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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Nguyen, Duy M.H. Nguyen, Hoang Mai, Truong T.N. Cao, Tri Nguyen, Binh T. Ho, Nhat Swoboda, Paul Albarqouni, Shadi Xie, Pengtao Sonntag, Daniel Department of Computer Science University of Stuttgart Germany AISIA Lab. University of Science - VNU HCM Viet Nam Department of Multimedia Engineering Dongguk University Korea Republic of Department of Statistics and Data Sciences University of Texas Austin United States Max Planck Institute for Informatics Germany Helmholtz AI Helmholtz Munich Germany Clinic for Diagnostic and Interventional Radiology University of Bonn Germany Department of Electrical and Computer Engineering University of California San Diego United States Department of Computer Science Oldenburg University Germany German Research Center for Artificial Intelligence Germany

ISBN: (纸本)9781577358800

Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data. However, most current SSL techniques in the medical field have been designed for either 2D images or 3D volumes. In practice, this restricts the capability to fully leverage unlabeled data from numerous sources, which may include both 2D and 3D data. Additionally, the use of these pre-trained networks is constrained to downstream tasks with compatible data dimensions. In this paper, we propose a novel framework for unsupervised joint learning on 2D and 3D data modalities. Given a set of 2D images or 2D slices extracted from 3D volumes, we construct an SSL task based on a 2D contrastive clustering problem for distinct classes. The 3D volumes are exploited by computing vectored embedding at each slice and then assembling a holistic feature through deformable self-attention mechanisms in Transformer, allowing incorporating long-range dependencies between slices inside 3D volumes. These holistic features are further utilized to define a novel 3D clustering agreement-based SSL task and masking embedding prediction inspired by pre-trained language models. Experiments on downstream tasks, such as 3D brain segmentation, lung nodule detection, 3D heart structures segmentation, and abnormal chest X-ray detection, demonstrate the effectiveness of our joint 2D and 3D SSL approach. We improve plain 2D Deep-ClusterV2 and SwAV by a significant margin and also surpass various modern 2D and 3D SSL approaches. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Embeddings

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Exploring the Security of Blockchain Applications: A Review of Current Solutions and Open Challenges

Exploring the Security of Blockchain Applications: A Review ...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Milind Shah Mayur Chauhan Vaishnavi Solanki Bhargav Bharvad Priyanka Jadav Manav Shah Department of Computer Engineering Sardar Vallabhbhai Patel Institute of Technology (S.V.I.T) Vasad Gujarat India Department of Computer Engineering Gujarat Power Engineering & Research Institute(GPERI) A Constitute College of Gujarat Technological University Mehsana Gujarat India IoT Security e-Infochips Pvt Ltd Ahmedabad Gujarat India Department of Computer Engineering Devang Patel Institute of Advance Technology & Research (DEPSTAR) Faculty of Technology & Engineering (FTE) Charotar University of Science & Technology (CHARUSAT) Changa India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Blockchain technology has gained extensive recognition across a range of industries and is currently considered as one of the most essential technologies in the computing industry. The incorporation of private computing into blockchain technology is very necessary because of the huge challenges that blockchain technology faces in terms of preserving the privacy of consumers while simultaneously ensuring the immutability and transparency of data. This technology enhances the protection of users' privacy and the safety of their data within blockchain networks, while enhancing transparency and accountability. Additionally, it has the capability of enabling blockchain applications to carry out actions that require complex cooperation and smart contracts while yet maintaining the confidentiality of user information. The review article offers an introductory overview of the security of blockchain-based applications, encompassing the fundamental principles of blockchain security, the associated challenges, and prospective solutions. The article introduces research questions related to these topics and incorporates an extensive literature analysis of more than 15 academic articles. In addition, it discusses the use of blockchain technology in secure systems, current developments in blockchain security, and presents case examples from previous research that focus on protecting blockchain applications. In conclusion, the research highlights the challenges and unexplored research domains in the field of blockchain security, concluding in a brief summary of its findings.

关键词： Industries Data privacy Reviews Smart contracts Blockchains Security Protection Faces

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Uncertainty-Aware Gait-Based Age Estimation and its Applications

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity Science 2021年第4期3卷 479-494页

作者： Xu, Chi Sakata, Atsuya Makihara, Yasushi Takemura, Noriko Muramatsu, Daigo Yagi, Yasushi Lu, Jianfeng Institute of Scientific and Industrial Research Osaka University Ibaraki Japan Department of Computer and Information Science Faculty of Science and Technology Seikei University Musashino Japan School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Gait-based age estimation is a key technique for many applications. It is well known that age estimation uncertainty is highly dependent on age (i.e., small for children and large for adults), and it is important to know the uncertainty for the above-mentioned applications. Therefore, we propose a method for uncertainty-aware gait-based age estimation by introducing a label distribution learning framework. Specifically, we design a network that takes an appearance-based gait feature as input and outputs discrete label distributions in the integer age domain. We then train the network to minimize a loss function, which is defined as the dissimilarity between the estimated age distribution and the ground-truth age distribution, in addition to the conventional mean absolute error for the estimated age. Additionally, we demonstrate that uncertainty-aware gait-based age estimation is beneficial for two applications: person search by age query and people counting by age group. Experiments on the world's largest gait database, OULP-Age, demonstrated that the proposed method can successfully represent age estimation uncertainty, and outperforms or is comparable with state-of-the-art methods in terms of age estimation accuracy. Moreover, we demonstrated the effectiveness of the uncertainty-aware framework in applications to person search and people counting through experiments on the database. © 2019 IEEE.

关键词： Query processing

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Fuzzy Attack Tree: Assessing Cyberattack Risk Using Attack Tree and Fuzzy Logic 3

Fuzzy Attack Tree: Assessing Cyberattack Risk Using Attack T...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Naik, Nitin Jenkins, Paul Grace, Paul Naik, Dishita Song, Jingping Prajapat, Shaligram Mishra, Durgesh Yang, Longzhi Boongoen, Tossapon Iam-On, Natthakan School of Informatics and Digital Engineering Aston University United Kingdom Cardiff School of Technologies Cardiff Metropolitan University United Kingdom Birmingham City University United Kingdom Software College Northeastern University China International Institute of Professional Studies Devi Ahilya University India School of Computer Science & Information Technology Symbiosis University of Applied Sciences India Northumbria University Department of Computer and Information Sciences United Kingdom Aberystwyth University Department of Computer Science United Kingdom

ISBN: (纸本)9798350343274

Organisations and users have been experiencing significant rises in cyberattacks and their severity, which means that they require a greater awareness and understanding of the anatomy of cyberattacks, to prevent and mitigate their effects. In analysing cyberattacks, there are a number of different approaches that may be used to assess their potential risks and effects. However, these are utilised in specific types of cyberattacks and their analysis, which means they cannot be applied in every situation or cyberattack. Moreover, several other factors may influence the decision to use these approaches, such as cost, complexity, skills and adaptability. As a result, continuous research to design and enhance these approaches is undertaken to produce a generic, cost-effective, easy and adaptable approach. This paper presents one such approach to assess the risk of cyberattacks utilising an attack tree and fuzzy logic. An attack tree is a systematic and illustrative method for describing an attack on a system and analysing its taxonomy and other aspects. Subsequently, the probability and risk of each leaf node in the attack tree are calculated using the proposed formulas. Finally, fuzzy logic enables decision making based on imprecise data and heuristics to obtain the overall risk of attack. This proposed approach comprises systematic steps to accomplish an assessment of any cyberattack and its associated risks in an uncomplicated and effective manner, enabling its prevention and mitigation to be determined. The paper illustrates an application of the proposed approach to assess the risk of an information theft attack on an organisation, which can then be utilised to assess the risk of other cyberattacks. © 2023 IEEE.

关键词： Decision making

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Exploring Music Similarity through Siamese CNNs using Triplet Loss on Music Samples

Exploring Music Similarity through Siamese CNNs using Triple...

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International research Conference on Smart Computing and Systems engineering (SCSE)

作者： Gibran Kasif Ganesha Thondilege School of Computer Science and Engineering University of Westminster London UK Department of Computing Informatics Institute of Technology Colombo Sri Lanka

In the rapidly evolving digital music landscape, identifying similarities between musical pieces is essential to help musicians avoid unintended copyright infringement and maintain the originality of their work. However, detecting such similarities remains a complex and computationally challenging problem. A novel approach to address this issue is a song similarity detection system that utilizes a Siamese Convolutional Neural Network (CNN) with Triplet Loss for effective audio input comparison. The model is trained on a custom dataset from WhoSampled, an extensive database of information on sampled music, cover songs, and remixes. The dataset comprises pairs of audio samples and interpolations, making it suitable for the Siamese CNN approach. Incorporating Triplet Loss enhances the model’s performance by learning discriminative features for improved comparison. The performance of this system is assessed using a confidence interval-based metric, achieving a 96.86% accuracy at a 99.7% confidence level in determining the similarity between music samples. The solution provides a helpful tool for musicians to actively compare their creations with existing songs, helping to reduce the likelihood of unintentional plagiarism and possible legal issues.

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Multi-Path Attention Inverse Discrimination Network for Offline Signature Verification

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Intelligent Automation & Soft Computing 2023年第6期36卷 3057-3071页

作者： Xiaorui Zhang Yingying Wang Wei Sun Qi Cui Xindong Wei School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of WindsorWindsorN9B 3P4Canada School of Teacher Education Nanjing University of Information Science&TechnologyNanjing210044China

Signature verification,which is a method to distinguish the authenticity of signature images,is a biometric verification technique that can effectively reduce the risk of forged signatures in financial,legal,and other business ***,compared with ordinary images,signature images have the following characteristics:First,the strokes are slim,i.e.,there is less effective ***,the signature changes slightly with the time,place,and mood of the signer,i.e.,it has high intraclass *** challenges lead to the low accuracy of the existing methods based on convolutional neural net-works(CNN).This study proposes an end-to-end multi-path attention inverse dis-crimination network that focuses on the signature stroke parts to extract features by reversing the foreground and background of signature images,which effectively solves the problem of little effective *** solve the problem of high intraclass variability of signature images,we add multi-path attention modules between discriminative streams and inverse streams to enhance the discriminative features of signature ***,a multi-path discrimination loss function is proposed,which does not require the feature representation of the samples with the same class label to be infinitely close,as long as the gap between inter-class distance and the intra-class distance is bigger than the set classification threshold,which radically resolves the problem of high intra-class difference of signature *** addition,this loss can also spur the network to explore the detailed infor-mation on the stroke parts,such as the crossing,thickness,and connection of *** respectively tested on CEDAR,BHSig-Bengali,BHSig-Hindi,and GPDS Synthetic datasets with accuracies of 100%,96.24%,93.86%,and 83.72%,which are more accurate than existing signature verification *** is more helpful to the task of signature authentication in justice and finance.

关键词： Offline signatures biometric verification multi-path discrimination loss attention mechanisms inverse discrimination

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Opportunistic Routing for Mobile Edge Computing: A Community Detected and Task Priority Aware Approach

SSRN

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SSRN 2024年

作者： Wu, Jia Dai, Tingyi Guan, Peiyuan Chen, Ziru Gou, Fangfang Taherkordi, Amir The Department of Informatics University of Oslo Oslo0316 Norway The Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago United States The School of Computer Science and Technology GuiZhou University Guiyang550025 China The Research Center for Artificial Intelligence Faculty of IT Monash University MelbourneVIC3800 Australia

As the 5G era develops quickly, there has been a significant growth in the amount of network data. Concurrently, traditional routing algorithms are encountering growing challenges. Traditional routing algorithms typically rely on maintaining stable end-to-end paths to facilitate smooth data transmission. However, in wireless multi-hop networks, the mobility of nodes and the dynamic nature of network topology make maintaining stable end-to-end paths exceedingly difficult, if not impossible. Consequently, data transmission efficiency decreases while network overhead increases. To address these challenges, opportunistic routing has emerged. Opportunistic routing is self-organizing networks that rely on chance encounters between nodes to transmit messages, and the store-and-carry forwarding technique is used for data transmission between nodes. Typically, most traditional opportunistic routing algorithms divide nodes into communities. However, such approaches often overlook node characteristics, potentially leading to disparate nodes being assigned to separate communities despite positive correlations. Thus, accurately dividing communities remains a significant challenge. To address this, we propose the Efficient Community Detection Data Transmitting (ECDDT) algorithm. During community division, ECDDT combines social attributes and network topology using mobile edge computing. Subsequently, to expedite task execution, we schedule these tasks in a reasonable manner. Through simulation experiments across four diverse real datasets, we evaluate the algorithm's efficacy. Our findings demonstrate favorable performance in delivery rate and routing overhead. © 2024, The Authors. All rights reserved.

关键词： Mobile edge computing

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A Mobility-Aware Reinforcement Learning Proactive Solution for State Data Migration in Edge Computing

A Mobility-Aware Reinforcement Learning Proactive Solution f...

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IEEE International Workshop on computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Elias Dritsas Kostas Ramantas Christos Verikoukis Athena Research and Innovation Center Industrial Systems Institute Patras Greece Iquadrat Barcelona Spain Department of Computer Engineering and Informatics University of Patras Patras Greece

ISBN: (数字)9798350377644

ISBN: (纸本)9798350377651

To handle the demands of modern applications for storage, computing, low latency, and bandwidth, various services are offloaded from the cloud to edge servers, bringing them closer to end-users. This shift in computing resources closer to the users is known as Edge Computing whose services are highly affected by users' mobility. As users move and change geographical locations, the services provided by an edge server may be compromised or become distant from the user, leading to a decrease in the quality of service (QoS) for applications. Service migration is a common approach to ensure that essential services remain close to users, thereby improving the application QoS. The synergy of Edge Computing with Artificial Intelligence (AI) can open up new capabilities for the Lifecycle Management (LCM) of applications, besides the adoption of the innovative microservice-based architecture for building them and, especially, stateful to ensure seamless service continuity and minimal latency during user's mobility. The leverage of AI-based LCM of such applications can significantly benefit edge computing by automating and optimising, in terms of resource allocation, the process of decision-making during deployment and run-time operation of microservices running on edge devices. In this direction, we elaborate a mobility-induced proactive migration mechanism empowered by efficient Deep Learning (DL) and Reinforcement Learning (RL) algorithms to identify the optimal edge node for application and state data migration without violating Service Level Agreement - SLA (e.g., latency) and ensure efficient resource usage at the edge.

关键词： Microservice architectures Reinforcement learning Quality of service Predictive models Trajectory Servers Resource management Artificial intelligence Edge computing Service level agreements

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Improving Prediction Quality of Face Image Preference Using Combinatorial Fusion Algorithm 16th

Improving Prediction Quality of Face Image Preference Using...

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16th International Conference on Brain informatics, BI 2023

作者： Zhang, Zihan Schweikert, Christina Shimojo, Shinsuke Hsu, D. Frank Laboratory of Informatics and Data Mining Department of Computer and Information Science Fordham University YorkNY10023 United States Division of Computer Science Mathematics and Science St. John’s University QueensNY11439 United States Division of Biology and Biological Engineering/Computation and Neural Systems California Institute of Technology PasadenaCA91125 United States

ISBN: (纸本)9783031430749

When two face images are shown to a subject, the subject’s preference choice is influenced by many factors. Although it is a challenging task, one way to detect a subject’s preference is to analyze the eye movement gaze cascade in the recorded video sequence together with a set of extracted attributes (features). Combinatorial fusion algorithm (CFA) is a new information fusion and machine learning paradigm for combining multiple scoring systems using rank-score characteristic (RSC) function and cognitive diversity (CD). In this paper, we apply CFA in the analysis of the eye movement gaze cascade sequence. In particular, we characterize each of the attributes and measure the diversity between two attributes using RSC function and CD respectively. The novelty of the combinatorial fusion approach is the new paradigm of incorporating both score function and rank function as well as using both score combination and rank combination. In addition, our results demonstrate that weighted rank combination has some advantage over weighted score combination when the weight is measured using diversity strength. Since diversity strength depends only on cognitive diversity between two attributes but not on any specific sequence in the eye movement gaze cascade, our result facilitates learning and modeling in an unsupervised manner. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Information fusion

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Auto-Handover Gateway LoRaWAN based on Autonomous Vehicles

Auto-Handover Gateway LoRaWAN based on Autonomous Vehicles

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Information technology International Seminar (ITIS)

作者： Iwan Purnama Rahmadani Pane Adri Gabriel Sooai Firman Edi Ahmad Abdul Chamid Puput Dani Prasetyo Adi Department of Information Technology Faculty of Science and Technology Universitas Labuhanbatu Labuhanbatu Indonesia Department of Computer Engineering Faculty of Computer Science Universitas Katolik Widya Mandira Kupang NTT Indonesia Department of Engineering Management Faculty of Industrial Technology Batam Institute of Technology Batam City Indonesia Department of Informatics Engineering Muria Kudus University Kudus Indonesia Department of Telecommunication National Research and Innovation Agency (BRIN) Bandung Indonesia

ISBN: (数字)9798331521295

ISBN: (纸本)9798331521301

This article presents a simulation study on the use of LoRaWAN technology in autonomous vehicles. The research focuses on developing an Auto-Handover Gateway for Autonomous Vehicles, utilizing LoRaWAN parameters such as bit rates, spreading factor (SF), packet loss, and received signal strength. These parameters are tested in simulations of autonomous vehicle movements, which include both regular and random motion patterns. The aim is to assess the performance of LoRaWAN in different scenarios and its impact on communication in autonomous vehicle systems. The simulation results show several key trends. As the vehicle moves further, the spreading factor (SF) increases, and bit rates tend to decrease, indicating a drop in communication performance over longer distances. The research also identifies that the gateway with the closest proximity to the vehicle is selected, both in random and non-random movement scenarios. Additionally, packet loss increases as the distance between the vehicle and the gateway grows, highlighting the challenges of maintaining reliable communication over long distances. These findings provide insights into optimizing LoRaWAN parameters for real-time communication in autonomous vehicles, contributing to the development of intelligent vehicle networks.

关键词： Seminars Simulation Bit rate LoRaWAN Packet loss Logic gates Market research Real-time systems Reliability Autonomous vehicles

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