1. Journal of Universal Computer Science
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全文截稿：2017-09-15
期刊：Journal of Universal Computer Science
专刊：Special Issue on Intelligent Services for Smart Cities
领域：计算机科学理论
难度：★★
影响因子：0.546
网址：http://www.jucs.org/jucs
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It has been important to understand urban data (e.g., weather, traffic, and energy) that can be acquired from our cities. Various methods for processing such urban data have gained great attention of scientific community over the last several years. The urban data challenges ranges from handling the size and variety of urban data, to dealing with the complexity of the underlying physical models, to addressing the concerns on protecting individual's privacy, and so on. However, the past few years have seen significant advances in addressing such challenges by researchers who are stepping up their efforts in understanding the biology of cities using big data. It is our hope that through this workshop, the knowledge, experiences, and lessons can be shared to make data mining an essential and easy-to-use tool for the Smart City applications.
In this special issue, we will concentrate various research and industrial issues on smart city management systems in many kinds of intelligent systems. The aim of this issue is to bring together researchers and practitioners in areas of information management and knowledge-based systems to share their research achievements and experiences. This will give an opportunity to push further the discussion upon of knowledge integration across many academic and industrial communities. We cordially invite all potential authors to submit papers to this issue. All papers will be peer reviewed by the Board of Reviewers.
The Special Issue is devoted to intelligent services and systems for smart cities. We want to offer an opportunity for researchers and practitioners to identify new promising research directions as well as to publish recent advances in this area. The scope of this issue includes, but is not limited, to the following topics:
- Big Data processing for Smart Cities
- Mining Big data streams for Smart Cities
- Smart traffic data processing
- Smart energy data processing
- Smart buildings
2. Multimedia Tools and Applications
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全文截稿：2017-09-15
期刊：Multimedia Tools and Applications
专刊：Special Issue on Advances in Computational Intelligence for Multimodal Biomedical Imaging (ATSIP 2017)
领域：计算机图形学与多媒体
难度：★★★
CCF分类：C类
影响因子：1.331
网址：http://www.springer.com/journal/11042/about
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Nowadays, many modalities such as CT, X-ray scanners, MRI/fMRI, PET scan, etc. generate complex images with a large amount of data that are becoming extremely difficult to handle. This growing mass of data requires new strategies for the diagnosis of diseases and new therapies.
In recent years, particular attention has been paid to computational intelligence methods in multimodal biomedical imaging applications. Inspired by artificial intelligence, mathematics, biology and other fields, these methods can find relationships between different categories of this complex data and provide a set of tools for the diagnosis and monitoring of the disease.
The topics of this special issue include the following computational intelligence based methods for multimodal biomedical
imaging systems and applications, but are not limited to:
- Bio-inspired methods and neural modelling
- Learning theory for biomedical image processing
- Machine, deep and manifold learning for biomedical imaging systems
- Pattern recognition and big data in medical imaging systems methodologies
- Compressive sensing and time series analysis
- Evolutionary algorithms and metaheuristics optimization for medical imaging
- Neural networks and genetic algorithms for biomedical imaging systems
- Applications (diagnosis, classification, denoising, registration, segmentation, security, augmented reality-aided surgery, brain-computer interface etc ...)
- Modalities (X-ray, CT, MRI, fMRI, PET scan etc ...)
3. Software: Practice and Experience
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全文截稿：2017-09-15
期刊：Software: Practice and Experience
专刊：Special Issue on Metaheuristics in Cloud Computing
领域：软件工程/系统软件/程序设计语言
难度：★★★★
CCF分类：B类
影响因子：0.652
网址：http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1097-024X/
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The cloud computing paradigm is increasingly becoming mainstream and a growing number of companies and research organizations seek to gain value from its unique characteristics, service models, and deployment forms. This gives rise to many different optimization problems both from the consumers' and providers' perspective.
Cloud service providers (CSPs) aim to reduce operational expenses and improve economies of scale to maximize profits and provide competitive prices in a highly competitive market. Therefore, CSPs are particularly concerned with a cost-effective, energy-efficient, fault-tolerant utilization and management of physical and virtual computing resources and services, whereby quality of service (QoS) requirements of consumers have to be satisfied. From the consumers' point of view, particularly an efficient selection and utilization of cloud providers and cloud services as well as the management thereof is of interest. Several requirements in terms of costs and service quality as well as legal constraints need to be taken into account. This involves also specific characteristics of cloud computing, such as the options of scalability, loadbalancing, and replication. Some of those consumers may also act as a provider of software applications hosted in a third-party cloud by using cloud infrastructure and platform services. In the context of this ecosystem, the role of cloud broker schemes has been intensively discussed, in particular with regard to decision support. That is, a cloud broker (e.g., in form of decision support or a third-party) can interact between consumers and providers as well as between consumers to increase the value creation.
We invite high quality papers either on the use of metaheuristics for solving related optimization problems or the application and assessment of metaheuristics within cloud environments for solving well-known optimization problems.
4. Multimedia Tools and Applications
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全文截稿：2017-09-20
期刊：Multimedia Tools and Applications
专刊：Special Issue on Soft Computing Techniques and Applications on Multimedia Data Analyzing Systems
领域：计算机图形学与多媒体
难度：★★★
CCF分类：C类
影响因子：1.331
网址：http://www.springer.com/journal/11042/about
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In computer science and engineering research, soft computing is the use of inexact solutions to computationally hard tasks such as the solution of NP-complete problems, for which there is no known algorithm that can compute an exact solution in polynomial time. Soft computing differs from conventional computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind. The three major components of soft computing are fuzzy logic, neural network, and probabilistic reasoning, which complement each other. Fuzzy logic is used for error analysis and neural network for knowledge learning. Probabilistic reasoning is used to solve uncertainties and chestnut problems. It is concluded that fuzzy logic can simulate the function of human processing language, and neural network and probability inference model imitate human process data, knowledge learning and reasoning process, so it can deal with multivariable and nonlinear system problem with soft computing theory. The integration of soft computing and multimedia systems is the trend especially when the deep learning arises.
The aim of this special issue is to provide a premier international platform for wide range of professions including scholars, researchers, academicians and industry researchers to discuss and present the different types of cutting-edge soft computing techniques toward multimedia data analyzing systems. The special issue is open to submit novel and high quality research contributions. We target the researchers from both the industry and academia and anticipate that this special issue will open new entrance for further research and technology improvements in this important area.
Preferred topics in this issue include (but are not limited to):
- Online multimedia stream classification
- On-line single-pass active learning from multimedia data streams
- Multimedia big data mining, advanced analytics and visualization
- Operating systems and real-time processing for multimedia data-intensive applications
- Reliability in multimedia model predictions and parameters
- Dynamic dimension reduction and feature selection in multimedia streams
- User activities recognition for multimedia systems
- Semi-supervised learning from multimedia data streams
- Interplay between multimedia components for novel big data applications
- Soft computing model for multimedia assisted prediction
- Performance characterization, evaluation, optimization and design trade-offs
- MapReduce and parallel models for multimedia big data processing
- Web applications for multimedia systems
- Compiler support for multimedia data-intensive in high performance systems
- Multimedia information retrieval and feature extraction
- Remote sensing multimedia system model and platform
- Multimedia data stream modelling and identification
- Robotics, intelligent system and advanced manufacturing with multimedia
- Deep learning model and the applications in multimedia systems
5. Multimedia Tools and Applications
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全文截稿：2017-09-27
期刊：Multimedia Tools and Applications
专刊：Special Issue on Large-Scale Heterogeneous Multimedia Data Computing and Understanding
领域：计算机图形学与多媒体
难度：★★★
CCF分类：C类
影响因子：1.331
网址：http://www.springer.com/journal/11042/about
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We are living in the era of large-scale multimedia data in a heterogeneous space. This era not only provides new opportunities to jointly represent objects from distinct aspects, but also brings great challenges in data understanding and analysis. Apart from the traditional audios, images, and videos, data in this era exhibit unprecedented modalities, such as user connections, user behaviors, and geographical annotations. To gain insightful understanding and analysis into the data, recent years have witnessed the wave of a variety of machine learning algorithms and frameworks, ranging from deep learning to quantum learning models. It therefore becomes vital to report the very recent progress in advanced machine learning methodologies and state-of-the-arts for handling large-scale heterogeneous multimedia data. We are targeting at inviting original research outputs in this field, including new theories, applications, benchmark datasets, and new industrial deployments on the topic.
The list of possible topics includes, but not limited to:
1. Large-scale heterogeneous media data computing
- Multimodal media data acquisition
- Image/video feature extraction
- Heterogeneous data feature learning
- Deep learning methods for media computing
2. Large-scale heterogeneous media data management
- Multimodal data indexing
- Hash methods with multimodal data
- Search with multimodal data
- Cross-view or cross-modal Search
3. Large-scale heterogeneous media understanding
- Retrieval, classification and recognition with multimodal data
- Multi-View or Multimodal data fusion technique
- Multimodal dictionary learning technique
- Human-Computer-Interaction with multimodal data
- Learning methods using multimodal data
- Deep learning methods for heterogeneous feature fusion
6. Pattern Recognition Letters
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全文截稿：2017-09-30
期刊：Pattern Recognition Letters
专刊：Special Issue on Robustness, Security and Regulation Aspects in Current Biometric Systems (RSRA-BS)
领域：人工智能
难度：★★★
CCF分类：C类
影响因子：1.586
网址：http://www.journals.elsevier.com/pattern-recognition-letters/
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Biometric systems consist in acquiring key physiological and/or behavioural features of humans, and use them for the automatic identification or verification of identity claims for physical protection. The urge for protection of sensitive infrastructure is calling for robust and secure biometric systems. In the first case, robustness is achieved by tolerating and dealing with the noise in the feature acquisition without affecting the correct outcome. This is achieved by investigating the number of false positive and false negative that noised feature acquisition causes and by proposing proper tolerance method to reduce such numbers. In the second case, a series of attacks can be directed towards a biometric system in order to bring it in error and alter the obtained result, by augmenting the number of false positive or the one of false negative. Moreover, a biometric system holds a number of data upon which the identification is performed, which may be considered sensitive and should keep private by the system. Currently, a series of proposal are being investigated in order to rise the offered level of robustness and security within such systems by using innovative pattern recognition systems and/or using multiple classifiers paving the way to multi-modal or multi-criteria biometrics.
This is to respond to the more demanding market needs with respect to security and robustness by retaining high accuracy, scalability and usability. Last, recently a novel research topic is meeting greater attention and interest: when designing and deploying biometric systems it is important to consider the cultural, social and legal contexts of these systems. There is an increasing awareness of the social and legal aspects related to biometric systems, due to the fact that they are firmly tied to our physical bodies. There are considerable privacy concerns related to biometric systems: the legal status of biometric data, the storage of biometric data, compulsory and voluntary issues and the necessity of using biometric technology. Those concerns are calling out for legal regulations to discipline the use and design of biometric systems.
The list of possible topics includes, but not limited to:
- Robustness of the Biometric Systems and Its improvement
- Regulatory and Legal Framework of Biometric Systems
- Security and Trustworthiness of Biometric Systems
- Privacy-enhancing biometrics
- Biometric Systems for Security and Privacy
- Pattern Recognition Innovations in Biometry
- Novel biometric acquisition and storage
- De-identification and Privacy in Soft Biometrics
- Anti-Spoofing and Template Security
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全文截稿：2017-08-30
期刊：Soft Computing
专刊：Special Issue on Advanced Computer Science and Applications for Soft Computing of Converged IT environments
领域：人工智能
难度：★★★
CCF分类：C类
影响因子：1.63
网址：http://www.springer.com/journal/500/about
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This special issue is to gather new soft computing trends and methodological recent advances on a wide range of problems arising in different fields to handle practical data of converged environment. Many advanced computational methods have been successfully applied to a range of optimization and classification problems in soft computing, but there are still many practical problems tackled by traditional methods that are generally difficult to solve experimentally in practical converged data. More specifically, many computational problems arising in fields of scientific programming have been addressed in AI, HPC, large-scale data mining and etc that handles practical converged data. Submissions are welcomed on scientific programming applied to optimization in practical data. We invite researchers and practitioners to submit their original research articles and theoretical articles.
Potential topics include, but are not limited to:
- Mobile and ubiquitous computing
- Dependable, reliable and autonomic computing
- Security and trust management
- Multimedia systems and services
- Networking and communications
- Database and data mining
- Game and software engineering
- Grid and scalable computing
- Embedded system and software
- Artificial intelligence
- Distributed and parallel algorithms
