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Securing Data Based on Lightweight Algorithm for Internet of Things Networks 1

2nd International Conference on Human-Centric Smart Computing, ICHCSC 2023

作者： Alahdal, Abdullah Kumar, D. Sai AlAhdal, Abdulrazzaq H. A. Krichen, Moez Almansour, Hamad University College of Engineering Osmania University Hyderabad India Faculty of CSE Hodeidah University Hodaidah Yemen Faculty of Computer Science and Information Technology Al-Baha University Al-Baha65528 Saudi Arabia ReDCAD Laboratory National School of Engineers of Sfax University of Sfax Sfax Tunisia Applied College Najran University Najran Saudi Arabia

ISBN: (数字)9789819977116

ISBN: (纸本)9789819977109

IoT technology is one recent technology that connects millions of various devices together, resulting in a huge amount of data exchange among them. As a result, this data requires high levels of protection. However, conventional cryptography can use up a substantial amount of energy because of its powerful mathematical computations and rise in the number of rounds, making it difficult for IoT devices with limited resources and small size. The present paper proposes a lightweight cryptographic algorithm. Such algorithm employs basic mathematical operations, like shifting, XOR, swapping, XNOR, and uses a mix of Substitution-Permutation and Feistel architectural methods to harden the encryption process. The proposed model’s structure, security, and flexibility are compared to those of other cryptographic algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wireless sensor networks

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On the -differential spectra of two classes of power functions over finite fields

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Applicable Algebra in Engineering, Communication and Computing 2025年 1-30页

作者： Liu, Qian Huang, Zhiwei Chen, Zhixiong Jiang, Rong Zhang, Liupiao Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou University Fuzhou China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Fujian Key Laboratory of Financial Information Processing Putian University Putian China School of Computer Science and Technology Dongguan University of Technology Dongguan China

Recently, a new concept called multiplicative differential was introduced by Ellingsen et al. [7]. As an extension of the differential uniformity, it is theoretically appealing to determine the properties of c-differential uniformity and the corresponding c-differential spectrum. In this paper, based on certain quadratic character sums and two special elliptic curves over $$\mathbb {F}_p$$ , the $$(-1)$$ -differential spectra of the following two classes of power functions over $$\mathbb {F}_{p^n}$$ is completely determined: (1) $$f_1(x)=x^{\frac{p^n+3}{2}}$$ , where $$p>3$$ and $$p\equiv 3\pmod 4$$ ; (2) $$f_2(x)=x^{p^n-3}$$ , where $$p>3$$ . The obtained result shows that the $$(-1)$$ -differential spectra of $$f_1(x)$$ and $$f_2(x)$$ can be expressed explicitly in terms of n. Moreover, an upper bound of the c-differential uniformity of $$f_2(x)$$ is given.

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Enhanced Non-Contact Vital Sign Monitoring via Depth Camera-Assisted Millimeter-Wave Radar

Enhanced Non-Contact Vital Sign Monitoring via Depth Camera-...

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2024 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2024

作者： Zhang, Hanwen Dou, Chenglong Liu, Yucheng Wu, Yuan Qian, Liping Shi, Zhiguo Lu, Rongxing University of Macau State Key Laboratory of Internet of Things for Smart City China Zhuhai UM Science and Technology Research Institute Zhuhai China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China University of New Brunswick Faculty of Computer Science FrederictonNBE3B 5A3 Canada

ISBN: (纸本)9798331507886

Non-contact vital sign monitoring with mmWave radar has been regarded as an effective method for identifying lung and heart diseases due to its strong penetration and non-invasive capabilities. Nevertheless, relying solely on millimeter-wave radar for vital sign monitoring is susceptible to interference from multipath reflections, which degrades the measurement accuracy. To address this issue and enhance measurement accuracy, we propose a system design that leverages the depth camera for precise target location and subsequently employs dynamic beamforming techniques to effectively monitor the specified region. The experimental results demonstrate that our proposed system achieves an average error of 2.44 beats per minute (BPM) for heart rate monitoring and 0.22 breaths per minute (BPM) for respiratory rate monitoring. Compared to systems without beamforming, our system reduces the mean error for heart rate monitoring by 4.21 BPM and for respiratory rate monitoring by 0.51 BPM. © 2024 IEEE.

关键词： Heart

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PLIP: language-image pre-training for person representation learning 24

PLIP: language-image pre-training for person representation ...

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Proceedings of the 38th International Conference on Neural information Processing Systems

作者： Jialong Zuo Jiahao Hong Feng Zhang Changqian Yu Hanyu Zhou Changxin Gao Nong Sang Jingdong Wang National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Skywork AI Department of Computer Vision Baidu Inc.

ISBN: (纸本)9798331314385

Language-image pre-training is an effective technique for learning powerful representations in general domains. However, when directly turning to person representation learning, these general pre-training methods suffer from unsatisfactory performance. The reason is that they neglect critical person-related characteristics, i.e., fine-grained attributes and identities. To address this issue, we propose a novel language-image pre-training framework for person representation learning, termed PLIP. Specifically, we elaborately design three pretext tasks: 1) Text-guided Image Colorization, aims to establish the correspondence between the person-related image regions and the fine-grained color-part textual phrases. 2) Image-guided Attributes Prediction, aims to mine fine-grained attribute information of the person body in the image; and 3) Identity-based vision-Language Contrast, aims to correlate the cross-modal representations at the identity level rather than the instance level. Moreover, to implement our pre-train framework, we construct a large-scale person dataset with image-text pairs named SYNTH-PEDES by automatically generating textual annotations. We pre-train PLIP on SYNTH-PEDES and evaluate our models by spanning downstream person-centric tasks. PLIP not only significantly improves existing methods on all these tasks, but also shows great ability in the zero-shot and domain generalization settings.

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation 14

Towards a Deep Learning Pain-Level Detection Deployment at U...

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14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Ismail, Leila Waseem, Muhammad Danish Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately. © 2023 Elsevier B.V.. All rights reserved.

关键词： computer vision

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A Study of Encapsulation Temperature Field of Dry-type Air-core Reactor with the Structure of Equivalent and Aluminum Wire-Insulation

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Distributed Generation and Alternative Energy Journal 2022年第5期37卷 1665-1680页

作者： Wan, Xiaorong Wang, Han Li, Yingna Li, Chuan Zhao, Zhengang Cui, Zhigang Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming650500 China Electric Power Research Institute of Yunnan Power Grid Co. Ltd Kunming650217 China

During the operation of the Dry-type Air-core Reactor (DAR), the insulation material deteriorates due to local overheating of the encapsulation, and the reactor is burned in severe conditions. Therefore, studying the temperature rise of the reactor plays an important role in the reliability of its operation and the economy of design and production. According to the structural characteristics of the reactor, the three-dimensional thermal field model of the aluminum wire-insulation structure of the reactor is established, and the encapsulation temperature field distribution of the reactor under steady state is obtained. Compare the simulation result with the encapsulation temperature field distribution of the equivalent structure. The results show that the highest temperature of the aluminum wire-insulation structure encapsulation rises to 73.51◦C, which is located in the middle of the reactor. Each encapsulation hotspot is located about 20% from the upper edge of the encapsulation. In the temperature rise test, a Fiber Bragg Grating (FGB) temperature sensor is installed about 20% from the upper edge of the encapsulation. The test shows that the most significant value of temperature rise of the reactor is 73◦C, which is located in the middle of the reactor. The temperature rise test results verify the accuracy of the simulated calculation value of the temperature field of the aluminum wire-insulated reactor and provide a reference for the temperature rise calculation and temperature rise monitoring of the DAR. © 2022 River Publishers.

关键词： Fiber Bragg gratings

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Analyzing Infrared Linescan Profiles of Steel Strips for Enhanced Cooling Pattern Prediction

Analyzing Infrared Linescan Profiles of Steel Strips for Enh...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Rubén Usamentiaga Adrian G. Gayo Francisco J. DelaCalle Dario G. Lema Stefano Sfarra Hai Zhang Department of Computer Science and Engineering University of Oviedo Gijón Asturias Spain Department of Industrial and Information Engineering and Economics (DIIIE) University of L'Aquila L'Aquila Italy Department of Electrical and Computer Engineering Computer Vision and Systems Laboratory (CVSL) Laval University Quebec Canada

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

Real-time temperature information is crucial for optimizing cooling processes during steel strip rolling, ensuring the attainment of desired microstructural properties and surface quality at an optimal cooling rate. Infrared line scanners emerge as the preferred choice for temperature measurement in highspeed rolling operations, delivering temperature readings with high resolution and enabling the capture of detailed temperature profiles. By analyzing these profiles, cooling systems can be finely adjusted and precisely controlled to optimize the rolling operation. However, developing effective cooling strategies becomes challenging when dealing with temperature profiles comprising numerous discrete data points, often numbering in the thousands per profile. This study presents an innovative approach that integrates the detection of steel strip boundaries within temperature profiles and subsequent temperature pattern characterization using polynomial fitting. A significant advantage is demonstrated by leveraging the coefficients of Legendre polynomials, which provide a concise description of temperature profile shapes, facilitating straightforward approaches to cooling strategies. By integrating boundary detection with temperature characterization, the system enhances its ability to predict tailored cooling patterns, optimizing cooling efficiency, and enhancing product quality in the manufacturing process. Rigorous testing using both synthetic data and real-world applications in cold and hot rolling validates the proposed system's practical utility and reliability. These results underscore its potential to enhance efficiency and quality in industrial steel manufacturing operations.

关键词： Temperature measurement Temperature sensors Strips Cooling Shape Polynomials Real-time systems Steel Monitoring Testing

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Depth-Aware Concealed Crop Detection in Dense Agricultural Scenes

Depth-Aware Concealed Crop Detection in Dense Agricultural S...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Liqiong Wang Jinyu Yang Yanfu Zhang Fangyi Wang Feng Zheng Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University College of Computer and Information Technology China Three Gorges University Southern University of Science and Technology University of Birmingham Departmental of Computer Science College of William and Mary

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Concealed Object Detection (COD) aims to identify objects visually embedded in their background. Existing COD datasets and methods predominantly focus on animals or humans, ignoring the agricultural domain, which often contains numerous, small, and concealed crops with severe occlusions. In this paper, we introduce Concealed Crop Detection (CCD), which extends classic COD to agricultural domains. Experimental study shows that unimodal data provides insufficient information for CCD. To address this gap, we first collect a large-scale RGB-D dataset, ACOD-12K, containing high-resolution crop images and depth maps. Then, we propose a foundational framework named Recurrent Iterative Segmentation Network (RISNet). To tackle the challenge of dense objects, we employ multi-scale receptive fields to capture objects of varying sizes, thus enhancing the detection performance for dense objects. By fusing depth features, our method can acquire spatial information about concealed objects to mitigate disturbances caused by intricate backgrounds and occlusions. Furthermore, our model adopts a multi-stage iterative approach, using predictions from each stage as gate attention to reinforce position information, thereby improving the detection accuracy for small objects. Extensive experimental results demonstrate that our RISNet achieves new state-of-the-art performance on both newly proposed CCD and classic COD tasks. All resources will be available at https://***/Kki2Eve/RISNet.

关键词： Charge coupled devices Visualization Crops Object detection Predictive models Logic gates Pattern recognition

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A Conversational Human-computer Interface for Smart Energy System Simulation Environments

A Conversational Human-Computer Interface for Smart Energy S...

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2023 Winter Simulation Conference, WSC 2023

作者： Dengler, Gabriel Bazan, Peter German, Reinhard Lalbakhsh, Pooia Liebmann, Ariel Friedrich-Alexander-Universität Erlangen-Nürnberg Laboratory of Computer Networks and Communication Systems Martensstr. 3 BY Erlangen91058 Germany Monash University Faculty of Information Technology Department of Data Science and Ai ClaytonVIC3168 Australia

ISBN: (纸本)9798350369663

This paper introduces a conversational framework that enhances the usability of smart energy system simulations. This study is centered around OpenAI's Generative Pre-trained Transformer (GPT), a fine-tuned conversational model that allows users to communicate with the system in a natural way. Therefore, users can describe their simulation scenarios in plain language and GPT seamlessly translates these descriptions into Python scripts, used as inputs to the simulation environment, in our case, AnyLogic Simulation Software. Our framework is based on the i7-AnyEnergy core framework to compute distribution flows and relevant statistics. The proposed human-machine interface facilitates and accelerates simulation modeling, as demonstrated through the two scenarios we have provided in this paper. Overall, our conversational framework has the potential to significantly improve the user experience of smart energy system simulation environments. By simplifying the interaction between users and complex simulation models, we enable users to obtain valuable insights rapidly and more easily. © 2023 IEEE.

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Deep learning-based activity recognition and fine motor identification using 2D skeletons of cynomolgus monkeys

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Zoological Research 2023年第5期44卷 967-980页

作者： Chuxi Li Zifan Xiao Yerong Li Zhinan Chen Xun Ji Yiqun Liu Shufei Feng Zhen Zhang Kaiming Zhang Jianfeng Feng Trevor W.Robbins Shisheng Xiong Yongchang Chen Xiao Xiao School of Information Science and Technology Micro Nano System Center Fudan UniversityShanghai 200433China Department of Anesthesiology Huashan HospitalKey Laboratory of Computational Neuroscience and Brain-Inspired IntelligenceMinistry of EducationBehavioral and Cognitive Neuroscience CenterInstitute of Science and Technology for Brain-Inspired IntelligenceMOE Frontiers Center for Brain ScienceFudan UniversityShanghai 200433China Kuang Yaming Honors School Nanjing UniversityNanjingJiangsu 210023China Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China State Key Laboratory of Primate Biomedical Research Institute of Primate Translational MedicineKunming University of Science and TechnologyKunmingYunnan 650500China New Vision World LLC. Aliso ViejoCalifornia 92656USA Behavioural and Clinical Neuroscience Institute University of CambridgeCambridgeCB21TNUK

Video-based action recognition is becoming a vital tool in clinical research and neuroscientific study for disorder detection and ***,action recognition currently used in non-human primate(NHP)research relies heavily on intense manual labor and lacks standardized *** this work,we established two standard benchmark datasets of NHPs in the laboratory:Monkeyin Lab(Mi L),which includes 13 categories of actions and postures,and MiL2D,which includes sequences of two-dimensional(2D)skeleton ***,based on recent methodological advances in deep learning and skeleton visualization,we introduced the Monkey Monitor Kit(Mon Kit)toolbox for automatic action recognition,posture estimation,and identification of fine motor activity in *** the datasets and Mon Kit,we evaluated the daily behaviors of wild-type cynomolgus monkeys within their home cages and experimental environments and compared these observations with the behaviors exhibited by cynomolgus monkeys possessing mutations in the MECP2 gene as a disease model of Rett syndrome(RTT).Mon Kit was used to assess motor function,stereotyped behaviors,and depressive phenotypes,with the outcomes compared with human manual *** Kit established consistent criteria for identifying behavior in NHPs with high accuracy and efficiency,thus providing a novel and comprehensive tool for assessing phenotypic behavior in monkeys.

关键词： Action recognition Fine motor identification Two-stream deep model 2D skeleton Non-human primates Rett syndrome

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