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Efficient quantum digital signatures over long distances with likely bit strings

Physical Review Applied 2024年第2期21卷 024012-024012页

作者： Ji-Qian Qin Zong-Wen Yu Xiang-Bin Wang State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing 100084 China Data Communication Science and Technology Research Institute Beijing 100191 China Jinan Institute of Quantum Technology SAICT Jinan 250101 China Shanghai Branch CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai 201315 China Shenzhen Institute for Quantum Science and Engineering and Physics Department Southern University of Science and Technology Shenzhen 518055 China Frontier Science Center for Quantum Information Beijing 100084 China

Quantum digital signatures (QDSs) can provide information-theoretic security of messages against forgery and repudiation. Compared with previous QDS protocols that focus on signing one-bit messages, hash function–based QDS protocols can save quantum resources and are able to sign messages of arbitrary length. Using the idea of likely bit strings, we propose an efficient QDS protocol with hash functions over long distances. Our method of likely bit strings can be applied to any quantum key distribution–based QDS protocol to significantly improve the signature rate and dramatically increase the secure signature distance of QDS protocols. In order to save computing resources, we propose an improved method where Alice participates in the verification process of Bob and Charlie. This eliminates the computational complexity relating to the huge number of all likely strings. We demonstrate the advantages of our method and our improved method with the example of sending-or-not-sending QDS. Under typical parameters, both our method and our improved method can improve the signature rate by more than 100 times and increase the signature distance by about 150km compared with hash function–based QDS protocols without likely bit strings.

关键词： Quantum cryptography Quantum protocols

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The IDC System for Sentiment Classification and Sarcasm Detection in Arabic 6

The IDC System for Sentiment Classification and Sarcasm Dete...

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6th Arabic Natural Language Processing Workshop, WANLP 2021

作者： Israeli, Abraham Nahum, Yotam Fine, Shai Bar, Kfir Data Science Institute Interdisciplinary Center Herzliya Israel Department of Software and Information System Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

ISBN: (纸本)9781954085091

Sentiment classification and sarcasm detection attract a lot of attention by the NLP research community. However, solving these two problems in Arabic and on the basis of socialnetwork data (i.e., Twitter) is still of lower interest. In this paper we present designated solutions for sentiment classification and sarcasm detection tasks that were introduced as part of a shared task by Abu Farha et al. (2021). We adjust the existing state-of-the-art transformer pretrained models for our needs. In addition, we use a variety of machine-learning techniques such as down-sampling, augmentation, bagging, and usage of meta-features to improve the models performance. We achieve an F1-score of 0.75 over the sentiment classification problem where the F1-score is calculated over the positive and negative classes (the neutral class is not taken into account). We achieve an F1-score of 0.66 over the sarcasm detection problem where the F1-score is calculated over the sarcastic class only. In both cases, the above reported results are evaluated over the ArSarcasm-v2-an extended dataset of the ArSarcasm (Farha and Magdy, 2020) that was introduced as part of the shared task. This reflects an improvement to the state-of-the-art results in both tasks. © WANLP 2021 - 6th Arabic Natural Language Processing Workshop

关键词： Natural language processing systems

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DeepQ Feature Selection and Recognition of Handwritten Prediction Using Convolutional Neural Networks

SSRN

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SSRN 2023年

作者： Sasi Kumar, A. Aithal, P.S. Institute of Computer Science & Information Science Srinivas University Mangalore575 001 India Department of Cloud Technology & Data Science Institute of Engineering & Technology Srinivas University Srinivas Nagar Mukka Mangalore Surathkal India Srinivas University Mangalore575 001 India Institute of Computer Science & Information Science Srinivas University Mangalore575001 India

Purpose: Deep learning (DL) is referred to as the "hot subject" in pattern recognition and machine learning. The unmatched potential of deep learning allows for the resolution of the majority of complex machine learning issues, and it is evident that it will receive attention within the framework for mobile devices. There are tools for pattern recognition that can be used by smart applications of the next generation to make huge changes. Design/Methodology/Approach: The deep feature convolutional network (DEEPQ-CNN) extracts the high representation and features of the hierarchical image from the relevant training data when data-driven learning is enabled. In addition, the DEEPQ-CNN characterization strategies are adapted to a couple of datasets in the boundaries and construction. The running time of the neural network and the network's weight are essential requirements for mobile computing. Finding/Results: In the proposed system, the design of the image processing module is based on the characteristics of a mobile device's convolutional neural network. However, the use of mobile devices for data collection, processing, and construction is described. Last but not least, the computing conditions and mobile device data features are taken into account. Original Value: For optical character recognition (OCR), specific datasets support the lightweight network structure. CNN is utilized to approve the proposed framework when examinations are made with the results of past methods to perceive the optical person. © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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MAIN: Multimodal Attention-based Fusion Networks for Diagnosis Prediction

MAIN: Multimodal Attention-based Fusion Networks for Diagnos...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： An, Ying Zhang, Haojia Sheng, Yu Wang, Jianxin Chen, Xianlai Central South University Big Data Institute Changsha410083 China Central South University School of Computer Science and Engineering Changsha410083 China

ISBN: (纸本)9781665401265

Predicting the future diagnoses from patients' historical Electronic Health Records (EHR) is a significant task in healthcare. EHR consist of multiple modal data, each modality has different features and contains a wealth of information of patients. However, most of the existing EHR-based prediction methods either only use unimodal data, or fail to fully explore the correlation between different modalities when fusing multimodal data. To address these challenges, we propose a Multimodal Attention-based fusIon Networks (MAIN) for diagnosis prediction. In this model, we first design different feature extraction modules for each modality. Then, an inter-modal correlation module which contains two layers is applied to capture the intermodal correlation. Finally, a multimodal fusion module based on weighted averaging is utilized to integrate the representations derived from different modalities and their correlation to obtain the patient representation for diagnosis prediction. We evaluate our proposed model on two medical datasets, and the experimental results demonstrate the effectiveness of MAIN. © 2021 IEEE.

关键词： Forecasting

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Combining the AIML Model with Acoustic Signal to Evaluate the Bowel Sound

Combining the AIML Model with Acoustic Signal to Evaluate th...

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International Conference on Computational and Characterization Techniques in engineering & sciences (CCTES)

作者： S.Pavan Kumar Reddy N. Sreevani Revathi V Samuel Babu Navdeep Singh Ali Kareem Department of Artificial Intelligence and Data Science(AI&DS) B V Raju Institute of Technology Narsapur Campus Hyderabad Department of CSE(Cyber Security) Institute of Aeronautical Engineering Hyderabad India Department of Applied Sciences New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Lovely Professional University Phagwara India Hilla University College Babylon Iraq

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Automated methods for assessing bowel sounds (BS) were well developed by the early 2000s. Several teams, using diverse analytical techniques, achieved high levels of accuracy. BS has shown potential in doing non-invasive research on irritable bowel syndrome, studying gastrointestinal motility, and performing surgical procedures. This article introduces hybrid convolution and recursive neural networks that are used for BS analysis. It was among the first deep-learning approaches to be extensively studied. An experimental workflow and a novel dataset from a contact microphone device were used to assess the results. data were obtained at night, the most neuro gastroenterological intriguing time. Previous studies solely preserved daytime information, ignoring this period. The programme accurately detects bowel noises. In addition, they acquired good diagnostic specificity. Medical professionals confirmed the data, supporting the clinical diagnosis. Medical professionals may submit patient recordings and have them analyzed online using the client-server solution. Technologies are established, but BS research needs a consistent methodology, a worldwide venue for debate, & a free platform for data interchange, hence it is not widely employed. A common BS research framework may start with the server.

关键词： Wireless sensor networks Intestines Accuracy Working environment noise Noise Neural networks Motors Gastrointestinal tract Recording Medical diagnostic imaging

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GrabQC: Graph based Query Contextualization for automated ICD coding

arXiv

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arXiv 2022年

作者： Chelladurai, Jeshuren Santhiappan, Sudarsun Ravindran, Balaraman Dept. of Computer Science and Engineering Indian Institute of Technology Madras India Robert Bosch Centre for Data Science and AI India

Automated medical coding is a process of codifying clinical notes to appropriate diagnosis and procedure codes automatically from the standard taxonomies such as ICD (International Classification of Diseases) and CPT (Current Procedure Terminology). The manual coding process involves the identification of entities from the clinical notes followed by querying a commercial or non-commercial medical codes Information Retrieval (IR) system that follows the Centre for Medicare and Medicaid Services (CMS) guidelines. We propose to automate this manual process by automatically constructing a query for the IR system using the entities auto-extracted from the clinical notes. We propose GrabQC, a Graph based Query Contextualization method that automatically extracts queries from the clinical text, contextualizes the queries using a Graph Neural Network (GNN) model and obtains the ICD Codes using an external IR system. We also propose a method for labelling the dataset for training the model. We perform experiments on two datasets of clinical text in three different setups to assert the effectiveness of our approach. The experimental results show that our proposed method is better than the compared baselines in all three settings. © 2022, CC BY.

关键词： Search engines

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Edge Intelligence for Traffic Flow Detection: A Deep Learning Approach

Edge Intelligence for Traffic Flow Detection: A Deep Learnin...

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Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security (iQ-CCHESS), International Conference on

作者： R Ranjitha Pooja Ahuja R Shreeshayana D Anil Department of Information Science and Engineering Nagarjuna College of Engineering and Technology Bengaluru India Department of CSE-Data Science Nagarjuna College of Engineering and Technology Bengaluru India Department of Electrical and Electronics Engineering ATME College of Engineering Mysuru India Research Scholar (Bangalore University) CMR Institute of Technology Bengaluru India

Intelligent Transportation Systems (ITS) are crucial for regulating public transportation, enhancing security, and addressing urban challenges. ITS offers smart guidance to mitigate traffic congestion, minimize environmental impact, and improve overall transport efficiency. In cloud-based ITS, traffic flow detection involves sending video data from edge devices to cloud data centers. However, this approach faces hurdles due to increased monitoring demands. To overcome this, a solution is proposed: employing deep learning on edge nodes. The approach includes a YOLOv4-based vehicle detection model, retrained for multi-object tracking using DeepSORT. Edge computing migrates and installs the detection and tracking systems, ensuring accurate performance at the edge. This approach optimizes traffic flow detection, addressing challenges faced by conventional cloud-based systems.

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SRAM optimized porting and execution of machine learning classifiers on MCU-based IoT devices: Demo abstract 21

SRAM optimized porting and execution of machine learning cla...

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12th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2021, part of CPS-IoT Week 2021

作者： Sudharsan, Bharath Patel, Pankesh Breslin, John G. Ali, Muhammad Intizar Confirm Sfi Research Centre for Smart Manufacturing Data Science Institute Nui Galway Ireland School of Electronic Engineering Dublin City University Ireland

ISBN: (纸本)9781450383530

With the introduction of edge analytics, IoT devices are becoming smarter and ready for AI applications. However, any increase in the training data results in a linear increase in the space complexity of the trained Machine Learning (ML) models, which means they cannot be deployed on IoT devices that have limited memory. To alleviate such memory issues, we recently proposed an SRAM-optimized classifier porting, stitching, and efficient deployment method in [3]. This is currently the most resource-friendly approach that enables large classifiers to be comfortably executed on microcontroller unit (MCU) based IoT devices, and perform ultra-fast classifications (1 - 4x times faster than state-of-the-art libraries) while consuming 0 bytes of SRAM. In this demo, realizing our recent SRAM-optimized approach, we port and execute 7 dataset-trained classifiers on 7 popular MCUs, and report their inference performance. It is apparent from the demo results that realizing our approach makes even the slowest Atmega328P MCU perform faster unit inference than a NVIDIA Jetson Nano GPU and Raspberry Pi 4 CPU. © 2021 ACM.

关键词： Static random access storage

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Porting and Execution of Anomalies Detection Models on Embedded Systems in IoT: Demo Abstract 21

Porting and Execution of Anomalies Detection Models on Embed...

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6th ACM/IEEE International Conference on Internet of Things Design and Implementation, IoTDI 2021

作者： Sudharsan, Bharath Patel, Pankesh Wahid, Abdul Yahya, Muhammad Breslin, John G. Ali, Muhammad Intizar Confirm SFI Research Centre for Smart Manufacturing Data Science Institute NUI Galway Ireland School of Electronic Engineering Dublin City University Ireland

ISBN: (纸本)9781450383547

In the Industry 4.0 era, Microcontrollers (MCUs) based tiny embedded sensor systems have become the sensing paradigm to interact with the physical world. In 2020, 25.6 billion MCUs were shipped, and over 250 billion MCUs are already operating in the wild. Such low-power, low-cost MCUs are being used as the brain to control diverse applications and soon will become the global digital nervous system. In an Industrial IoT setup, such tiny MCU-based embedded systems are equipped with anomaly detection models and mounted on production plant machines for monitoring the machine's health/condition. These models process the machine's health data (from temperature, RPM, vibration sensors) and raise timely alerts when it predicts/detects data patterns that show deviations from the normal operation state. In this demo, we train One Class Support Vector Machines (OC-SVM) based anomaly detection models and port the trained models to their MCU executable versions. We then deploy and execute the ported models on 4 popular MCUs and report their on-board inference performance along with their memory (Flash and SRAM) consumption. The steps/procedure that we show in the demo is generic, and the viewers can use it to efficiently port a wide variety of datasets-trained classifiers and execute them on different resource-constrained MCU and small CPU-based devices. © 2021 Owner/Author. © 2021 ACM.

关键词： Static random access storage

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A Blockchain-Based Secure Communication Framework For IOMT With Improved Security Via Hybrid Metaheuristic Models

A Blockchain-Based Secure Communication Framework For IOMT W...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Mahalle, Parikshit N. Kumar, V.P. Arul Kataria, Ansh Srividhya, S. Dev, Sudhanshu Malathi, H. Vishwakarma Institute of Technology Department of Artificial Intelligence & Data Science Pune India Karpagam Institute of Technology Department of Information Technology Coimbatore641105 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Ece Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Sciences Biotechnology and Genetics Karnataka Bangalore India

ISBN: (纸本)9798350388602

Multiple healthcare devices are integrated into an IoMT network to enhance monitoring of patients and real-time treatment. These networks make crucial health care choices using many devices. To protect these devices and networks, researchers have used encoding, hashing, confidentiality safeguarding, attribute-based admittance controller, and more. However, continuous monitoring security frameworks are complicated or unchangeable. Present methods' security relies on their interior setup, which is seldom extendable for new assaults. This explore recommends a hybrid metaheuristic approach for health care IoT safety. Changing encryption and hashing standards may dynamically rearrange the blockchain paradigm. EHO and GWO are used to continually optimize blockchain-based IoMT implementation security and QoS performance. In the proposed paradigm, dual fitness functions increase safety and QoS for numerous attacks. Fitness functions adjust encryption and hashing settings to enhance performance against various attacks. Blockchain-based deployment may be scaled and used in real time by integrating EH and GW optimization models for dynamic attack situations. The prototypical is evaluated beneath masquerade, Sybil, MITM, and DDoS assaults and associated to leading models. © 2024 IEEE.

关键词： EMR IOT IS MIS MO and SQ

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