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Deep supervised fused similarity hashing for cross-modal retrieval

Multimedia Tools and Applications 2024年第39期83卷 86537-86555页

作者： Ng, Wing W. Y. Xu, Yongzhi Tian, Xing Wang, Hui Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering South China University of Technology Guangzhou China School of Artificial Intelligence South China Normal University Guangzhou China School of Electronics Electrical Engineering and Computer Science Queens University Belfast Belfast United Kingdom

The need for cross-modal retrieval increases significantly with the rapid growth of multimedia information on the Internet. However, most of existing cross-modal retrieval methods neglect the correlation between label similarity and intra-modality similarity in common semantic subspace training, which makes the trained common semantic subspace unable to preserve semantic similarity of original data effectively. Therefore, a novel cross-modal hashing method is proposed in this paper, namely, Deep Supervised Fused Similarity Hashing (DSFSH). The DSFSH mainly consists of two parts. Firstly, a fused similarity method is proposed to exploit the intrinsic inter-modality correlation of data while preserving the intra-modality relationship of data at the same time. Secondly, a novel quantization max-margin loss is proposed. The gap between cosine similarity and Hamming similarity is closed by minimizing this loss. Extensive experimental results on three benchmark datasets show that the proposed method yields better retrieval performance comparing to state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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Toward a smart multi-unmanned aerial vehicle system

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IEEE Potentials 2022年第1期41卷 22-25页

作者： Jaiswal, Shashwat Sidhanta, Subhajit Department of Electrical Engineering and Computer Science Indian Institute of Technology Bhilai492015 India

Drones, or unmanned aerial vehicles (UAVs) in general, have been increasingly gaining popularity and attention in both the commercial and nonprofit sectors. They have particularly been envisioned to be of critical importance in military and disaster zones for many purposes, including surveying, relief, and combat. © 1988-2012 IEEE.

关键词： Antennas

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A Recurrent Neural Network for Multimodal Anomaly Detection by Using Spatio-Temporal Audio-Visual Data

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computers, Materials & Continua 2024年第11期81卷 2493-2515页

作者： Sameema Tariq Ata-Ur-Rehman Maria Abubakar Waseem Iqbal Hatoon S.Alsagri Yousef A.Alduraywish Haya Abdullah AAlhakbani Department of Electrical Engineering University of Engineering and TechnologyLahore54890Pakistan Department of Electrical Engineering National University of Science and TechnologyNational University of Sciences and TechnologyIslamabad24090Pakistan Department of Business and Computing Ravensbourne University LondonRavensbourne UniversityLondonSE100EWEngland Electrical and Computer Engineering Department College of EngineeringSultan Qaboos UniversityMuscat123Oman College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11673Saudi Arabia

In video surveillance,anomaly detection requires training machine learning models on spatio-temporal video ***,sometimes the video-only data is not sufficient to accurately detect all the abnormal ***,we propose a novel audio-visual spatiotemporal autoencoder specifically designed to detect anomalies for video surveillance by utilizing audio data along with video *** paper presents a competitive approach to a multi-modal recurrent neural network for anomaly detection that combines separate spatial and temporal autoencoders to leverage both spatial and temporal features in audio-visual *** proposed model is trained to produce low reconstruction error for normal data and high error for abnormal data,effectively distinguishing between the two and assigning an anomaly *** is conducted on normal datasets,while testing is performed on both normal and anomalous *** anomaly scores from the models are combined using a late fusion technique,and a deep dense layer model is trained to produce decisive scores indicating whether a sequence is normal or *** model’s performance is evaluated on the University of California,San Diego Pedestrian 2(UCSD PED 2),University of Minnesota(UMN),and Tampere University of technology(TUT)Rare Sound Events datasets using six evaluation *** is compared with state-of-the-art methods depicting a high Area Under Curve(AUC)and a low Equal Error Rate(EER),achieving an(AUC)of 93.1 and an(EER)of 8.1 for the(UCSD)dataset,and an(AUC)of 94.9 and an(EER)of 5.9 for the UMN *** evaluations demonstrate that the joint results from the combined audio-visual model outperform those from separate models,highlighting the competitive advantage of the proposed multi-modal approach.

关键词： Acoustic-visual anomaly detection sequence-to-sequence autoencoder reconstruction error late fusion regularity score

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Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4322-4343页

作者： Al-Maliki, Shawqi Qayyum, Adnan Ali, Hassan Abdallah, Mohamed Qadir, Junaid Hoang, Dinh Thai Niyato, Dusit Al-Fuqaha, Ala Division College of Science and Engineering Doha34110 Qatar Information Technology University Lahore54000 Pakistan University of New South Wales Sydney Sydney2052 Australia Qatar University Department of Computer Science and Engineering College of Engineering Doha34110 Qatar University of Technology Sydney School of Electrical and Data Engineering Sydney2052 Australia Nanyang Technological University College of Computing and Data Science 639798 Singapore

Deep neural networks (DNNs) have been the driving force behind many of the recent advances in machine learning. However, research has shown that DNNs are vulnerable to adversarial examples - input samples that have been perturbed to force DNN-based models to make errors. As a result, adversarial machine learning (AdvML) has gained a lot of attention, and researchers have investigated these vulnerabilities in various settings and modalities. In addition, DNNs have also been found to incorporate embedded bias and often produce unexplainable predictions, which can result in antisocial AI applications. The emergence of new AI technologies that leverage large language models (LLMs), such as ChatGPT and GPT-4, increases the risk of producing antisocial applications at scale. AdvML for social good (AdvML4G) is an emerging field that repurposes the AdvML bug to invent prosocial applications. Regulators, practitioners, and researchers should collaborate to encourage the development of prosocial applications and hinder the development of antisocial ones. In this work, we provide the first comprehensive review of the emerging field of AdvML4G. This paper encompasses a taxonomy that highlights the emergence of AdvML4G, a discussion of the differences and similarities between AdvML4G and AdvML, a taxonomy covering social good-related concepts and aspects, an exploration of the motivations behind the emergence of AdvML4G at the intersection of ML4G and AdvML, and an extensive summary of the works that utilize AdvML4G as an auxiliary tool for innovating prosocial applications. Finally, we elaborate upon various challenges and open research issues that require significant attention from the research community. © 2020 IEEE.

关键词： Taxonomies

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Liver Ailment Prediction Using Random Forest Model

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computers, Materials & Continua 2023年第1期74卷 1049-1067页

作者： Fazal Muhammad Bilal Khan Rashid Naseem Abdullah A Asiri Hassan A Alshamrani Khalaf A Alshamrani Samar M Alqhtani Muhammad Irfan Khlood M Mehdar Hanan Talal Halawani Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan City University of Science and Information Technology PeshawarPakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia

Today,liver disease,or any deterioration in one’s ability to survive,is extremely common all around the *** research has indicated that liver disease is more frequent in younger people than in older *** the liver’s capability begins to deteriorate,life can be shortened to one or two days,and early prediction of such diseases is *** several machine learning(ML)approaches,researchers analyzed a variety of models for predicting liver disorders in their early *** a result,this research looks at using the Random Forest(RF)classifier to diagnose the liver disease early *** dataset was picked from the University of California,Irvine ***’s accomplishments are contrasted to those of Multi-Layer Perceptron(MLP),Average One Dependency Estimator(A1DE),Support Vector Machine(SVM),Credal Decision Tree(CDT),Composite Hypercube on Iterated Random Projection(CHIRP),K-nearest neighbor(KNN),Naïve Bayes(NB),J48-Decision Tree(J48),and Forest by Penalizing Attributes(Forest-PA).Some of the assessment measures used to evaluate each classifier include Root Relative Squared Error(RRSE),Root Mean Squared Error(RMSE),accuracy,recall,precision,specificity,Matthew’s Correlation Coefficient(MCC),F-measure,and *** has an RRSE performance of 87.6766 and an RMSE performance of 0.4328,however,its percentage accuracy is *** widely acknowledged result of this work can be used as a starting point for subsequent *** a result,every claim that a new model,framework,or method enhances forecastingmay be benchmarked and demonstrated.

关键词： Liver ailment random forest machine learning

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Slow Convergence of Interacting Kalman Filters in Word-of-Mouth Social Learning 60

Slow Convergence of Interacting Kalman Filters in Word-of-Mo...

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60th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2024

作者： Krishnamurthy, Vikram Rojas, Cristian R. School of Electrical and Computer Engineering Cornell University United States School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden

ISBN: (纸本)9798331541033

We consider word-of-mouth social learning involving m Kalman filter agents that operate sequentially. The first Kalman filter receives the raw observations, while each subsequent Kalman filter receives a noisy measurement of the conditional mean of the previous Kalman filter. The prior is updated by the m-th Kalman filter. When m=2, and the observations are noisy measurements of a Gaussian random variable, the covariance goes to zero as k-1/3 for 7kappa;observations, instead of O(k-1) in the standard Kalman filter. In this paper we prove that for m agents, the covariance decreases to zero as k-(2m-1), i.e, the learning slows down exponentially with the number of agents. We also show that by artificially weighing the prior at each time, the learning rate can be made optimal as k-1. The implication is that in word-of-mouth social learning, artificially re-weighing the prior can yield the optimal learning rate. © 2024 IEEE.

关键词： Kalman filters

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Cyberbullying and Cyberviolence Detection:A Triangular User-Activity-Content View

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1384-1405页

作者： Shuwen Wang Xingquan Zhu Weiping Ding Amir Alipour Yengejeh Department of Electrical Engineering and Computer Science Florida Atlantic UniversityBoca RatonFL 33431 USA School of Information Science and Technology Nantong UniversityNantong 226019China

Recent years have witnessed the increasing popularity of mobile and networking devices,as well as social networking sites,where users engage in a variety of activities in the cyberspace on a daily and real-time *** such systems provide tremendous convenience and enjoyment for users,malicious usages,such as bullying,cruelty,extremism,and toxicity behaviors,also grow noticeably,and impose significant threats to individuals and *** this paper,we review computational approaches for cyberbullying and cyberviolence detection,in order to understand two major factors:1)What are the defining features of online bullying users,and 2)How to detect cyberbullying and *** achieve the goal,we propose a user-activities-content(UAC)triangular view,which defines that users in the cyberspace are centered around the UAC triangle to carry out activities and generate ***,we categorize cyberbully features into three main categories:1)User centered features,2)Content centered features,and 3)Activity centered *** that,we review methods for cyberbully detection,by taking supervised,unsupervised,transfer learning,and deep learning,etc.,into *** UAC centered view provides a coherent and complete summary about features and characteristics of online users(their activities),approaches to detect bullying users(and malicious content),and helps defend cyberspace from bullying and toxicity.

关键词： Classification clustering cyberbullying natural language processing social network

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An Intelligent Navigation System for the Visually Impaired Based on Machine Vision

An Intelligent Navigation System for the Visually Impaired B...

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7th International Conference on Electronics technology, ICET 2024

作者： Li, Zhuyu Xiong, Bing Huang, Jialing Xie, Zhuojiang Guo, Tingli Gu, Hao Changsha University of Science and Technology School of Computer and Communications Engineering Changsha China Changsha University of Science and Technology School of Electrical and Information Engineering Changsha China

ISBN: (纸本)9798350363951

In recent years, the number of visually impaired individuals has increased, and the urbanization process and lack of portable assistive tools have increased the risks associated with their mobility. This paper introduces the traditional blind cane as a carrier and integrates Internet of Things (IoT) and artificial intelligence (AI) technologies by installing sensor modules such as Bluetooth, cameras, ultrasonic obstacle avoidance, and voice broadcasting on the cane. Based on cloud service platforms and the YOLOv5 algorithm, an intelligent navigation cane has been developed that can achieve intelligent obstacle avoidance, traffic and road condition recognition, and other functions. Furthermore, an accessible navigation app has been developed that integrates various public transportation resources and enables Bluetooth connection, one-click cane retrieval, and travel navigation. This forms a complete smart navigation system for visually impaired individuals, providing them with security, independence, and long-distance travel support. © 2024 IEEE.

关键词： Machine vision

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HCNNet: hybrid convolution neural network for automatic identification of ischaemia in diabetic foot ulcer wounds

HCNNet: hybrid convolution neural network for automatic iden...

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作者： Das, Sujit Kumar Namasudra, Suyel Sangaiah, Arun Kumar Computer Science and Engineering Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India Computer Science and Engineering National Institute of Technology Agartala Tripura India International Graduate School of AI National Yunlin University of Science and Technology Douliu Taiwan Electrical and Science Engineering Lebanese American University Byblos Lebanon

The diabetic foot ulcer (DFU) is a significant medical complication for diabetic patients, which often leads to lower limb amputation. The manual identification of ischaemia in DFU is laborious, time-consuming, and costly. This study aims to develop an efficient deep learning (DL) method for the early identification of ischaemia in DFU. Therefore, a novel Convolutional Neural Network (CNN) architecture (HCNNet) is proposed by integrating multiple hybridised blocks from inception, residual, and dense modules along with appropriately placed Squeeze-and-Excitation (SE) block and intermediate transition layers. The proposed HCNNet is trained several times using various optimizer and learning rate settings to optimise its performance. It achieves promising Area Under the ROC Curve (AUC) scores of 0.999 for ischaemia identification. The experimental results show that the proposed HCNNet outperforms existing State-Of-The-Art (SOTA) methods. © 2024, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Medical imaging

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Development of Automotive-mobility Training in the TRIREME project 30

Development of Automotive-mobility Training in the TRIREME p...

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30th IEEE International Symposium for Design and technology in Electronic Packaging, SIITME 2024

作者： Medgyes, Balint Farkas, Csaba Spanyik, Marek Danieli, Stefano Cantiani, Elena Stolfa, Jakub Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Electronics Technology Budapest Hungary VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Department of Computer Science Ostrava Czech Republic Mylia-Adecco Formazione Milan Italy

ISBN: (纸本)9798331539511

To achieve climate neutrality by 2050, the consistent transformation of the mobility sector is one of the most important pillars of the European Green deal. As a result of green and sustainable approaches, novel automotive concepts and massive digitalisation in the mobility ecosystem is undergoing a continuous development towards eco-friendly solutions and novel business models, and the changing landscape of automotive industry comes with a significant social impact as well. The overall horizontal collaboration on skills agenda in the Automotive-Mobility Ecosystem is assured throughout the Automotive Skills Alliance (ASA) [1], which is the Large-scale Pact for Skills Partnership. The latest strategic project of the partnership to support its activities is the Digital & Green Skills Towards Future of the Mobility Ecosystem (TRIREME) which promotes a collaboration between industry stakeholders and training providers including Vocational education Trainings (VETs), Higher Educations (HEs) and universities), moreover, also emphasizing the widespread public representation of different project segments and evaluating the social impacts and regional impact of the changes in the Automotive-Mobility Ecosystem. In parallel to the development of sectoral skills strategy, our department (Department of Electronics technology-ETT) is contributing to deliver specific training materials and courses within Work Package 4 (WP4), which is based on the gathered and processed intelligence from previous desk research, survey activity and participation in professional events. The methodology of course development relies on the concept of modularity, our professional competences in the field of Automotive-mobility, and the fulfillment of requirements to cover the needed skills, the competence and the occupational profile subsets. © 2024 IEEE.

关键词： Automotive Mobility Modular Courses Skills Training Development

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