Huansheng Ning

About Huansheng Ning

Huansheng Ning, With an exceptional h-index of 58 and a recent h-index of 48 (since 2020), a distinguished researcher at University of Science and Technology Beijing, specializes in the field of Internet of Things, Cyberspace, Cyber Intelligence, Cyber Philosophy and Health.

His recent articles reflect a diverse array of research interests and contributions to the field:

Role of computer vision in smart cities: applications and research challenges

Maximizing UAV Fog Deployment Efficiency for Critical Rescue Operations

Skin Conductance-Based Acupoint and Non-Acupoint Recognition Using Machine Learning

An Improved Masking Strategy for Self-supervised Masked Reconstruction in Human Activity Recognition

Selective Task offloading for Maximum Inference Accuracy and Energy efficient Real-Time IoT Sensing Systems

MCformer: Multivariate Time Series Forecasting with Mixed-Channels Transformer

Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Model

A Multimodal Latent-Features-Based Service Recommendation System for the Social Internet of Things

Huansheng Ning Information

University

University of Science and Technology Beijing

Position

(北京科技大学)

Citations(all)

13800

Citations(since 2020)

9678

Cited By

7442

hIndex(all)

58

hIndex(since 2020)

48

i10Index(all)

177

i10Index(since 2020)

142

Email

University Profile Page

University of Science and Technology Beijing

Huansheng Ning Skills & Research Interests

Internet of Things

Cyberspace

Cyber Intelligence

Cyber Philosophy and Health

Top articles of Huansheng Ning

Role of computer vision in smart cities: applications and research challenges

Authors

Ubaid Abbas,Irfan Mehmood,HuanSheng Ning

Published Date

2024

Computer vision in smart cities: applications and research challenges Page 1 Vol.:(0123456789) Multimedia Tools and Applications https://doi.org/10.1007/s11042-024-18276-y 1 3 Role of computer vision in smart cities: applications and research challenges © Springer Science+Business Media, LLC, part of Springer Nature 2024 Multimedia Tools and Applications gratefully acknowledges the editorial work of the scholars listed below on the special issue entitled “Role of Computer Vision in Smart Cities: Applications and Research Challenges” (SI 1158T). Of 88 papers submitted, 25 were accepted for this issue after a stringent peer review process. Corresponding Guest Editor Ubaid Abbas Mount Allison University/GPRC, Canada Email: UAbbasi@gprc.ab.ca Guest Editors Irfan Mehmood University of Bradford, UK Email: I.Mehmood4@bradford.ac.uk HuanSheng Ning University of Science and Technology Beijing, …

Maximizing UAV Fog Deployment Efficiency for Critical Rescue Operations

Authors

Abdenacer Naouri,Huansheng Ning,Nabil Abdelkader Nouri,Amar Khelloufi,Abdelkarim Ben Sada,Salim Naouri,Attia Qammar,Sahraoui Dhelim

Journal

arXiv preprint arXiv:2402.16052

Published Date

2024/2/25

In disaster scenarios and high-stakes rescue operations, integrating Unmanned Aerial Vehicles (UAVs) as fog nodes has become crucial. This integration ensures a smooth connection between affected populations and essential health monitoring devices, supported by the Internet of Things (IoT). Integrating UAVs in such environments is inherently challenging, where the primary objectives involve maximizing network connectivity and coverage while extending the network's lifetime through energy-efficient strategies to serve the maximum number of affected individuals. In this paper, We propose a novel model centred around dynamic UAV-based fog deployment that optimizes the system's adaptability and operational efficacy within the afflicted areas. First, we decomposed the problem into two subproblems. Connectivity and coverage subproblem, and network lifespan optimization subproblem. We shape our UAV fog deployment problem as a uni-objective optimization and introduce a specialized UAV fog deployment algorithm tailored specifically for UAV fog nodes deployed in rescue missions. While the network lifespan optimization subproblem is efficiently solved via a one-dimensional swapping method. Following that, We introduce a novel optimization strategy for UAV fog node placement in dynamic networks during evacuation scenarios, with a primary focus on ensuring robust connectivity and maximal coverage for mobile users, while extending the network's lifespan. Finally, we introduce Adaptive Whale Optimization Algorithm (WOA) for fog node deployment in a dynamic network. Its agility, rapid convergence, and low computational …

Skin Conductance-Based Acupoint and Non-Acupoint Recognition Using Machine Learning

Authors

Feifei Shi,Huansheng Ning,Ruoxiu Xiao,Tao Zhu,Nannan Li

Journal

IEEE Journal of Biomedical and Health Informatics

Published Date

2024/3/18

Acupoints (APs) prove to have positive effects on disease diagnosis and treatment, while intelligent techniques for the automatic detection of APs are not yet mature, making them more dependent on manual positioning. In this paper, we realize the skin conductance-based APs and non-APs recognition with machine learning, which could assist in APs detection and localization in clinical practice. Firstly, we collect skin conductance of traditional Five-Shu Point and their corresponding non-APs with wearable sensors, establishing a dataset containing over 36000 samples of 12 different AP types. Then, electrical features are extracted from the time domain, frequency domain, and nonlinear perspective respectively, following which typical machine learning algorithms (SVM, RF, KNN, NB, and XGBoost) are demonstrated to recognize APs and non-APs. The results demonstrate XGBoost with the best precision of 66.38 …

An Improved Masking Strategy for Self-supervised Masked Reconstruction in Human Activity Recognition

Authors

Jinqiang Wang,Tao Zhu,Huansheng Ning

Journal

arXiv preprint arXiv:2312.04147

Published Date

2023/12/7

Masked reconstruction serves as a fundamental pretext task for self-supervised learning, enabling the model to enhance its feature extraction capabilities by reconstructing the masked segments from extensive unlabeled data. In human activity recognition, this pretext task employed a masking strategy centered on the time dimension. However, this masking strategy fails to fully exploit the inherent characteristics of wearable sensor data and overlooks the inter-channel information coupling, thereby limiting its potential as a powerful pretext task. To address these limitations, we propose a novel masking strategy called Channel Masking. It involves masking the sensor data along the channel dimension, thereby compelling the encoder to extract channel-related features while performing the masked reconstruction task. Moreover, Channel Masking can be seamlessly integrated with masking strategies along the time dimension, thereby motivating the self-supervised model to undertake the masked reconstruction task in both the time and channel dimensions. Integrated masking strategies are named Time-Channel Masking and Span-Channel Masking. Finally, we optimize the reconstruction loss function to incorporate the reconstruction loss in both the time and channel dimensions. We evaluate proposed masking strategies on three public datasets, and experimental results show that the proposed strategies outperform prior strategies in both self-supervised and semi-supervised scenarios.

Selective Task offloading for Maximum Inference Accuracy and Energy efficient Real-Time IoT Sensing Systems

Authors

Abdelkarim Ben Sada,Amar Khelloufi,Abdenacer Naouri,Huansheng Ning,Sahraoui Dhelim

Journal

arXiv preprint arXiv:2402.16904

Published Date

2024/2/24

The recent advancements in small-size inference models facilitated AI deployment on the edge. However, the limited resource nature of edge devices poses new challenges especially for real-time applications. Deploying multiple inference models (or a single tunable model) varying in size and therefore accuracy and power consumption, in addition to an edge server inference model, can offer a dynamic system in which the allocation of inference models to inference jobs is performed according to the current resource conditions. Therefore, in this work, we tackle the problem of selectively allocating inference models to jobs or offloading them to the edge server to maximize inference accuracy under time and energy constraints. This problem is shown to be an instance of the unbounded multidimensional knapsack problem which is considered a strongly NP-hard problem. We propose a lightweight hybrid genetic algorithm (LGSTO) to solve this problem. We introduce a termination condition and neighborhood exploration techniques for faster evolution of populations. We compare LGSTO with the Naive and Dynamic programming solutions. In addition to classic genetic algorithms using different reproduction methods including NSGA-II, and finally we compare to other evolutionary methods such as Particle swarm optimization (PSO) and Ant colony optimization (ACO). Experiment results show that LGSTO performed 3 times faster than the fastest comparable schemes while producing schedules with higher average accuracy.

MCformer: Multivariate Time Series Forecasting with Mixed-Channels Transformer

Authors

Wenyong Han,Tao Zhu Member,Liming Chen,Huansheng Ning,Yang Luo,Yaping Wan

Journal

arXiv preprint arXiv:2403.09223

Published Date

2024/3/14

The massive generation of time-series data by largescale Internet of Things (IoT) devices necessitates the exploration of more effective models for multivariate time-series forecasting. In previous models, there was a predominant use of the Channel Dependence (CD) strategy (where each channel represents a univariate sequence). Current state-of-the-art (SOTA) models primarily rely on the Channel Independence (CI) strategy. The CI strategy treats all channels as a single channel, expanding the dataset to improve generalization performance and avoiding inter-channel correlation that disrupts long-term features. However, the CI strategy faces the challenge of interchannel correlation forgetting. To address this issue, we propose an innovative Mixed Channels strategy, combining the data expansion advantages of the CI strategy with the ability to counteract inter-channel correlation forgetting. Based on this strategy, we introduce MCformer, a multivariate time-series forecasting model with mixed channel features. The model blends a specific number of channels, leveraging an attention mechanism to effectively capture inter-channel correlation information when modeling long-term features. Experimental results demonstrate that the Mixed Channels strategy outperforms pure CI strategy in multivariate time-series forecasting tasks.

Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Model

Authors

Zita Lifelo,Huansheng Ning,Sahraoui Dhelim

Journal

arXiv preprint arXiv:2404.09045

Published Date

2024/4/13

Timely identification is essential for the efficient handling of mental health illnesses such as depression. However, the current research fails to adequately address the prediction of mental health conditions from social media data in low-resource African languages like Swahili. This study introduces two distinct approaches utilising model-agnostic meta-learning and leveraging large language models (LLMs) to address this gap. Experiments are conducted on three datasets translated to low-resource language and applied to four mental health tasks, which include stress, depression, depression severity and suicidal ideation prediction. we first apply a meta-learning model with self-supervision, which results in improved model initialisation for rapid adaptation and cross-lingual transfer. The results show that our meta-trained model performs significantly better than standard fine-tuning methods, outperforming the baseline fine-tuning in macro F1 score with 18\% and 0.8\% over XLM-R and mBERT. In parallel, we use LLMs' in-context learning capabilities to assess their performance accuracy across the Swahili mental health prediction tasks by analysing different cross-lingual prompting approaches. Our analysis showed that Swahili prompts performed better than cross-lingual prompts but less than English prompts. Our findings show that in-context learning can be achieved through cross-lingual transfer through carefully crafted prompt templates with examples and instructions.

A Multimodal Latent-Features-Based Service Recommendation System for the Social Internet of Things

Authors

Amar Khelloufi,Huansheng Ning,Abdenacer Naouri,Abdelkarim Ben Sada,Attia Qammar,Abdelkader Khalil,Lingfeng Mao,Sahraoui Dhelim

Journal

IEEE Transactions on Computational Social Systems

Published Date

2024/2/21

The Social Internet of Things (SIoT) is revolutionizing how we interact with our everyday lives. By adding the social dimension to connecting devices, the SIoT has the potential to drastically change the way we interact with smart devices. This connected infrastructure allows for unprecedented levels of convenience, automation, and access to information, allowing us to do more with less effort. However, this revolutionary new technology also brings an eager need for service recommendation systems. As the SIoT grows in scope and complexity, it becomes increasingly important for businesses and individuals, and SIoT objects alike to have reliable sources for products, services, and information that are tailored to their specific needs. Few works have been proposed to provide service recommendations for SIoT environments. However, these efforts have been confined to only focusing on modeling user-item …

P2LHAP: Wearable sensor-based human activity recognition, segmentation and forecast through Patch-to-Label Seq2Seq Transformer

Authors

Shuangjian Li,Tao Zhu,Mingxing Nie,Huansheng Ning,Zhenyu Liu,Liming Chen

Journal

arXiv preprint arXiv:2403.08214

Published Date

2024/3/13

Traditional deep learning methods struggle to simultaneously segment, recognize, and forecast human activities from sensor data. This limits their usefulness in many fields such as healthcare and assisted living, where real-time understanding of ongoing and upcoming activities is crucial. This paper introduces P2LHAP, a novel Patch-to-Label Seq2Seq framework that tackles all three tasks in a efficient single-task model. P2LHAP divides sensor data streams into a sequence of "patches", served as input tokens, and outputs a sequence of patch-level activity labels including the predicted future activities. A unique smoothing technique based on surrounding patch labels, is proposed to identify activity boundaries accurately. Additionally, P2LHAP learns patch-level representation by sensor signal channel-independent Transformer encoders and decoders. All channels share embedding and Transformer weights across all sequences. Evaluated on three public datasets, P2LHAP significantly outperforms the state-of-the-art in all three tasks, demonstrating its effectiveness and potential for real-world applications.

Efficient fog node placement using nature-inspired metaheuristic for IoT applications

Authors

Abdenacer Naouri,Nabil Abdelkader Nouri,Amar Khelloufi,Abdelkarim Ben Sada,Huansheng Ning,Sahraoui Dhelim

Journal

Cluster Computing

Published Date

2024/4/8

Managing the explosion of data from the edge to the cloud requires intelligent supervision, such as fog node deployments, which is an essential task to assess network operability. To ensure network operability, the deployment process must be carried out effectively regarding two main factors: connectivity and coverage. The network connectivity is based on fog node deployment, which determines the network’s physical topology, while the coverage determines the network accessibility. Both have a significant impact on network performance and guarantee the network quality of service. Determining an optimum fog node deployment method that minimizes cost, reduces computation and communication overhead, and provides a high degree of network connection coverage is extremely hard. Therefore, maximizing coverage and preserving network connectivity is a non-trivial problem. In this paper, we propose a fog …

Metaverse for Intelligent Transportation Systems (ITS): A Comprehensive Review of Technologies, Applications, Implications, Challenges and Future Directions

Authors

Doreen Sebastian Sarwatt,Yujia Lin,Jianguo Ding,Yunchuan Sun,Huansheng Ning

Published Date

2024/1/16

Intelligent transportation systems (ITS) have made significant advancements in enhancing transportation safety, reliability, and efficiency. However, challenges persist in security, privacy, data management, and integration. Metaverse, an emerging technology enabling immersive and simulated experiences, presents promising solutions to overcome these challenges. By establishing secure communication channels, facilitating virtual simulations for safe testing and training, and enabling centralized data management with real-time analytics, metaverse offers a transformative approach to address these challenges. While metaverse has found extensive applications across industries, its potential in transportation remains largely untapped. This comprehensive review delves into the integration of the metaverse in ITS, exploring key technologies like virtual reality, digital twin, blockchain, and artificial intelligence, and …

BusCache: V2V-based infrastructure-free content dissemination system for Internet of Vehicles

Authors

Abdenacer Naouri,Nabil Abdelkader Nouri,Amar Khelloufi,Abdelkarim Ben Sada,Salim Naouri,Huansheng Ning,Sahraoui Dhelim

Journal

IEEE Access

Published Date

2024/3/7

Internet of Vehicles (IoV) offers many services aiming to enhance the safety and comfort of drivers and passengers such as accident alarms, congestion avoidance, and multimedia, entertainment applications. Cooperatively sharing and retrieving large-scale files in IoV is a challenging task due extremely volatile nature of IoV. Therefore, it is paramount to develop a vehicle-to-vehicle (V2V) content delivery mechanism that can adapt to IoV communication requirements. One of the challenging tasks in this regard is to locate content pieces and gather information about peers. Previous proposed systems broadcast beacon messages to the whole network to locate certain content pieces, which consume the bandwidth and limit the network resources. To overcome these issues, in this paper, we propose BusCache, a traffic-aware content delivery system for IoV. BusCache uses buses as trackers for the content distribution …

DFS-WR: A novel dual feature selection and weighting representation framework for classification

Authors

Zhimin Zhang,Fan Zhang,Lingfeng Mao,Cheng Chen,Huansheng Ning

Journal

Information Fusion

Published Date

2024/4/1

Classification methods relying on multidimensional features have been widely applied in commerce, healthcare, and transportation. Nevertheless, haphazardly selecting inappropriate features may not only hinder the improvement of classification performance but also disrupt the convergence of classifier parameters, leading to prolonged classifier runtime and an increased storage burden. Therefore, this paper proposed a novel framework (dubbed as DFS-WR) to perform dual selection and weighted representation of multidimensional features. Firstly, DFS-WR employs Laplacian scores to conduct a de-redundant operation as the first feature selection. Subsequently, DFS-WR utilizes a correlation coefficient matrix and redundant sorting to eliminate duplicate deployment of similar features in classification through the second selection. Finally, for hierarchical expression, DFS-WR assigns weights by virtue of …

Web3-enabled Metaverse: The Internet of Digital Twins in a Decentralised Metaverse

Authors

Nyothiri Aung,Sahraoui Dhelim,Huansheng Ning,Abdelaziz Kerrache,Said Boumaraf,Liming Chen,M-Tahar Kechadi

Journal

Authorea Preprints

Published Date

2024/1/2

The convergence of Web3, Metaverse, and Digital Twins technologies is bringing a transformative revolution to Cyber-Physical-Social systems. Web3, which is driven by blockchain and decentralization, allows users to have control over their data and digital assets. Meanwhile, the Metaverse is creating a virtual space where people can interact, work, and live together. Digital twins offer a real-time digital representation of physical objects or spaces. When these three concepts intersect, they create a dynamic and interconnected digital ecosystem where the physical and virtual worlds blend seamlessly. This paper focuses on discussing the convergence of Metaverse, Web3 technologies, and Digital Twin. We will focus on the architecture of a Web3-enabled Metaverse, which leverages the decentralized nature of Web3 to offer a distributed Metaverse. The digital twin technology will realize a Cyber-Physical-Social …

PerMl-Fed: enabling personalized multi-level federated learning within heterogenous IoT environments for activity recognition

Authors

Chang Zhang,Tao Zhu,Hangxing Wu,Huansheng Ning

Journal

Cluster Computing

Published Date

2024/3/1

Federated Learning (FL) has emerged as a promising approach to addressing issues related to centralized machine learning such as data privacy, security and access. Nevertheless, it also brings new challenges incurred by heterogeneity among data statistical levels, devices and models in the context of multi-level federated learning (MlFed) architecture. In this paper, we conceive a new Personalized Multi-level Federated Learning (PerMl-Fed) framework, which extends existing MlFed architecture with three specialized personalized FL methods to address the three challenges. Specially, we design a Transfer Multi-level Federated Learning (TrMlFed) model to mitigate statistical heterogeneity across multiple layers of FL. We propose an Asynchronous Multi-level Federated Learning (AsMlFed) approach which allows asynchronous update in MlFed, thus alleviating device heterogeneity. We develop a Deep Mutual Multi …

Harmamba: Efficient wearable sensor human activity recognition based on bidirectional selective ssm

Authors

Shuangjian Li,Tao Zhu,Furong Duan,Liming Chen,Huansheng Ning,Yaping Wan

Journal

arXiv preprint arXiv:2403.20183

Published Date

2024/3/29

Wearable sensor human activity recognition (HAR) is a crucial area of research in activity sensing. While transformer-based temporal deep learning models have been extensively studied and implemented, their large number of parameters present significant challenges in terms of system computing load and memory usage, rendering them unsuitable for real-time mobile activity recognition applications. Recently, an efficient hardware-aware state space model (SSM) called Mamba has emerged as a promising alternative. Mamba demonstrates strong potential in long sequence modeling, boasts a simpler network architecture, and offers an efficient hardware-aware design. Leveraging SSM for activity recognition represents an appealing avenue for exploration. In this study, we introduce HARMamba, which employs a more lightweight selective SSM as the foundational model architecture for activity recognition. The goal is to address the computational resource constraints encountered in real-time activity recognition scenarios. Our approach involves processing sensor data flow by independently learning each channel and segmenting the data into "patches". The marked sensor sequence's position embedding serves as the input token for the bidirectional state space model, ultimately leading to activity categorization through the classification head. Compared to established activity recognition frameworks like Transformer-based models, HARMamba achieves superior performance while also reducing computational and memory overhead. Furthermore, our proposed method has been extensively tested on four public activity datasets: PAMAP2 …

Linear semantic transformation for semi-supervised medical image segmentation

Authors

Cheng Chen,Yunqing Chen,Xiaoheng Li,Huansheng Ning,Ruoxiu Xiao

Journal

Computers in Biology and Medicine

Published Date

2024/5/1

Medical image segmentation is a focus research and foundation in developing intelligent medical systems. Recently, deep learning for medical image segmentation has become a standard process and succeeded significantly, promoting the development of reconstruction, and surgical planning of disease diagnosis. However, semantic learning is often inefficient owing to the lack of supervision of feature maps, resulting in that high-quality segmentation models always rely on numerous and accurate data annotations. Learning robust semantic representation in latent spaces remains a challenge. In this paper, we propose a novel semi-supervised learning framework to learn vital attributes in medical images, which constructs generalized representation from diverse semantics to realize medical image segmentation. We first build a self-supervised learning part that achieves context recovery by reconstructing space …

Negative selection by clustering for contrastive learning in human activity recognition

Authors

Jinqiang Wang,Tao Zhu,Liming Luke Chen,Huansheng Ning,Yaping Wan

Journal

IEEE Internet of Things Journal

Published Date

2023/1/26

Contrastive learning is an emerging and important self-supervised learning paradigm that has been successfully applied to sensor-based human activity recognition (HAR) because it can achieve competitive performance relative to supervised learning. Contrastive learning methods generally involve instance discrimination, which means that the instances are regarded as negatives of each other, and thus their representations are pulled away from each other during the training process. However, instance discrimination could cause overclustering, meaning that the representations of instances from the same class could be overly separated. To alleviate this overclustering phenomenon, we propose a new contrastive learning framework to select negatives by clustering in HAR, which is named clustering for contrastive learning in human activity recognition (ClusterCLHAR). First, ClusterCLHAR clusters the instance …

Chatbots to chatgpt in a cybersecurity space: Evolution, vulnerabilities, attacks, challenges, and future recommendations

Authors

Attia Qammar,Hongmei Wang,Jianguo Ding,Abdenacer Naouri,Mahmoud Daneshmand,Huansheng Ning

Journal

arXiv preprint arXiv:2306.09255

Published Date

2023/5/29

Chatbots shifted from rule-based to artificial intelligence techniques and gained traction in medicine, shopping, customer services, food delivery, education, and research. OpenAI developed ChatGPT blizzard on the Internet as it crossed one million users within five days of its launch. However, with the enhanced popularity, chatbots experienced cybersecurity threats and vulnerabilities. This paper discussed the relevant literature, reports, and explanatory incident attacks generated against chatbots. Our initial point is to explore the timeline of chatbots from ELIZA (an early natural language processing computer program) to GPT-4 and provide the working mechanism of ChatGPT. Subsequently, we explored the cybersecurity attacks and vulnerabilities in chatbots. Besides, we investigated the ChatGPT, specifically in the context of creating the malware code, phishing emails, undetectable zero-day attacks, and generation of macros and LOLBINs. Furthermore, the history of cyberattacks and vulnerabilities exploited by cybercriminals are discussed, particularly considering the risk and vulnerabilities in ChatGPT. Addressing these threats and vulnerabilities requires specific strategies and measures to reduce the harmful consequences. Therefore, the future directions to address the challenges were presented.

Cerebrovascular Segmentation in TOF-MRA with Topology Regularization Adversarial Model

Authors

Cheng Chen,Yunqing Chen,Shuang Song,Jianan Wang,Huansheng Ning,Ruoxiu Xiao

Published Date

2023/10/26

Time-of-flight magnetic resonance angiography (TOF-MRA) is a common cerebrovascular imaging. Accurate and automatic cerebrovascular segmentation in TOF-MRA images is an important auxiliary method in clinical practice. Due to the complex semantics and noise interference, the existing segmentation methods often fail to pay attention to topological correlation, resulting in the neglect of branch vessels and vascular topology destruction. In this paper, we proposed a topology regularization adversarial model for cerebrovascular segmentation in TOF-MRA images. Firstly, we trained a self-supervised model to learn spatial semantic layout in TOF-MRA images by image context restoration. Subsequently, we exploited initialization based on the self-supervised model and constructed an adversarial model to accomplish parameter optimization. Considering the limitations of uneven distribution of cerebrovascular …

See List of Professors in Huansheng Ning University(University of Science and Technology Beijing)

Huansheng Ning FAQs

What is Huansheng Ning's h-index at University of Science and Technology Beijing?

The h-index of Huansheng Ning has been 48 since 2020 and 58 in total.

What are Huansheng Ning's top articles?

The articles with the titles of

Role of computer vision in smart cities: applications and research challenges

Maximizing UAV Fog Deployment Efficiency for Critical Rescue Operations

Skin Conductance-Based Acupoint and Non-Acupoint Recognition Using Machine Learning

An Improved Masking Strategy for Self-supervised Masked Reconstruction in Human Activity Recognition

Selective Task offloading for Maximum Inference Accuracy and Energy efficient Real-Time IoT Sensing Systems

MCformer: Multivariate Time Series Forecasting with Mixed-Channels Transformer

Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Model

A Multimodal Latent-Features-Based Service Recommendation System for the Social Internet of Things

...

are the top articles of Huansheng Ning at University of Science and Technology Beijing.

What are Huansheng Ning's research interests?

The research interests of Huansheng Ning are: Internet of Things, Cyberspace, Cyber Intelligence, Cyber Philosophy and Health

What is Huansheng Ning's total number of citations?

Huansheng Ning has 13,800 citations in total.

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