13,175 research outputs found
Audio-visual multi-modality driven hybrid feature learning model for crowd analysis and classification
The high pace emergence in advanced software systems, low-cost hardware and decentralized cloud computing technologies have broadened the horizon for vision-based surveillance, monitoring and control. However, complex and inferior feature learning over visual artefacts or video streams, especially under extreme conditions confine majority of the at-hand vision-based crowd analysis and classification systems. Retrieving event-sensitive or crowd-type sensitive spatio-temporal features for the different crowd types under extreme conditions is a highly complex task. Consequently, it results in lower accuracy and hence low reliability that confines existing methods for real-time crowd analysis. Despite numerous efforts in vision-based approaches, the lack of acoustic cues often creates ambiguity in crowd classification. On the other hand, the strategic amalgamation of audio-visual features can enable accurate and reliable crowd analysis and classification. Considering it as motivation, in this research a novel audio-visual multi-modality driven hybrid feature learning model is developed for crowd analysis and classification. In this work, a hybrid feature extraction model was applied to extract deep spatio-temporal features by using Gray-Level Co-occurrence Metrics (GLCM) and AlexNet transferrable learning model. Once extracting the different GLCM features and AlexNet deep features, horizontal concatenation was done to fuse the different feature sets. Similarly, for acoustic feature extraction, the audio samples (from the input video) were processed for static (fixed size) sampling, pre-emphasis, block framing and Hann windowing, followed by acoustic feature extraction like GTCC, GTCC-Delta, GTCC-Delta-Delta, MFCC, Spectral Entropy, Spectral Flux, Spectral Slope and Harmonics to Noise Ratio (HNR). Finally, the extracted audio-visual features were fused to yield a composite multi-modal feature set, which is processed for classification using the random forest ensemble classifier. The multi-class classification yields a crowd-classification accurac12529y of (98.26%), precision (98.89%), sensitivity (94.82%), specificity (95.57%), and F-Measure of 98.84%. The robustness of the proposed multi-modality-based crowd analysis model confirms its suitability towards real-world crowd detection and classification tasks
Towards Sybil Resilience in Decentralized Learning
Federated learning is a privacy-enforcing machine learning technology but
suffers from limited scalability. This limitation mostly originates from the
internet connection and memory capacity of the central parameter server, and
the complexity of the model aggregation function. Decentralized learning has
recently been emerging as a promising alternative to federated learning. This
novel technology eliminates the need for a central parameter server by
decentralizing the model aggregation across all participating nodes. Numerous
studies have been conducted on improving the resilience of federated learning
against poisoning and Sybil attacks, whereas the resilience of decentralized
learning remains largely unstudied. This research gap serves as the main
motivator for this study, in which our objective is to improve the Sybil
poisoning resilience of decentralized learning.
We present SybilWall, an innovative algorithm focused on increasing the
resilience of decentralized learning against targeted Sybil poisoning attacks.
By combining a Sybil-resistant aggregation function based on similarity between
Sybils with a novel probabilistic gossiping mechanism, we establish a new
benchmark for scalable, Sybil-resilient decentralized learning.
A comprehensive empirical evaluation demonstrated that SybilWall outperforms
existing state-of-the-art solutions designed for federated learning scenarios
and is the only algorithm to obtain consistent accuracy over a range of
adversarial attack scenarios. We also found SybilWall to diminish the utility
of creating many Sybils, as our evaluations demonstrate a higher success rate
among adversaries employing fewer Sybils. Finally, we suggest a number of
possible improvements to SybilWall and highlight promising future research
directions
Fault diagnosis in aircraft fuel system components with machine learning algorithms
There is a high demand and interest in considering the social and environmental effects of the component’s lifespan. Aircraft are one of the most high-priced
businesses that require the highest reliability and safety constraints. The complexity of aircraft systems designs also has advanced rapidly in the last decade. Consequently, fault detection, diagnosis and modification/ repair procedures are becoming more challenging. The presence of a fault within an aircraft system can result in changes to system performances and cause operational downtime or accidents in a worst-case scenario.
The CBM method that predicts the state of the equipment based on data collected is widely used in aircraft MROs. CBM uses diagnostics and prognostics models
to make decisions on appropriate maintenance actions based on the Remaining Useful Life (RUL) of the components.
The aircraft fuel system is a crucial system of aircraft, even a minor failure in the fuel system can affect the aircraft's safety greatly. A failure in the fuel system that
impacts the ability to deliver fuel to the engine will have an immediate effect on system performance and safety. There are very few diagnostic systems that
monitor the health of the fuel system and even fewer that can contain detected faults. The fuel system is crucial for the operation of the aircraft, in case of failure,
the fuel in the aircraft will become unusable/unavailable to reach the destination.
It is necessary to develop fault detection of the aircraft fuel system. The future aircraft fuel system must have the function of fault detection. Through the information of sensors and Machine Learning Techniques, the aircraft fuel system’s fault type can be detected in a timely manner.
This thesis discusses the application of a Data-driven technique to analyse the healthy and faulty data collected using the aircraft fuel system model, which is
similar to Boeing-777. The data is collected is processed through Machine learning Techniques and the results are comparedPhD in Manufacturin
Smart Farm-Care using a Deep Learning Model on Mobile Phones
Deep learning and its models have provided exciting solutions in various image processing applications like image segmentation, classification, labeling, etc., which paved the way to apply these models in agriculture to identify diseases in agricultural plants. The most visible symptoms of the disease initially appear on the leaves. To identify diseases found in leaf images, an accurate classification system with less size and complexity is developed using smartphones. A labeled dataset consisting of 3171 apple leaf images belonging to 4 different classes of diseases, including the healthy ones, is used for classification. In this work, four variants of MobileNet models - pre-trained on the ImageNet database, are retrained to diagnose diseases. The model’s variants differ based on their depth and resolution multiplier. The results show that the proposed model with 0.5 depth and 224 resolution performs well - achieving an accuracy of 99.6%. Later, the K-means algorithm is used to extract additional features, which helps improve the accuracy to 99.7% and also measures the number of pixels forming diseased spots, which helps in severity prediction. Doi: 10.28991/ESJ-2023-07-02-013 Full Text: PD
Spectral Normalized-Cut Graph Partitioning with Fairness Constraints
Normalized-cut graph partitioning aims to divide the set of nodes in a graph
into disjoint clusters to minimize the fraction of the total edges between
any cluster and all other clusters. In this paper, we consider a fair variant
of the partitioning problem wherein nodes are characterized by a categorical
sensitive attribute (e.g., gender or race) indicating membership to different
demographic groups. Our goal is to ensure that each group is approximately
proportionally represented in each cluster while minimizing the normalized cut
value. To resolve this problem, we propose a two-phase spectral algorithm
called FNM. In the first phase, we add an augmented Lagrangian term based on
our fairness criteria to the objective function for obtaining a fairer spectral
node embedding. Then, in the second phase, we design a rounding scheme to
produce clusters from the fair embedding that effectively trades off
fairness and partition quality. Through comprehensive experiments on nine
benchmark datasets, we demonstrate the superior performance of FNM compared
with three baseline methods.Comment: 17 pages, 7 figures, accepted to the 26th European Conference on
Artificial Intelligence (ECAI 2023
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
Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)
The book attempts to introduce a gentle introduction to the field of Facial
Micro Expressions Recognition (FMER) using Color and Depth images, with the aid
of MATLAB programming environment. FMER is a subset of image processing and it
is a multidisciplinary topic to analysis. So, it requires familiarity with
other topics of Artifactual Intelligence (AI) such as machine learning, digital
image processing, psychology and more. So, it is a great opportunity to write a
book which covers all of these topics for beginner to professional readers in
the field of AI and even without having background of AI. Our goal is to
provide a standalone introduction in the field of MFER analysis in the form of
theorical descriptions for readers with no background in image processing with
reproducible Matlab practical examples. Also, we describe any basic definitions
for FMER analysis and MATLAB library which is used in the text, that helps
final reader to apply the experiments in the real-world applications. We
believe that this book is suitable for students, researchers, and professionals
alike, who need to develop practical skills, along with a basic understanding
of the field. We expect that, after reading this book, the reader feels
comfortable with different key stages such as color and depth image processing,
color and depth image representation, classification, machine learning, facial
micro-expressions recognition, feature extraction and dimensionality reduction.
The book attempts to introduce a gentle introduction to the field of Facial
Micro Expressions Recognition (FMER) using Color and Depth images, with the aid
of MATLAB programming environment.Comment: This is the second edition of the boo
Disentangling Societal Inequality from Model Biases: Gender Inequality in Divorce Court Proceedings
Divorce is the legal dissolution of a marriage by a court. Since this is
usually an unpleasant outcome of a marital union, each party may have reasons
to call the decision to quit which is generally documented in detail in the
court proceedings. Via a substantial corpus of 17,306 court proceedings, this
paper investigates gender inequality through the lens of divorce court
proceedings. While emerging data sources (e.g., public court records) on
sensitive societal issues hold promise in aiding social science research,
biases present in cutting-edge natural language processing (NLP) methods may
interfere with or affect such studies. We thus require a thorough analysis of
potential gaps and limitations present in extant NLP resources. In this paper,
on the methodological side, we demonstrate that existing NLP resources required
several non-trivial modifications to quantify societal inequalities. On the
substantive side, we find that while a large number of court cases perhaps
suggest changing norms in India where women are increasingly challenging
patriarchy, AI-powered analyses of these court proceedings indicate striking
gender inequality with women often subjected to domestic violence.Comment: This paper is accepted at IJCAI 2023 (AI for good track
Improvement the Community Detection with Graph Autoencoder in Social Network Using Correlation-Based Feature Selection Method
مقدمة:
في هذا البحث ، نهدف إلى تحسين طرق اكتشاف المجتمع باستخدام Graph Autoencoder. يعد اكتشاف المجتمع مرحلة حاسمة لفهم الشبكات الاجتماعية وتكوينها.
طرق العمل:
نقترح إطار عمل اكتشاف المجتمع باستخدام نموذج Graph Autoencoder (CDGAE)، حيث قمنا بدمج ميزة العقد مع هيكل الشبكة كمدخل لطريقتنا. تستخدم CDGAE إستراتيجية قائمة على قياس المركزية للتعامل مع مجموعة البيانات الخالية من الميزات من خلال توفير ميزات اصطناعية لعقدها. تم تحسين أداء النموذج من خلال تطبيق تحديد الميزة على ميزات العقدة.
يتمثل الابتكار الأساسي لـ CDGAE في إضافة عدد المجتمعات التي تم حسابها باستخدام Bethe Hessian Matrix في طبقة عنق الزجاجة لبنية Graph Autoencoder (GAE) ، لاستخراج المجتمعات مباشرةً دون استخدام أي خوارزميات تجميع.
الاستنتاجات:
وفقًا للنتائج التجريبية ، تؤدي إضافة ميزات اصطناعية إلى عقد مجموعة البيانات إلى تحسين الأداء. بالإضافة إلى ذلك ، حصلنا على نتائج افضل بكثير في اكتشاف المجتمع باستخدام طريقة اختيار الميزة وبتعميق نموذج. أظهرت النتائج التجريبية أن نهجنا يتفوق على الخوارزميات الموجودة.Background:
In this paper, we aim to improve community detection methods using Graph Autoencoder. Community detection is a crucial stage in comprehend the purpose and composition of social networks.
Materials and Methods:
We propose a Community Detection framework using the Graph Autoencoder (CDGAE) model, we combined the nodes feature with the network topology as input to our method. A centrality measurement-based strategy is used by CDGAE to deal with the featureless dataset by providing artificial attributes to its nodes. The performance of the model was improved by applying feature selection to node features
The basic innovation of CDGAE is that added the number of communities counted using the Bethe Hessian Matrix in the bottleneck layer of the graph autoencoder (GAE) structure, to directly extract communities without using any clustering algorithms.
Results:
According to experimental findings, adding artificial features to the dataset's nodes improves performance. Additionally, the outcomes in community detection were much better with the feature selection method and a deeper model. Experimental evidence has shown that our approach outperforms existing algorithms.
Conclusion:
In this study, we suggest a community detection framework using graph autoencoder (CDMEC). In order to take advantage of GAE's ability to combine node features with the network topology, we add node features to the featureless graph nodes using centrality measurement. By applying the feature selection to the features of the nodes, the performance of the model has improved significantly, due to the elimination of data noise. Additionally, the inclusion of the number of communities in the bottleneck layer of the GAE structure allowed us to do away with clustering algorithms, which helped decrease the complexity time. deepening the model also improved the community detection. Because social media platforms are dynamic
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