An automated string-based approach to White Matter fiber-bundles clustering

Cauteruccio, Francesco; Stamile, Claudio; Terracina, Giorgio; Ursino, Domenico; Sappey-Mariniery, Dominique

doi:10.1109/ijcnn.2015.7280545

Cited by 10 publications

(7 citation statements)

References 30 publications

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“…However, this approach requires considerable construction time due to the complex bit selection procedure. Similar issues are addressed in different research areas [45,46].…”

Section: Many-field Packet Classification Based On Hardwarementioning

confidence: 87%

Packet Classification Using TCAM of Narrow Entries

Lin,

Pan,

Wang

2023

Technologies

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Packet classification based on rules of packet header fields is the key technology for enabling software-defined networking (SDN). Ternary content addressable memory (TCAM) is a widely used hardware for packet classification; however, commercially available TCAM chips have only limited storage. As the number of supported header fields in SDN increases, the number of supported rules in a TCAM chip is reduced. In this work, we present a novel scheme to enable packet classification using TCAM with entries that are narrower than rules by storing the most representative field of a ruleset in TCAM. Due to the fact that not all rules can be distinguished using one field, our scheme employs a TCAM-based multimatch packet classification technique to ensure correctness. We further develop approaches to reduce redundant TCAM accesses for multimatch packet classification. Although our scheme requires additional TCAM accesses, it supports packet classification upon long rules with narrow TCAM entries, and drastically reduces the required TCAM storage. Our experimental results show that our scheme requires a moderate number of additional TCAM accesses and consumes much less storage compared to the basic TCAM-based packet classification. Thus, it can provide the required scalability for long rules required by potential applications of SDN.

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“…However, this approach requires considerable construction time due to the complex bit selection procedure. Similar issues are addressed in different research areas [45,46].…”

Section: Many-field Packet Classification Based On Hardwarementioning

confidence: 87%

Packet Classification Using TCAM of Narrow Entries

Lin,

Pan,

Wang

2023

Technologies

View full text Add to dashboard Cite

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“…In future research, we can identify conflicting event pairs from historical events and optimize the feature weights of active users to more accurately determine their preferences for leisure events. Secondly, incorporating new similarity methods [52,53] can enhance the calculation of similarity between heterogeneous event sequences. These methods can provide more robust and accurate measures of similarity, leading to improved event recommendations.…”

Section: Discussionmentioning

confidence: 99%

MFM: A Multiple-Features Model for Leisure Event Recommendation in Geotagged Social Networks

Wu,

Peng,

Niu

et al. 2023

Electronics

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Event-based social networks (EBSNs) are rich in information about users and leisure events. The willingness of users to participate in leisure events is influenced by many factors such as event time, location, content, organizer, and social relationship factors of users. Event recommendation systems in EBSNs can help leisure event organizers to accurately find users who want to participate in events. However, to address the existing cold-start problems and improve the accuracy of event recommendations, we propose a multiple-feature-based leisure event recommendation model (MFM). We introduce the user’s social contacts into the user preference features and construct a user feature space by integrating the features of the user preferences for events and organizers and preferences of the user’s closest friends. Moreover, considering the behavioral differences between active and inactive users, we extracted the respective features and trained the feature weight models. Finally, the experimental results showed that in comparison with the baseline models, the precision of the MFM is higher by at least 7.9%.

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“…Xue et al [42] intro-duced a multi-level attention-graph network to reduce noise within and between modalities. Cauteruccio et al [43] introduced a string-comparison metric that could be employed to enhance the processing of heterogeneous audio samples, mitigating modality-related noise. However, these models did not investigate the impact of ASR errors on the MSA model.…”

Section: Related Workmentioning

confidence: 99%

Multimodal Sentiment Analysis in Realistic Environments Based on Cross-Modal Hierarchical Fusion Network

Huang,

Lu,

Sun

et al. 2023

Electronics

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In the real world, multimodal sentiment analysis (MSA) enables the capture and analysis of sentiments by fusing multimodal information, thereby enhancing the understanding of real-world environments. The key challenges lie in handling the noise in the acquired data and achieving effective multimodal fusion. When processing the noise in data, existing methods utilize the combination of multimodal features to mitigate errors in sentiment word recognition caused by the performance limitations of automatic speech recognition (ASR) models. However, there still remains the problem of how to more efficiently utilize and combine different modalities to address the data noise. In multimodal fusion, most existing fusion methods have limited adaptability to the feature differences between modalities, making it difficult to capture the potential complex nonlinear interactions that may exist between modalities. To overcome the aforementioned issues, this paper proposes a new framework named multimodal-word-refinement and cross-modal-hierarchy (MWRCMH) fusion. Specifically, we utilized a multimodal word correction module to reduce sentiment word recognition errors caused by ASR. During multimodal fusion, we designed a cross-modal hierarchical fusion module that employed cross-modal attention mechanisms to fuse features between pairs of modalities, resulting in fused bimodal-feature information. Then, the obtained bimodal information and the unimodal information were fused through the nonlinear layer to obtain the final multimodal sentiment feature information. Experimental results on the MOSI-SpeechBrain, MOSI-IBM, and MOSI-iFlytek datasets demonstrated that the proposed approach outperformed other comparative methods, achieving Has0-F1 scores of 76.43%, 80.15%, and 81.93%, respectively. Our approach exhibited better performance, as compared to multiple baselines.

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An automated string-based approach to White Matter fiber-bundles clustering

Cited by 10 publications

References 30 publications

Packet Classification Using TCAM of Narrow Entries

Packet Classification Using TCAM of Narrow Entries

MFM: A Multiple-Features Model for Leisure Event Recommendation in Geotagged Social Networks

Multimodal Sentiment Analysis in Realistic Environments Based on Cross-Modal Hierarchical Fusion Network

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