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arXiv 2023年

作者： Mu, Fangwen Chen, Xiao Shi, Lin Wang, Song Wang, Qing State Key Laboratory of Intelligent Game Beijing China Science and Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Lassonde School of Engineering York University Toronto Canada

Existing automatic code comment generators mainly focus on producing a general description of functionality for a given code snippet without considering developer intentions. However, in real-world practice, comments are complicated, which often contain information reflecting various intentions of developers, e.g., functionality summarization, design rationale, implementation details, code properties, etc. To bridge the gap between automatic code comment generation and real-world comment practice, we define Developer-Intent Driven Code Comment Generation, which can generate intent-aware comments for the same source code with different intents. To tackle this challenging task, we propose DOME, an approach that utilizes Intent-guided Selective Attention to explicitly select intent-relevant information from the source code, and produces various comments reflecting different intents. Our approach is evaluated on two real-world Java datasets, and the experimental results show that our approach outperforms the state-of-the-art baselines. A human evaluation also confirms the significant potential of applying DOME in practical usage, enabling developers to comment code effectively according to their own needs. Copyright © 2023, The Authors. All rights reserved.

关键词： Bridges

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StyleGene: Crossover and Mutation of Region-level Facial Genes for Kinship Face Synthesis

StyleGene: Crossover and Mutation of Region-level Facial Gen...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hao Li Xianxu Hou Zepeng Huang Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

High-fidelity kinship face synthesis has many potential applications, such as kinship verification, missing child identification, and social media analysis. However, it is challenging to synthesize high-quality descendant faces with genetic relations due to the lack of large-scale, high-quality annotated kinship data. This paper proposes RFG (Region-level Facial Gene) extraction framework to address this issue. We propose to use IGE (Image-based Gene Encoder), LGE (Latent-based Gene Encoder) and Gene Decoder to learn the RFGs of a given face image, and the relationships between RFGs and the latent space of Style-GAN2. As cycle-like losses are designed to measure the $\mathcal{L}_{2}$ distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i.e. no paired kinship face data is required. Based upon the proposed RFGs, a crossover and mutation module is further designed to inherit the facial parts of parents. A Gene Pool has also been used to introduce the variations into the mutation of RFGs. The diversity of the faces of descendants can thus be significantly increased. Qualitative, quantitative, and subjective experiments on FIW, TSKinFace, and FF-Databases clearly show that the quality and diversity of kinship faces generated by our approach are much better than the existing state-of-the-art methods.

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Lossless Compression Technique For ECG Signals Using A-law Companding Algorithm 3

Lossless Compression Technique For ECG Signals Using A-law C...

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3rd International Conference on Innovative Research in Applied Science, engineering and Technology, IRASET 2023

作者： Hanine, Mustapha El Ouldzira, Hicham Boudaoud, Abdelghani Abdelmounim, Elhassane Higher Institute of Health Sciences Hassan First University of Settat Laboratory of Health Sciences and Technologies Department of Biomedical Instrumentation and Medical Physics Settat Morocco Department of Industrial Engineering Safi Morocco High School of Technology Sultan Moulay Slimane University Laboratory of Engeneering and Applied Physics Department of Mechatronics Beni Mellal Morocco Hassan First University Laboratory of System Analysis and Information Technology Faculty of Sciences and Technology Department of Applied Physics Settat Morocco

ISBN: (纸本)9798350398366

In this work, we present a lossless compression technique based on the use of the A-law companding algorithm, associated with the inter-leads correlation to compress ECG signals by reducing the dynamic range and number of encoded signals. Experiments are conducted on the selected records from the PTB Arrhythmia Database, and the performance is evaluated using the compression ratio (CR) and the percent root mean square difference (PRD). the results show that this technique efficiently retains pathological informations, whether for patients in normal condition or with arrhythmias, a total compression ratio CR of 4.92 is achieved with a PRD of 0 %. © 2023 IEEE.

关键词： Electrocardiograms

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A hybrid Chinese named entity recognition method for Internet of Things

A hybrid Chinese named entity recognition method for Interne...

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2022 International Conference on Algorithms, Microchips and Network Applications

作者： Wang, Ying Wang, Zehao Li, Hong Liu, Peipei Li, Yachao Zuo, Fang Institute of Intelligence Networks System Henan University Henan475001 China Henan Experimental Teaching Demonstration Centre of Modern Network Technology Henan University Henan475001 China Intelligent Data Processing Engineering Research Center of Henan Province Henan University Henan475001 China Institute of Information Engineering Chinese Academy of Sciences Beijing China Henan International Joint Laboratory of Theories and Key Technologies on Intelligence Networks Henan University Henan475001 China

ISBN: (纸本)9781510653290

In order to facilitate government departments to assess security risks and prevent infiltration, it's necessary to recognize the IoT device from open data by the method of Named entity recognition (NER). In this study, a hybrid method of chinese NER which contains neural networks and templates is proposed to extract IoT device information from open web. The model first initiates characters of input through BERT, which can create character embedding while reduce dependency with external datasets. Then, the initiated vectors are fed into a multi-layers BiLSTM model to encode the contextual representation of each character, and a linear CRF layer after BiLSTM is used to assign the scores to every character for entity annotation. In addition, a dictionary and rule base is constructed based on the characteristics of IoT devices to correct the annotation results. We experimented the methods on the dataset of IoT, the results prove the proposed model achieves better recognition effect than other models, and the F-scores value is 88.9%. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Internet of things

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CAD-GPT: Synthesising CAD Construction Sequence with Spatial Reasoning-Enhanced Multimodal LLMs 39

CAD-GPT: Synthesising CAD Construction Sequence with Spatial...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Siyu Chen, Cailian Le, Xinyi Xu, Qimin Xu, Lei Zhang, Yanzhou Yang, Jie School of Electronics Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China SJTU-Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai China Institute of Cyber Science and Technology Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai China University of Minnesota Twin Cities Saint PaulMN United States

ISBN: (纸本)157735897X

Computer-aided design (CAD) significantly enhances the efficiency, accuracy, and innovation of design processes by enabling precise 2D and 3D modeling, extensive analysis, and optimization. Existing methods for creating CAD models rely on latent vectors or point clouds, which are difficult to obtain and costly to store. Recent advances in Multimodal Large Language Models (MLLMs) have inspired researchers to use natural language instructions and images for CAD model construction. However, these models still struggle with inferring accurate 3D spatial location and orientation, leading to inaccuracies in determining the spatial 3D starting points and extrusion directions for constructing geometries. This work introduces CAD-GPT, a CAD synthesis method with spatial reasoning-enhanced MLLM that takes either a single image or a textual description as input. To achieve precise spatial inference, our approach introduces a 3D Modeling Spatial Mechanism. This method maps 3D spatial positions and 3D sketch plane rotation angles into a 1D linguistic feature space using a specialized spatial unfolding mechanism, while discretizing 2D sketch coordinates into an appropriate planar space to enable precise determination of spatial starting position, sketch orientation, and 2D sketch coordinate translations. Extensive experiments demonstrate that CAD-GPT consistently outperforms existing state-of-the-art methods in CAD model synthesis, both quantitatively and qualitatively. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： High level synthesis

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Design and Simulation of Millimeter-Wave Magnetrons 3

Design and Simulation of Millimeter-Wave Magnetrons

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3rd IEEE Ukraine Conference on Electrical and Computer engineering, UKRCON 2021

作者： Kopot, Mykhailo Kobzev, Igor Chetverykov, Grigorij Gritsunov, Alexander Bilotserkivska, Anzhela Kharkiv National University of Radio Electronics Department of Software Engineering Kharkiv Ukraine V.N.Karazin Kharkiv National University Department of Information Technologies and Control System Kharkiv Ukraine Kharkiv National University of Radio Electronics Department of Microelectronics Electronic Devices and Appliances Kharkiv Ukraine

ISBN: (纸本)9781665400947

Specific features and problems of the design of millimeter-wave magnetrons are described. Cold secondary-emission cathode usage is considered as a promising factor for the device life prolongation. Field-emission cathodes instead of thermionic ones are proposed as the trigger units for further increase of the magnetron working life. The problems of ensuring enough frequency separation between the operating eigenmode and neighboring modes are discussed;using the rising-sun delay system and operating on spatial harmonics are proposed as possible solutions. The methods of providing enough emitting current from cathodes without their fast burnout are considered;the solution of the problem could lie in using inverted design of the magnetron. Examples of 'cold' (electrodynamic) calculations of the delay systems of millimeter-wave magnetrons are given. 'Hot' (electronic) simulations with PIC algorithms have been performed using TULIPgm code for magnetron and amplitron modeling. © 2021 IEEE.

关键词： Millimeter waves

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Distilled Transformers with Locally Enhanced Global Representations for Face Forgery Detection

arXiv

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arXiv 2024年

作者： Zhang, Yaning Li, Qiufu Yu, Zitong Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China School of Computing and Information Technology Great Bay University Guangdong Dongguan523000 China

Face forgery detection (FFD) is devoted to detecting the authenticity of face images. Although current CNN-based works achieve outstanding performance in FFD, they are susceptible to capturing local forgery patterns generated by various manipulation methods. Though transformer-based detectors exhibit improvements in modeling global dependencies, they are not good at exploring local forgery artifacts. Hybrid transformer-based networks are designed to capture local and global manipulated traces, but they tend to suffer from the attention collapse issue as the transformer block goes deeper. Besides, soft labels are rarely available. In this paper, we propose a distilled transformer network (DTN) to capture both rich local and global forgery traces and learn general and common representations for different forgery faces. Specifically, we design a mixture of expert (MoE) module to mine various robust forgery embeddings. Moreover, a locally-enhanced vision transformer (LEVT) module is proposed to learn locally-enhanced global representations. We design a lightweight multi-attention scaling (MAS) module to avoid attention collapse, which can be plugged and played in any transformer-based models with only a slight increase in computational costs. In addition, we propose a deepfake self-distillation (DSD) scheme to provide the model with abundant soft label information. Extensive experiments show that the proposed method surpasses the state of the arts on five deepfake datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Face recognition

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Offsite Evaluation of Localization systems: Criteria, systems, and Results From IPIN 2021 and 2022 Competitions

IEEE Journal on Indoor and Seamless Positioning and Navigati...

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IEEE Journal on Indoor and Seamless Positioning and Navigation 2024年 2卷 92-129页

作者： Potorti, Francesco Crivello, Antonino Lee, Soyeon Vladimirov, Blagovest Park, Sangjoon Chen, Yushi Wang, Long Chen, Runze Zhao, Fang Zhuge, Yue Luo, Haiyong Perez-Navarro, Antoni Jimenez, Antonio R. Wang, Han Liang, Hengyi De Cock, Cedric Plets, David Cui, Yan Xiong, Zhi Li, Xiaodong Ding, Yiming Franco, Fernando Javier Alvarez Polo, Fernando Jesus Aranda Rodriguez, Felipe Parralejo Moreira, Adriano Pendao, Cristiano Silva, Ivo Ortiz, Miguel Zhu, Ni Li, Ziyou Renaudin, Valerie Wei, Dongyan Ji, Xinchun Zhang, Wenchao Wang, Yan Ding, Longyang Kuang, Jian Zhang, Xiaobing Dou, Zhi Yang, Chaoqun Kram, Sebastian Stahlke, Maximilian Mutschler, Christopher Coene, Sander Li, Chenglong Venus, Alexander Leitinger, Erik Tertinek, Stefan Witrisal, Klaus Wang, Yi Wang, Shaobo Jin, Beihong Zhang, Fusang Su, Chang Wang, Zhi Li, Siheng Li, Xiaodong Li, Shitao Pan, Mengguan Zheng, Wang Luo, Kai Ma, Ziyao Gao, Yanbiao Chang, Jiaxing Ren, Hailong Guo, Wenfang Torres-Sospedra, Joaquin Pisa56124 Italy Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School Beijing100876 China Chinese Academy of Sciences Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Beijing100190 China Universitat Oberta de Catalunya Faculty of Computer Sciences Multimedia and Telecommunication Barcelona08035 Spain Arganda del Rey28500 Spain Huawei Technologies Company Ltd. Shenzhen100015 China IMEC-WAVES/Ghent University Department of Information Technology Gent9052 Belgium Nanjing University of Aeronautics and Astronautics College of Automation Nanjing210016 China University of Extremadura Badajoz06006 Spain University of Trás-os-Montes and Alto Douro Department of Engineering School of Sciences and Technology Vila Real5000-801 Portugal Universidade Do Minho Centro Algoritmi Guimarães4800-058 Portugal Ame Department of the University Gustave Eiffel Geoloc Laboratory Bouguenais44344 France Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China Wuhan University Gnss Research Center Wuhan430072 China AutoNavi Software Company Ltd. China Fraunhofer Institute for Integrated Circuits Iis Fraunhofer Iis Nuremberg90411 Germany Erlangen91058 Germany Graz University of Technology Christian Doppler Laboratory for Location-Aware Electronic Systems Graz8010 Austria Institute of Software Chinese Academy of Sciences Beijing100190 China Purple Mountain Laboratories Pervasive Communication Research Center Nanjing210000 China Beijing University of Posts and Telecommunications Beijing100876 China

Indoor positioning is a thriving research area, which is slowly gaining market momentum. Its applications are mostly customized, ad hoc installations;ubiquitous applications analogous to Global Navigation Satellite system for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the indoor positioning and indoor navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site tracks. In contrast with on-site tracks where competitors bring their systems on-site - which were impossible to organize during 2021 and 2022 - in off-site tracks competitors download prerecorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem. © 2023 CCBY.

关键词： Smartphones

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Speaker's Emotions Recognition Module Based on the GoogleLeNet Neural Network

Speaker's Emotions Recognition Module Based on the GoogleLeN...

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2022 International Conference on Smart information systems and technologies, SIST 2022

作者： Tereikovskyi, Ihor Tereikovska, Liudmyla Kryvoruchko, Olena Tereikovskyi, Oleh Tyshchenko, Dmytro Franchuk, Tamara National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of System Programming And Specialized Computer Systems Kyiv Ukraine Kyiv National University Of Construction And Architecture Dept. Of Information Technology For Design And Applied Mathematics Kyiv Ukraine Kyiv National University Of Trade And Economics Dept. Of Software Engineering And Cyber Security Kyiv Ukraine National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of Department Of Computer Engineering Kyiv Ukraine

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The article is devoted to the development of means for recognition of the emotions of the speaker, based on the neural network analysis of fixed fragments of the voice signal. The possibility of improving recognition tools through the use of a convolutional neural network of the GoogLeNet type is shown. The expediency of training the network on the examples of the TESS database is determined. A procedure for representing a voice signal in the form of a square grayscale image has been developed. Based on the use of the specified database, the modification of the GoogLeNet network was carried out. The architecture and software implementation of the recognition module have been developed. With the help of computer experiments, it has been established that after 150 epochs of training on a fairly limited training sample, the modified GoogLeNet makes it possible to achieve an accuracy of recognition of the speaker's emotions of approximately 0.97 on test examples. The obtained accuracy is comparable to the accuracy of the best modern systems of this purpose and confirms the promise of using neural network models like GoogLeNet for emotion recognition based on voice signal analysis. The need for further research aimed at reducing the resource intensity of the neural network model is shown. © 2022 IEEE.

关键词： Emotion Recognition

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The Impact of 5G Technology on Agile Project Management: A Cross-Industry Analysis

The Impact of 5G Technology on Agile Project Management: A C...

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Intelligent Communication technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Mahesh Kumar Mishra Krishnamoorthy Selvaraj Kathari Santosh Mohd Aarif S.Suma Christal Mary B Kiran Bala Delta System and Software Inc McKinney Texas USA Dept of ERP Encloud service LLC Keller Texas US Department of MBA CMR Institute of Technology Bengaluru Bengaluru India IOSRP Noida UP India Department of Information Technology Panimalar Engineering College Poonamalle Chennai Department of Artificial Intelligence and Data Science K.Ramakrishnan college of Engineering Trichy

Project management and organisation are completely transformed by the introduction of 5G technology, which has brought in a new era of connection. This research investigates how 5G networks will affect Enterprise Resource Planning (ERP), Agile Project Management (APM) and the larger context of the Technology-Organization-Environment (TOE) paradigm through a cross-industry examination. Agile approaches may benefit from improved adaptive design, cooperation and connectivity as a result of 5G technology being included into project management procedures. A team of developers can work together more easily and make decisions more quickly with the real-time data transfer made possible by 5G networks' higher capacity and speed. This study looks at how Agile methods for managing projects are more effective and flexible when using 5G's low latency and high data throughput. This research explores how Enterprise Resource Planning (ERP) systems may be affected by 5G. With 5G networks' enhanced capabilities, current information synchronisation is made possible in new ways, enabling ERP systems to function with increased precision and speed. This study investigates how supply chain administration, resource allocation and general organisational agility are improved by 5G-enabled ERP. This study evaluates how various sectors' organisational structures, procedures, and objectives are affected by the deployment of 5G infrastructure. This research outlines critical elements that influence the successful implementation of 5G into project management and ERP systems, including technology preparedness, organisational culture, and external factors. This research provides important insights into the potential for transformation of 5G technology through a thorough cross-industry assessment. It also gives organisations an established basis on which to adapt their Agile Project Management and Enterprise Resource Planning practises in the dynamic context of today's digital world.

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