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Integrating cycleGAN and BERT for Chinese text style transfer

Multimedia Tools and Applications 2024年 1-20页

作者： Chou, Chien-Hsing Chou, Cheng-Hou Hsieh, Yi-Zeng Yang, Tzu-Shien Department of Mathematics and Information Education National Taipei University of Education Taipei Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Electrical and Computer Engineering Tamkang University New Taipei Taiwan

In this study, we integrate the Bidirectional Encoder Representations from Transformers (BERT) model with the Cycle Generative Adversarial Network (CycleGAN) to create a system for Chinese text style transfer. Natural language processing (NLP) involves converting human languages into data interpretable by computers, enabling applications like text classification, chatbots, and dialogue systems. Recent advancements, such as Google's transformer model and the BERT technique, have significantly improved NLP capabilities through self-attention mechanisms and unsupervised pretraining. Text style transfer modifies the style of texts without altering their semantics. Previous methods like StyIns and models based on disentangled representation learning highlight the challenges of retaining text meaning during style transfer. Our system leverages CycleGAN’s unsupervised learning to convert unpaired data between wuxia and fantasy styles while preserving semantics. Using the pretrained BERT model from the Chinese Knowledge and Information Processing (CKIP) Lab, our experimental results demonstrate successful style conversion, maintaining the original meanings of texts. This integration of BERT and CycleGAN shows promise for further advancements in NLP applications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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Effectiveness Verification of the Dynamic Volt-VAR Control to Reduce SVC Capacity 4

Effectiveness Verification of the Dynamic Volt-VAR Control t...

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4th International Conference on High Voltage engineering and Power Systems, ICHVEPS 2023

作者： Maeda, Haruka Sitompul, Sandro Fujita, Goro Shibaura Institute of Technology Graduate School of Engineering and Science Department of Electrical Engineering and Computer Science Tokyo Japan Shibaura Institute of Technology Department of Electrical Engineering Tokyo Japan

ISBN: (纸本)9798350318678

The use of high-penetration solar photovoltaics (PV) to transmission networks is complicated by voltage rise. Infractions of voltage occur when voltage levels surpass the permitted limits. Several solutions have been suggested to avoid voltage breaches, including volt-VAR control on inverters. In this research, the impact of utilizing dynamic volt-VAR control as opposed to static volt-VAR control in a high-penetration photovoltaic (PV) system is compared to the impact of applying static VAR compensator (SVC). With dynamic volt-VAR control, which takes into account the inverter's available reactive power capacity, it is possible to adjust the reactive power contribution of the PV inverter. The quantity of available capacity will affect the force of the control, as expressed by the droop gain. The transmission system's controls are evaluated for one year to determine the number of uncompensated voltage occurrences. The number of violations that the control cannot compensate for affects how much reactive power the SVC must provide. © 2023 IEEE.

关键词： Reactive power

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Distortion minimization for multimedia transmission in NOMA HAP-UAV integrated aerial access networks

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Chinese Journal of Aeronautics 2022年第9期35卷 81-94页

作者： Guobin ZHANG Zhu HAN Hang XIN Xin WEI Boya DI School of Electrical Engineering and Intelligentization Dongguan University of TechnologyDongguan 523808China Department of Electrical and Computer Engineering and Department of Computer Science University of HoustonTX77004USA School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China Department of Computing Imperial CollegeLondon SW72AzUK

Aerial access networks have been envisioned as a promising 6G solution to enhance the ground communication systems in both coverage and capacity. To better utilize the spectrum and fully explore different channel characteristics, this paper constructs an integrated network comprising the High Altitude Platform(HAP) and Unmanned Air Vehicles(UAVs) with the NonOrthogonal Multiple Access(NOMA) technology. In order to improve the transmission quality of images and videos, a power management scheme is proposed to minimize the distortion of the transmissions from the HAP and UAVs to the terminals. The power control is formulated as a non-convex problem constrained by the maximal transmit power and the minimal terminal rate requirements. The variable substitution and the first-order Tailor’s expansion is used to transform it into a sequence of convex problems, which are subsequently solved through the gradient projection method. Simulation demonstrates the signal distortion and error rate improvement achieved by the proposed algorithm.

关键词： Aerial Access Networks High Altitude Platform Non-Orthogonal Multiple Access Sixth-Generation Unmanned Air Vehicles

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Binary Program Vulnerability Mining Based on Neural Network

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computers, Materials & Continua 2024年第2期78卷 1861-1879页

作者： Zhenhui Li Shuangping Xing Lin Yu Huiping Li Fan Zhou Guangqiang Yin Xikai Tang Zhiguo Wang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Electrical and Computer Engineering University ofWaterlooWaterlooN2L 3G1Canada

Software security analysts typically only have access to the executable program and cannot directly access the source code of the *** poses significant challenges to security *** it is crucial to identify vulnerabilities in such non-source code programs,there exists a limited set of generalized tools due to the low versatility of current vulnerability mining ***,these tools suffer from some *** terms of targeted fuzzing,the path searching for target points is not streamlined enough,and the completely random testing leads to an excessively large search ***,when it comes to code similarity analysis,there are issues with incomplete code feature extraction,which may result in information *** this paper,we propose a cross-platform and cross-architecture approach to exploit vulnerabilities using neural network obfuscation *** leveraging the Angr framework,a deobfuscation technique is introduced,along with the adoption of a VEX-IR-based intermediate language conversion *** combination allows for the unified handling of binary programs across various architectures,compilers,and compilation ***,binary programs are processed to extract multi-level spatial features using a combination of a skip-gram model with self-attention mechanism and a bidirectional Long Short-Term Memory(LSTM)***,the graph embedding network is utilized to evaluate the similarity of program *** on these similarity scores,a target function is determined,and symbolic execution is applied to solve the target *** solved content serves as the initial seed for targeted *** binary program is processed by using the de-obfuscation technique and intermediate language transformation method,and then the similarity of program functions is evaluated by using a graph embedding network,and symbolic execution is performed based on these similarity *** approach facilitates

关键词： Vulnerability mining de-obfuscation neural network graph embedding network symbolic execution

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An Optimal DPM Based Energy-Aware Task Scheduling for Performance Enhancement in Embedded MPSoC

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computers, Materials & Continua 2023年第1期74卷 2097-2113页

作者： Hamayun Khan Irfan Ud Din Arshad Ali Mohammad Husain Department of Electrical Engineering Faculty of Engineering&Technology Superior University Lahore54000Pakistan Department of Computer Science Faculty of Computer Science&IT Superior University Lahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahAl Madinah Al Munawarah42351Saudi Arabia

Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip(MPSoC)has become an integral chip design issue for multiprocessor *** performance measurement of computational systems is changing with the advancement in *** to shrinking and smaller chip size power densities onchip are increasing rapidly that increasing chip temperature in multi-core embedded *** operating speed of the device decreases when power consumption reaches a threshold that causes a delay in complementary metal oxide semiconductor(CMOS)circuits because high on-chip temperature adversely affects the life span of the *** this paper an energy-aware dynamic power management technique based on energy aware earliest deadline first(EA-EDF)scheduling is proposed for improving the performance and reliability by reducing energy and power consumption in the system on chip(SOC).Dynamic power management(DPM)enables MPSOC to reduce power and energy consumption by adopting a suitable core configuration for task *** migration avoids peak temperature values in the multicore *** utilization factor(ui)on central processing unit(CPU)core consumes more energy and increases the temperature *** technique switches the core bymigrating such task to a core that has less temperature and is in a low power *** proposed EA-EDF scheduling technique migrates load on different cores to attain stability in temperature among multiple cores of the CPU and optimized the duration of the idle and sleep periods to enable the low-temperature *** effectiveness of the EA-EDF approach reduces the utilization and energy consumption compared to other existing methods and *** simulation results show the improvement in performance by optimizing 4.8%on u_(i) 9%,16%,23%and 25%at 520 MHz operating frequency as compared to other energy-aware techniques for MPSoCs when the least number of tasks is in running state and can

关键词： Dynamic power management dynamic voltage&frequency scaling dynamic thermal management multiprocessor system on chip complementary metal oxide semiconductor reliability

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Detection of DDoS Attack: A Comparative Evaluation of Machine Learning Techniques

Detection of DDoS Attack: A Comparative Evaluation of Machin...

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2024 IEEE International Conference on Signal Processing, Information, Communication and Systems, SPICSCON 2024

作者： Mimi, Afsana Moon, Iffath Tanjim Mridha, Md Musfiqur Rahman Jafor, Md Shakiul Department of Computer Science & Engineering Bangladesh Department of Electrical and Electronic Engineering Bangladesh Pundra University of Science & Technology Bogura Bangladesh Varendra University Rajshahi Bangladesh

ISBN: (纸本)9798331510213

Distributed denial-of-service (DDoS) attacks are a common and increasing threat to online services, targeting multiple layers of the OSI model. As communication technology advances, these attacks are getting more frequent and complicated, making detection and protection more challenging. Therefore, the need to study DDoS attack detection is growing. This paper evaluated multiple machine learning models for detecting DDoS attacks using binary classifications. We have used nine machine learning models for binary classification namely Support Vector Machine, Random Forest, Gaussian Naïve Bayes, Extra Trees, AdaBoost, XGBoost, Gradient Boosting, Logistic Regression, and Multilayer Perceptron. Besides, we have selected the KDDCup99 dataset for this research. The dimensions of data determine a detection system's usefulness, consequently, feature selection and preprocessing are used to reduce the dataset's dimensionality, which minimizes the mathematical complexity. The evaluation of models is based on the accuracy, recall, precision, f1-score, false alarm rate, false positive ratio, false negative ratio, and error rate of datasets. Finally, using these evaluation criteria, we have compared the performance of the machine learning models and achieved the best results in binary classification using the XGBoost model with the highest accuracy (99.98%), precision (99.97%), recall (99.96%), f1-score (99.97%), and lowest false alarm rate (0.01%), error rate (0.02%), false positive ratio (0.03%), and false negative ratio (0.03%). © 2024 IEEE.

关键词： Contrastive Learning

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Non-Hermitian polarization quantized by time-reversal symmetry

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science China(Physics,Mechanics & Astronomy) 2024年第12期67卷 141-147页

作者： Jing Cheng Jinbing Hu Zhigao Hu Lin Chen Minghui Lu Songlin Zhuang Technical Center for Multifunctional Magneto-Optical Spectroscopy(Shanghai) Engineering Research Center of Nanophotonics&Advanced Instrument(Ministry of Education)Department of Physics and Electronic ScienceEast of China Normal UniversityShanghai200241China College of Optical-Electrical Information and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan430074China National Laboratory of Solid State Microstructures Science and Engineering Nanjing UniversityNanjing210093China

It is well known that in one-dimensional(1D) crystalline insulators,the electric polarization is a manifestation of Berry phase,which can not be quantized by time-reversal symmetry(TRS) as in Hermitian physics TRS does not induce any topological phase in one *** this paper we report that even though associated with complex eigenenergies a 1D non-Hermitian insulator obeying only TRS is capable of presenting quantized bulk *** underlying physical reason is unveiled:TRS guarantees the complex energies to come in pair(E,E*),and the corresponding decaying and amplifying wave functions also come in pair and have the same variation rate,hence,giving rise to a stable wannier *** electron transport is performed by means of charge pumping process,which verifies the physical mechanism *** last,we discuss the possible experimental implementation of the proposed model by means of twisted-π gauge flux.

关键词： non-Hermitian polarization time-reversal symmetry complex hopping

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HEXGEN: Generative Inference of Large Language Model over Heterogeneous Environment 41

HEXGEN: Generative Inference of Large Language Model over He...

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41st International Conference on Machine Learning, ICML 2024

作者： Jiang, Youhe Yan, Ran Yao, Xiaozhe Zhou, Yang Chen, Beidi Yuan, Binhang Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Computer Science ETH Zurich Zürich Switzerland Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States

Serving generative inference of the large language model is a crucial component of contemporary AI applications. This paper focuses on deploying such services in a heterogeneous and cross-datacenter setting to mitigate the substantial inference costs typically associated with a single centralized datacenter. Towards this end, we propose HEXGEN, a flexible distributed inference engine that uniquely supports the asymmetric partition of generative inference computations over both tensor model parallelism and pipeline parallelism and allows for effective deployment across diverse GPUs interconnected by a fully heterogeneous network. We further propose a sophisticated scheduling algorithm grounded in constrained optimization that can adaptively assign asymmetric inference computation across the GPUs to fulfill inference requests while maintaining acceptable latency levels. We conduct an extensive evaluation to verify the efficiency of HEXGEN by serving the state-of-the-art LLAMA-2 (70B) model. The results suggest that HEXGEN can choose to achieve up to 2.3× lower latency deadlines or tolerate up to 4× more request rates compared with the homogeneous baseline given the same budget. Our implementation is available at https://***/Relaxed-System-Lab/HexGen. Copyright 2024 by the author(s)

关键词： Constrained optimization

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Guest Editorial: Special Issue on Evolutionary Computation for Games

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IEEE Transactions on Games 2023年第1期15卷 1-4页

作者： Schrum, Jacob Liu, Jialin Browne, Cameron Ekart, Aniko Gallagher, Marcus Southwestern University Department of Mathematics and Computer Science GeorgetownTX United States Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China Maastricht University Department of Data Science and Knowledge Engineering Maastricht Netherlands Aston University Department of Computer Science United Kingdom The University of Queensland School of Information Technology and Electrical Engineering Australia

The eight papers in this special section focus on applications of evolutionary computation to games to demonstrate several ways in which evolution can push boundaries and explore new areas of what is possible in the realm of games research, with a focus on game-playing, automatic agent parameter tuning, automatic game testing, and procedural content generation.

关键词： Special issues and sections Evolutionary computation Games

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5731-5748页

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen the College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore the School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore the School of Automation Guangdong University of Technology Guangzhou510641 China the Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

—Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025 Institute of electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Markov processes

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