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检索条件"任意字段=Proceedings of the 6th International Conference on Algorithms, Computing and Systems"

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A3-108 Machine Translation System for Similar Language Translation Shared Task 2021 6

A3-108 Machine Translation System for Similar Language Trans...

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6th conference on Machine Translation, WMT 2021

作者： Yadav, Saumitra Shrivastava, Manish Machine Translation - Natural Language Processing Lab Language Technologies Research Centre Kohli Center on Intelligent Systems International Institute of Information Technology Hyderabad India

ISBN: (纸本)9781954085947

In this paper, we describe our submissions for the Similar Language Translation Shared Task 2021. We built 3 systems in each direction for the Tamil - Telugu language pair. this paper outlines experiments with various tokenization schemes to train statistical models. We also report the configuration of the submitted systems and results produced by them. © 2021 Association for Computational Linguistics

关键词： Computational linguistics

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Advanced Attention for Causality Classification of Verb Nodes of Knowledge Graph 23

Advanced Attention for Causality Classification of Verb Node...

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proceedings of the 7th international conference on algorithms, computing and systems

作者： Jiawei Fan Xianghui Ren Hao Zhang Huisheng Ma Xinlei Wei Yifeng Yue The 15th Research Institute of China Electronics Technology Group Corporation China

ISBN: (纸本)9798400709098

In this paper, the causal relationship between verb nodes in knowledge graph is determined according to the verb nodes and the sentences in which they are located. For the operation of paying attention to the whole sentence, the verb is not studied specifically, which is disadvantageous to the judgment of causality. therefore, this paper proposes an advanced attention method based on attention mechanism to judge the causality of knowledge graph. It can not only focus on the extraction of verb related information, but also learn the whole sentence features. the experiment verifies the method, and the results show that the method in this paper has advantages.

关键词： attention classification knowledge graphs verbs

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Keep your Enemies closer: On the minimal Distance of Adversaries when using Channel-based Key Extraction in SISO 6G systems

Keep your Enemies closer: On the minimal Distance of Adversa...

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IEEE international conference on Wireless and Mobile computing, Networking And Communications (WiMob)

作者： Felix Klement Shoya Takebuchi Tolga Arul Stefan Katzenbeisser Faculty of Computer Science and Mathematics University of Passau Passau Germany Ministry of Internal Affairs and Communications Japan Tokyo Japan Computer Science Department Technical University of Darmstadt Darmstadt Germany

We conduct a comprehensive analysis of Channel-based Key Extraction across various frequency ranges that have potential applications in the 6th generation mobile standard. Specifically, we examine the minimum distance required between an attacker and a legitimate entity to achieve secure key generation through channel measurement. By simulating various metrics across diverse configurations, we observe that in our generic model, the required distance for an attacker to attain sufficient channel correlation reduces as frequency increases. Consequently, an attacker must be within close proximity, mere centimeters away, from a legitimate node to acquire relevant information necessary for generating a potentially identical key.

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thALES ALENIA SPACE 6th GENERATION DIGITAL TRANSPARENT PROCESSOR (DTP6G) - A MULTI-MISSION, MULTI-MODE VERSATILE PRODUCT AT thE HEART OF TELECOM SOLUTIONS 39

THALES ALENIA SPACE 6TH GENERATION DIGITAL TRANSPARENT PROCE...

引用

39th international Communications Satellite systems conference, ICSSC 2022

作者： Nicolas, Cédric Enjolras, Vivien Voisin, Philippe Boustie, Christelle Jestin, Guy Thales Alenia Space 26 Avenue Champollion ToulouseCedex 1 31037 France CNES 18 avenue Edouard Belin ToulouseCedex 9 31401 France DGA-Maitrise de l'Information BP7 RennesCedex 35998 France

ISBN: (纸本)9781839538841

Flexibility and in-flight reconfigurability offered by digital processing have become key features in today's telecom satellite payloads. In recent years, thales Alenia Space has developed several generations of on-board digital transparent processors (DTP) by introducing the most advanced and disruptive technologies. thales Alenia Space DTPs are state-of-the-art sub-systems at the heart of payloads using analogue-to-digital (ADC) and digital-to-analogue (DAC) channelizers on their input and output RF accesses and making extensive use of digital processing to support channel routing with fine bandwidth granularity. After several payloads based on 5th generation processors, Space Inspire (INstant SPace In-orbit REconfiguration)) solution uses a 6th generation processors offering another step in flexibility through Digital Beam Forming (DBFN). this latest processor generation leverages DTP3G and 5G assets as well as in-flight heritage and provides many more features. Developped with support of CNES & DGA (French DoD), DTP6G covers Civil but also MilsatCom/GovSatCom missions thanks to high performances and small routing granularity. Its main current usecases are GEO but it is compatible with LEO to MEO orbits. Although DBFN is a key addition of DTP6G, it can still be operated with fixed-beam antennas or with an analog BFN. Another new feature is the embedded High Speed Command/Control link (HiLink) providing high TMTC throughput over several waveforms (Full CCSDS or CCSDS over DVBS2). Internal encryption is proposed (AES) but access to an external encryption unit (NSA-type) is also possible. this C&C link combined with a high-performance/large-storage-capacity processing board ensures fast operability and a high-level of observability. DTP6G processor is also ready for future implementations of a Regenerative companion, Beamhopping and Multi-user scenarios. the paper will present thales Alenia Space transparent processor roadmap leading to DTP6G, its mission coverage,

关键词： Cryptography

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MRI-Based Brain Tumor Classification Using Various Deep Learning Convolutional Networks and CNN 6th

MRI-Based Brain Tumor Classification Using Various Deep Lear...

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6th international conference on Intelligent computing and Optimization, ICO 2023

作者： Saiful, Md. Haider, Sakib Rahman, S. M. Arafat Reza, Nahid Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram4349 Bangladesh

ISBN: (纸本)9783031362453

Yearly, brain tumors cause many fatalities and a significant portion of these victims come from rural regions. However, beginning brain tumor diagnosis technology is not as effective as anticipated. We thus set out to develop an accurate approach that would aid doctors in recognizing brain tumors. Even though there have been several types of research on this topic, we tried to develop a classification approach that is significantly more accurate and error-free and is trained using a sizable amount of authentic datasets rather than an enhanced data-modified version of the VGG-16 convolutional neural network architecture was used to analyze a dataset of 6328 MRI images that were categorized into three different types: Pituitary, Glioma, and Meningioma. the results were highly impressive, with the model achieving an overall accuracy of 99.5%. the precision rates for each type were also outstanding, with a precision rate of 99.4% for gliomas, 96.7% for meningiomas, and 100% for pituitaries. these results suggest that the modified VGG-16 architecture is highly effective in accurately classifying MRI images of the brain into these three distinct categories. Additionally, it outperformed various other current CNN designs and cutting-edge research in terms of outcomes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Magnetic resonance imaging

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Deciphering Handwritten Text: A Convolutional Neural Network Framework for Handwritten Character Recognition 6th

Deciphering Handwritten Text: A Convolutional Neural Network...

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6th international conference on Intelligent computing and Optimization, ICO 2023

作者： Hossain, Md Jakir Zaman, Sarah Samiha Akash, Fardin Rahman Alam, Farhana Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka1341 Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram4349 Bangladesh

ISBN: (纸本)9783031362453

HCR (Handwritten Character Recognition) is considered one of the most challenging research areas, given the vast array of potential applications. Character recognition has been the focus of research since the beginning of Artificial Intelligence. Numerous studies, including HCR, have been conducted in this sector. A typical procedure requires two steps: feature extraction and Classification. Many forms of neural networks have been used in this cause over the years, with notable results. CNN has altered the scenario in recent years. It has had remarkable success in this industry due to its cutting-edge extraction of features and Classification. To produce recognized characters, CNN uses images for input and sends them through a sequence of layers, including a convolutional layer, a nonlinear function, a pooling layer, and interconnected layers. We utilized a dataset containing 372,450 handwritten character images covering the entire alphabet in English. We created a model using the CNN model and achieved 99% test accuracy. CNN is an efficient and powerful approach for HCR. Our model's high accuracy suggests that it has the potential to be applied in various practical scenarios such as postal address reading, digital libraries, and traffic sign detection. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Character recognition

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CITISIA 2021 - IEEE conference on Innovative Technologies in Intelligent System and Industrial Application, proceedings

CITISIA 2021 - IEEE Conference on Innovative Technologies in...

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6th IEEE international conference on Innovative Technologies in Intelligent System and Industrial Application, CITISIA 2021

ISBN: (纸本)9781665417846

the proceedings contain 35 papers. the topics discussed include: an innovative framework to improve course and student outcomes;data security and privacy in cloud computing focused on transportation sector with the aid of block chain approach;generating product description with generative pre-trained transformer 2;threats and data trading detection methods in the dark web;prediction of forest fire using machine learning algorithms: the search for the better algorithm;predicting hypoglycaemia using classification;improving weeds identification with a repository of agricultural pre-trained deep neural networks;enhancing the security of data in cloud computing environments using remote data auditing;customer data extraction techniques based on natural language processing for e-commerce business analytics;and a review of blockchain-based batch authentication techniques for securing the internet of vehicles.

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Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments

Deep Reinforcement Learning-based Obstacle Avoidance for Rob...

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IEEE international conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Keqin Li Jiajing Chen Dezhi Yu Tao Dajun Xinyu Qiu Jieting Lian Ryan Ji Shengyuan Zhang Zhenyu Wan Baiwei Sun Bo Hong Fanghao Ni Jiaqi Han Department of Computer Science AMA University Quezon Philippines Courant Institute New York University 251 Mercer St New York NY USA School of Information University of California Berkeley University Avenue and Oxford St Berkeley CA United States Master of Science in Mechanical Engineering School of Engineering Carnegie Mellon University 5000 Forbes Avenue Pittsburgh PA USA College of Engineering Northeastern University Seattle USA Master of Science in Database Technology School of Professional Studies New York University 50 West 4th Street New York NY USA College of Engineering Cornell University 616 Thurston Ave Ithaca NY US Master of Professional Science in Applied Statistics-Data Science College of Computing and Information Science Cornell University 616 Thurston Ave Ithaca NY USA Independent Researcher Beijing China Master of Computer Science Donald Bren School of Information and Computer Sciences University of California Irvine 510 Aldrich Hall Irvine CA USA School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA Shanghai Institute of Technology Shanghai China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

this paper proposes a deep reinforcement learning based on the warehouse environment, the mobile robot obstacle avoidance Algorithm. Firstly, the value function network is improved based on the pedestrian interaction. We can learn to obtain the relative importance of the current state and the historical trajectory state as well as the joint impact on the robot’s obstacle avoidance strategy. Secondly, the reward function of reinforcement learning is designed based on the spatial behaviour of pedestrians, and the robot is punished for the state where the angle changes too much, so as to achieve the requirement of comfortable obstacle avoidance. Finally, the feasibility and effectiveness of the deep reinforcement learning-based mobile robot obstacle avoidance algorithm in the warehouse environment in the complex environment of the warehouse are verified through simulation experiments.

关键词： Legged locomotion Pedestrians Heuristic algorithms Robot sensing systems Feature extraction Trajectory Safety Data mining Collision avoidance Information technology

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Performance Evaluation of the Classic McEliece Key Encapsulation Algorithm 11

Performance Evaluation of the Classic McEliece Key Encapsula...

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11th IEEE international conference on Intelligent Data Acquisition and Advanced computing systems - Technology and Applications (IDAACS)

作者： Kuznetsov, Alexandr Lutsenko, Maria Bagmut, Mykhaylo Zhora, Victor Kharkov Natl Univ Svobody Sq 6 UA-61022 Kharkiv Ukraine JSC Inst Informat Technol Bakulin St 12 UA-61166 Kharkiv Ukraine State Serv Special Commun & Informat Protect Ukra Solomianska 13 Str UA-03110 Kiev Ukraine

ISBN: (纸本)9781665426053

In late 2016, the US NIST announced a post-quantum cryptography open competition to select public-key cryptoalgorithms (digital signature, asymmetric encryption, and key encapsulation) suitable for use when quantum computing is widely available. Based on the results of the 2nd round of the competition, several solutions were selected that could potentially be standardized as post-quantum cryptographic algorithms. In this article, we are looking at one of the bids based on codes. the Classic McEliece key encapsulation algorithm is a variant of the well-known (over 40 years) public key cryptosystem in codes. We investigate the various characteristics of the Classic McEliece, in particular, we provide estimates of its performance using various computing platforms and testing technologies.

关键词： post-quantum cryptography key encapsulation algorithm performance testing of cryptoalgorithms

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Parallel Generation and Analysis of Optimal Chordal Ring Networks Using Python Tools on Kunpeng Processors 17th

Parallel Generation and Analysis of Optimal Chordal Ring N...

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Parallel computing Technologies - 17th international conference, PaCT 2023, proceedings

作者： Monakhov, Oleg Monakhova, Emilia Kireev, Sergey Institute of Computational Mathematics and Mathematical Geophysics of SB RAS Lavrentieva 6 Novosibirsk630090 Russia

ISBN: (纸本)9783031416729

Parallel versions of the reduced exhaustive search algorithm based on the Python tools are implemented to optimize chordal ring networks, which are of practical interest in the design of systems on a chip and supercomputer systems. An analysis of the effectiveness of parallel programs with different numbers of MPI processes on Kunpeng processors was carried out. the speed-up of several parallel computing schemes was experimentally evaluated and analyzed. the large dataset of all optimal chordal networks with numbers of up $$6 \cdot 10^4$$ nodes was generated for the first time. A preliminary analysis of experimentally obtained dataset has been carried out and the existence of new families of optimal chordal ring networks with analytical descriptions of parameters has been discovered. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Python

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