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Satellite Network Security Routing Technology Based on Deep Learning and Trust Management

SENSORS 2023年第20期23卷 8474-8474页

作者： Liu, Zhiguo Rong, Junlin Jiang, Yingru Zhang, Luxi Dalian Univ Commun & Network Lab Dalian 116622 Peoples R China

The conventional trust model employed in satellite network security routing algorithms exhibits limited accuracy in detecting malicious nodes and lacks adaptability when confronted with unknown attacks. To address this challenge, this paper introduces a secure satellite network routing technology founded on deep learning and trust management. The approach embraces the concept of distributed trust management, resulting in all satellite nodes in this paper being equipped with trust management and anomaly detection modules for assessing the security of neighboring nodes. In a more detailed breakdown, this technology commences by preprocessing the communication behavior of satellite network nodes using D-S evidence theory, effectively mitigating interference factors encountered during the training of VAE modules. Following this preprocessing step, the trust vector, which has undergone prior processing, is input into the VAE module. Once the VAE module's training is completed, the satellite network can assess safety factors by employing the safety module during the collection of trust evidence. Ultimately, these security factors can be integrated with the pheromone component within the ant colony algorithm to guide the ants in discovering pathways. Simulation results substantiate that the proposed satellite network secure routing algorithm effectively counters the impact of malicious nodes on data transmission within the network. When compared to the traditional trust management model of satellite network secure routing algorithms, the algorithm demonstrates enhancements in average end-to-end delay, packet loss rate, and throughput.

关键词： trust management D-S evidence theory variational autoencoder secure routing

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PCovNet: A presymptomatic COVID-19 detection framework using deep learning model using wearables data

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年 147卷 105682-105682页

作者： Abir, Farhan Fuad Alyafei, Khalid Chowdhury, Muhammad E. H. Khandakar, Amith Ahmed, Rashid Hossain, Muhammad Maqsud Mahmud, Sakib Rahman, Ashiqur Abbas, Tareq O. Zughaier, Susu M. Naji, Khalid Kamal Univ Dhaka Dept Elect & Elect Engn Dhaka 1000 Bangladesh Qatar Univ Coll Engn Dept Mech & Ind Engn Doha 2713 Qatar Qatar Univ Dept Elect Engn Doha 2713 Qatar Qatar Univ Biomed Res Ctr Doha 2713 Qatar North South Univ NSU Genome Res Inst NGRI Dhaka 1229 Bangladesh Tohoku Univ Inst Multidisciplinary Res Adv Mat Sendai Japan Surg Dept Urol Div Sidra Med Doha 26999 Qatar Qatar Univ Coll Med Dept Basic Med Sci QU Hlth Doha 2713 Qatar Qatar Univ Coll Engn Doha 2713 Qatar

While the advanced diagnostic tools and healthcare management protocols have been struggling to contain the COVID-19 pandemic, the spread of the contagious viral pathogen before the symptom onset acted as the Achilles' heel. Although reverse transcription-polymerase chain reaction (RT-PCR) has been widely used for COVID-19 diagnosis, they are hardly administered before any visible symptom, which provokes rapid transmission. This study proposes PCovNet, a Long Short-term Memory variational autoencoder (LSTM-VAE)-based anomaly detection framework, to detect COVID-19 infection in the presymptomatic stage from the Resting Heart Rate (RHR) derived from the wearable devices, i.e., smartwatch or fitness tracker. The framework was trained and evaluated in two configurations on a publicly available wearable device dataset consisting of 25 COVID-positive individuals in the span of four months including their COVID-19 infection phase. The first configuration of the framework detected RHR abnormality with average Precision, Recall, and F-beta scores of 0.946, 0.234, and 0.918, respectively. However, the second configuration detected aberrant RHR in 100% of the subjects (25 out of 25) during the infectious period. Moreover, 80% of the subjects (20 out of 25) were detected during the presymptomatic stage. These findings prove the feasibility of using wearable devices with such a deep learning framework as a secondary diagnosis tool to circumvent the presymptomatic COVID-19 detection problem.

关键词： Presymptomatic COVID-19 Smartwatch Resting heart rate Anomaly detection Long short -term memory variational autoencoder

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Inverse design of dual-phase steel microstructures using generative machine learning model and Bayesian optimization

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INTERNATIONAL JOURNAL OF PLASTICITY 2023年 171卷

作者： Kusampudi, Navyanth Diehl, Martin Max Planck Inst Eisenforsch GmbH Max Planck Str 1 D-40237 Dusseldorf Germany Katholieke Univ Leuven Dept Mat Sci Kasteelpark Arenberg 44 B-3001 Leuven Belgium Katholieke Univ Leuven Dept Comp Sci Celestijnenlaan 200 A B-3001 Louvain Belgium

The design of optimal microstructures requires first, the identification of microstructural features that influence the material's properties and, then, a search for a combination of these features that give rise to desired properties. For microstructures with complex morphologies, where the number of features is large, deriving these structure-property relationships is a challenging task. To address this challenge, we propose a generative machine learning model that can automatically identify low-dimensional descriptors of microstructural features that can be used to establish structure-property relationships. Based on this model, we present an integrated, data-driven framework for microstructure characterization, reconstruction, and design that is applicable to heterogeneous materials with polycrystalline microstructures. The proposed method is evaluated on a case study of designing dual-phase steel microstructures created with the multi-level Voronoi tessellation method. To this end, we train a variational autoencoder to identify the descriptors from these synthetic dual-phase steel microstructures. Subsequently, we employ Bayesian optimization to search for the optimal combination of the descriptors and generate microstructures with specific yield stress and low susceptibility for damage initiation. The presented results show how microstructure descriptors, determined by the variational autoencoder model, act as design variables for an optimization algorithm that identifies microstructures with desired properties.

关键词： Crystal plasticity variational autoencoder Voronoi tessellation Random forest

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VAPER: A deep learning model for explainable probabilistic regression

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JOURNAL OF COMPUTATIONAL SCIENCE 2022年第0期63卷

作者： Jung, Seungwon Noh, Yoona Moon, Jaeuk Hwang, Eenjun Korea Univ Sch Elect Engn Seoul 02841 South Korea

A probabilistic regression model provides decision-makers with the regression output along with its quantitative uncertainty for given input variables. Even though this uncertainty may help to avoid serious consequences such as misdiagnosis or blackout due to overconfidence in the output, it only provides a measure of the uncertainty of the output, and has a limitation in that it cannot explain the reasons for the output and its uncertainty. If they can be presented along with their reasons, more suitable alternatives to the output can be found. However, despite the development of artificial intelligence methods to explain machine learning models and their outputs, few probabilistic regression models with this functionality have been proposed. Therefore, in this paper, we propose a variational autoencoder-based model for explainable probabilistic regression, called VAPER. VAPER provides a parametric probability distribution of an output variable over input variables and interprets it using layer-wise relevance propagation to investigate the effect of each input variable. To evaluate the effectiveness of the pro-posed model, we performed extensive experiments using several datasets. The experimental results demonstrated that VAPER has competitive regression performance compared to existing models, even with effective explainability.

关键词： Probabilistic regression Explainable artificial intelligence Layer -wise relevance propagation variational autoencoder

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Learning to balance the coherence and diversity of response generation in generation-based chatbots

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2020年第4期17卷

作者： Wang, Shuliang Li, Dapeng Geng, Jing Yang, Longxing Leng, Hongyong Beijing Inst Technol Sch Comp Sci & Technol 5 South St Beijing 100081 Peoples R China Beijing Inst Technol Acad E Govt Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

Generating response with both coherence and diversity is a challenging task in generation-based chatbots. It is more difficult to improve the coherence and diversity of dialog generation at the same time in the response generation model. In this article, we propose an improved method that improves the coherence and diversity of dialog generation by changing the model to use gamma sampling and adding attention mechanism to the knowledge-guided conditional variational autoencoder. The experimental results demonstrate that our proposed method can significantly improve the coherence and diversity of knowledge-guided conditional variational autoencoder for response generation in generation-based chatbots at the same time.

关键词： variational autoencoder dialog system deep learning response generation chatbots

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Optimization of VAE-CGAN structure for missing time-series data complementation of UAV jujube garden aerial surveys

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TURKISH JOURNAL OF AGRICULTURE AND FORESTRY 2023年第5期47卷 746-760页

作者： Ling, Shunkang Wang, Nianyi Li, Jingbin Ding, Longpeng Shihezi Univ Coll Mech & Elect Engn Shihezi Peoples R China Xinjiang Prod & Construct Corps Key Lab Modern Agr Machinery Shihezi Peoples R China

UAV aerial survey technology has been widely used in agricultural production, in the aerial survey mission sensor by signal interference, environmental changes and other factors will have the problem of missing flight data. In order to accurately complement the time-series data, the paper proposes a complementary model based on VAE-CGAN optimization, which uses a combination of conditional generative adversarial network (CGAN) and variational autoencoder (VAE), incorporates QRNN as a regressor for VAE-CGAN reduction, adds ProbSparse Self-attention (PSA) to reduce computational complexity, and uses a new discriminator structure. Comparative experiments on real aerial survey project datasets show that the model is universally applicable on data with different parameter time series missing and outperforms other comparative models in terms of sample generation capability and prediction results with different missing rates.

关键词： Jujube garden aerial survey time-series data missing value complementation variational autoencoder conditional generation adversarial network

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Novel method of performance-optimized metastructure design for electromagnetic wave absorption in specific band using deep learning

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartB期137卷

作者： On, Han-Ik Jeong, Leekyo Seo, Tae-Moon Jo, Yehrin Choi, Wonwoo Kang, Dong-Joong Park, Jun-Hyub Lee, Hak-Joo Pusan Natl Univ Sch Mech Engn 2 Busandaehak Ro 63beon Gil Busan 46241 South Korea Ctr Adv Meta Mat 156 Gajeongbuk Ro Daejeon 34103 South Korea Kyungsung Univ Dept Mech & Automot Engn 309 Suyeong Ro Busan 48434 South Korea Korea Adv Inst Sci & Technol Cho Chun Shik Grad Sch Mobil 193 Munji Ro Daejeon South Korea

In this paper, we propose a new method that utilizes deep learning techniques to design a metastructure for an electromagnetic absorber. This method enables the effective design of a metastructure with the desired performance (spectrum of S11<- 10 dB) in the frequency band specified by the designer, within a wideband range from 2 to 40 GHz. The proposed absorber consists of two dielectric layers with varied conductive patterns and a back reflector. Critical to the absorber's microwave performance is the binary pattern configuration, organized in a 20-pixel square, along with the sheet resistance and layer thickness of each layer, contributing to a significant design freedom exceeding 1037 degrees of freedom. Our model for performance-optimized design involves three steps: Initially, with limited data from 26,000 sets, a variational autoencoder (VAE) was trained to map S11 spectra and arrange a latent space linked to metastructure. Subsequently, we developed a spectrum prediction network to correlate patterns with S11 spectra, leveraging a pre-trained decoder from the auxiliary VAE in the first step. The final step trains a network for designing a metastructure with broadband absorption. To verify the performance of a metastructure designed by the developed method, we compared their performances with those obtained through Finite Difference Time Domain (FDTD) simulation and the developed network. And also to further validate our approach experimentally, the designed metastructures were fabricated by silkscreen printing using carbon paste ink, and some bands (1-18 GHz, 26.5-40 GHz) were measured to compare with the performance predicted by the VAE network.

关键词： Broadband electromagnetic metastructure absorber Finite difference time domain Deep learning variational autoencoder Performance-driven design Covariance matrix adaptation evolution strategy

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Unsupervised domain adaptation for activity recognition across heterogeneous datasets

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PERVASIVE AND MOBILE COMPUTING 2020年 64卷 101147-101147页

作者： Sanabria, Andrea Rosales Ye, Juan Univ St Andrews Sch Comp Sci St Andrews Fife Scotland

Sensor-based human activity recognition is to recognise human daily activities through a collection of ambient and wearable sensors. It is the key enabler for many healthcare applications, especially in ambient assisted living. The advance of sensing and communication technologies has driven the deployment of sensors in many residential and care home settings. However, the challenge still resides in the lack of sufficient, high-quality activity annotations on sensor data, which most of the existing activity recognition algorithms rely on. In this paper, we propose an Unsupervised Domain adaptation technique for Activity Recognition, called UDAR, which supports sharing and transferring activity models from one dataset to another heterogeneous dataset without the need of activity labels on the latter. This approach has combined knowledge- and data-driven techniques to achieve coarse- and fine-grained feature alignment. We have evaluated UDAR on five third-party, real-world datasets and have demonstrated high recognition accuracy and robustness against sensor noise, compared to the state-of-the-art domain adaptation techniques. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Human activity recognition Domain adaptation Ensemble learning variational autoencoder

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A review of molecular representation in the age of machine learning

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WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2022年第5期12卷 e1603-e1603页

作者： Wigh, Daniel S. Goodman, Jonathan M. Lapkin, Alexei A. Univ Cambridge Dept Chem Engn & Biotechnol Cambridge CB3 0AS England Univ Cambridge Yusuf Hamied Dept Chem Cambridge England

Research in chemistry increasingly requires interdisciplinary work prompted by, among other things, advances in computing, machine learning, and artificial intelligence. Everyone working with molecules, whether chemist or not, needs an understanding of the representation of molecules in a machine-readable format, as this is central to computational chemistry. Four classes of representations are introduced: string, connection table, feature-based, and computer-learned representations. Three of the most significant representations are simplified molecular-input line-entry system (SMILES), International Chemical Identifier (InChI), and the MDL molfile, of which SMILES was the first to successfully be used in conjunction with a variational autoencoder (VAE) to yield a continuous representation of molecules. This is noteworthy because a continuous representation allows for efficient navigation of the immensely large chemical space of possible molecules. Since 2018, when the first model of this type was published, considerable effort has been put into developing novel and improved methodologies. Most, if not all, researchers in the community make their work easily accessible on GitHub, though discussion of computation time and domain of applicability is often overlooked. Herein, we present questions for consideration in future work which we believe will make chemical VAEs even more accessible. This article is categorized under: Data Science > Chemoinformatics

关键词： chemoinformatics fingerprints machine learning molecular representation variational autoencoder

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User preference-aware content caching strategy for video delivery in cache-enabled IoT networks

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COMPUTER NETWORKS 2024年 240卷

作者： Firouzjaee, Mostafa Taghizade Jamshidi, Kamal Moghim, Neda Shetty, Sachin Univ Isfahan Dept Comp Engn Esfahan *** Iran Old Dominion Univ Virginia Modeling Anal & Simulat Ctr Dept Computat Modeling & Simulat Engn Suffolk VA 23435 USA

The escalating growth of content-dependent services and applications within the Internet of Things (IoT) platform has led to a surge in traffic, necessitating real-time data processing. Content caching has emerged as an effective solution to counteract this traffic upswing. Caching not only improves network efficiency but also enhances user service quality. Critical to the development of an optimal caching algorithm is the accurate prediction of future content popularity. This prediction hinges on the ability to anticipate users' content preferences, which is a pivotal method for assessing content popularity. In this study, we introduce a novel caching strategy termed User Preference-aware content Caching Strategy (UPCS) tailored for an IoT platform, where users access multimedia services offered by remote Content Providers (CPs). The UPCS encompasses three key algorithms: a content popularity prediction algorithm that utilizes variational autoencoders (VAE) to forecast users' future content preferences based on their prior requests, an online algorithm for dynamic cached content replacement, and a cooperative caching algorithm to augment caching efficiency. The proposed content caching strategy outperforms alternative methods, exhibiting superior cache hit rates and reduced Content Retrieval Delays (CRD).

关键词： Content caching Cooperative caching IoT Networks Popularity prediction Deep learning variational autoencoder

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