40 research outputs found
Frequency Domain-based Dataset Distillation
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
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
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
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 compared to 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
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
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
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
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
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
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