7,717 research outputs found
HOFA: Twitter Bot Detection with Homophily-Oriented Augmentation and Frequency Adaptive Attention
Twitter bot detection has become an increasingly important and challenging
task to combat online misinformation, facilitate social content moderation, and
safeguard the integrity of social platforms. Though existing graph-based
Twitter bot detection methods achieved state-of-the-art performance, they are
all based on the homophily assumption, which assumes users with the same label
are more likely to be connected, making it easy for Twitter bots to disguise
themselves by following a large number of genuine users. To address this issue,
we proposed HOFA, a novel graph-based Twitter bot detection framework that
combats the heterophilous disguise challenge with a homophily-oriented graph
augmentation module (Homo-Aug) and a frequency adaptive attention module
(FaAt). Specifically, the Homo-Aug extracts user representations and computes a
k-NN graph using an MLP and improves Twitter's homophily by injecting the k-NN
graph. For the FaAt, we propose an attention mechanism that adaptively serves
as a low-pass filter along a homophilic edge and a high-pass filter along a
heterophilic edge, preventing user features from being over-smoothed by their
neighborhood. We also introduce a weight guidance loss to guide the frequency
adaptive attention module. Our experiments demonstrate that HOFA achieves
state-of-the-art performance on three widely-acknowledged Twitter bot detection
benchmarks, which significantly outperforms vanilla graph-based bot detection
techniques and strong heterophilic baselines. Furthermore, extensive studies
confirm the effectiveness of our Homo-Aug and FaAt module, and HOFA's ability
to demystify the heterophilous disguise challenge.Comment: 11 pages, 7 figure
DeepOnto: A Python Package for Ontology Engineering with Deep Learning
Applying deep learning techniques, particularly language models (LMs), in
ontology engineering has raised widespread attention. However, deep learning
frameworks like PyTorch and Tensorflow are predominantly developed for Python
programming, while widely-used ontology APIs, such as the OWL API and Jena, are
primarily Java-based. To facilitate seamless integration of these frameworks
and APIs, we present Deeponto, a Python package designed for ontology
engineering. The package encompasses a core ontology processing module founded
on the widely-recognised and reliable OWL API, encapsulating its fundamental
features in a more "Pythonic" manner and extending its capabilities to include
other essential components including reasoning, verbalisation, normalisation,
projection, and more. Building on this module, Deeponto offers a suite of
tools, resources, and algorithms that support various ontology engineering
tasks, such as ontology alignment and completion, by harnessing deep learning
methodologies, primarily pre-trained LMs. In this paper, we also demonstrate
the practical utility of Deeponto through two use-cases: the Digital Health
Coaching in Samsung Research UK and the Bio-ML track of the Ontology Alignment
Evaluation Initiative (OAEI).Comment: under review at Semantic Web Journa
The Globalization of Artificial Intelligence: African Imaginaries of Technoscientific Futures
Imaginaries of artificial intelligence (AI) have transcended geographies of the Global North and become increasingly entangled with narratives of economic growth, progress, and modernity in Africa. This raises several issues such as the entanglement of AI with global technoscientific capitalism and its impact on the dissemination of AI in Africa. The lack of African perspectives on the development of AI exacerbates concerns of raciality and inclusion in the scientific research, circulation, and adoption of AI. My argument in this dissertation is that innovation in AI, in both its sociotechnical imaginaries and political economies, excludes marginalized countries, nations and communities in ways that not only bar their participation in the reception of AI, but also as being part and parcel of its creation.
Underpinned by decolonial thinking, and perspectives from science and technology studies and African studies, this dissertation looks at how AI is reconfiguring the debate about development and modernization in Africa and the implications for local sociotechnical practices of AI innovation and governance. I examined AI in international development and industry across Kenya, Ghana, and Nigeria, by tracing Canada’s AI4D Africa program and following AI start-ups at AfriLabs. I used multi-sited case studies and discourse analysis to examine the data collected from interviews, participant observations, and documents.
In the empirical chapters, I first examine how local actors understand the notion of decolonizing AI and show that it has become a sociotechnical imaginary. I then investigate the political economy of AI in Africa and argue that despite Western efforts to integrate the African AI ecosystem globally, the AI epistemic communities in the continent continue to be excluded from dominant AI innovation spaces. Finally, I examine the emergence of a Pan-African AI imaginary and argue that AI governance can be understood as a state-building experiment in post-colonial Africa. The main issue at stake is that the lack of African perspectives in AI leads to negative impacts on innovation and limits the fair distribution of the benefits of AI across nations, countries, and communities, while at the same time excludes globally marginalized epistemic communities from the imagination and creation of AI
A conceptual framework for developing dashboards for big mobility data
Dashboards are an increasingly popular form of data visualization. Large, complex, and dynamic mobility data present a number of challenges in dashboard design. The overall aim for dashboard design is to improve information communication and decision making, though big mobility data in particular require considering privacy alongside size and complexity. Taking these issues into account, a gap remains between wrangling mobility data and developing meaningful dashboard output. Therefore, there is a need for a framework that bridges this gap to support the mobility dashboard development and design process. In this paper we outline a conceptual framework for mobility data dashboards that provides guidance for the development process while considering mobility data structure, volume, complexity, varied application contexts, and privacy constraints. We illustrate the proposed framework’s components and process using example mobility dashboards with varied inputs, end-users and objectives. Overall, the framework offers a basis for developers to understand how informational displays of big mobility data are determined by end-user needs as well as the types of data selection, transformation, and display available to particular mobility datasets
Is attention all you need in medical image analysis? A review
Medical imaging is a key component in clinical diagnosis, treatment planning
and clinical trial design, accounting for almost 90% of all healthcare data.
CNNs achieved performance gains in medical image analysis (MIA) over the last
years. CNNs can efficiently model local pixel interactions and be trained on
small-scale MI data. The main disadvantage of typical CNN models is that they
ignore global pixel relationships within images, which limits their
generalisation ability to understand out-of-distribution data with different
'global' information. The recent progress of Artificial Intelligence gave rise
to Transformers, which can learn global relationships from data. However, full
Transformer models need to be trained on large-scale data and involve
tremendous computational complexity. Attention and Transformer compartments
(Transf/Attention) which can well maintain properties for modelling global
relationships, have been proposed as lighter alternatives of full Transformers.
Recently, there is an increasing trend to co-pollinate complementary
local-global properties from CNN and Transf/Attention architectures, which led
to a new era of hybrid models. The past years have witnessed substantial growth
in hybrid CNN-Transf/Attention models across diverse MIA problems. In this
systematic review, we survey existing hybrid CNN-Transf/Attention models,
review and unravel key architectural designs, analyse breakthroughs, and
evaluate current and future opportunities as well as challenges. We also
introduced a comprehensive analysis framework on generalisation opportunities
of scientific and clinical impact, based on which new data-driven domain
generalisation and adaptation methods can be stimulated
Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion
Time Series Classification (TSC) has received much attention in the past two
decades and is still a crucial and challenging problem in data science and
knowledge engineering. Indeed, along with the increasing availability of time
series data, many TSC algorithms have been suggested by the research community
in the literature. Besides state-of-the-art methods based on similarity
measures, intervals, shapelets, dictionaries, deep learning methods or hybrid
ensemble methods, several tools for extracting unsupervised informative summary
statistics, aka features, from time series have been designed in the recent
years. Originally designed for descriptive analysis and visualization of time
series with informative and interpretable features, very few of these feature
engineering tools have been benchmarked for TSC problems and compared with
state-of-the-art TSC algorithms in terms of predictive performance. In this
article, we aim at filling this gap and propose a simple TSC process to
evaluate the potential predictive performance of the feature sets obtained with
existing feature engineering tools. Thus, we present an empirical study of 11
feature engineering tools branched with 9 supervised classifiers over 112 time
series data sets. The analysis of the results of more than 10000 learning
experiments indicate that feature-based methods perform as accurately as
current state-of-the-art TSC algorithms, and thus should rightfully be
considered further in the TSC literature
Using machine learning to predict pathogenicity of genomic variants throughout the human genome
Geschätzt mehr als 6.000 Erkrankungen werden durch Veränderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das Spleißen der mRNA in eine andere Isoform begünstigen. All diese Prozesse müssen überprüft werden, um die zum beschriebenen Phänotyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer Pathogenität.
Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier präsentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst außerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores.
Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells für das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. Außerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-Spleißing verbessern. Außerdem werden Varianteneffektmodelle aufgrund eines neuen, auf Allelhäufigkeit basierten, Trainingsdatensatz entwickelt.
Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfügbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity.
Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants.
The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency.
In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org
Dataset Distillation: A Comprehensive Review
Recent success of deep learning is largely attributed to the sheer amount of
data used for training deep neural networks.Despite the unprecedented success,
the massive data, unfortunately, significantly increases the burden on storage
and transmission and further gives rise to a cumbersome model training process.
Besides, relying on the raw data for training \emph{per se} yields concerns
about privacy and copyright. To alleviate these shortcomings, dataset
distillation~(DD), also known as dataset condensation (DC), was introduced and
has recently attracted much research attention in the community. Given an
original dataset, DD aims to derive a much smaller dataset containing synthetic
samples, based on which the trained models yield performance comparable with
those trained on the original dataset. In this paper, we give a comprehensive
review and summary of recent advances in DD and its application. We first
introduce the task formally and propose an overall algorithmic framework
followed by all existing DD methods. Next, we provide a systematic taxonomy of
current methodologies in this area, and discuss their theoretical
interconnections. We also present current challenges in DD through extensive
experiments and envision possible directions for future works.Comment: 23 pages, 168 references, 8 figures, under revie
GripNet: Graph information propagation on supergraph for heterogeneous graphs
Heterogeneous graph representation learning aims to learn low-dimensional vector representations of different types of entities and relations to empower downstream tasks. Existing popular methods either capture semantic relationships but indirectly leverage node/edge attributes in a complex way, or leverage node/edge attributes directly without taking semantic relationships into account. When involving multiple convolution operations, they also have poor scalability. To overcome these limitations, this paper proposes a flexible and efficient Graph information propagation Network (GripNet) framework. Specifically, we introduce a new supergraph data structure consisting of supervertices and superedges. A supervertex is a semantically-coherent subgraph. A superedge defines an information propagation path between two supervertices. GripNet learns new representations for the supervertex of interest by propagating information along the defined path using multiple layers. We construct multiple large-scale graphs and evaluate GripNet against competing methods to show its superiority in link prediction, node classification, and data integration
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