40 research outputs found

    Frequency Domain-based Dataset Distillation

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    This paper presents FreD, a novel parameterization method for dataset distillation, which utilizes the frequency domain to distill a small-sized synthetic dataset from a large-sized original dataset. Unlike conventional approaches that focus on the spatial domain, FreD employs frequency-based transforms to optimize the frequency representations of each data instance. By leveraging the concentration of spatial domain information on specific frequency components, FreD intelligently selects a subset of frequency dimensions for optimization, leading to a significant reduction in the required budget for synthesizing an instance. Through the selection of frequency dimensions based on the explained variance, FreD demonstrates both theoretical and empirical evidence of its ability to operate efficiently within a limited budget, while better preserving the information of the original dataset compared to conventional parameterization methods. Furthermore, based on the orthogonal compatibility of FreD with existing methods, we confirm that FreD consistently improves the performances of existing distillation methods over the evaluation scenarios with different benchmark datasets. We release the code at https://github.com/sdh0818/FreD.Comment: Accepted at NeurIPS 202

    A Survey on Causal Discovery Methods for Temporal and Non-Temporal Data

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    Causal Discovery (CD) is the process of identifying the cause-effect relationships among the variables from data. Over the years, several methods have been developed primarily based on the statistical properties of data to uncover the underlying causal mechanism. In this study we introduce the common terminologies in causal discovery, and provide a comprehensive discussion of the approaches designed to identify the causal edges in different settings. We further discuss some of the benchmark datasets available for evaluating the performance of the causal discovery algorithms, available tools to perform causal discovery readily, and the common metrics used to evaluate these methods. Finally, we conclude by presenting the common challenges involved in CD and also, discuss the applications of CD in multiple areas of interest

    A review on deep-learning-based cyberbullying detection

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    Bullying is described as an undesirable behavior by others that harms an individual physically, mentally, or socially. Cyberbullying is a virtual form (e.g., textual or image) of bullying or harassment, also known as online bullying. Cyberbullying detection is a pressing need in today’s world, as the prevalence of cyberbullying is continually growing, resulting in mental health issues. Conventional machine learning models were previously used to identify cyberbullying. However, current research demonstrates that deep learning surpasses traditional machine learning algorithms in identifying cyberbullying for several reasons, including handling extensive data, efficiently classifying text and images, extracting features automatically through hidden layers, and many others. This paper reviews the existing surveys and identifies the gaps in those studies. We also present a deep-learning-based defense ecosystem for cyberbullying detection, including data representation techniques and different deep-learning-based models and frameworks. We have critically analyzed the existing DL-based cyberbullying detection techniques and identified their significant contributions and the future research directions they have presented. We have also summarized the datasets being used, including the DL architecture being used and the tasks that are accomplished for each dataset. Finally, several challenges faced by the existing researchers and the open issues to be addressed in the future have been presented

    Label Attention Network for sequential multi-label classification: you were looking at a wrong self-attention

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    Most of the available user information can be represented as a sequence of timestamped events. Each event is assigned a set of categorical labels whose future structure is of great interest. For instance, our goal is to predict a group of items in the next customer's purchase or tomorrow's client transactions. This is a multi-label classification problem for sequential data. Modern approaches focus on transformer architecture for sequential data introducing self-attention for the elements in a sequence. In that case, we take into account events' time interactions but lose information on label inter-dependencies. Motivated by this shortcoming, we propose leveraging a self-attention mechanism over labels preceding the predicted step. As our approach is a Label-Attention NETwork, we call it LANET. Experimental evidence suggests that LANET outperforms the established models' performance and greatly captures interconnections between labels. For example, the micro-AUC of our approach is 0.95360.9536 compared to 0.75010.7501 for a vanilla transformer. We provide an implementation of LANET to facilitate its wider usage

    Hierarchical Classification of Research Fields in the "Web of Science" Using Deep Learning

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    This paper presents a hierarchical classification system that automatically categorizes a scholarly publication using its abstract into a three-tier hierarchical label set (discipline, field, subfield) in a multi-class setting. This system enables a holistic categorization of research activities in the mentioned hierarchy in terms of knowledge production through articles and impact through citations, permitting those activities to fall into multiple categories. The classification system distinguishes 44 disciplines, 718 fields and 1,485 subfields among 160 million abstract snippets in Microsoft Academic Graph (version 2018-05-17). We used batch training in a modularized and distributed fashion to address and allow for interdisciplinary and interfield classifications in single-label and multi-label settings. In total, we have conducted 3,140 experiments in all considered models (Convolutional Neural Networks, Recurrent Neural Networks, Transformers). The classification accuracy is > 90% in 77.13% and 78.19% of the single-label and multi-label classifications, respectively. We examine the advantages of our classification by its ability to better align research texts and output with disciplines, to adequately classify them in an automated way, and to capture the degree of interdisciplinarity. The proposed system (a set of pre-trained models) can serve as a backbone to an interactive system for indexing scientific publications in the future.Comment: Under review in QS

    A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning

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    Knowledge Graph Embedding (KGE) has proven to be an effective approach to solving the Knowledge Graph Completion (KGC) task. Relational patterns which refer to relations with specific semantics exhibiting graph patterns are an important factor in the performance of KGE models. Though KGE models' capabilities are analyzed over different relational patterns in theory and a rough connection between better relational patterns modeling and better performance of KGC has been built, a comprehensive quantitative analysis on KGE models over relational patterns remains absent so it is uncertain how the theoretical support of KGE to a relational pattern contributes to the performance of triples associated to such a relational pattern. To address this challenge, we evaluate the performance of 7 KGE models over 4 common relational patterns on 2 benchmarks, then conduct an analysis in theory, entity frequency, and part-to-whole three aspects and get some counterintuitive conclusions. Finally, we introduce a training-free method Score-based Patterns Adaptation (SPA) to enhance KGE models' performance over various relational patterns. This approach is simple yet effective and can be applied to KGE models without additional training. Our experimental results demonstrate that our method generally enhances performance over specific relational patterns. Our source code is available from GitHub at https://github.com/zjukg/Comprehensive-Study-over-Relational-Patterns.Comment: This paper is accepted by ISWC 202

    The State of the Art in Creating Visualization Corpora for Automated Chart Analysis

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    We present a state-of-the-art report on visualization corpora in automated chart analysis research. We survey 56 papers that created or used a visualization corpus as the input of their research techniques or systems. Based on a multi-level task taxonomy that identifies the goal, method, and outputs of automated chart analysis, we examine the property space of existing chart corpora along five dimensions: format, scope, collection method, annotations, and diversity. Through the survey, we summarize common patterns and practices of creating chart corpora, identify research gaps and opportunities, and discuss the desired properties of future benchmark corpora and the required tools to create them.Comment: To appear at EuroVis 202

    DIVA: A Dirichlet Process Based Incremental Deep Clustering Algorithm via Variational Auto-Encoder

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    Generative model-based deep clustering frameworks excel in classifying complex data, but are limited in handling dynamic and complex features because they require prior knowledge of the number of clusters. In this paper, we propose a nonparametric deep clustering framework that employs an infinite mixture of Gaussians as a prior. Our framework utilizes a memoized online variational inference method that enables the "birth" and "merge" moves of clusters, allowing our framework to cluster data in a "dynamic-adaptive" manner, without requiring prior knowledge of the number of features. We name the framework as DIVA, a Dirichlet Process-based Incremental deep clustering framework via Variational Auto-Encoder. Our framework, which outperforms state-of-the-art baselines, exhibits superior performance in classifying complex data with dynamically changing features, particularly in the case of incremental features. We released our source code implementation at: https://github.com/Ghiara/divaComment: update supplementary material

    MAtch, eXpand and Improve: Unsupervised Finetuning for Zero-Shot Action Recognition with Language Knowledge

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    Large scale Vision-Language (VL) models have shown tremendous success in aligning representations between visual and text modalities. This enables remarkable progress in zero-shot recognition, image generation & editing, and many other exciting tasks. However, VL models tend to over-represent objects while paying much less attention to verbs, and require additional tuning on video data for best zero-shot action recognition performance. While previous work relied on large-scale, fully-annotated data, in this work we propose an unsupervised approach. We adapt a VL model for zero-shot and few-shot action recognition using a collection of unlabeled videos and an unpaired action dictionary. Based on that, we leverage Large Language Models and VL models to build a text bag for each unlabeled video via matching, text expansion and captioning. We use those bags in a Multiple Instance Learning setup to adapt an image-text backbone to video data. Although finetuned on unlabeled video data, our resulting models demonstrate high transferability to numerous unseen zero-shot downstream tasks, improving the base VL model performance by up to 14\%, and even comparing favorably to fully-supervised baselines in both zero-shot and few-shot video recognition transfer. The code will be released later at \url{https://github.com/wlin-at/MAXI}.Comment: Accepted at ICCV 202

    Visuell individbestemmelse av gaupe (Lynx lynx) ved hjelp av viltkamerabilder

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    Bestandsestimering av store rovdyr er vanskelig, ettersom store rovdyr er skye dyr med store leveområder. Det brukes flere ulike metoder for å estimere bestandene av store rovdyr, som DNA-innsamling, sporing på snø og viltkamera-overvåkning. Bilder fra viltkamera benyttes også til individbestemmelse for enkelte arter. Dette forutsetter at arten har permanente markører som er unike for hvert individ, ikke endrer seg igjennom dyrets levetid og er synlige på viltkamerabilder. Denne metodikken benyttes ofte til å estimere bestander av mønstrete kattedyr, for eksempel gaupe (Lynx lynx). Forskere individbestemmer fotograferte gauper ut ifra pelsmønster og estimerer bestand og tetthet ut ifra antall individbestemte dyr. Denne studien undersøker nøyaktigheten av individbestemmelse av gauper fotografert med viltkamera. Dette ble testet med viltkamerabilder av 40 kjente gauper fra 13 dyreparker i Norge, Sverige, Danmark, Finland, Tyskland og England. Bilder av de ulike individene ble sammenliknet i en nettbasert undersøkelse hvor deltakerne skulle avgjøre om to gaupe-observasjoner var av samme individ, av ulike individer eller uidentifiserbare. Resultatene fra undersøkelsen viste at sammenlikninger med minst en uniform gaupe hadde høyest sannsynlighet for å bli regnet som uidentifiserbare. Deltakerne mente at nesten halvparten av alle sammenlikninger med to uniforme gauper var uidentifiserbare. Sammenlikninger med minst en IR-natt-observasjon hadde signifikant høyere sannsynlighet for å være uidentifiserbare enn sammenlikninger med to dag-observasjoner. Deltakers erfaring så derimot ikke ut til å påvirke sannsynligheten for å besvare en sammenlikning. Deltakere med erfaring fra visuell individgjenkjenning av gaupe har derimot høy sannsynlighet for å svare rett >95%. Sammenlikninger med to uniforme gauper viste signifikant høyere sannsynlighet for å bli feilidentifisert enn sammenlikninger med minst en mønstret gaupe. To IR-natt-observasjoner viste også en tendens til å gi høyere sannsynlighet for feil svar sammenliknet med to dag-observasjoner. Selv om andelen feil var lav, kan feilene gi store utslag ved en reell bestandsestimering. Feil som innebærer at deltakeren svarer at to observasjoner av samme individ er to ulike individer skaper ikke-eksisterende «spøkelses-individer». Dette kan derfor føre til store overestimat av en bestand. Metodikken bør fungere til å skille familiegrupper, eller til å estimere bestander med lav andel uniforme gauper. Dette forutsetter at individbestemmelsen blir utført av to eller flere erfarne personer.To estimate the population of large carnivores is difficult. Large carnivores are elusive animals with large home-ranges. Several different methods are used to estimate the populations of large carnivores, including collection of DNA samples, tracking in snow, and trail camera monitoring. Photos from trail cameras are used for individual identification of some species. This method works if the species has permanent markers that are individually unique and do not change throughout the animal's lifetime and that the markers are visible on trail camera photos. This method is used to estimate the population of patterned felids, like lynx (Lynx lynx). Researchers identify photographed lynx individuals based on their fur pattern, and estimate population and density based on the number of identified animals. This study examines the accuracy of individual identification of lynx photographed with trail cameras. The study used trail camera photos of 40 lynx from 13 zoos in Norway, Sweden, Denmark, Finland, Germany, and England. Photos of the different individuals were then compared in an online survey where participants were asked whether there were two observations of the same lynx, two different lynxes, or if the observations were unidentifiable. The results of the survey showed that comparisons with at least one uniform lynx had the highest likelihood of being considered unidentifiable. Participants thought that almost half of all comparisons with two uniform lynxes were unidentifiable. Comparisons with at least one IR-night observation had a significantly higher likelihood of being unidentifiable than comparisons with two daytime observations. However, participants experience did not seem to affect the likelihood of answering a comparison. However, participants with experience in visual lynx identification, had a high likelihood of answering correctly (>95%). Comparisons with two uniform lynxes showed a significantly higher likelihood of being misidentified than comparisons with at least one patterned lynx. Two IR-night observations also showed a tendency to give a higher likelihood of incorrect answers compared to two daytime observations. Although the proportion of errors was low, errors that result in the participant answering that two observations of the same individual are two different individuals create non-existent “ghost individuals." This can therefore lead to overestimation of a population. The methodology should work to distinguish family groups or estimate populations with a low proportion of uniform lynx. This assumes that individual identification is performed by two or more experienced persons
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