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‘澳门威斯尼斯人游戏’盘算机 | SCI期刊专刊信息4条

发布时间:2021-09-25 05:55 作者:澳门威斯尼斯人游戏 点击: 【 字体:

本文摘要:SCI、SSCI翻译润色、焦点投刊指导Call4Papers致力于资助所有科研人员揭晓学术论文,向大家实时提供各领域知名集会的deadline以及期刊的约稿信息SCI、SSCI翻译润色、焦点投刊指导盘算机体系结构,并行与漫衍式盘算Future Generation Computer SystemsSpecial Issue on Sentiment on Social Web Social Computing - Data Science for the Social We

澳门威斯尼斯人游戏

SCI、SSCI翻译润色、焦点投刊指导Call4Papers致力于资助所有科研人员揭晓学术论文,向大家实时提供各领域知名集会的deadline以及期刊的约稿信息SCI、SSCI翻译润色、焦点投刊指导盘算机体系结构,并行与漫衍式盘算Future Generation Computer SystemsSpecial Issue on Sentiment on Social Web & Social Computing - Data Science for the Social Web全文截稿: 2020-01-15影响因子: 4.639CCF分类: C类中科院JCR分区: • 大类 : 工程技术 - 2区 • 小类 : 盘算机:理论方法 - 2区网址: http://www.journals.elsevier.com/future-generation-computer-systems/In today’s era, every day 2.5 quintillion bytes of data has been generated which is an unimaginable number to human beings. Thanks to the efforts of researcher and developers in continual development of big data techniques, platform and architectures, handling and processing big data become more achievable. There is vast amount of useful information and knowledge that could be extracted from raw (unstructured) data in both public and private sectors, via two important concepts: semantic and sentiment analysis.Semantic analysis focuses on the study of dataset under various logical meanings and clusters as well as extraction of relevant meanings from the given dataset. On the other hand, sentiment analysis aims at identifying and extracting opinions within dataset, and creating typical attributes of opinion holder, subject and polarity. The analysis offers attractive advantages of scalability, real-time, consistent criteria (avoid human bias) and leads to an increase popularity in market research, product analytics, workforce analytics, customer service, voice of customer/employee, brand analytics and social media monitoring. There are several main challenges that are subjected to further research, for instance, subjectivity and tone, context and polarity, irony and sarcasm, comparison, emojis, and definition of neutral.This special issue is intended to report high-quality and original research on semantic and sentiment analysis including emerging trends and technologies, with results and discussion via practical applications and case studies. Topics include but are not limited to:• Semantic and sentiment analysis of social media/network• Big data architectures for semantic and sentiment applications• Rule-based, automatic and hybrid approach for semantic and sentiment analysis• Deep learning approach for semantic and sentiment analysis• Reliability model for near future context and polarity predictions• Semantic and sentiment analysis in document level, sentence level and sub-sentence level• Figure of merits for semantic and sentiment analysis• Modalities and heterogeneity: multi-modal and cross-modal analysis盘算机科学与技术Physical CommunicationSpecial Issue on Deep Learning Methods for Physical-Layer Wireless Communications- Recent Advances and Future Trends (submission due: February 29, 2020)全文截稿: 2020-02-29影响因子: 1.522CCF分类: 无中科院JCR分区: • 大类 : 工程技术 - 4区 • 小类 : 工程:电子与电气 - 4区 • 小类 : 电信学 - 4区网址: https://www.journals.elsevier.com/physical-communicationDeep Learning (DL) and deep reinforcement learning (DRL) methods, well known from computer science (CS) disciplines, are beginning to emerge in wireless communications. These approaches were first widely applied to the upper layers of wireless communication systems for various purposes, such as routing establishment/optimization, and deployment of cognitive radio and communication network. These system models and algorithms designed with DL technology greatly improve the performance of communication systems based on traditional methods.New features of future communications, such as complex scenarios with unknown channel models, high speed and accurate processing requirements, make traditional methods no longer suitable, and provides many more potential applications of DL. DL te

澳门威斯尼斯人游戏

chnology has become a new hotspot in the research of physical-layer wireless communications and challenges conventional communication theories. Currently, DL-based ‘black-box’ methods show promising performance improvements but have certain limitations, such as the lack of solid analytical tools and the use of architectures specifically designed for communication and implementation research. With the development of DL technology, in addition to the traditional neural network-based data-driven model, the model-driven deep network model and the DRL model (i.e. DQN) which combined DL with reinforcement learning, are more suitable for dealing with future complex communication systems. As in most cases of wireless resource allocation, there are no definite samples to train the model, hence DRL, which trains the model by maximizing the reward associated with different actions, can be adopted.This Special Issue focuses on the application of DL/DRL methods to physical-layer wireless communications to make future communications more intelligent. We invite submissions of high-quality original technical and survey articles, which have not been published previously, on DL/DRL techniques and their applications for wireless communication and signal processing.The topics of interests include, but are not limited to:· Deep Learning based 5G wireless technologies· Deep Learning based beamforming in mmWave massive MIMO· Deep Learning based hybrid precoding in massive MIMO system, mmWave system· Deep Learning based non-orthogonal multiple access (NOMA) techniques· Deep Learning based MIMO-NOMA frameworks· Deep Learning based sparse channel estimation· Deep Learning based communication frameworks· Deep Learning based multiuser detection· Deep Learning based modulation and coding· Deep Learning based direction-of-arrival estimation· Deep Learning based channel modeling· Deep Learning based signal classification· Deep Learning based unmanned aerial vehicles (UAVs) techniques· Deep Learning based energy-efficient network operations· Deep Learning based ultra-dense cell communication· Deep Learning based testbeds and experimental evaluations软件工程Science of Computer ProgrammingSpecial issue on “Coordination and Self-Adaptiveness of Software Applications”全文截稿: 2020-02-29影响因子: 0.74CCF分类: B类中科院JCR分区: • 大类 : 工程技术 - 4区 • 小类 : 盘算机:软件工程 - 4区网址: http://www.journals.elsevier.com/science-of-computer-programming/This is an open call for contributions to the special issue "Coordination and Self-Adaptiveness of Software Applications". The special issue belongs to the Elsevier journal "Science of Computer Programming" (ISSN: 0167-6423).***Aims and scope***Nowadays software systems are distributed, concurrent, mobile, and often involve the composition of heterogeneous components and stand-alone (micro)services. Service coordination, service orchestration and self-adaptation constitute the core characteristics of distributed and service-oriented systems. Theoretical/practical approaches to modelling and reasoning about (self-)adaptive behaviour help to simplify the development of complex distributed systems, enable their validation and evaluation, and improve interoperability, reusability and maintainability of such systems. The goal of the this special issue is to allow researchers and practitioners to discuss common problems and present novel solutions in the aforementioned fields.Topics of interest include (but are not limited to) both theoretical and practical solutions for what follows:* Coordination, orchestration, composition and adaptation of components, services or microservices.* Business processes and concurrent system mod

澳门威斯尼斯人游戏

elling.* Languages and models for component and service interaction, their semantics, expressiveness, validation and verification, type checking, static and dynamic analysis.* Cloud/fog/edge computing, and large-scale distributed systems.* Dynamic software architectures, self-adaptive, self-monitoring and self-organizing systems.* Peer-to-peer and multi-agent systems, and blockchains.* QoS observation, storage, history-based analysis in self-adaptive systems.盘算机科学与技术Optical Switching and NetworkingSpecial Issue on Fog to Cloud Aware Optical Networks全文截稿: 2020-03-01影响因子: 1.113CCF分类: 无中科院JCR分区: • 大类 : 工程技术 - 4区 • 小类 : 盘算机:信息系统 - 4区 • 小类 : 光学 - 4区 • 小类 : 电信学 - 4区网址: https://www.journals.elsevier.com/optical-switching-and-networkingToday we experience the ubiquitous usage of cloud services provided by worldwide geographically spread data centers. However, due to the long distances between the users and the data storage/computing infrastructure, high latency is experienced by transported traffic, so the QoE is relevantly impacted for some applications. This has lead, two decades ago, to the installation of computation and storage resources closer to the user. More recently, driven by advances in the IoT technologies, distributing data storage/processing by deploying computing and storage resources in proximity of the end users has become a major requirement for latency-sensitive services, leading to a new computation paradigm know as fog computing.This usage scenario originated the widely referred three-tier architecture of fog computing, which basically unveils the communication interfaces among cloud nodes (data centers), fog nodes (local servers and network devices) and end nodes (networked objects and end-user devices). Looking at this architecture from the transport network perspective, one can observe several communication paradigms, i.e., fog to fog (F2F), fog to cloud (F2C) and cloud to cloud (C2C) communication channels, which require a proper transport infrastructure in order to deliver the required service to the end devices.The flexibility provided by the elastic optical networks (EON) allows the allocation of the optical network resources tailored to dynamic transmission requests. Furthermore, considering the coherent transmission technology and the sliceable transmission and switching components, EON can provide an efficient usage of spectrum and energy for the data transport, thus being an enabler for the IoT traffic transport, by providing an efficient data transport infrastructure to F2C and C2C computing systems. Moreover, Fiber-Wireless (FiWi) broadband access networks is the key technology that will enable F2F computing systems.In order to fulfil this task new research has to be conducted focused on the F2F-F2C-C2C communication scenarios, proposing efficient data transport solutions that are aware of the fog computing systems. Moreover, it is also necessary to explore the design of optical nodes and control strategies in a WAN + MAN multi-domain network scenario.The topics of interest include, but are not limited to:- Application (F2C) aware EON- Efficient resource allocation solutions for Fog to Cloud in Metro EON- High availability / low latency / low energy design of Metro EON- Coordinated Wide + Metro EON control- Design and dimensioning of nodes in Metro EON- F2C optical network convergence- F2C optical network architectures- Service-oriented optical networks architecture for fog, cloud and fog to cloud- Energy efficiency of F2C solutions- Software defined control planes for F2C optical networks- Application (F2F) aware FiWi networks- New architectures for FiWi networksSCI、SSCI翻译润色、焦点投刊指
本文关键词:‘,澳门,威斯,尼,斯人,游戏,澳门威斯尼斯人游戏,’,盘算机,SCI,SCI

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