Aravindan Raghuveer, Rishi Saket, Jay Nandy, Balaraman Ravindran
Venue: Conference KDD 2022
Year: 2022

We formulate a new inference task in the domain of multivariate time series forecasting (MTSF), called Variable Subset Forecast (VSF), where only a small subset of the variables is available during inference. Variables are absent during inference because of long-term data loss (eg. sensor failures) or high -> low-resource domain shift between train / test. To the best of our knowledge, robustness of MTSF models in presence of such failures, has not been studied in the literature. Through extensive evaluation, we first show that the performance of state of the art methods degrade significantly in the VSF setting. We propose a non-parametric, wrapper technique that can be applied on top any existing forecast models. Through systematic experiments across 4 datasets and 5 forecast models, we show that our technique is able to recover close to 95\% performance of the models even when only 15\% of the original variables are present.
Morn: Molecular property prediction based on textual-topological-spatial multi-view learning
Authors: Runze Ma, Yidan Zhang, Xinye Wang, Zhenyang Yu, Lei Duan
Venue: Conference CIKM 2022
Year: 2022

Predicting molecular properties has significant implications for the discovery and generation of drugs and further research in the domain of medicinal chemistry. Learning representations of molecules plays a central role in deep learning-driven property prediction. However, the diversity of molecular features (e.g., chemical system languages, structure notations) brings inconsistency in molecular representation. Moreover, the scarcity of labeled molecular data limits the accuracy of the molecular property prediction model. To address the above issues, we proposed a two-stage method, named MORN, for learning molecular representations for molecular property prediction from a multi-view perspective. In the first stage, textual-topological-spatial multi-views were proposed to learn the molecular representations, so as to capture both chemical system language and structure notation features simultaneously. In the second stage, an adaptive strategy was used to fuse molecular representations learned from multi-views to predict molecular properties. To alleviate the limitation of the scarcity of labeled molecular data, the label restriction was introduced in both multi-view representation learning and fusion stages. The performance of MORN was assessed by seven benchmark molecular datasets and one self-built molecular dataset. Experimental results demonstrated that MORN is effective in molecular property prediction.
I3: Intent-introspective retrieval conditioned on instructions
Authors: Kaihang Pan, Juncheng Li, Wenjie Wang, Hao Fei, Hongye Song, Wei Ji, Jun Lin, Xiaozhong Liu, Tat-Seng Chua, Siliang Tang
Venue: Conference SIGIR 2024
Year: 2024

Recent studies indicate that dense retrieval models struggle to perform well on a wide variety of retrieval tasks that lack dedicated training data, as different retrieval tasks often entail distinct search intents. To address this challenge, in this work we leverage instructions to flexibly describe retrieval intents and introduce I3, a unified retrieval system that performs Intent-Introspective retrieval across various tasks, conditioned on Instructions without any task-specific training. I3 innovatively incorporates a pluggable introspector in a parameter-isolated manner to comprehend specific retrieval intents by jointly reasoning over the input query and instruction, and seamlessly integrates the introspected intent into the original retrieval model for intent-aware retrieval. Furthermore, we propose progressively-pruned intent learning. It utilizes extensive LLM-generated data to train I3 phase-by-phase, embodying two key designs: progressive structure pruning and drawback extrapolation-based data refinement. Extensive experiments show that in the BEIR benchmark, I3 significantly outperforms baseline methods designed with task-specific retrievers, achieving state-of-the-art zero-shot performance without any task-specific tuning.
Perceiving commerial activeness over satellite images
Authors: Zhiyuan He, Su Yang, Weishan Zhang, Jiulong Zhang
Venue: Conference WWW 2018
Year: 2018

Different urban regions usually have different commercial hotness due to the different social contexts inside. As satellite imagery promises high-resolution, low-cost, real-time, and ubiquitous data acquisition, this study aims to solve commercial hotness prediction as well as the correlated social contexts mining problem via visual pattern analysis on satellite images. The goal is to reveal the underlying law correlating visual patterns of satellite images with commercial hotness so as to infer the commercial hotness map of a whole city for government regulation and business planning. We propose a novel deep learning-based model, which learns semantic information from raw satellite images to enable predicting regional commercial hotness. First, we collect satellite images from Google Map and label such images with POI categories according to the annotations from OpenStreetMap. Then, we train a model of deep convolutional networks that leverage raw images to infer the social attributes of the region of interest. Finally, we use three classical regression methods to predict regional commercial hotness from the corresponding social contexts reflected in satellite images in Shanghai, where the applied deep features are learned from the examples of Beijing to guarantee the generality. The result shows that the proposed model is robust enough to reach 82% precision at average. To the best of our knowledge, it is the first work focused on discovering relations between commercial hotness and satellite images. A web service is developed to demonstrate how business planning can be done in reference to the predicted commercial hotness of a given region.
Urban perception of commercial activeness from satellite images and streetscapes
Authors: Wenshan Wang, Su Yang, Zhiyuan He, Minjie Wang, Jiulong Zhang, Weishan Zhang
Venue: Conference WWW 2018
Year: 2018

People can percept social attributes from streetscapes such as safety, richness, and happiness by means of visual perception, which inspires the research in terms of urban perception. To the best of our knowledge, this is the first work focused on revealing the relationship between visual patterns of satellite images as well as streetscapes and commercial activeness. We propose to make use of bag of features (BoF) in the context of computer vision and sparse representation in the sense of machine learning to predict commercial activeness of urban commercial districts. After obtaining the urban commercial districts via clustering, we predict the commercial activeness degrees of them using four image features, namely, Histogram of Oriented Gradients (HOG), Autoencoder, GIST, and multifractal spectra for satellite images and street view images, respectively. The performance evaluation with four large-scale datasets demonstrates that the presented computational framework can not only predict the commercial activeness with satisfactory precision compared with that based on Point of Interest (POI) data but also discover the visual patterns related.
Efficient approximation algorithms for the diameter-bounded max-coverage group Steiner tree problem
Authors: Ke Zhang, Xiaoqing Wang, Gong Cheng
Venue: Conference WWW 2023
Year: 2023

The Diameter-bounded max-Coverage Group Steiner Tree (DCGST) problem has recently been proposed as an expressive way of formulating keyword-based search and exploration of knowledge graphs. It aims at finding a diameter-bounded tree which covers the most given groups of vertices and has the minimum weight. In contrast to its specialization—the classic Group Steiner Tree (GST) problem which has been extensively studied, the emerging DCGST problem still lacks an efficient algorithm. In this paper, we propose Cba, the first approximation algorithm for the DCGST problem, and we prove its worst-case approximation ratio. Furthermore, we incorporate a best-first search strategy with two pruning methods into PrunedCBA, an improved approximation algorithm. Our extensive experiments on real and synthetic graphs demonstrate the effectiveness and efficiency of PrunedCBA.
Courtsighttv: An interactive visualization software for labeling key basketball moments
Authors: Alexander Russakoff, Kenny Miller, Vahid Mahzoon, Parsa Esmaeilkhani, Christine Cho, Jaffar Alzeidi, Sandro Hauri, Slobodan Vucetic
Venue: Conference CIKM 2024
Year: 2024

Advancements in sensor technology are leading to massive collection of tracking data in sports. There is an increasing interest in analyzing the tracking data to gain competitive advantage. Analyzing and labeling key game moments can provide deep insights into player performance, team dynamics, as well as game strategy. However, the process of manually labeling and analyzing these moments is costly and time-consuming. In this paper, we describe a visual interface for user-friendly and efficient labeling of key moments in basketball games aided by neural networks. We report results of a user study evaluating the labeling interface.
Finding the best of both worlds: Faster and more robust top-k document retrieval
Authors: Omar Khattab, Mohammad Hammoud, Tamer Elsayed
Venue: Conference SIGIR 2020
Year: 2020

Many top-k document retrieval strategies have been proposed based on the WAND and MaxScore heuristics and yet, from recent work, it is surprisingly difficult to identify the "fastest" strategy. This becomes even more challenging when considering various retrieval criteria, like different ranking models and values of k. In this paper, we conduct the first extensive comparison between ten effective strategies, many of which were never compared before to our knowledge, examining their efficiency under five representative ranking models. Based on a careful analysis of the comparison, we propose LazyBM, a remarkably simple retrieval strategy that bridges the gap between the best performing WAND-based and MaxScore-based approaches. Empirically, LazyBM considerably outperforms all of the considered strategies across ranking models, values of k, and index configurations under both mean and tail query latency.
Contextualizing internet memes across social media platforms
Authors: Saurav Joshi, Filip Ilievski, Luca Luceri
Venue: Conference WWW 2024
Year: 2024

Internet memes have emerged as a novel format for communication and expressing ideas on the web. Their fluidity and creative nature are reflected in their widespread use, often across platforms and occasionally for unethical or harmful purposes. While computational work has already analyzed their high-level virality over time and developed specialized classifiers for hate speech detection, there have been no efforts to date that aim to holistically track, identify, and map internet memes posted on social media. To bridge this gap, we investigate whether internet memes across social media platforms can be contextualized by using a semantic repository of knowledge, namely, a knowledge graph. We collect thousands of potential internet meme posts from two social media platforms, namely Reddit and Discord, and develop an extract-transform-load procedure to create a data lake with candidate meme posts. By using vision transformer-based similarity, we match these candidates against the memes cataloged in IMKG --- a recently released knowledge graph of internet memes. We leverage this grounding to highlight the potential of our proposed framework to study the prevalence of memes on different platforms, map them to IMKG, and provide context about memes on social media.
A digital platform for remote healthcare monitoring
Authors: Shirin Enshaeifar, Payam M. Barnaghi, Severin Skillman, David J. Sharp, Ramin Nilforooshan, Helen Rostill
Venue: Conference WWW 2020
Year: 2020

We describe a digital platform developed in collaboration with clinicians and user groups to provide remote healthcare monitoring and support in a dementia care application. The platform uses data from sensory devices that are deployed in participants’ homes and utilises a set machine learning and analytical algorithms to identify risks of adverse health conditions such as Urinary Tract Infections (UTIs) and hypertension in people with dementia. The platform includes a clinical interface that is used by a monitoring team to view alerts and notifications that are generated by the algorithms and to also browse the in-home activity and physiological data in a secure and privacy-aware system. The platform complies to the information governance requirements of the UK National Healthcare Service (NHS) and is registered as a class 1 medical device. The platform has been deployed and tested in over 150 homes.
Capalign: Improving cross modal alignment via informative captioning for harmful meme detection
Authors: Junhui Ji, Xuanrui Lin, Usman Naseem
Venue: Conference WWW 2024
Year: 2024

Harmful memes detection is challenging due to the semantic gap between different modalities. Previous studies mainly focus on feature extraction and fusion to learn discriminative information from memes. However, they ignore the misalignment of the modalities caused by the modality gap and suffer from data scarcity, resulting in insufficient learning of fusion-based models. Recently, researchers transformed images into textual captions and used language models for predictions, resulting in non-informative image captions. To address these gaps, this paper proposes an instructions-based abstracting approach CapAlign, in zero-shot visual question-answering settings. Precisely, we prompt a large language model (LLM) to ask informative questions to a pre-trained vision-language model and use the dialogues to generate a high-quality image caption. Further, to align the generated caption with the textual content of a meme, we used an LLM with instructions to generate informative captions of the meme and then prepend it with the attributes of the visual content of a meme to a prompt-based LM for prediction. Experimental findings on two benchmark datasets show that our approach produces informative captions and outperforms state-of-the-art methods for detecting harmful memes.
Social media discourses on interracial intimacy: Tracking racism and sexism through chinese geo-located social media data
Authors: Zheng Wei, Yixuan Xie, Danyun Xiao, Simin Zhang, Pan Hui, Muzhi Zhou
Venue: Conference WWW 2024
Year: 2024

We examine the social media discourse surrounding interracial relationships in China, specifically on the popular platform Douyin. By analyzing comments on short video posts, the study focuses on four types of interracial relationships: Black men and Chinese women, Black women and Chinese men, White men and Chinese women, and White women and Chinese men. The study also explores potential regional differences in these discourses, using IP geolocation data made available to the public since April 2022. Our content analysis revealed that the Black men and Chinese women couples attracted the most negative comments and the White women and Chinese men couples received the least negative comments. We also observed substantial regional differences in the discourses towards these interracial relationships. We investigated several regional socioeconomic development indicators and noted that local GDP, population sizes, and the level of openness to Western cultures explained the variation in the negative sentiment level. This work advances our understanding of the interplay of race, gender, and immigration in constructing public discourses on social media and offers important insights into how these discourses evolve along with socioeconomic development.