Dark-Side Avoidance of Mobile Applications With Data Biases Elimination in Socio-Cyber World

Ma, Ying; Yu, Chuyi; Sangaiah, Arun Kumar; Wu, Youke

doi:10.1109/tcss.2023.3264696

Cited by 9 publications

(2 citation statements)

References 30 publications

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“…The information supplement helps you to quickly familiarize yourself with scenic spots for the first time. Besides the field of daily life, cross-modal retrieval has important applications in many domains such as medicine [1], finance [2], and information security [3]. Therefore, it is an interesting and challenging problem to construct an effective cross-modal retrieval system.…”

Section: Introductionmentioning

confidence: 99%

Cross-modal retrieval based on multi-dimensional feature fusion hashing

Ren,

2024

Front. Phys.

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Along with the continuous breakthrough and popularization of information network technology, multi-modal data, including texts, images, videos, and audio, is growing rapidly. We can retrieve different modal data to meet our needs, so cross-modal retrieval has important theoretical significance and application value. In addition, because the data of different modalities can be mutually retrieved by mapping them to a unified Hamming space, hash codes have been extensively used in the cross-modal retrieval field. However, existing cross-modal hashing models generate hash codes based on single-dimension data features, ignoring the semantic correlation between data features in different dimensions. Therefore, an innovative cross-modal retrieval method using Multi-Dimensional Feature Fusion Hashing (MDFFH) is proposed. To better get the image’s multi-dimensional semantic features, a convolutional neural network, and Vision Transformer are combined to construct an image multi-dimensional fusion module. Similarly, we apply the multi-dimensional text fusion module to the text modality to obtain the text’s multi-dimensional semantic features. These two modules can effectively integrate the semantic features of data in different dimensions through feature fusion, making the generated hash code more representative and semantic. Extensive experiments and corresponding analysis results on two datasets indicate that MDFFH’s performance outdoes other baseline models.

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Section: Introductionmentioning

confidence: 99%

Cross-modal retrieval based on multi-dimensional feature fusion hashing

Ren,

2024

Front. Phys.

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“…With the widespread adoption of technologies such as service-oriented architecture (SOA), the Internet of Services (IoS), and cloud computing, an increasing number of enterprises are launching various virtual services to meet user demands. Data, artificial intelligence, and mobile applications also continue to provide new technological support for enterprises to maintain a competitive advantage [2][3][4]. Nowadays, in the face of the increasing number of services with similar functions, it is difficult for users to make appropriate choices.…”

Section: Introductionmentioning

confidence: 99%

Location-Aware Deep Interaction Forest for Web Service QoS Prediction

Zhu,

Ding,

Yang

2024

Applied Sciences

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With the rapid development of the web service market, the number of web services shows explosive growth. QoS is an important factor in the recommendation scene; how to accurately recommend a high-quality service for users among the massive number of web services has become a tough problem. Previous methods usually acquired feature interaction information by network structures like DNN to improve the QoS prediction accuracy, but this generates unnecessary computations. Aiming at addressing the above problem, inspired by the multigrained scanning mechanism in a deep forest, we propose a location-aware deep interaction forest approach for web service QoS prediction (LDIF). This approach offers the following innovations: The model fuses the location similarity of users and services as a latent feature representation of them. In addition, we designed a scanning interaction structure (SIS), which obtains multiple local feature combinations from the interaction between user and service features, uses interactive computing to extract feature interaction information, and concatenates the feature interaction information with original features, which aims to enhance the dimension of the features. Equipped with these, we compose a layer-by-layer cascade by using SIS to fuse low- and high-order feature interaction information, and the early-stop mechanism controls the cascade depth to avoid unnecessary computation. The experiments demonstrate that our model outperforms eight other state-of-the-art methods on MAE and RMSE common metrics on real public datasets.

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RETRACTED ARTICLE: Automatically human action recognition (HAR) with view variation from skeleton means of adaptive transformer network

et al. 2023

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Human Action Recognition (HAR) using skeletons has become increasingly appealing to a growing number of researchers in recent years. It is particularly challenging to recognize actions when they are captured from different angles because there are so many variations in their representations. This paper proposes an automatic strategy for determining virtual observation viewpoints that are based on learning and data-driven to solve the problem of view variation throughout an act. Our VA-CNN and VA-RNN networks, which use convolutional and recurrent neural networks with the Long Short-term Memory Article Title (LSTM), offer an alternative to the conventional method of reorienting skeletons according to a human-defined earlier benchmark. Using the unique view adaption module, each network first identifies the best observation perspectives and then transforms the skeletons for endto-end detection with the main classification network based on those viewpoints. Using the skeletons of different perspectives, the suggested view adaptive models can provide significantly more consistent virtual viewpoints. By removing views, the models allow networks to learn action-specific properties more efficiently. Furthermore, we developed a two-stream scheme (referred to as VA-fusion) that integrates the performance of two networks to obtain an improved prediction. Random rotation of skeletal sequences is used to avoid overfitting during training and improve the reliability of view adaption models. An extensive experiment demonstrates that our proposed view-adaptive networks outperform existing solutions on five challenging benchmarks.

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Dark-Side Avoidance of Mobile Applications With Data Biases Elimination in Socio-Cyber World

Cited by 9 publications

References 30 publications

Cross-modal retrieval based on multi-dimensional feature fusion hashing

Cross-modal retrieval based on multi-dimensional feature fusion hashing

Location-Aware Deep Interaction Forest for Web Service QoS Prediction

RETRACTED ARTICLE: Automatically human action recognition (HAR) with view variation from skeleton means of adaptive transformer network

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